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3fc3a0bb-0e3f-4045-81e0-4f82832ed829 | kpdrop-an-approach-to-improving-absent | 2112.01476 | null | https://arxiv.org/abs/2112.01476v3 | https://arxiv.org/pdf/2112.01476v3.pdf | KPDrop: Improving Absent Keyphrase Generation | Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. Keyphrases can be either present or absent from the given document. While the extraction of present keyphrases has received much attention in the past, only recently a stronger focus has been placed on the generation of absent keyphrases. However, generating absent keyphrases is challenging; even the best methods show only a modest degree of success. In this paper, we propose a model-agnostic approach called keyphrase dropout (or KPDrop) to improve absent keyphrase generation. In this approach, we randomly drop present keyphrases from the document and turn them into artificial absent keyphrases during training. We test our approach extensively and show that it consistently improves the absent performance of strong baselines in both supervised and resource-constrained semi-supervised settings. | ['Jishnu Ray Chowdhury', 'Cornelia Caragea', 'Tuhin Kundu', 'Seoyeon Park'] | 2021-12-02 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 5.08632421e-01 1.17006458e-01 -4.52240705e-01 -1.88316349e-02
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9cf1aa09-4912-49ef-a4a9-448452ead093 | how-far-can-camels-go-exploring-the-state-of | 2306.04751 | null | https://arxiv.org/abs/2306.04751v1 | https://arxiv.org/pdf/2306.04751v1.pdf | How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources | In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with state-of-the-art proprietary models, these claims are often accompanied by limited evaluation, making it difficult to compare models across the board and determine the utility of various resources. We provide a large set of instruction-tuned models from 6.7B to 65B parameters in size, trained on 12 instruction datasets ranging from manually curated (e.g., OpenAssistant) to synthetic and distilled (e.g., Alpaca) and systematically evaluate them on their factual knowledge, reasoning, multilinguality, coding, and open-ended instruction following abilities through a collection of automatic, model-based, and human-based metrics. We further introduce T\"ulu, our best performing instruction-tuned model suite finetuned on a combination of high-quality open resources. Our experiments show that different instruction-tuning datasets can uncover or enhance specific skills, while no single dataset (or combination) provides the best performance across all evaluations. Interestingly, we find that model and human preference-based evaluations fail to reflect differences in model capabilities exposed by benchmark-based evaluations, suggesting the need for the type of systemic evaluation performed in this work. Our evaluations show that the best model in any given evaluation reaches on average 83% of ChatGPT performance, and 68% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap. We release our instruction-tuned models, including a fully finetuned 65B T\"ulu, along with our code, data, and evaluation framework at https://github.com/allenai/open-instruct to facilitate future research. | ['Hannaneh Hajishirzi', 'Iz Beltagy', 'Noah A. Smith', 'Kelsey MacMillan', 'David Wadden', 'Khyathi Raghavi Chandu', 'Tushar Khot', 'Jack Hessel', 'Pradeep Dasigi', 'Hamish Ivison', 'Yizhong Wang'] | 2023-06-07 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-4.36943144e-01 -1.89310655e-01 -6.54828966e-01 -4.50959146e-01
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1555995f-b332-4f6a-b51a-2cf094871aa7 | measuring-numerical-common-sense-is-a-word | null | null | https://openreview.net/forum?id=B1xbTlBKwB | https://openreview.net/pdf?id=B1xbTlBKwB | Measuring Numerical Common Sense: Is A Word Embedding Approach Effective? | Numerical common sense (e.g., ``a person with a height of 2m is very tall'') is essential when deploying artificial intelligence (AI) systems in society. To predict ranges of small and large values for a given target noun and unit,
previous studies have implemented a rule-based method that processed numeric values appearing in a natural language by using template matching. To obtain numerical knowledge, crawled textual data from web pages are frequently used as the input in the above method. Although this is an important task, few studies have addressed the availability of numerical common sense extracted from corresponding textual information. To this end, we first used a crowdsourcing service to obtain sufficient data for a subjective agreement on numerical common sense. Second, to examine whether common sense is attributed to current word embedding, we examined the performance of a regressor trained on the obtained data. In comparison with humans, the performance of an automatic relevance determination regression model was good, particularly when the unit was yen (a maximum correlation coefficient of 0.57). Although all the regression approach with word embedding does not predict values with high correlation coefficients, this word-embedding method could potentially contribute to construct numerical common sense for AI deployment. | ['Tatsuya Harada', 'Chin-Yew Lin', 'Hiroaki Yamane'] | 2019-09-25 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.76990896e-01 3.04080158e-01 -1.03378668e-01 -3.34677666e-01
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46f8eb45-d19f-405b-bab5-e0baa7f683ff | fast-and-robust-online-inference-with | 2106.03156 | null | https://arxiv.org/abs/2106.03156v3 | https://arxiv.org/pdf/2106.03156v3.pdf | Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling | We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms. We leverage insights from time series regression in econometrics and construct asymptotically pivotal statistics via random scaling. Our approach is fully operational with online data and is rigorously underpinned by a functional central limit theorem. Our proposed inference method has a couple of key advantages over the existing methods. First, the test statistic is computed in an online fashion with only SGD iterates and the critical values can be obtained without any resampling methods, thereby allowing for efficient implementation suitable for massive online data. Second, there is no need to estimate the asymptotic variance and our inference method is shown to be robust to changes in the tuning parameters for SGD algorithms in simulation experiments with synthetic data. | ['Youngki Shin', 'Myung Hwan Seo', 'Yuan Liao', 'Sokbae Lee'] | 2021-06-06 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.76073059e-01 -3.70822281e-01 -2.00282782e-01 -2.65421122e-01
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75bef24d-6130-46d9-92f6-059069c34176 | deepvo-a-deep-learning-approach-for-monocular | 1611.06069 | null | http://arxiv.org/abs/1611.06069v1 | http://arxiv.org/pdf/1611.06069v1.pdf | DeepVO: A Deep Learning approach for Monocular Visual Odometry | Deep Learning based techniques have been adopted with precision to solve a
lot of standard computer vision problems, some of which are image
classification, object detection and segmentation. Despite the widespread
success of these approaches, they have not yet been exploited largely for
solving the standard perception related problems encountered in autonomous
navigation such as Visual Odometry (VO), Structure from Motion (SfM) and
Simultaneous Localization and Mapping (SLAM). This paper analyzes the problem
of Monocular Visual Odometry using a Deep Learning-based framework, instead of
the regular 'feature detection and tracking' pipeline approaches. Several
experiments were performed to understand the influence of a known/unknown
environment, a conventional trackable feature and pre-trained activations tuned
for object classification on the network's ability to accurately estimate the
motion trajectory of the camera (or the vehicle). Based on these observations,
we propose a Convolutional Neural Network architecture, best suited for
estimating the object's pose under known environment conditions, and displays
promising results when it comes to inferring the actual scale using just a
single camera in real-time. | ['Vikram Mohanty', 'Shubh Agrawal', 'Shaswat Datta', 'Arna Ghosh', 'Vishnu Dutt Sharma', 'Debashish Chakravarty'] | 2016-11-18 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [ 3.76309790e-02 -2.85246372e-02 -1.97435275e-01 -4.73460913e-01
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ec89c9ac-37c6-4476-9869-7768458c7515 | moral-hazard-on-productivity-among-work-from | 2209.05684 | null | https://arxiv.org/abs/2209.05684v1 | https://arxiv.org/pdf/2209.05684v1.pdf | Moral Hazard on Productivity Among Work-From-Home Workers Amid the COVID-19 Pandemic | After the outbreak of COVID 19, firms appear to monitor Work From Home (WFH) workers more than ever out of anxiety that workers may shirk at home or implement moral hazard at home. Using the Survey of Working Arrangements and Attitudes (SWAA, Barrero et al., 2021), the evidence of WFH workers' ex post moral hazard as well as its specific aspects are examined. The results show that the ex post moral hazard among the WFH workers is generally found. Interestingly, however, the moral hazard on specific type of productivity, efficiency, is not detected for the workers at firms with WFH friendly policy for long term. Moreover, the advantages & challenges for the WFH culture report that workers with health or disability issues improve their productivity, whereas certain conditions specific to the WFH environment must be met. | ['Jieun Lee'] | 2022-09-13 | null | null | null | null | ['culture'] | ['speech'] | [-1.30536417e-02 8.42819750e-01 -3.48756313e-01 7.96420053e-02
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895f5c24-f3f7-4843-8816-0b26a52d07dd | findings-of-the-the-ruatd-shared-task-2022-on | 2206.01583 | null | https://arxiv.org/abs/2206.01583v1 | https://arxiv.org/pdf/2206.01583v1.pdf | Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian | We present the shared task on artificial text detection in Russian, which is organized as a part of the Dialogue Evaluation initiative, held in 2022. The shared task dataset includes texts from 14 text generators, i.e., one human writer and 13 text generative models fine-tuned for one or more of the following generation tasks: machine translation, paraphrase generation, text summarization, text simplification. We also consider back-translation and zero-shot generation approaches. The human-written texts are collected from publicly available resources across multiple domains. The shared task consists of two sub-tasks: (i) to determine if a given text is automatically generated or written by a human; (ii) to identify the author of a given text. The first task is framed as a binary classification problem. The second task is a multi-class classification problem. We provide count-based and BERT-based baselines, along with the human evaluation on the first sub-task. A total of 30 and 8 systems have been submitted to the binary and multi-class sub-tasks, correspondingly. Most teams outperform the baselines by a wide margin. We publicly release our codebase, human evaluation results, and other materials in our GitHub repository (https://github.com/dialogue-evaluation/RuATD). | ['Ekaterina Artemova', 'Elena Tutubalina', 'Ivan Smurov', 'Tatiana Shavrina', 'Anastasiya Valeeva', 'Marat Saidov', 'Alena Fenogenova', 'Daniil Chernianskii', 'Vladislav Mikhailov', 'Tatiana Shamardina'] | 2022-06-03 | null | null | null | null | ['paraphrase-generation', 'dialogue-evaluation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 4.75811720e-01 4.85305101e-01 -4.97761881e-03 -1.98147327e-01
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3d30afdb-5de9-49f5-8ba3-db14841310e8 | transfer-learning-of-lexical-semantic | 2209.02495 | null | https://arxiv.org/abs/2209.02495v2 | https://arxiv.org/pdf/2209.02495v2.pdf | Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification | Argument mining tasks require an informed range of low to high complexity linguistic phenomena and commonsense knowledge. Previous work has shown that pre-trained language models are highly effective at encoding syntactic and semantic linguistic phenomena when applied with transfer learning techniques and built on different pre-training objectives. It remains an issue of how much the existing pre-trained language models encompass the complexity of argument mining tasks. We rely on experimentation to shed light on how language models obtained from different lexical semantic families leverage the performance of the identification of argumentative discourse units task. Experimental results show that transfer learning techniques are beneficial to the task and that current methods may be insufficient to leverage commonsense knowledge from different lexical semantic families. | ['António Branco', 'Ruben Branco', 'João Rodrigues'] | 2022-09-06 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 3.46029997e-01 5.27893841e-01 -5.07169724e-01 -3.87850493e-01
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631a4442-b002-487a-9586-b9a5a2ffb830 | exploration-with-multi-sample-target-values | 2202.02693 | null | https://arxiv.org/abs/2202.02693v1 | https://arxiv.org/pdf/2202.02693v1.pdf | Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning | Distributional reinforcement learning (RL) aims to learn a value-network that predicts the full distribution of the returns for a given state, often modeled via a quantile-based critic. This approach has been successfully integrated into common RL methods for continuous control, giving rise to algorithms such as Distributional Soft Actor-Critic (DSAC). In this paper, we introduce multi-sample target values (MTV) for distributional RL, as a principled replacement for single-sample target value estimation, as commonly employed in current practice. The improved distributional estimates further lend themselves to UCB-based exploration. These two ideas are combined to yield our distributional RL algorithm, E2DC (Extra Exploration with Distributional Critics). We evaluate our approach on a range of continuous control tasks and demonstrate state-of-the-art model-free performance on difficult tasks such as Humanoid control. We provide further insight into the method via visualization and analysis of the learned distributions and their evolution during training. | ['Frank Wood', 'Michiel Van de Panne', 'Michael Teng'] | 2022-02-06 | null | null | null | null | ['distributional-reinforcement-learning', 'humanoid-control'] | ['methodology', 'robots'] | [-2.34722406e-01 3.55754048e-01 -4.21064436e-01 -1.60766020e-01
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d3e4002e-5f27-432d-b6df-9ab22d3a5bff | biggreen-at-semeval-2021-task-1-lexical | 2104.09040 | null | https://arxiv.org/abs/2104.09040v1 | https://arxiv.org/pdf/2104.09040v1.pdf | BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models | This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-based model with a deep neural network model founded on BERT. While BERT itself performs competitively, our feature engineering-based model helps in extreme cases, eg. separating instances of easy and neutral difficulty. Our handcrafted features comprise a breadth of lexical, semantic, syntactic, and novel phonological measures. Visualizations of BERT attention maps offer insight into potential features that Transformers models may learn when fine-tuned for lexical complexity prediction. Our ensembled predictions score reasonably well for the single word subtask, and we demonstrate how they can be harnessed to perform well on the multi word expression subtask too. | ['Soroush Vosoughi', 'Weicheng Ma', 'Aadil Islam'] | 2021-04-19 | null | https://aclanthology.org/2021.semeval-1.86 | https://aclanthology.org/2021.semeval-1.86.pdf | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-1.53378144e-01 1.87757298e-01 -3.00919581e-02 -5.42185366e-01
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dae9e539-9125-4b80-9e28-5635d568f2fd | machine-reading-with-background-knowledge | 1612.05348 | null | http://arxiv.org/abs/1612.05348v1 | http://arxiv.org/pdf/1612.05348v1.pdf | Machine Reading with Background Knowledge | Intelligent systems capable of automatically understanding natural language
text are important for many artificial intelligence applications including
mobile phone voice assistants, computer vision, and robotics. Understanding
language often constitutes fitting new information into a previously acquired
view of the world. However, many machine reading systems rely on the text alone
to infer its meaning. In this paper, we pursue a different approach; machine
reading methods that make use of background knowledge to facilitate language
understanding. To this end, we have developed two methods: The first method
addresses prepositional phrase attachment ambiguity. It uses background
knowledge within a semi-supervised machine learning algorithm that learns from
both labeled and unlabeled data. This approach yields state-of-the-art results
on two datasets against strong baselines; The second method extracts
relationships from compound nouns. Our knowledge-aware method for compound noun
analysis accurately extracts relationships and significantly outperforms a
baseline that does not make use of background knowledge. | ['Ndapandula Nakashole', 'Tom M. Mitchell'] | 2016-12-16 | null | null | null | null | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 3.46468180e-01 6.05155587e-01 -7.35158920e-01 -6.61671877e-01
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78355024-9f31-418f-8812-09c93f3717b7 | generative-model-for-zero-shot-sketch-based | 1904.08542 | null | http://arxiv.org/abs/1904.08542v1 | http://arxiv.org/pdf/1904.08542v1.pdf | Generative Model for Zero-Shot Sketch-Based Image Retrieval | We present a probabilistic model for Sketch-Based Image Retrieval (SBIR)
where, at retrieval time, we are given sketches from novel classes, that were
not present at training time. Existing SBIR methods, most of which rely on
learning class-wise correspondences between sketches and images, typically work
well only for previously seen sketch classes, and result in poor retrieval
performance on novel classes. To address this, we propose a generative model
that learns to generate images, conditioned on a given novel class sketch. This
enables us to reduce the SBIR problem to a standard image-to-image search
problem. Our model is based on an inverse auto-regressive flow based
variational autoencoder, with a feedback mechanism to ensure robust image
generation. We evaluate our model on two very challenging datasets, Sketchy,
and TU Berlin, with novel train-test split. The proposed approach significantly
outperforms various baselines on both the datasets. | ['Piyush Rai', 'Vinay Kumar Verma', 'Ashish Mishra', 'Aakansha Mishra'] | 2019-04-18 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.19786966e-01 -3.69476676e-01 -1.72489434e-01 -2.45800316e-01
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366ff198-dc82-447a-a743-71982abd0279 | calibrating-sequence-likelihood-improves | 2210.00045 | null | https://arxiv.org/abs/2210.00045v1 | https://arxiv.org/pdf/2210.00045v1.pdf | Calibrating Sequence likelihood Improves Conditional Language Generation | Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible sequences given the context, the model probabilities often do not accurately rank-order generated sequences by quality. This has been empirically observed in beam search decoding as output quality degrading with large beam sizes, and decoding strategies benefiting from heuristics such as length normalization and repetition-blocking. In this work, we introduce sequence likelihood calibration (SLiC) where the likelihood of model generated sequences are calibrated to better align with reference sequences in the model's latent space. With SLiC, decoding heuristics become unnecessary and decoding candidates' quality significantly improves regardless of the decoding method. Furthermore, SLiC shows no sign of diminishing returns with model scale, and presents alternative ways to improve quality with limited training and inference budgets. With SLiC, we exceed or match SOTA results on a wide range of generation tasks spanning abstractive summarization, question generation, abstractive question answering and data-to-text generation, even with modest-sized models. | ['Peter J. Liu', 'Mohammad Saleh', 'Shashi Narayan', 'Rishabh Joshi', 'Misha Khalman', 'Yao Zhao'] | 2022-09-30 | null | null | null | null | ['abstractive-text-summarization', 'data-to-text-generation', 'question-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 7.79938161e-01 5.28787613e-01 -4.25543606e-01 -4.50231612e-01
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d897c272-2952-4c4b-865f-1a12e7d6482b | iterative-weak-self-supervised-classification | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0167865521000507 | https://www.di.ubi.pt/~hugomcp/doc/Events_PRL.pdf | Iterative weak/self-supervised classification framework for abnormal events detection | The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et al., we introduce one iterative learning framework composed of two experts working in the weak and self-supervised paradigms and providing additional amounts of learning data to each other, where the novel instances at each iteration are filtered by a Bayesian framework that supports the iterative data augmentation task; 2) we describe a novel term that is added to the baseline loss to spread the scores in the unit interval, which is crucial for the performance of the iterative framework; 3) we propose a Random Forest ensemble that fuses at the score level the top performing methods and reduces the EER values about 20% over the state-of-the-art; and 4) we announce the availability of the ”UBI-Fights” dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/. | ['Hugo Proença', 'Bruno Degardin'] | 2021-01-03 | null | null | null | null | ['self-supervised-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'semi-supervised-video-classification', 'abnormal-event-detection-in-video', 'semi-supervised-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 4.23716366e-01 2.49870777e-01 -1.85733125e-01 -4.92791265e-01
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552b4229-99d9-4bbf-805f-67abe338194c | protranslator-zero-shot-protein-function | 2204.10286 | null | https://arxiv.org/abs/2204.10286v1 | https://arxiv.org/pdf/2204.10286v1.pdf | ProTranslator: zero-shot protein function prediction using textual description | Accurately finding proteins and genes that have a certain function is the prerequisite for a broad range of biomedical applications. Despite the encouraging progress of existing computational approaches in protein function prediction, it remains challenging to annotate proteins to a novel function that is not collected in the Gene Ontology and does not have any annotated proteins. This limitation, a side effect from the widely-used multi-label classification problem setting of protein function prediction, hampers the progress of studying new pathways and biological processes, and further slows down research in various biomedical areas. Here, we tackle this problem by annotating proteins to a function only based on its textual description so that we do not need to know any associated proteins for this function. The key idea of our method ProTranslator is to redefine protein function prediction as a machine translation problem, which translates the description word sequence of a function to the amino acid sequence of a protein. We can then transfer annotations from functions that have similar textual description to annotate a novel function. We observed substantial improvement in annotating novel functions and sparsely annotated functions on CAFA3, SwissProt and GOA datasets. We further demonstrated how our method accurately predicted gene members for a given pathway in Reactome, KEGG and MSigDB only based on the pathway description. Finally, we showed how ProTranslator enabled us to generate the textual description instead of the function label for a set of proteins, providing a new scheme for protein function prediction. We envision ProTranslator will give rise to a protein function "search engine" that returns a list of proteins based on the free text queried by the user. | ['Sheng Wang', 'Hanwen Xu'] | 2022-04-20 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 4.61803287e-01 3.81835550e-02 1.10256311e-03 -5.19725561e-01
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10137e35-80f9-4fb2-9332-6fdd0c96c28b | fedst-federated-shapelet-transformation-for | 2302.10631 | null | https://arxiv.org/abs/2302.10631v3 | https://arxiv.org/pdf/2302.10631v3.pdf | FedST: Secure Federated Shapelet Transformation for Time Series Classification | This paper explores how to customize time series classification (TSC) methods with the help of external data in a privacy-preserving federated learning (FL) scenario. To the best of our knowledge, we are the first to study on this essential topic. Achieving this goal requires us to seamlessly integrate the techniques from multiple fields including Data Mining, Machine Learning, and Security. In this paper, we systematically investigate existing TSC solutions for the centralized scenario and propose FedST, a novel FL-enabled TSC framework based on a shapelet transformation method. We recognize the federated shapelet search step as the kernel of FedST. Thus, we design a basic protocol for the FedST kernel that we prove to be secure and accurate. However, we identify that the basic protocol suffers from efficiency bottlenecks and the centralized acceleration techniques lose their efficacy due to the security issues. To speed up the federated protocol with security guarantee, we propose several optimizations tailored for the FL setting. Our theoretical analysis shows that the proposed methods are secure and more efficient. We conduct extensive experiments using both synthetic and real-world datasets. Empirical results show that our FedST solution is effective in terms of TSC accuracy, and the proposed optimizations can achieve three orders of magnitude of speedup. | ['Hongzhi Wang', 'Zhiyu Liang'] | 2023-02-21 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 4.26674485e-02 -3.94868910e-01 -1.34106740e-01 -1.22259289e-01
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5e4a4169-5c06-4888-96a0-c827e1480c5b | a-toolkit-for-data-driven-discovery-of | 2111.04870 | null | https://arxiv.org/abs/2111.04870v2 | https://arxiv.org/pdf/2111.04870v2.pdf | A toolkit for data-driven discovery of governing equations in high-noise regimes | We consider the data-driven discovery of governing equations from time-series data in the limit of high noise. The algorithms developed describe an extensive toolkit of methods for circumventing the deleterious effects of noise in the context of the sparse identification of nonlinear dynamics (SINDy) framework. We offer two primary contributions, both focused on noisy data acquired from a system x' = f(x). First, we propose, for use in high-noise settings, an extensive toolkit of critically enabling extensions for the SINDy regression method, to progressively cull functionals from an over-complete library and yield a set of sparse equations that regress to the derivate x'. These innovations can extract sparse governing equations and coefficients from high-noise time-series data (e.g. 300% added noise). For example, it discovers the correct sparse libraries in the Lorenz system, with median coefficient estimate errors equal to 1% - 3% (for 50% noise), 6% - 8% (for 100% noise); and 23% - 25% (for 300% noise). The enabling modules in the toolkit are combined into a single method, but the individual modules can be tactically applied in other equation discovery methods (SINDy or not) to improve results on high-noise data. Second, we propose a technique, applicable to any model discovery method based on x' = f(x), to assess the accuracy of a discovered model in the context of non-unique solutions due to noisy data. Currently, this non-uniqueness can obscure a discovered model's accuracy and thus a discovery method's effectiveness. We describe a technique that uses linear dependencies among functionals to transform a discovered model into an equivalent form that is closest to the true model, enabling more accurate assessment of a discovered model's accuracy. | ['J. Nathan Kutz', 'Charles B. Delahunt'] | 2021-11-08 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.79256767e-01 -2.64489532e-01 4.40108478e-01 3.68484929e-02
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4b2304cd-879e-441b-99fd-de8edd8ebeb1 | binary-single-dimensional-convolutional | 2206.07518 | null | https://arxiv.org/abs/2206.07518v1 | https://arxiv.org/pdf/2206.07518v1.pdf | Binary Single-dimensional Convolutional Neural Network for Seizure Prediction | Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction. However, these methods still cannot be implemented as part of implantable or efficient wearable devices due to their large hardware and corresponding high-power consumption. They usually require complex feature extraction process, large memory for storing high precision parameters and complex arithmetic computation, which greatly increases required hardware resources. Moreover, available yield poor prediction performance, because they adopt network architecture directly from image recognition applications fails to accurately consider the characteristics of EEG signals. We propose in this paper a hardware-friendly network called Binary Single-dimensional Convolutional Neural Network (BSDCNN) intended for epileptic seizure prediction. BSDCNN utilizes 1D convolutional kernels to improve prediction performance. All parameters are binarized to reduce the required computation and storage, except the first layer. Overall area under curve, sensitivity, and false prediction rate reaches 0.915, 89.26%, 0.117/h and 0.970, 94.69%, 0.095/h on American Epilepsy Society Seizure Prediction Challenge (AES) dataset and the CHB-MIT one respectively. The proposed architecture outperforms recent works while offering 7.2 and 25.5 times reductions on the size of parameter and computation, respectively. | ['Mohamad Sawan', 'Yankun Xu', 'Jie Yang', 'Shiqi Zhao'] | 2022-06-08 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-1.25427932e-01 -4.05345351e-01 1.45798415e-01 -3.72827441e-01
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5631195c-ff76-4879-a7af-a9b361eacb02 | occlusion-robust-3d-human-pose-estimation | 2304.12069 | null | https://arxiv.org/abs/2304.12069v1 | https://arxiv.org/pdf/2304.12069v1.pdf | Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation | Occlusion is an omnipresent challenge in 3D human pose estimation (HPE). In spite of the large amount of research dedicated to 3D HPE, only a limited number of studies address the problem of occlusion explicitly. To fill this gap, we propose to combine exploitation of spatio-temporal features with synthetic occlusion augmentation during training to deal with occlusion. To this end, we build a spatio-temporal 3D HPE model, StridedPoseGraphFormer based on graph convolution and transformers, and train it using occlusion augmentation. Unlike the existing occlusion-aware methods, that are only tested for limited occlusion, we extensively evaluate our method for varying degrees of occlusion. We show that our proposed method compares favorably with the state-of-the-art (SoA). Our experimental results also reveal that in the absence of any occlusion handling mechanism, the performance of SoA 3D HPE methods degrades significantly when they encounter occlusion. | ['Alois Knoll', 'Alejandro Mendoza Garcia', 'Patricia Gschoßmann', 'Soubarna Banik'] | 2023-04-24 | null | null | null | null | ['3d-human-pose-estimation', 'occlusion-handling'] | ['computer-vision', 'computer-vision'] | [-5.15654758e-02 2.29232207e-01 8.64424035e-02 -6.28491268e-02
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1134ae55-792f-494d-a6a3-9544e2424d51 | towards-context-aware-interaction-recognition-1 | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Zhuang_Towards_Context-Aware_Interaction_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhuang_Towards_Context-Aware_Interaction_ICCV_2017_paper.pdf | Towards Context-Aware Interaction Recognition for Visual Relationship Detection | Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current methods can be categorized as either: (i) training a single classifier on the combination of the interaction and its context; or (ii) aiming to recognize the interaction independently of its explicit context. Both methods suffer limitations: the former scales poorly with the number of combinations and fails to generalize to unseen combinations, while the latter often leads to poor interaction recognition performance due to the difficulty of designing a context-independent interaction classifier. To mitigate those drawbacks, this paper proposes an alternative, context-aware interaction recognition framework. The key to our method is to explicitly construct an interaction classifier which combines the context, and the interaction. The context is encoded via word2vec into a semantic space, and is used to derive a classification result for the interaction. The proposed method still builds one classifier for one interaction (as per type (ii) above), but the classifier built is adaptive to context via weights which are context dependent. The benefit of using the semantic space is that it naturally leads to zero-shot generalizations in which semantically similar contexts (subject-object pairs) can be recognized as suitable contexts for an interaction, even if they were not observed in the training set. Our method also scales with the number of interaction-context pairs since our model parameters do not increase with the number of interactions. Thus our method avoids the limitation of both approaches. We demonstrate experimentally that the proposed framework leads to improved performance for all investigated interaction representations and datasets.
| ['Chunhua Shen', 'Lingqiao Liu', 'Bohan Zhuang', 'Ian Reid'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.74165523e-01 -2.43581474e-01 2.41075503e-03 -3.21277738e-01
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577401a9-e213-443b-9e53-5e3632eee8ae | how-do-i-update-my-model-on-the-resilience-of | 2109.03501 | null | https://arxiv.org/abs/2109.03501v1 | https://arxiv.org/pdf/2109.03501v1.pdf | How do I update my model? On the resilience of Predictive Process Monitoring models to change | Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviours over time. As a solution to this problem, we evaluate the use of three different strategies that allow the periodic rediscovery or incremental construction of the predictive model so as to exploit new available data. The evaluation focuses on the performance of the new learned predictive models, in terms of accuracy and time, against the original one, and uses a number of real and synthetic datasets with and without explicit Concept Drift. The results provide an evidence of the potential of incremental learning algorithms for predicting process monitoring in real environments. | ['Fabrizio Maria Maggi', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Williams Rizzi1'] | 2021-09-08 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 5.93362808e-01 3.85902151e-02 5.23659363e-02 -4.82988022e-02
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b410b40e-e7b3-4885-8e21-9e52b0e5944d | pests-persian-english-cross-lingual-corpus | 2305.07893 | null | https://arxiv.org/abs/2305.07893v1 | https://arxiv.org/pdf/2305.07893v1.pdf | PESTS: Persian_English Cross Lingual Corpus for Semantic Textual Similarity | One of the components of natural language processing that has received a lot of investigation recently is semantic textual similarity. In computational linguistics and natural language processing, assessing the semantic similarity of words, phrases, paragraphs, and texts is crucial. Calculating the degree of semantic resemblance between two textual pieces, paragraphs, or phrases provided in both monolingual and cross-lingual versions is known as semantic similarity. Cross lingual semantic similarity requires corpora in which there are sentence pairs in both the source and target languages with a degree of semantic similarity between them. Many existing cross lingual semantic similarity models use a machine translation due to the unavailability of cross lingual semantic similarity dataset, which the propagation of the machine translation error reduces the accuracy of the model. On the other hand, when we want to use semantic similarity features for machine translation the same machine translations should not be used for semantic similarity. For Persian, which is one of the low resource languages, no effort has been made in this regard and the need for a model that can understand the context of two languages is felt more than ever. In this article, the corpus of semantic textual similarity between sentences in Persian and English languages has been produced for the first time by using linguistic experts. We named this dataset PESTS (Persian English Semantic Textual Similarity). This corpus contains 5375 sentence pairs. Also, different models based on transformers have been fine-tuned using this dataset. The results show that using the PESTS dataset, the Pearson correlation of the XLM ROBERTa model increases from 85.87% to 95.62%. | ['Behrouz Minaei Bidgoli', 'Poorya Piroozfar', 'Mohammad Abdous'] | 2023-05-13 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-8.44279975e-02 -1.84050992e-01 -8.76407698e-02 -6.00547314e-01
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ff30baa0-edbc-4ce2-b9a6-b93237f0d1ea | improving-transformer-based-networks-with | 2302.08639 | null | https://arxiv.org/abs/2302.08639v2 | https://arxiv.org/pdf/2302.08639v2.pdf | Improving Transformer-based Networks With Locality For Automatic Speaker Verification | Recently, Transformer-based architectures have been explored for speaker embedding extraction. Although the Transformer employs the self-attention mechanism to efficiently model the global interaction between token embeddings, it is inadequate for capturing short-range local context, which is essential for the accurate extraction of speaker information. In this study, we enhance the Transformer with the enhanced locality modeling in two directions. First, we propose the Locality-Enhanced Conformer (LE-Confomer) by introducing depth-wise convolution and channel-wise attention into the Conformer blocks. Second, we present the Speaker Swin Transformer (SST) by adapting the Swin Transformer, originally proposed for vision tasks, into speaker embedding network. We evaluate the proposed approaches on the VoxCeleb datasets and a large-scale Microsoft internal multilingual (MS-internal) dataset. The proposed models achieve 0.75% EER on VoxCeleb 1 test set, outperforming the previously proposed Transformer-based models and CNN-based models, such as ResNet34 and ECAPA-TDNN. When trained on the MS-internal dataset, the proposed models achieve promising results with 14.6% relative reduction in EER over the Res2Net50 model. | ['Jian Wu', 'John H. L. Hansen', 'Gang Liu', 'Yong Zhao', 'Mufan Sang'] | 2023-02-17 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-1.82260871e-01 -8.23349729e-02 2.11326167e-01 -5.40676355e-01
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a4ec6255-27f1-4faa-a65a-8789a2990dbe | spatial-temporal-hypergraph-self-supervised | 2204.08587 | null | https://arxiv.org/abs/2204.08587v2 | https://arxiv.org/pdf/2204.08587v2.pdf | Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction | Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to alleviate the increasing concern about the public safety. While many efforts have been devoted to proposing various spatial-temporal forecasting techniques to explore dependence across locations and time periods, most of them follow a supervised learning manner, which limits their spatial-temporal representation ability on sparse crime data. Inspired by the recent success in self-supervised learning, this work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework (ST-HSL) to tackle the label scarcity issue in crime prediction. Specifically, we propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. Furthermore, we design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination. We perform extensive experiments on two real-life crime datasets. Evaluation results show that our ST-HSL significantly outperforms state-of-the-art baselines. Further analysis provides insights into the superiority of our ST-HSL method in the representation of spatial-temporal crime patterns. The implementation code is available at https://github.com/LZH-YS1998/STHSL. | ['Jian Pei', 'Yong Xu', 'Lianghao Xia', 'Chao Huang', 'Zhonghang Li'] | 2022-04-18 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 5.35658449e-02 -2.15491444e-01 -5.78821063e-01 -4.84795302e-01
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a77a0a22-8d1c-45c6-9999-ce8fca1d4cee | problems-in-evaluating-grammatical-error | null | null | https://aclanthology.org/C12-1038 | https://aclanthology.org/C12-1038.pdf | Problems in Evaluating Grammatical Error Detection Systems | null | ['Martin Chodorow', 'Markus Dickinson', 'Joel Tetreault', 'Ross Israel'] | 2012-12-01 | problems-in-evaluating-grammatical-error-1 | https://aclanthology.org/C12-1038 | https://aclanthology.org/C12-1038.pdf | coling-2012-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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228d8b05-6cb8-42f3-adb5-37dd6365d6cf | delphic-offline-reinforcement-learning-under | 2306.01157 | null | https://arxiv.org/abs/2306.01157v1 | https://arxiv.org/pdf/2306.01157v1.pdf | Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding | A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the observed outcomes. Hidden confounding can compromise the validity of any causal conclusion drawn from data and presents a major obstacle to effective offline RL. In the present paper, we tackle the problem of hidden confounding in the nonidentifiable setting. We propose a definition of uncertainty due to hidden confounding bias, termed delphic uncertainty, which uses variation over world models compatible with the observations, and differentiate it from the well-known epistemic and aleatoric uncertainties. We derive a practical method for estimating the three types of uncertainties, and construct a pessimistic offline RL algorithm to account for them. Our method does not assume identifiability of the unobserved confounders, and attempts to reduce the amount of confounding bias. We demonstrate through extensive experiments and ablations the efficacy of our approach on a sepsis management benchmark, as well as on electronic health records. Our results suggest that nonidentifiable hidden confounding bias can be mitigated to improve offline RL solutions in practice. | ['Guy Tennenholtz', 'Gunnar Rätsch', 'Bernhard Schölkopf', 'Hugo Yèche', 'Alizée Pace'] | 2023-06-01 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 1.04462713e-01 6.35790944e-01 -5.02087653e-01 -1.04447097e-01
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bc88a528-3ff6-4e7a-aea4-cdde4b947d0c | one-bit-covariance-reconstruction-with-non | 2303.16455 | null | https://arxiv.org/abs/2303.16455v1 | https://arxiv.org/pdf/2303.16455v1.pdf | One-Bit Covariance Reconstruction with Non-zero Thresholds: Algorithm and Performance Analysis | Covariance matrix reconstruction is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitation, recent studies have proposed the use of non-zero clipping thresholds. However, the relationship between the estimation error and the sampling threshold is not yet known. In this paper, we undertake an analysis of the mean squared error by computing the Fisher information matrix for a given threshold. Our results reveal that the optimal threshold can vary considerably, depending on the variances and correlation coefficients. As a result, it is inappropriate to use a constant threshold to encompass parameters that vary widely. To mitigate this issue, we present a recovery scheme that incorporates time-varying thresholds. Our approach differs from existing methods in that it utilizes the exact values of the threshold, rather than its statistical properties, to enhance the estimation performance. Our simulations, including the direction-of-arrival estimation problem, demonstrate the efficacy of the developed scheme, especially in complex scenarios where the covariance elements are widely separated. | ['Hing Cheung So', 'Cheng Qian', 'David Ramírez', 'Lei Huang', 'Yu-Hang Xiao'] | 2023-03-29 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 4.41006869e-01 -5.14914870e-01 -1.85784395e-03 -2.33019993e-01
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f79c535e-d83e-482f-8d7b-d3d27873e48c | sevggnet-lstm-a-fused-deep-learning-model-for | 2210.17111 | null | https://arxiv.org/abs/2210.17111v1 | https://arxiv.org/pdf/2210.17111v1.pdf | SEVGGNet-LSTM: a fused deep learning model for ECG classification | This paper presents a fused deep learning algorithm for ECG classification. It takes advantages of the combined convolutional and recurrent neural network for ECG classification, and the weight allocation capability of attention mechanism. The input ECG signals are firstly segmented and normalized, and then fed into the combined VGG and LSTM network for feature extraction and classification. An attention mechanism (SE block) is embedded into the core network for increasing the weight of important features. Two databases from different sources and devices are employed for performance validation, and the results well demonstrate the effectiveness and robustness of the proposed algorithm. | ['Yicong Zhou', 'Wei Wang', 'Junxin Chen', 'Yiming Chen', 'Tongyue He'] | 2022-10-31 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.79404342e-01 -1.77573249e-01 -1.67962193e-01 -3.36827874e-01
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14b905dc-1e80-4a3a-b5fe-8ef1fdf448a3 | contextualized-emotion-recognition-in | null | null | https://aclanthology.org/2020.sigdial-1.23 | https://aclanthology.org/2020.sigdial-1.23.pdf | Contextualized Emotion Recognition in Conversation as Sequence Tagging | Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on. In this paper, we propose a method to model ERC task as sequence tagging where a Conditional Random Field (CRF) layer is leveraged to learn the emotional consistency in the conversation. We employ LSTM-based encoders that capture self and inter-speaker dependency of interlocutors to generate contextualized utterance representations which are fed into the CRF layer. For capturing long-range global context, we use a multi-layer Transformer encoder to enhance the LSTM-based encoder. Experiments show that our method benefits from modeling the emotional consistency and outperforms the current state-of-the-art methods on multiple emotion classification datasets. | ['Jing Xiao', 'Shaojun Wang', 'Jun Ma', 'Jiayu Zhang', 'Yan Wang'] | 2020-07-01 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.14579916e-01 3.40571135e-01 -9.30639878e-02 -1.10980356e+00
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9beea27b-9424-4207-ba97-e2d53b4d49f0 | coupling-natural-language-processing-and | null | null | https://aclanthology.org/W15-2815 | https://aclanthology.org/W15-2815.pdf | Coupling Natural Language Processing and Animation Synthesis in Portuguese Sign Language Translation | null | ['C', "Lu{\\'\\i}sa Coheur", 'In{\\^e}s Almeida', 'Sara eias'] | 2015-09-01 | null | null | null | ws-2015-9 | ['sign-language-translation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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165ffc2e-18b6-4ef4-a2ce-c2eef31c6e13 | linguistic-resources-for-entity-linking | null | null | https://aclanthology.org/L12-1118 | https://aclanthology.org/L12-1118.pdf | Linguistic Resources for Entity Linking Evaluation: from Monolingual to Cross-lingual | To advance information extraction and question answering technologies toward a more realistic path, the U.S. NIST (National Institute of Standards and Technology) initiated the KBP (Knowledge Base Population) task as one of the TAC (Text Analysis Conference) evaluation tracks. It aims to encourage research in automatic information extraction of named entities from unstructured texts with the ultimate goal of integrating such information into a structured Knowledge Base. The KBP track consists of two types of evaluation: Named Entity Linking (NEL) and Slot Filling. This paper describes the linguistic resource creation efforts at the Linguistic Data Consortium (LDC) in support of Named Entity Linking evaluation of KBP, focusing on annotation methodologies, process, and features of corpora from 2009 to 2011, with a highlighted analysis of the cross-lingual NEL data. Progressing from monolingual to cross-lingual Entity Linking technologies, the 2011 cross-lingual NEL evaluation targeted multilingual capabilities. Annotation accuracy is presented in comparison with system performance, with promising results from cross-lingual entity linking systems. | ['Heng Ji', 'Kira Griffitt', 'Joe Ellis', 'Xuansong Li', 'Stephanie Strassel'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['cross-lingual-entity-linking', 'knowledge-base-population'] | ['natural-language-processing', 'natural-language-processing'] | [-4.76414412e-01 6.05465531e-01 -3.70091707e-01 -3.92271221e-01
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18a4d915-64cf-4333-be11-934b33ef2bd8 | heterogeneous-datasets-for-federated-survival | 2301.12166 | null | https://arxiv.org/abs/2301.12166v2 | https://arxiv.org/pdf/2301.12166v2.pdf | Heterogeneous Datasets for Federated Survival Analysis Simulation | Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In this context, federated learning can be exploited to tremendously improve the quality of the models trained on distributed data while preserving user privacy. However, federated survival analysis is still in its early development, and there is no common benchmarking dataset to test federated survival models. This work provides a novel technique for constructing realistic heterogeneous datasets by starting from existing non-federated datasets in a reproducible way. Specifically, we propose two dataset-splitting algorithms based on the Dirichlet distribution to assign each data sample to a carefully chosen client: quantity-skewed splitting and label-skewed splitting. Furthermore, these algorithms allow for obtaining different levels of heterogeneity by changing a single hyperparameter. Finally, numerical experiments provide a quantitative evaluation of the heterogeneity level using log-rank tests and a qualitative analysis of the generated splits. The implementation of the proposed methods is publicly available in favor of reproducibility and to encourage common practices to simulate federated environments for survival analysis. | ['Matteo Matteucci', 'André Martin', 'Francesco Lattari', 'Eugenio Lomurno', 'Alberto Archetti'] | 2023-01-28 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-2.09532261e-01 -1.36077225e-01 -4.05288815e-01 -5.91699004e-01
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27d8e492-a3a0-4572-af65-ac5154ab88bf | interpretability-analysis-of-heartbeat | null | null | https://doi.org/10.1109/ACCESS.2019.2933473 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8790681 | Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network | Arrhythmia is a disease that threatens human life. Therefore, timely diagnosis of arrhythmia is of great significance in preventing heart disease and sudden cardiac death. The BiLSTM-Attention neural network model with heartbeat activity's global sequence features can effectively improve the accuracy of heartbeat classification. Firstly, the noise is removed by the continuous wavelet transform method. Secondly, the peak of the R wave is detected by the tagged database, and then the P-QRS-T wave morphology and the RR interval are extracted. This feature set is heartbeat activity's global sequence features, which combines single heartbeat morphology and 21 consecutive RR intervals. Finally, the Bi-LSTM algorithm and the BiLSTM-Attention algorithm are used to identify heartbeat category respectively, and the MIT-BIH arrhythmia database is used to verify the algorithm. The results show that the BiLSTM-Attention model combined with heartbeat activity's global sequence features has higher interpretability than other methods discussed in this paper. | ['Zongmin Wang', 'Runchuan Li', 'Bing Zhou', 'Xingjin Zhang', 'Honghua Dai'] | 2019-08-07 | null | null | null | ieee-access-2019-8 | ['arrhythmia-detection', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 7.04176426e-02 -4.69091982e-01 -7.09094107e-02 1.45933136e-01
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c0658ba3-8e2b-4bd2-a421-7aee6f736e30 | a-fluctuation-smoothing-approach-for | null | null | https://aclanthology.org/W16-4911 | https://aclanthology.org/W16-4911.pdf | A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading | We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem. A major drawback of the existing techniques is the significant effect that variations in model answers could have on their performances. The proposed fluctuation smoothing approach, based on classical sequential pattern mining, exploits lexical overlap in students{'} answers to any typical question. We empirically demonstrate using multiple datasets that the proposed approach improves the overall performance and significantly reduces (up to 63{\%}) variation in performance (standard deviation) of unsupervised ASAG techniques. We bring in additional benchmarks such as (a) paraphrasing of model answers and (b) using answers by k top performing students as model answers, to amplify the benefits of the proposed approach. | ['ipan', 'D', 'Shourya Roy', 'Y. Narahari', 'S apat'] | 2016-12-01 | null | null | null | ws-2016-12 | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.31907065e-02 3.80894467e-02 -1.46661364e-02 -4.15799499e-01
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a4eca47b-fb78-4bc0-8ed5-5a9bf9448ddd | accurate-unsupervised-joint-named-entity | null | null | https://aclanthology.org/W12-4403 | https://aclanthology.org/W12-4403.pdf | Accurate Unsupervised Joint Named-Entity Extraction from Unaligned Parallel Text | null | ['Christopher D. Manning', 'Robert Munro'] | 2012-07-01 | accurate-unsupervised-joint-named-entity-1 | https://aclanthology.org/W12-4403 | https://aclanthology.org/W12-4403.pdf | ws-2012-7 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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9581ff8b-1cc4-4076-8878-3048009f8212 | paired-point-lifting-for-enhanced-privacy | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lee_Paired-Point_Lifting_for_Enhanced_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lee_Paired-Point_Lifting_for_Enhanced_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.pdf | Paired-Point Lifting for Enhanced Privacy-Preserving Visual Localization | Visual localization refers to the process of recovering camera pose from input image relative to a known scene, forming a cornerstone of numerous vision and robotics systems. While many algorithms utilize sparse 3D point cloud of the scene obtained via structure-from-motion (SfM) for localization, recent studies have raised privacy concerns by successfully revealing high-fidelity appearance of the scene from such sparse 3D representation. One prominent approach for bypassing this attack was to lift 3D points to randomly oriented 3D lines thereby hiding scene geometry, but latest work have shown such random line cloud has a critical statistical flaw that can be exploited to break through protection. In this work, we present an alternative lightweight strategy called Paired-Point Lifting (PPL) for constructing 3D line clouds. Instead of drawing one randomly oriented line per 3D point, PPL splits 3D points into pairs and joins each pair to form 3D lines. This seemingly simple strategy yields 3 benefits, i) new ambiguity in feature selection, ii) increased line cloud sparsity, and iii) non-trivial distribution of 3D lines, all of which contributes to enhanced protection against privacy attacks. Extensive experimental results demonstrate the strength of PPL in concealing scene details without compromising localization accuracy, unlocking the true potential of 3D line clouds. | ['Je Hyeong Hong', 'Chanhyuk Yun', 'Jaihoon Kim', 'Chunghwan Lee'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [ 3.13596517e-01 -1.65162638e-01 -4.79735360e-02 -5.69804683e-02
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07baddd8-0b44-41f5-8e40-9278965ab39c | student-become-decathlon-master-in-retinal | 2203.03631 | null | https://arxiv.org/abs/2203.03631v3 | https://arxiv.org/pdf/2203.03631v3.pdf | Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation | Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of them only focus on single-target domain adaptation and cannot be applied to the scenario with multiple target domains. In this paper, we propose RVms, a novel unsupervised multi-target domain adaptation approach to segment retinal vessels (RVs) from multimodal and multicenter retinal images. RVms mainly consists of a style augmentation and transfer (SAT) module and a dual-teacher knowledge distillation (DTKD) module. SAT augments and clusters images into source-similar domains and source-dissimilar domains via Bezier and Fourier transformations. DTKD utilizes the augmented and transformed data to train two teachers, one for source-similar domains and the other for source-dissimilar domains. Afterwards, knowledge distillation is performed to iteratively distill different domain knowledge from teachers to a generic student. The local relative intensity transformation is employed to characterize RVs in a domain invariant manner and promote the generalizability of teachers and student models. Moreover, we construct a new multimodal and multicenter vascular segmentation dataset from existing publicly-available datasets, which can be used to benchmark various domain adaptation and domain generalization methods. Through extensive experiments, RVms is found to be very close to the target-trained Oracle in terms of segmenting the RVs, largely outperforming other state-of-the-art methods. | ['Xiaoying Tang', 'Huaqing He', 'Pujin Cheng', 'Li Lin', 'Linkai Peng'] | 2022-03-07 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 3.32650900e-01 7.77485669e-02 -3.60327899e-01 -4.35631037e-01
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4076eae6-032b-4fd1-a5a2-afd1e3088230 | facial-descriptors-for-human-interaction | 1509.05366 | null | http://arxiv.org/abs/1509.05366v1 | http://arxiv.org/pdf/1509.05366v1.pdf | Facial Descriptors for Human Interaction Recognition In Still Images | This paper presents a novel approach in a rarely studied area of computer
vision: Human interaction recognition in still images. We explore whether the
facial regions and their spatial configurations contribute to the recognition
of interactions. In this respect, our method involves extraction of several
visual features from the facial regions, as well as incorporation of scene
characteristics and deep features to the recognition. Extracted multiple
features are utilized within a discriminative learning framework for
recognizing interactions between people. Our designed facial descriptors are
based on the observation that relative positions, size and locations of the
faces are likely to be important for characterizing human interactions. Since
there is no available dataset in this relatively new domain, a comprehensive
new dataset which includes several images of human interactions is collected.
Our experimental results show that faces and scene characteristics contain
important information to recognize interactions between people. | ['Nazli Ikizler-Cinbis', 'Gokhan Tanisik', 'Cemil Zalluhoglu'] | 2015-09-17 | null | null | null | null | ['human-interaction-recognition'] | ['computer-vision'] | [ 3.33944075e-02 -4.36604947e-01 -1.08176842e-01 -7.95539439e-01
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5045ae77-dcee-47f2-b53c-5c127690e21f | near-optimal-fully-first-order-algorithms-for | 2306.14853 | null | https://arxiv.org/abs/2306.14853v1 | https://arxiv.org/pdf/2306.14853v1.pdf | Near-Optimal Fully First-Order Algorithms for Finding Stationary Points in Bilevel Optimization | Bilevel optimization has various applications such as hyper-parameter optimization and meta-learning. Designing theoretically efficient algorithms for bilevel optimization is more challenging than standard optimization because the lower-level problem defines the feasibility set implicitly via another optimization problem. One tractable case is when the lower-level problem permits strong convexity. Recent works show that second-order methods can provably converge to an $\epsilon$-first-order stationary point of the problem at a rate of $\tilde{\mathcal{O}}(\epsilon^{-2})$, yet these algorithms require a Hessian-vector product oracle. Kwon et al. (2023) resolved the problem by proposing a first-order method that can achieve the same goal at a slower rate of $\tilde{\mathcal{O}}(\epsilon^{-3})$. In this work, we provide an improved analysis demonstrating that the first-order method can also find an $\epsilon$-first-order stationary point within $\tilde {\mathcal{O}}(\epsilon^{-2})$ oracle complexity, which matches the upper bounds for second-order methods in the dependency on $\epsilon$. Our analysis further leads to simple first-order algorithms that can achieve similar near-optimal rates in finding second-order stationary points and in distributed bilevel problems. | ['Jingzhao Zhang', 'Yaohua Ma', 'Lesi Chen'] | 2023-06-26 | null | null | null | null | ['meta-learning', 'bilevel-optimization'] | ['methodology', 'methodology'] | [-2.85476029e-01 2.53018558e-01 -2.27220610e-01 5.24467528e-02
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4086fd19-0c2c-4135-8e83-b30d808d5433 | audio2gestures-generating-diverse-gestures | 2108.06720 | null | https://arxiv.org/abs/2108.06720v1 | https://arxiv.org/pdf/2108.06720v1.pdf | Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders | Generating conversational gestures from speech audio is challenging due to the inherent one-to-many mapping between audio and body motions. Conventional CNNs/RNNs assume one-to-one mapping, and thus tend to predict the average of all possible target motions, resulting in plain/boring motions during inference. In order to overcome this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code. The shared code mainly models the strong correlation between audio and motion (such as the synchronized audio and motion beats), while the motion-specific code captures diverse motion information independent of the audio. However, splitting the latent code into two parts poses training difficulties for the VAE model. A mapping network facilitating random sampling along with other techniques including relaxed motion loss, bicycle constraint, and diversity loss are designed to better train the VAE. Experiments on both 3D and 2D motion datasets verify that our method generates more realistic and diverse motions than state-of-the-art methods, quantitatively and qualitatively. Finally, we demonstrate that our method can be readily used to generate motion sequences with user-specified motion clips on the timeline. Code and more results are at https://jingli513.github.io/audio2gestures. | ['Linchao Bao', 'Zhenyu He', 'Ying Zhang', 'Xuefei Zhe', 'Wenjie Pei', 'Di Kang', 'Jing Li'] | 2021-08-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Li_Audio2Gestures_Generating_Diverse_Gestures_From_Speech_Audio_With_Conditional_Variational_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Li_Audio2Gestures_Generating_Diverse_Gestures_From_Speech_Audio_With_Conditional_Variational_ICCV_2021_paper.pdf | iccv-2021-1 | ['gesture-generation'] | ['robots'] | [-2.24353105e-01 -1.56435162e-01 -2.73796678e-01 -1.54477447e-01
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80449f99-d517-4e95-acab-b02bccad1ed1 | mobileface-3d-face-reconstruction-with | 1809.08809 | null | http://arxiv.org/abs/1809.08809v1 | http://arxiv.org/pdf/1809.08809v1.pdf | MobileFace: 3D Face Reconstruction with Efficient CNN Regression | Estimation of facial shapes plays a central role for face transfer and
animation. Accurate 3D face reconstruction, however, often deploys iterative
and costly methods preventing real-time applications. In this work we design a
compact and fast CNN model enabling real-time face reconstruction on mobile
devices. For this purpose, we first study more traditional but slow morphable
face models and use them to automatically annotate a large set of images for
CNN training. We then investigate a class of efficient MobileNet CNNs and adapt
such models for the task of shape regression. Our evaluation on three datasets
demonstrates significant improvements in the speed and the size of our model
while maintaining state-of-the-art reconstruction accuracy. | ['Ivan Laptev', 'Nikolai Chinaev', 'Alexander Chigorin'] | 2018-09-24 | null | null | null | null | ['face-transfer'] | ['computer-vision'] | [-5.81360683e-02 -8.15961603e-03 2.59124450e-02 -4.42176014e-01
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1b7b219c-7c1a-46a4-af08-4fd173cb7e84 | recurrent-environment-simulators | 1704.02254 | null | http://arxiv.org/abs/1704.02254v2 | http://arxiv.org/pdf/1704.02254v2.pdf | Recurrent Environment Simulators | Models that can simulate how environments change in response to actions can
be used by agents to plan and act efficiently. We improve on previous
environment simulators from high-dimensional pixel observations by introducing
recurrent neural networks that are able to make temporally and spatially
coherent predictions for hundreds of time-steps into the future. We present an
in-depth analysis of the factors affecting performance, providing the most
extensive attempt to advance the understanding of the properties of these
models. We address the issue of computationally inefficiency with a model that
does not need to generate a high-dimensional image at each time-step. We show
that our approach can be used to improve exploration and is adaptable to many
diverse environments, namely 10 Atari games, a 3D car racing environment, and
complex 3D mazes. | ['Sébastien Racaniere', 'Shakir Mohamed', 'Daan Wierstra', 'Silvia Chiappa'] | 2017-04-07 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 1.47504240e-01 9.45584557e-04 3.45019370e-01 -1.26584053e-01
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e1c4e3de-9d6f-4ea9-8239-6615ad578d2c | the-future-of-human-centric-explainable | 2307.00364 | null | https://arxiv.org/abs/2307.00364v1 | https://arxiv.org/pdf/2307.00364v1.pdf | The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations | Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous, and pervasive in aspects of daily life, explainability is necessary to avoid or minimize adverse effects of model mistakes. Unfortunately, current approaches in human-centric XAI (e.g. predictive tasks in healthcare, education, or personalized ads) tend to rely on a single explainer. This is a particularly concerning trend when considering that recent work has identified systematic disagreement in explainability methods when applied to the same points and underlying black-box models. In this paper, we therefore present a call for action to address the limitations of current state-of-the-art explainers. We propose to shift from post-hoc explainability to designing interpretable neural network architectures; moving away from approximation techniques in human-centric and high impact applications. We identify five needs of human-centric XAI (real-time, accurate, actionable, human-interpretable, and consistent) and propose two schemes for interpretable-by-design neural network workflows (adaptive routing for interpretable conditional computation and diagnostic benchmarks for iterative model learning). We postulate that the future of human-centric XAI is neither in explaining black-boxes nor in reverting to traditional, interpretable models, but in neural networks that are intrinsically interpretable. | ['Tanja Käser', 'Jibril Frej', 'Vinitra Swamy'] | 2023-07-01 | null | null | null | null | ['explainable-artificial-intelligence'] | ['computer-vision'] | [ 4.81277108e-02 8.32987368e-01 -6.28705695e-02 -9.22160208e-01
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bc9a565a-5966-4d3a-a470-dc3680b0653b | do-we-need-entire-training-data-for | 2303.06241 | null | https://arxiv.org/abs/2303.06241v2 | https://arxiv.org/pdf/2303.06241v2.pdf | Do we need entire training data for adversarial training? | Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suffer from vulnerability against adversarial attacks. In the past few years, numerous approaches have been proposed to tackle this problem by training networks using adversarial training. Almost all the approaches generate adversarial examples for the entire training dataset, thus increasing the training time drastically. We show that we can decrease the training time for any adversarial training algorithm by using only a subset of training data for adversarial training. To select the subset, we filter the adversarially-prone samples from the training data. We perform a simple adversarial attack on all training examples to filter this subset. In this attack, we add a small perturbation to each pixel and a few grid lines to the input image. We perform adversarial training on the adversarially-prone subset and mix it with vanilla training performed on the entire dataset. Our results show that when our method-agnostic approach is plugged into FGSM, we achieve a speedup of 3.52x on MNIST and 1.98x on the CIFAR-10 dataset with comparable robust accuracy. We also test our approach on state-of-the-art Free adversarial training and achieve a speedup of 1.2x in training time with a marginal drop in robust accuracy on the ImageNet dataset. | ['Apurva Narayan', 'Vipul Gupta'] | 2023-03-10 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 4.06813264e-01 1.20794021e-01 2.53838122e-01 -3.54018748e-01
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ad12d8d6-28bb-465c-8243-168e42f157af | furniturebench-reproducible-real-world | 2305.12821 | null | https://arxiv.org/abs/2305.12821v1 | https://arxiv.org/pdf/2305.12821v1.pdf | FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation | Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. However, these approaches have been limited to learning simple behaviors in current real-world manipulation benchmarks, such as pushing or pick-and-place. To enable more complex, long-horizon behaviors of an autonomous robot, we propose to focus on real-world furniture assembly, a complex, long-horizon robot manipulation task that requires addressing many current robotic manipulation challenges to solve. We present FurnitureBench, a reproducible real-world furniture assembly benchmark aimed at providing a low barrier for entry and being easily reproducible, so that researchers across the world can reliably test their algorithms and compare them against prior work. For ease of use, we provide 200+ hours of pre-collected data (5000+ demonstrations), 3D printable furniture models, a robotic environment setup guide, and systematic task initialization. Furthermore, we provide FurnitureSim, a fast and realistic simulator of FurnitureBench. We benchmark the performance of offline RL and IL algorithms on our assembly tasks and demonstrate the need to improve such algorithms to be able to solve our tasks in the real world, providing ample opportunities for future research. | ['Joseph J. Lim', 'Doohyun Lee', 'Youngwoon Lee', 'Minho Heo'] | 2023-05-22 | null | null | null | null | ['offline-rl', 'robot-manipulation', 'motion-planning'] | ['playing-games', 'robots', 'robots'] | [-5.24970852e-02 -2.54161894e-01 -2.88337290e-01 -5.57825901e-02
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1e3aadab-62d1-4363-a64a-c97f7610bfab | reproducible-biomarkers-leveraging-nonlinear | 2305.06954 | null | https://arxiv.org/abs/2305.06954v1 | https://arxiv.org/pdf/2305.06954v1.pdf | Reproducible biomarkers: Leveraging nonlinear descriptors in the face of non-ergodicity | Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-negative rates. The application of standard cut-off points and risk scores across populations hinges upon the assumption of such generalizability. Such generalizability, in turn, hinges upon this condition that the phenomenon investigated by current statistical methods is ergodic, i.e., its statistical measures converge over individuals and time within the finite limit of observations. However, emerging evidence indicates that biological processes abound with non-ergodicity, threatening this generalizability. Here, we present a solution for how to make generalizable inferences by deriving ergodic descriptions of non-ergodic phenomena. For this aim, we proposed capturing the origin of ergodicity-breaking in many biological processes: cascade dynamics. To assess our hypotheses, we embraced the challenge of identifying reliable biomarkers for heart disease and stroke, which, despite being the leading cause of death worldwide and decades of research, lacks reliable biomarkers and risk stratification tools. We showed that raw R-R interval data and its common descriptors based on mean and variance are non-ergodic and non-specific. On the other hand, the cascade-dynamical descriptors, the Hurst exponent encoding linear temporal correlations, and multifractal nonlinearity encoding nonlinear interactions across scales described the non-ergodic heart rate variability ergodically and were specific. This study inaugurates applying the critical concept of ergodicity in discovering and applying digital biomarkers of health and disease. | ['Damian G. Kelty-Stephen', 'Ken Kiyono', 'Eiichi Watanabe', 'Junichiro Hayano', 'Arash Sadri', 'Madhur Mangalam'] | 2023-05-11 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.53379411e-01 -3.22640330e-01 -1.94384515e-01 3.82037796e-02
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a07aef91-a78d-451f-907e-38c8518345f2 | end-to-end-deep-multi-score-model-for-no-1 | 2211.01374 | null | https://arxiv.org/abs/2211.01374v1 | https://arxiv.org/pdf/2211.01374v1.pdf | End-to-end deep multi-score model for No-reference stereoscopic image quality assessment | Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left and right images would differ, necessitating the learning of unique quality indicators for each view. Unlike existing stereoscopic IQA measures which focus mainly on estimating a global human score, we suggest incorporating left, right, and stereoscopic objective scores to extract the corresponding properties of each view, and so forth estimating stereoscopic image quality without reference. Therefore, we use a deep multi-score Convolutional Neural Network (CNN). Our model has been trained to perform four tasks: First, predict the left view's quality. Second, predict the quality of the left view. Third and fourth, predict the quality of the stereo view and global quality, respectively, with the global score serving as the ultimate quality. Experiments are conducted on Waterloo IVC 3D Phase 1 and Phase 2 databases. The results obtained show the superiority of our method when comparing with those of the state-of-the-art. The implementation code can be found at: https://github.com/o-messai/multi-score-SIQA | ['Aladine Chetouani', 'Oussama Messai'] | 2022-11-02 | end-to-end-deep-multi-score-model-for-no | https://ieeexplore.ieee.org/abstract/document/9897616 | https://www.researchgate.net/publication/364995366_End-to-end_deep_multi-score_model_for_No-reference_stereoscopic_image_quality_assessment | icip2022-2022-10 | ['stereoscopic-image-quality-assessment'] | ['computer-vision'] | [-1.01836637e-01 -1.97083652e-01 7.07311854e-02 -3.85659993e-01
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6606eef1-a562-472a-afd7-d231a13c2687 | fast-forward-through-opportunistic | null | null | https://aclanthology.org/P17-3019 | https://aclanthology.org/P17-3019.pdf | Fast Forward Through Opportunistic Incremental Meaning Representation Construction | null | ['Sergei Nirenburg', 'Petr Babkin'] | 2017-07-01 | null | null | null | acl-2017-7 | ['dialogue-understanding'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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3a4bd95c-14e4-40ed-a01a-ba56bf66346e | classification-and-explanation-of-distributed | 2306.17190 | null | https://arxiv.org/abs/2306.17190v1 | https://arxiv.org/pdf/2306.17190v1.pdf | Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods | DDoS attacks involve overwhelming a target system with a large number of requests or traffic from multiple sources, disrupting the normal traffic of a targeted server, service, or network. Distinguishing between legitimate traffic and malicious traffic is a challenging task. It is possible to classify legitimate traffic and malicious traffic and analysis the network traffic by using machine learning and deep learning techniques. However, an inter-model explanation implemented to classify a traffic flow whether is benign or malicious is an important investigation of the inner working theory of the model to increase the trustworthiness of the model. Explainable Artificial Intelligence (XAI) can explain the decision-making of the machine learning models that can be classified and identify DDoS traffic. In this context, we proposed a framework that can not only classify legitimate traffic and malicious traffic of DDoS attacks but also use SHAP to explain the decision-making of the classifier model. To address this concern, we first adopt feature selection techniques to select the top 20 important features based on feature importance techniques (e.g., XGB-based SHAP feature importance). Following that, the Multi-layer Perceptron Network (MLP) part of our proposed model uses the optimized features of the DDoS attack dataset as inputs to classify legitimate and malicious traffic. We perform extensive experiments with all features and selected features. The evaluation results show that the model performance with selected features achieves above 99\% accuracy. Finally, to provide interpretability, XAI can be adopted to explain the model performance between the prediction results and features based on global and local explanations by SHAP, which can better explain the results achieved by our proposed framework. | ['Seyit Camtepe', 'Fariza Sabrina', 'Amardeep Singh', 'Julian Jang-Jaccard', 'Yuanyuan Wei'] | 2023-06-27 | null | null | null | null | ['explainable-artificial-intelligence', 'feature-importance', 'decision-making'] | ['computer-vision', 'methodology', 'reasoning'] | [-3.40227067e-01 -2.21523315e-01 -4.21600997e-01 -3.68947417e-01
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ec62c717-17da-4aad-b3c7-e188d351dc3a | significant-ties-graph-neural-networks-for | 2211.06590 | null | https://arxiv.org/abs/2211.06590v1 | https://arxiv.org/pdf/2211.06590v1.pdf | Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling | Temporal networks are suitable for modeling complex evolving systems. It has a wide range of applications, such as social network analysis, recommender systems, and epidemiology. Recently, modeling such dynamic systems has drawn great attention in many domains. However, most existing approaches resort to taking discrete snapshots of the temporal networks and modeling all events with equal importance. This paper proposes Significant Ties Graph Neural Networks (STGNN), a novel framework that captures and describes significant ties. To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors' information and adaptively obtain the significance of node pairs. Experimental results on four real networks demonstrate the effectiveness of the proposed framework. | ['Li Tao', 'Yansong Wang', 'Tao Jia', 'Jiayun Wu'] | 2022-11-12 | null | null | null | null | ['epidemiology'] | ['medical'] | [-2.39083692e-01 -6.61640167e-02 -6.45326138e-01 -2.25695342e-01
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a381e7d4-a45d-4be5-bbc7-76e7ebe28ba1 | egocentric-pose-estimation-from-human-vision | 2104.05167 | null | https://arxiv.org/abs/2104.05167v1 | https://arxiv.org/pdf/2104.05167v1.pdf | Egocentric Pose Estimation from Human Vision Span | Estimating camera wearer's body pose from an egocentric view (egopose) is a vital task in augmented and virtual reality. Existing approaches either use a narrow field of view front facing camera that barely captures the wearer, or an extruded head-mounted top-down camera for maximal wearer visibility. In this paper, we tackle the egopose estimation from a more natural human vision span, where camera wearer can be seen in the peripheral view and depending on the head pose the wearer may become invisible or has a limited partial view. This is a realistic visual field for user-centric wearable devices like glasses which have front facing wide angle cameras. Existing solutions are not appropriate for this setting, and so, we propose a novel deep learning system taking advantage of both the dynamic features from camera SLAM and the body shape imagery. We compute 3D head pose, 3D body pose, the figure/ground separation, all at the same time while explicitly enforcing a certain geometric consistency across pose attributes. We further show that this system can be trained robustly with lots of existing mocap data so we do not have to collect and annotate large new datasets. Lastly, our system estimates egopose in real time and on the fly while maintaining high accuracy. | ['Vamsi Krishna Ithapu', 'Hao Jiang'] | 2021-04-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jiang_Egocentric_Pose_Estimation_From_Human_Vision_Span_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jiang_Egocentric_Pose_Estimation_From_Human_Vision_Span_ICCV_2021_paper.pdf | iccv-2021-1 | ['egocentric-pose-estimation'] | ['computer-vision'] | [-1.41864121e-01 2.15848889e-02 -2.39061508e-02 -3.11546117e-01
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638d6734-01c1-47bd-801a-8f72a1e511db | exploiting-modality-invariant-feature-for | 2210.15359 | null | https://arxiv.org/abs/2210.15359v1 | https://arxiv.org/pdf/2210.15359v1.pdf | Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities | Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always present in practice. In the studies to predict the missing data across modalities, the inherent difference between heterogeneous modalities, namely the modality gap, presents a challenge. To address this, we propose to use invariant features for a missing modality imagination network (IF-MMIN) which includes two novel mechanisms: 1) an invariant feature learning strategy that is based on the central moment discrepancy (CMD) distance under the full-modality scenario; 2) an invariant feature based imagination module (IF-IM) to alleviate the modality gap during the missing modalities prediction, thus improving the robustness of multimodal joint representation. Comprehensive experiments on the benchmark dataset IEMOCAP demonstrate that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions. We release the code at: https://github.com/ZhuoYulang/IF-MMIN. | ['Haizhou Li', 'Guanglai Gao', 'Jinming Zhao', 'Rui Liu', 'Haolin Zuo'] | 2022-10-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.13240847e-01 -9.53879207e-02 -3.47971588e-01 -4.98531610e-01
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16faf190-219f-4038-836a-4dfe489aec05 | the-speaker-independent-lipreading-play-off-a | 1810.10597 | null | http://arxiv.org/abs/1810.10597v1 | http://arxiv.org/pdf/1810.10597v1.pdf | The speaker-independent lipreading play-off; a survey of lipreading machines | Lipreading is a difficult gesture classification task. One problem in
computer lipreading is speaker-independence. Speaker-independence means to
achieve the same accuracy on test speakers not included in the training set as
speakers within the training set. Current literature is limited on
speaker-independent lipreading, the few independent test speaker accuracy
scores are usually aggregated within dependent test speaker accuracies for an
averaged performance. This leads to unclear independent results. Here we
undertake a systematic survey of experiments with the TCD-TIMIT dataset using
both conventional approaches and deep learning methods to provide a series of
wholly speaker-independent benchmarks and show that the best
speaker-independent machine scores 69.58% accuracy with CNN features and an SVM
classifier. This is less than state of the art speaker-dependent lipreading
machines, but greater than previously reported in independence experiments. | ['Madhi Saleh', 'Jake Burton', 'Nassir Navab', 'Helen L. Bear', 'David Frank'] | 2018-10-24 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.36543626e-01 -1.56637505e-01 -6.38327599e-01 -5.56160748e-01
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230f0362-3c3e-485d-ac6d-7e1ecb05e2ed | vvs-video-to-video-retrieval-with-irrelevant | 2303.08906 | null | https://arxiv.org/abs/2303.08906v1 | https://arxiv.org/pdf/2303.08906v1.pdf | VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression | In content-based video retrieval (CBVR), dealing with large-scale collections, efficiency is as important as accuracy. For this reason, several video-level feature-based studies have actively been conducted; nevertheless, owing to the severe difficulty of embedding a lengthy and untrimmed video into a single feature, these studies have shown insufficient for accurate retrieval compared to frame-level feature-based studies. In this paper, we show an insight that appropriate suppression of irrelevant frames can be a clue to overcome the current obstacles of the video-level feature-based approaches. Furthermore, we propose a Video-to-Video Suppression network (VVS) as a solution. The VVS is an end-to-end framework that consists of an easy distractor elimination stage for identifying which frames to remove and a suppression weight generation stage for determining how much to suppress the remaining frames. This structure is intended to effectively describe an untrimmed video with varying content and meaningless information. Its efficacy is proved via extensive experiments, and we show that our approach is not only state-of-the-art in video-level feature-based approaches but also has a fast inference time despite possessing retrieval capabilities close to those of frame-level feature-based approaches. | ['Yukyung Choi', 'Byungsoo Ko', 'Hyunwoo Kim', 'Gwangjin Lee', 'Geuntaek Lim', 'Won Jo'] | 2023-03-15 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [ 2.80590832e-01 -7.96438456e-01 -2.05588758e-01 2.12444421e-02
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e89f5d23-b7c5-4f3d-9c16-d2214140e50c | representation-learning-for-dynamic | 2112.10154 | null | https://arxiv.org/abs/2112.10154v4 | https://arxiv.org/pdf/2112.10154v4.pdf | Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting | The explosion of digital information and the growing involvement of people in social networks led to enormous research activity to develop methods that can extract meaningful information from interaction data. Commonly, interactions are represented by edges in a network or a graph, which implicitly assumes that the interactions are pairwise and static. However, real-world interactions deviate from these assumptions: (i) interactions can be multi-way, involving more than two nodes or individuals (e.g., family relationships, protein interactions), and (ii) interactions can change over a period of time (e.g., change of opinions and friendship status). While pairwise interactions have been studied in a dynamic network setting and multi-way interactions have been studied using hypergraphs in static networks, there exists no method, at present, that can predict multi-way interactions or hyperedges in dynamic settings. Existing related methods cannot answer temporal queries like what type of interaction will occur next and when it will occur. This paper proposes a temporal point process model for hyperedge prediction to address these problems. Our proposed model uses dynamic representation learning techniques for nodes in a neural point process framework to forecast hyperedges. We present several experimental results and set benchmark results. As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks. | ['Ambedkar Dukkipati', 'Tony Gracious'] | 2021-12-19 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 1.39096424e-01 1.67834684e-01 -3.21716070e-01 -4.78338033e-01
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058b1695-1004-442c-9448-619495167e96 | a-decomposition-based-hybrid-ensemble-cnn | 2203.09477 | null | https://arxiv.org/abs/2203.09477v2 | https://arxiv.org/pdf/2203.09477v2.pdf | A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition | Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging to extract more distinguishable features from different decomposed components for driver fatigue recognition. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. Four decomposition methods are employed to disassemble the EEG signals into components of different complexity. Instead of handcraft features, the CNNs in this framework directly learn from the decomposed components. In addition, a component-specific batch normalization layer is employed to reduce subject variability. Moreover, we employ two ensemble modes to integrate the outputs of all CNNs, comprehensively exploiting the diverse information of the decomposed components. Against the challenging cross-subject driver fatigue recognition task, the models under the framework all showed superior performance to the strong baselines. Specifically, the performance of different decomposition methods and ensemble modes was further compared. The results indicated that discrete wavelet transform-based ensemble CNN achieved the highest average classification accuracy of 83.48% among the compared methods. The proposed framework can be extended to any CNN architecture and be applied to any EEG-related tasks, opening the possibility of extracting more beneficial features from complex EEG data. | ['P. N. Suganthan', 'Ruobin Gao', 'Ruilin Li'] | 2022-03-14 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.59401262e-01 -4.37953383e-01 4.15097564e-01 -3.10438901e-01
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c9d4371d-eb7a-4368-8e82-eb1afe86c931 | image-based-indian-sign-language-recognition | 2304.14710 | null | https://arxiv.org/abs/2304.14710v1 | https://arxiv.org/pdf/2304.14710v1.pdf | Image-based Indian Sign Language Recognition: A Practical Review using Deep Neural Networks | People with vocal and hearing disabilities use sign language to express themselves using visual gestures and signs. Although sign language is a solution for communication difficulties faced by deaf people, there are still problems as most of the general population cannot understand this language, creating a communication barrier, especially in places such as banks, airports, supermarkets, etc. [1]. A sign language recognition(SLR) system is a must to solve this problem. The main focus of this model is to develop a real-time word-level sign language recognition system that would translate sign language to text. Much research has been done on ASL(American sign language). Thus, we have worked on ISL(Indian sign language) to cater to the needs of the deaf and hard-of-hearing community of India[2]. In this research, we provide an Indian Sign Language-based Sign Language recognition system. For this analysis, the user must be able to take pictures of hand movements using a web camera, and the system must anticipate and display the name of the taken picture. The acquired image goes through several processing phases, some of which use computer vision techniques, including grayscale conversion, dilatation, and masking. Our model is trained using a convolutional neural network (CNN), which is then utilized to recognize the images. Our best model has a 99% accuracy rate[3]. | ['M A Lekhana', 'Sanjam Kaur Bedi', 'Harleen Kaur', 'Mallikharjuna Rao K'] | 2023-04-28 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 5.90399131e-02 -3.81409794e-01 1.78034592e-04 -4.00861412e-01
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cb3868cb-b6b1-4382-a9ea-3fabdd29631f | rgb-d-local-implicit-function-for-depth | 2104.00622 | null | https://arxiv.org/abs/2104.00622v1 | https://arxiv.org/pdf/2104.00622v1.pdf | RGB-D Local Implicit Function for Depth Completion of Transparent Objects | Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new approach for depth completion of transparent objects from a single RGB-D image. Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed. Based on this representation, we present a novel framework that can complete missing depth given noisy RGB-D input. We further improve the depth estimation iteratively using a self-correcting refinement model. To train the whole pipeline, we build a large scale synthetic dataset with transparent objects. Experiments demonstrate that our method performs significantly better than the current state-of-the-art methods on both synthetic and real world data. In addition, our approach improves the inference speed by a factor of 20 compared to the previous best method, ClearGrasp. Code and dataset will be released at https://research.nvidia.com/publication/2021-03_RGB-D-Local-Implicit. | ['Dieter Fox', 'Shoubhik Debnath', 'Jozef van Eenbergen', 'Hammad Mazhar', 'Yu Xiang', 'Arsalan Mousavian', 'Luyang Zhu'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhu_RGB-D_Local_Implicit_Function_for_Depth_Completion_of_Transparent_Objects_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhu_RGB-D_Local_Implicit_Function_for_Depth_Completion_of_Transparent_Objects_CVPR_2021_paper.pdf | cvpr-2021-1 | ['transparent-objects'] | ['computer-vision'] | [ 2.67048270e-01 9.50079784e-02 4.79572207e-01 -5.50495923e-01
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3f76a725-6414-44b4-b060-ce045c9ecd52 | learning-spatial-and-temporal-variations-for | 2207.04673 | null | https://arxiv.org/abs/2207.04673v1 | https://arxiv.org/pdf/2207.04673v1.pdf | Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation | LiDAR-based 3D scene perception is a fundamental and important task for autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition tasks focus on single frame 3D point cloud data, and the temporal information is ignored in those methods. We argue that the temporal information across the frames provides crucial knowledge for 3D scene perceptions, especially in the driving scenario. In this paper, we focus on spatial and temporal variations to better explore the temporal information across the 3D frames. We design a temporal variation-aware interpolation module and a temporal voxel-point refiner to capture the temporal variation in the 4D point cloud. The temporal variation-aware interpolation generates local features from the previous and current frames by capturing spatial coherence and temporal variation information. The temporal voxel-point refiner builds a temporal graph on the 3D point cloud sequences and captures the temporal variation with a graph convolution module. The temporal voxel-point refiner also transforms the coarse voxel-level predictions into fine point-level predictions. With our proposed modules, the new network TVSN achieves state-of-the-art performance on SemanticKITTI and SemantiPOSS. Specifically, our method achieves 52.5\% in mIoU (+5.5% against previous best approaches) on the multiple scan segmentation task on SemanticKITTI, and 63.0% on SemanticPOSS (+2.8% against previous best approaches). | ['Lin Guosheng', 'Liu Fayao', 'Wang Hao', 'Wei Jiacheng', 'Shi Hanyu'] | 2022-07-11 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-1.52208731e-01 -3.91666025e-01 -3.39232981e-01 -7.25056887e-01
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3f75acfb-a7f7-4bc9-a147-171cba0f6f4f | panet-lidar-panoptic-segmentation-with-sparse | 2306.15348 | null | https://arxiv.org/abs/2306.15348v1 | https://arxiv.org/pdf/2306.15348v1.pdf | PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation | Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch and improve the performance on large objects, which are always over-segmented by clustering algorithms. Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the ``sampling-shifting-grouping" scheme to directly group thing points into instances from the raw point cloud efficiently. More specifically, balanced point sampling is introduced to generate sparse seed points with more uniform point distribution over the distance range. And a shift module, termed bubble shifting, is proposed to shrink the seed points to the clustered centers. Then we utilize the connected component label algorithm to generate instance proposals. Furthermore, an instance aggregation module is devised to integrate potentially fragmented instances, improving the performance of the SIP module on large objects. Extensive experiments show that PANet achieves state-of-the-art performance among published works on the SemanticKITII validation and nuScenes validation for the panoptic segmentation task. | ['Yong liu', 'Laijian Li', 'Xiaojun Hou', 'Mengmeng Wang', 'Yu Yang', 'Jianbiao Mei'] | 2023-06-27 | null | null | null | null | ['panoptic-segmentation', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.20622618e-02 -1.36859044e-01 -1.92371413e-01 -6.23008907e-01
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f1b9fb8a-b84e-4da3-949b-c32854f638f4 | promoting-saliency-from-depth-deep-1 | 2205.07179 | null | https://arxiv.org/abs/2205.07179v1 | https://arxiv.org/pdf/2205.07179v1.pdf | Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection | Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated at pixel-level. The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios. On the other hand, current unsupervised RGB-D SOD methods still heavily rely on handcrafted feature representations. This inspires us to propose in this paper a deep unsupervised RGB-D saliency detection approach, which requires no manual pixel-level annotation during training. It is realized by two key ingredients in our training pipeline. First, a depth-disentangled saliency update (DSU) framework is designed to automatically produce pseudo-labels with iterative follow-up refinements, which provides more trustworthy supervision signals for training the saliency network. Second, an attentive training strategy is introduced to tackle the issue of noisy pseudo-labels, by properly re-weighting to highlight the more reliable pseudo-labels. Extensive experiments demonstrate the superior efficiency and effectiveness of our approach in tackling the challenging unsupervised RGB-D SOD scenarios. Moreover, our approach can also be adapted to work in fully-supervised situation. Empirical studies show the incorporation of our approach gives rise to notably performance improvement in existing supervised RGB-D SOD models. | ['Li Cheng', 'Jie Liu', 'Chuan Guo', 'Qi Bi', 'Jingjing Li', 'Wei Ji'] | 2022-05-15 | promoting-saliency-from-depth-deep | https://openreview.net/forum?id=BZnnMbt0pW | https://openreview.net/pdf?id=BZnnMbt0pW | iclr-2022-4 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 5.52691817e-01 3.26388478e-01 -1.84388533e-01 -3.29656363e-01
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114a50a4-d8a3-4ca5-b5e4-5da9c6648e6e | sg-shuffle-multi-aspect-shuffle-transformer | 2211.04773 | null | https://arxiv.org/abs/2211.04773v1 | https://arxiv.org/pdf/2211.04773v1.pdf | SG-Shuffle: Multi-aspect Shuffle Transformer for Scene Graph Generation | Scene Graph Generation (SGG) serves a comprehensive representation of the images for human understanding as well as visual understanding tasks. Due to the long tail bias problem of the object and predicate labels in the available annotated data, the scene graph generated from current methodologies can be biased toward common, non-informative relationship labels. Relationship can sometimes be non-mutually exclusive, which can be described from multiple perspectives like geometrical relationships or semantic relationships, making it even more challenging to predict the most suitable relationship label. In this work, we proposed the SG-Shuffle pipeline for scene graph generation with 3 components: 1) Parallel Transformer Encoder, which learns to predict object relationships in a more exclusive manner by grouping relationship labels into groups of similar purpose; 2) Shuffle Transformer, which learns to select the final relationship labels from the category-specific feature generated in the previous step; and 3) Weighted CE loss, used to alleviate the training bias caused by the imbalanced dataset. | ['Josiah Poon', 'Soyeon Caren Han', 'Anh Duc Bui'] | 2022-11-09 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 5.16163468e-01 2.49840975e-01 -5.33088967e-02 -7.54542112e-01
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9a6eefc6-a662-4613-97a3-33c870a9e61f | mask-shadowgan-learning-to-remove-shadows | 1903.10683 | null | https://arxiv.org/abs/1903.10683v3 | https://arxiv.org/pdf/1903.10683v3.pdf | Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data | This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shadow-free domains, since the mapping between shadow and shadow-free images is not simply one-to-one. To address the problem, we formulate Mask-ShadowGAN, a new deep framework that automatically learns to produce a shadow mask from the input shadow image and then takes the mask to guide the shadow generation via re-formulated cycle-consistency constraints. Particularly, the framework simultaneously learns to produce shadow masks and learns to remove shadows, to maximize the overall performance. Also, we prepared an unpaired dataset for shadow removal and demonstrated the effectiveness of Mask-ShadowGAN on various experiments, even it was trained on unpaired data. | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaowei Hu', 'Yitong Jiang'] | 2019-03-26 | mask-shadowgan-learning-to-remove-shadows-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Hu_Mask-ShadowGAN_Learning_to_Remove_Shadows_From_Unpaired_Data_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Hu_Mask-ShadowGAN_Learning_to_Remove_Shadows_From_Unpaired_Data_ICCV_2019_paper.pdf | iccv-2019-10 | ['shadow-removal'] | ['computer-vision'] | [ 7.38876164e-01 3.90553087e-01 3.23086590e-01 -4.41039532e-01
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f13506b8-8b80-4969-bf12-bf22638fee53 | bayesian-calibration-of-differentiable-agent | 2305.15340 | null | https://arxiv.org/abs/2305.15340v1 | https://arxiv.org/pdf/2305.15340v1.pdf | Bayesian calibration of differentiable agent-based models | Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world. These difficulties have in turn generated research on approximate Bayesian inference methods for ABMs and on constructing differentiable approximations to arbitrary ABMs, but little work has been directed towards designing approximate Bayesian inference techniques for the specific case of differentiable ABMs. In this work, we aim to address this gap and discuss how generalised variational inference procedures may be employed to provide misspecification-robust Bayesian parameter inferences for differentiable ABMs. We demonstrate with experiments on a differentiable ABM of the COVID-19 pandemic that our approach can result in accurate inferences, and discuss avenues for future work. | ['Joel Dyer', 'Michael Wooldridge', 'Anisoara Calinescu', 'Ayush Chopra', 'Arnau Quera-Bofarull'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 5.05852327e-02 2.38891914e-01 -5.68024144e-02 -3.21621448e-01
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01429410-db61-4dd5-8631-fad44ff8ab6c | assessing-domain-adaptation-techniques-for | 2109.00869 | null | https://arxiv.org/abs/2109.00869v3 | https://arxiv.org/pdf/2109.00869v3.pdf | Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images | Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven artificial intelligence (AI) methods have been developed to automatically detect mitotic figures. However, these methods often generalise poorly, with performance reduced by variations in tissue types, staining protocols, or the scanners used to digitise whole-slide images. Domain adaptation approaches have been adopted in various applications to mitigate this issue of domain shift. We evaluate two unsupervised domain adaptation methods, CycleGAN and Neural Style Transfer, using the MIDOG 2021 Challenge dataset. This challenge focuses on detecting mitotic figures in whole-slide images digitised using different scanners. Two baseline mitosis detection models based on U-Net and RetinaNet were investigated in combination with the aforementioned domain adaptation methods. Both baseline models achieved human expert level performance, but had reduced performance when evaluated on images which had been digitised using a different scanner. The domain adaptation techniques were each found to be beneficial for detection with data from some scanners but not for others, with the only average increase across all scanners being achieved by CycleGAN on the RetinaNet detector. These techniques require further refinement to ensure consistency in mitosis detection. | ['Nishant Ravikumar', 'Nicolas M. Orsi', 'Kieran Zucker', 'Jack Breen'] | 2021-09-01 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 6.04406416e-01 1.16901062e-01 -1.23482540e-01 6.19772412e-02
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83a0cbb6-b828-4bb9-a1dd-d7b8fa04a63b | cluener2020-fine-grained-name-entity | 2001.04351 | null | https://arxiv.org/abs/2001.04351v4 | https://arxiv.org/pdf/2001.04351v4.pdf | CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese | In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like person, organization, and location, it contains more diverse categories. It is more challenging than current other Chinese NER datasets and could better reflect real-world applications. For comparison, we implement several state-of-the-art baselines as sequence labeling tasks and report human performance, as well as its analysis. To facilitate future work on fine-grained NER for Chinese, we release our dataset, baselines, and leader-board. | ['Weitang Liu', 'Yu tong', 'Liang Xu', 'Xuanwei Zhang', 'Lu Li', 'Yixuan Liao', 'Yin Tian', 'Qianqian Dong', 'Caiquan Liu', 'Cong Yu'] | 2020-01-13 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-6.20818555e-01 -1.44987121e-01 1.81788225e-02 -4.32526499e-01
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8a546655-0fe2-4fa9-8477-a14817980a08 | deepfake-text-detection-in-the-wild | 2305.13242 | null | https://arxiv.org/abs/2305.13242v1 | https://arxiv.org/pdf/2305.13242v1.pdf | Deepfake Text Detection in the Wild | Recent advances in large language models have enabled them to reach a level of text generation comparable to that of humans. These models show powerful capabilities across a wide range of content, including news article writing, story generation, and scientific writing. Such capability further narrows the gap between human-authored and machine-generated texts, highlighting the importance of deepfake text detection to avoid potential risks such as fake news propagation and plagiarism. However, previous work has been limited in that they testify methods on testbed of specific domains or certain language models. In practical scenarios, the detector faces texts from various domains or LLMs without knowing their sources. To this end, we build a wild testbed by gathering texts from various human writings and deepfake texts generated by different LLMs. Human annotators are only slightly better than random guessing at identifying machine-generated texts. Empirical results on automatic detection methods further showcase the challenges of deepfake text detection in a wild testbed. In addition, out-of-distribution poses a greater challenge for a detector to be employed in realistic application scenarios. We release our resources at https://github.com/yafuly/DeepfakeTextDetect. | ['Yue Zhang', 'Shuming Shi', 'Linyi Yang', 'Longyue Wang', 'Wei Bi', 'Leyang Cui', 'Qintong Li', 'Yafu Li'] | 2023-05-22 | null | null | null | null | ['face-swapping', 'story-generation'] | ['computer-vision', 'natural-language-processing'] | [-1.28225878e-01 2.15888396e-01 -1.51767820e-01 1.07662432e-01
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844f0c85-5da2-4738-906d-124eefc30a0f | dense-learning-based-semi-supervised-object | 2204.07300 | null | https://arxiv.org/abs/2204.07300v1 | https://arxiv.org/pdf/2204.07300v1.pdf | Dense Learning based Semi-Supervised Object Detection | Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data. Though various self-training based and consistency-regularization based SSOD methods have been proposed, most of them are anchor-based detectors, ignoring the fact that in many real-world applications anchor-free detectors are more demanded. In this paper, we intend to bridge this gap and propose a DenSe Learning (DSL) based anchor-free SSOD algorithm. Specifically, we achieve this goal by introducing several novel techniques, including an Adaptive Filtering strategy for assigning multi-level and accurate dense pixel-wise pseudo-labels, an Aggregated Teacher for producing stable and precise pseudo-labels, and an uncertainty-consistency-regularization term among scales and shuffled patches for improving the generalization capability of the detector. Extensive experiments are conducted on MS-COCO and PASCAL-VOC, and the results show that our proposed DSL method records new state-of-the-art SSOD performance, surpassing existing methods by a large margin. Codes can be found at \textcolor{blue}{https://github.com/chenbinghui1/DSL}. | ['Xian-Sheng Hua', 'Lei Zhang', 'Biao Wang', 'Xiang Chen', 'Pengyu Li', 'Binghui Chen'] | 2022-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 9.24326479e-03 -7.97879100e-02 -2.50390500e-01 -5.12370706e-01
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855e49d8-f03f-4f13-8b48-56081ff2a512 | mnist-mix-a-multi-language-handwritten-digit | 2004.03848 | null | https://arxiv.org/abs/2004.03848v1 | https://arxiv.org/pdf/2004.03848v1.pdf | MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset | In this letter, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples. With the same data format with MNIST, MNIST-MIX can be seamlessly applied in existing studies for handwritten digit recognition. By introducing digits from 10 different languages, MNIST-MIX becomes a more challenging dataset and its imbalanced classification requires a better design of models. We also present the results of applying a LeNet model which is pre-trained on MNIST as the baseline. | ['Weiwei Jiang'] | 2020-04-08 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-3.63007516e-01 -7.63879240e-01 -6.36402547e-01 -5.20348430e-01
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4b33d55d-260d-44fc-86a4-047f721506ba | fanet-quality-aware-feature-aggregation | 1811.09855 | null | https://arxiv.org/abs/1811.09855v2 | https://arxiv.org/pdf/1811.09855v2.pdf | FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking | This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose a novel deep network architecture called qualityaware Feature Aggregation Network (FANet) for robust RGBT tracking. Unlike existing RGBT trackers, our FANet aggregates hierarchical deep features within each modality to handle the challenge of significant appearance changes caused by deformation, low illumination, background clutter and occlusion. In particular, we employ the operations of max pooling to transform these hierarchical and multi-resolution features into uniform space with the same resolution, and use 1x1 convolution operation to compress feature dimensions to achieve more effective hierarchical feature aggregation. To model the interactions between RGB and thermal modalities, we elaborately design an adaptive aggregation subnetwork to integrate features from different modalities based on their reliabilities and thus are able to alleviate noise effects introduced by low-quality sources. The whole FANet is trained in an end-to-end manner. Extensive experiments on large-scale benchmark datasets demonstrate the high-accurate performance against other state-of-the-art RGBT tracking methods. | ['Jin Tang', 'Bin Luo', 'Yabin Zhu', 'Chenglong Li'] | 2018-11-24 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [ 3.96133661e-02 -6.19553983e-01 1.15611941e-01 -3.20836872e-01
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ddd88a59-311d-4977-8575-2539ecadd711 | active-learning-enhances-classification-of | 2303.01342 | null | https://arxiv.org/abs/2303.01342v1 | https://arxiv.org/pdf/2303.01342v1.pdf | Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning | In many histopathology tasks, sample classification depends on morphological details in tissue or single cells that are only visible at the highest magnification. For a pathologist, this implies tedious zooming in and out, while for a computational decision support algorithm, it leads to the analysis of a huge number of small image patches per whole slide image (WSI). Attention-based multiple instance learning (MIL), where attention estimation is learned in a weakly supervised manner, has been successfully applied in computational histopathology, but it is challenged by large numbers of irrelevant patches, reducing its accuracy. Here, we present an active learning approach to the problem. Querying the expert to annotate regions of interest in a WSI guides the formation of high-attention regions for MIL. We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. We test our approach on the CAMELYON17 dataset classifying metastatic lymph node sections in breast cancer. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. Active learning thus improves WSIs classification accuracy, leads to faster and more robust convergence, and speeds up the annotation process. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology. | ['Carsten Marr', 'Nassir Navab', 'Ario Sadafi'] | 2023-03-02 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.45627379e-01 5.76416850e-01 -2.47992918e-01 -3.71522576e-01
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588e6d7e-95a7-4008-b43b-22382d8eccc3 | automatic-image-cropping-for-visual-aesthetic | 1712.09048 | null | http://arxiv.org/abs/1712.09048v2 | http://arxiv.org/pdf/1712.09048v2.pdf | Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression | Despite recent progress, computational visual aesthetic is still challenging.
Image cropping, which refers to the removal of unwanted scene areas, is an
important step to improve the aesthetic quality of an image. However, it is
challenging to evaluate whether cropping leads to aesthetically pleasing
results because the assessment is typically subjective. In this paper, we
propose a novel cascaded cropping regression (CCR) method to perform image
cropping by learning the knowledge from professional photographers. The
proposed CCR method improves the convergence speed of the cascaded method,
which directly uses random-ferns regressors. In addition, a two-step learning
strategy is proposed and used in the CCR method to address the problem of
lacking labelled cropping data. Specifically, a deep convolutional neural
network (CNN) classifier is first trained on large-scale visual aesthetic
datasets. The deep CNN model is then designed to extract features from several
image cropping datasets, upon which the cropping bounding boxes are predicted
by the proposed CCR method. Experimental results on public image cropping
datasets demonstrate that the proposed method significantly outperforms several
state-of-the-art image cropping methods. | ['Hong-Yuan Mark Liao', 'Chunhua Shen', 'Yan Yan', 'Hanzi Wang', 'Guanjun Guo'] | 2017-12-25 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 5.84854186e-01 -9.40938368e-02 1.25608563e-01 -2.90576905e-01
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5bee2239-3000-4458-9f9c-d0682ba20c14 | protoformer-embedding-prototypes-for-1 | 2206.12710 | null | https://arxiv.org/abs/2206.12710v1 | https://arxiv.org/pdf/2206.12710v1.pdf | Protoformer: Embedding Prototypes for Transformers | Transformers have been widely applied in text classification. Unfortunately, real-world data contain anomalies and noisy labels that cause challenges for state-of-art Transformers. This paper proposes Protoformer, a novel self-learning framework for Transformers that can leverage problematic samples for text classification. Protoformer features a selection mechanism for embedding samples that allows us to efficiently extract and utilize anomalies prototypes and difficult class prototypes. We demonstrated such capabilities on datasets with diverse textual structures (e.g., Twitter, IMDB, ArXiv). We also applied the framework to several models. The results indicate that Protoformer can improve current Transformers in various empirical settings. | ['Zhishan Guo', 'Arthur Huang', 'Haiyan Bai', 'Nan Hua', 'Ning Sui', 'Ashkan Farhangi'] | 2022-06-25 | protoformer-embedding-prototypes-for | https://link.springer.com/chapter/10.1007/978-3-031-05933-9_35 | https://link.springer.com/chapter/10.1007/978-3-031-05933-9_35 | pakdd-2022-advances-in-knowledge-discovery | ['classification', 'self-learning'] | ['methodology', 'natural-language-processing'] | [-3.65952909e-01 -2.32339710e-01 -3.12426031e-01 -4.12081093e-01
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8c34ee25-b1ad-41e0-bba6-8a0372af2a88 | u-2-net-going-deeper-with-nested-u-structure | 2005.09007 | null | https://arxiv.org/abs/2005.09007v3 | https://arxiv.org/pdf/2005.09007v3.pdf | U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection | In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD). The architecture of our U$^2$-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU), (2) it increases the depth of the whole architecture without significantly increasing the computational cost because of the pooling operations used in these RSU blocks. This architecture enables us to train a deep network from scratch without using backbones from image classification tasks. We instantiate two models of the proposed architecture, U$^2$-Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U$^2$-Net$^{\dagger}$ (4.7 MB, 40 FPS), to facilitate the usage in different environments. Both models achieve competitive performance on six SOD datasets. The code is available: https://github.com/NathanUA/U-2-Net. | ['Osmar R. Zaiane', 'Xuebin Qin', 'Martin Jagersand', 'Chenyang Huang', 'Masood Dehghan', 'Zichen Zhang'] | 2020-05-18 | null | null | null | null | ['dichotomous-image-segmentation'] | ['computer-vision'] | [-7.79753327e-02 1.20295271e-01 1.24786556e-01 -9.58290398e-02
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db9b23dd-903e-468e-ae78-5eb1f49c5214 | bi3d-stereo-depth-estimation-via-binary | 2005.07274 | null | https://arxiv.org/abs/2005.07274v2 | https://arxiv.org/pdf/2005.07274v2.pdf | Bi3D: Stereo Depth Estimation via Binary Classifications | Stereo-based depth estimation is a cornerstone of computer vision, with state-of-the-art methods delivering accurate results in real time. For several applications such as autonomous navigation, however, it may be useful to trade accuracy for lower latency. We present Bi3D, a method that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth $D$, as existing stereo methods do, it classifies them as being closer or farther than $D$. This property offers a powerful mechanism to balance accuracy and latency. Given a strict time budget, Bi3D can detect objects closer than a given distance in as little as a few milliseconds, or estimate depth with arbitrarily coarse quantization, with complexity linear with the number of quantization levels. Bi3D can also use the allotted quantization levels to get continuous depth, but in a specific depth range. For standard stereo (i.e., continuous depth on the whole range), our method is close to or on par with state-of-the-art, finely tuned stereo methods. | ['Orazio Gallo', 'Jan Kautz', 'Pradeep Sen', 'Kihwan Kim', 'Alejandro Troccoli', 'Abhishek Badki'] | 2020-05-14 | bi3d-stereo-depth-estimation-via-binary-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.pdf | cvpr-2020-6 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 1.53602704e-01 -2.85094738e-01 1.53531823e-02 -3.56386065e-01
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7e07aac0-1008-4b12-a51e-31eac95737bc | supernmt-neural-machine-translation-with | null | null | https://aclanthology.org/P18-3010 | https://aclanthology.org/P18-3010.pdf | SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags | In this paper we incorporate semantic supersensetags and syntactic supertag features into EN{--}FR and EN{--}DE factored NMT systems. In experiments on various test sets, we observe that such features (and particularly when combined) help the NMT model training to converge faster and improve the model quality according to the BLEU scores. | ['Eva Vanmassenhove', 'Andy Way'] | 2018-07-01 | null | null | null | acl-2018-7 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 2.63485521e-01 2.45263323e-01 -4.22742635e-01 -6.18195295e-01
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c2c7b4cb-e82e-4da8-8ade-faa78ba72c4c | effect-of-analysis-window-and-feature | 2002.00461 | null | https://arxiv.org/abs/2002.00461v4 | https://arxiv.org/pdf/2002.00461v4.pdf | Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal | Electromyography (EMG) signals have been successfully employed for driving prosthetic limbs of a single or double degree of freedom. This principle works by using the amplitude of the EMG signals to decide between one or two simpler movements. This method underperforms as compare to the contemporary advances done at the mechanical, electronics, and robotics end, and it lacks intuition. Recently, research on myoelectric control based on pattern recognition (PR) shows promising results with the aid of machine learning classifiers. Using the approach termed as, EMG-PR, EMG signals are divided into analysis windows, and features are extracted for each window. These features are then fed to the machine learning classifiers as input. By offering multiple class movements and intuitive control, this method has the potential to power an amputated subject to perform everyday life movements. In this paper, we investigate the effect of the analysis window and feature selection on classification accuracy of different hand and wrist movements using time-domain features. We show that effective data preprocessing and optimum feature selection helps to improve the classification accuracy of hand movements. We use publicly available hand and wrist gesture dataset of $40$ intact subjects for experimentation. Results computed using different classification algorithms show that the proposed preprocessing and features selection outperforms the baseline and achieve up to $98\%$ classification accuracy. | ['Sarwan Ali', 'Safiullah Faizullah', 'Muhammad Asad Khan', 'Asad Ullah', 'Imdadullah Khan'] | 2020-02-02 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 4.90789473e-01 -7.81077296e-02 -6.32518113e-01 9.42242704e-03
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d968bc2d-7efe-4d33-8272-2b2181d3d7d8 | automated-program-repair-based-on-code-review | 2304.07840 | null | https://arxiv.org/abs/2304.07840v1 | https://arxiv.org/pdf/2304.07840v1.pdf | Automated Program Repair Based on Code Review: How do Pre-trained Transformer Models Perform? | Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review (natural language instruction about suggestive changes in code) can improve the program repair further. Large language models, trained with Natural Language (NL) and computer program corpora, have the capacity to contain inherent knowledge of both. In this study, we investigate if this inherent knowledge of code and NL can be utilized to improve automated program repair. We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both Programming Language (PL) and Natural Language (NL), on two such natural language-based program repair datasets and found that the pre-trained language models fine-tuned with datasets containing both code review and subsequent code changes notably outperform each of the previous models. We observed that the pre-trained models improve the previously best-reported results by 9.91% on the Review4Repair dataset and by 24.72% on the dataset by Tufano et al. This suggests that a pre-trained sequential model has a better understanding of natural language and can utilize it much better. We performed an ablation study to assess the contribution of the pre-training mechanism and the model architecture. We found that pre-training was significantly more important in the performance gain than the model architecture. The practical application of using pre-trained transformer models in the context of automated program repair is still a long way off. However, our study demonstrates the substantial value of employing pre-trained models, paving the path for future studies to use more of these. | ['Anindya Iqbal', 'Masum Hasan', 'Md. Mohib Hossain', 'Rishov Paul'] | 2023-04-16 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 2.13470533e-01 2.16224700e-01 -5.48205674e-01 -1.53525874e-01
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795fd1f0-abe0-4ef0-9eb5-d2a63d8efa3c | adversarial-skill-chaining-for-long-horizon | 2111.07999 | null | https://arxiv.org/abs/2111.07999v1 | https://arxiv.org/pdf/2111.07999v1.pdf | Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization | Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a policy encounters a starting state never seen during its training. For successful skill chaining, prior approaches attempt to widen the policy's starting state distribution. However, these approaches require larger state distributions to be covered as more policies are sequenced, and thus are limited to short skill sequences. In this paper, we propose to chain multiple policies without excessively large initial state distributions by regularizing the terminal state distributions in an adversarial learning framework. We evaluate our approach on two complex long-horizon manipulation tasks of furniture assembly. Our results have shown that our method establishes the first model-free reinforcement learning algorithm to solve these tasks; whereas prior skill chaining approaches fail. The code and videos are available at https://clvrai.com/skill-chaining | ['Yuke Zhu', 'Anima Anandkumar', 'Joseph J. Lim', 'Youngwoon Lee'] | 2021-11-15 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.65577304e-01 1.96250960e-01 -3.13296467e-01 2.11464494e-01
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9e711df0-c9f7-46f8-9b01-219d30caa930 | fair-a-causal-framework-for-accurately | 2306.11585 | null | https://arxiv.org/abs/2306.11585v1 | https://arxiv.org/pdf/2306.11585v1.pdf | FAIR: A Causal Framework for Accurately Inferring Judgments Reversals | Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framework for Accurately Inferring case Reversals (FAIR), which models the problem of judgments reversals based on real Chinese judgments. We mine the causes of judgments reversals by causal inference methods and inject the obtained causal relationships into the neural network as a priori knowledge. And then, our framework is validated on a challenging dataset as a legal judgment prediction task. The experimental results show that our framework can tap the most critical factors in judgments reversal, and the obtained causal relationships can effectively improve the neural network's performance. In addition, we discuss the generalization ability of large language models for legal intelligence tasks using ChatGPT as an example. Our experiment has found that the generalization ability of large language models still has defects, and mining causal relationships can effectively improve the accuracy and explain ability of model predictions. | ['Yaying Chen', 'Qionghui Zhang', 'Yuntao Shi', 'Nanfei Gu', 'Minghua He'] | 2023-06-20 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 9.77687538e-02 5.28118908e-02 -5.64521909e-01 -6.85907066e-01
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d64a875a-cb15-4195-bfe0-84be7f9e00e1 | semi-supervised-music-emotion-recognition | 2112.00702 | null | https://arxiv.org/abs/2112.00702v2 | https://arxiv.org/pdf/2112.00702v2.pdf | Semi-supervised music emotion recognition using noisy student training and harmonic pitch class profiles | We present Mirable's submission to the 2021 Emotions and Themes in Music challenge. In this work, we intend to address the question: can we leverage semi-supervised learning techniques on music emotion recognition? With that, we experiment with noisy student training, which has improved model performance in the image classification domain. As the noisy student method requires a strong teacher model, we further delve into the factors including (i) input training length and (ii) complementary music representations to further boost the performance of the teacher model. For (i), we find that models trained with short input length perform better in PR-AUC, whereas those trained with long input length perform better in ROC-AUC. For (ii), we find that using harmonic pitch class profiles (HPCP) consistently improve tagging performance, which suggests that harmonic representation is useful for music emotion tagging. Finally, we find that noisy student method only improves tagging results for the case of long training length. Additionally, we find that ensembling representations trained with different training lengths can improve tagging results significantly, which suggest a possible direction to explore incorporating multiple temporal resolutions in the network architecture for future work. | ['Hao Hao Tan'] | 2021-12-01 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 2.03753561e-01 -8.93855169e-02 -1.83078706e-01 -2.12911680e-01
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c5ebbee5-467b-4625-8992-23fb14efa505 | hopper-multi-hop-transformer-for-1 | 2103.10574 | null | https://arxiv.org/abs/2103.10574v2 | https://arxiv.org/pdf/2103.10574v2.pdf | Hopper: Multi-hop Transformer for Spatiotemporal Reasoning | This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about the location of objects as they move through the video while being occluded, contained or carried by other objects. Existing deep learning based approaches often suffer from spatiotemporal biases when applied to video reasoning problems. We propose Hopper, which uses a Multi-hop Transformer for reasoning object permanence in videos. Given a video and a localization query, Hopper reasons over image and object tracks to automatically hop over critical frames in an iterative fashion to predict the final position of the object of interest. We demonstrate the effectiveness of using a contrastive loss to reduce spatiotemporal biases. We evaluate over CATER dataset and find that Hopper achieves 73.2% Top-1 accuracy using just 1 FPS by hopping through just a few critical frames. We also demonstrate Hopper can perform long-term reasoning by building a CATER-h dataset that requires multi-step reasoning to localize objects of interest correctly. | ['Hans Peter Graf', 'Mubbasir Kapadia', 'Martin Renqiang Min', 'Alexandru Niculescu-Mizil', 'Farley Lai', 'Asim Kadav', 'Honglu Zhou'] | 2021-03-19 | hopper-multi-hop-transformer-for | https://openreview.net/forum?id=MaZFq7bJif7 | https://openreview.net/pdf?id=MaZFq7bJif7 | iclr-2021-1 | ['video-object-tracking'] | ['computer-vision'] | [-2.34394655e-01 -1.34759068e-01 -2.58859336e-01 -2.46120423e-01
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c7e4a936-c711-4627-bcc1-a196cc168743 | integrating-diverse-extraction-pathways-using | 2110.08144 | null | https://arxiv.org/abs/2110.08144v2 | https://arxiv.org/pdf/2110.08144v2.pdf | milIE: Modular & Iterative Multilingual Open Information Extraction | Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction. Based on this hypothesis, we propose a neural OpenIE system, milIE, that operates in an iterative fashion. Due to the iterative nature, the system is also modular -- it is possible to seamlessly integrate rule based extraction systems with a neural end-to-end system, thereby allowing rule based systems to supply extraction slots which milIE can leverage for extracting the remaining slots. We confirm our hypothesis empirically: milIE outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. Additionally, we are the first to provide an OpenIE test dataset for Arabic and Galician. | ['Ammar Shaker', 'Daniel Oñoro Rubio', 'Mathias Niepert', 'Makoto Takamoto', 'Vanesa Rodriguez-Tembras', 'Carolin Lawrence', 'Kiril Gashteovski', 'Bhushan Kotnis'] | 2021-10-15 | null | https://aclanthology.org/2022.acl-long.478 | https://aclanthology.org/2022.acl-long.478.pdf | acl-2022-5 | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.02847733e-01 9.79542553e-01 -4.18016165e-01 -1.90827459e-01
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7c25156b-8304-4c23-a2d4-9f57849c9a41 | visual-question-answering-based-on-formal | 2111.04785 | null | https://arxiv.org/abs/2111.04785v1 | https://arxiv.org/pdf/2111.04785v1.pdf | Visual Question Answering based on Formal Logic | Visual question answering (VQA) has been gaining a lot of traction in the machine learning community in the recent years due to the challenges posed in understanding information coming from multiple modalities (i.e., images, language). In VQA, a series of questions are posed based on a set of images and the task at hand is to arrive at the answer. To achieve this, we take a symbolic reasoning based approach using the framework of formal logic. The image and the questions are converted into symbolic representations on which explicit reasoning is performed. We propose a formal logic framework where (i) images are converted to logical background facts with the help of scene graphs, (ii) the questions are translated to first-order predicate logic clauses using a transformer based deep learning model, and (iii) perform satisfiability checks, by using the background knowledge and the grounding of predicate clauses, to obtain the answer. Our proposed method is highly interpretable and each step in the pipeline can be easily analyzed by a human. We validate our approach on the CLEVR and the GQA dataset. We achieve near perfect accuracy of 99.6% on the CLEVR dataset comparable to the state of art models, showcasing that formal logic is a viable tool to tackle visual question answering. Our model is also data efficient, achieving 99.1% accuracy on CLEVR dataset when trained on just 10% of the training data. | ['J. Clayton Kerce', 'Faramarz Fekri', 'Ali Payani', 'Muralikrishnna G. Sethuraman'] | 2021-11-08 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 4.18633103e-01 5.56388855e-01 1.55195311e-01 -3.31314951e-01
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4630d92c-de01-4fe3-a66a-0417e124583c | automatic-classification-of-geologic-units-in | 1901.03786 | null | http://arxiv.org/abs/1901.03786v1 | http://arxiv.org/pdf/1901.03786v1.pdf | Automatic classification of geologic units in seismic images using partially interpreted examples | Geologic interpretation of large seismic stacked or migrated seismic images
can be a time-consuming task for seismic interpreters. Neural network based
semantic segmentation provides fast and automatic interpretations, provided a
sufficient number of example interpretations are available. Networks that map
from image-to-image emerged recently as powerful tools for automatic
segmentation, but standard implementations require fully interpreted examples.
Generating training labels for large images manually is time consuming. We
introduce a partial loss-function and labeling strategies such that networks
can learn from partially interpreted seismic images. This strategy requires
only a small number of annotated pixels per seismic image. Tests on seismic
images and interpretation information from the Sea of Ireland show that we
obtain high-quality predicted interpretations from a small number of large
seismic images. The combination of a partial-loss function, a multi-resolution
network that explicitly takes small and large-scale geological features into
account, and new labeling strategies make neural networks a more practical tool
for automatic seismic interpretation. | ['Bas Peters', 'Justin Granek', 'Eldad Haber'] | 2019-01-12 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [ 6.03377819e-01 6.56037390e-01 2.39104107e-01 -8.97099197e-01
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d820a3ef-7eda-463f-9aa2-d424b6ca5f9d | overexposure-mask-fusion-generalizable | 2210.11511 | null | https://arxiv.org/abs/2210.11511v1 | https://arxiv.org/pdf/2210.11511v1.pdf | Overexposure Mask Fusion: Generalizable Reverse ISP Multi-Step Refinement | With the advent of deep learning methods replacing the ISP in transforming sensor RAW readings into RGB images, numerous methodologies solidified into real-life applications. Equally potent is the task of inverting this process which will have applications in enhancing computational photography tasks that are conducted in the RAW domain, addressing lack of available RAW data while reaping from the benefits of performing tasks directly on sensor readings. This paper's proposed methodology is a state-of-the-art solution to the task of RAW reconstruction, and the multi-step refinement process integrating an overexposure mask is novel in three ways: instead of from RGB to bayer, the pipeline trains from RGB to demosaiced RAW allowing use of perceptual loss functions; the multi-step processes has greatly enhanced the performance of the baseline U-Net from start to end; the pipeline is a generalizable process of refinement that can enhance other high performance methodologies that support end-to-end learning. | ['Jinwei Gu', 'Jun Jiang', 'Jinha Kim'] | 2022-10-20 | null | null | null | null | ['raw-reconstruction'] | ['computer-vision'] | [ 6.59623981e-01 2.80613810e-01 5.06001472e-01 -6.18049800e-01
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65c45cea-35ad-4c48-ac00-c7fd072fed9d | higher-order-correlation-analysis-for-multi | 2201.11949 | null | https://arxiv.org/abs/2201.11949v1 | https://arxiv.org/pdf/2201.11949v1.pdf | Higher Order Correlation Analysis for Multi-View Learning | Multi-view learning is frequently used in data science. The pairwise correlation maximization is a classical approach for exploring the consensus of multiple views. Since the pairwise correlation is inherent for two views, the extensions to more views can be diversified and the intrinsic interconnections among views are generally lost. To address this issue, we propose to maximize higher order correlations. This can be formulated as a low rank approximation problem with the higher order correlation tensor of multi-view data. We use the generating polynomial method to solve the low rank approximation problem. Numerical results on real multi-view data demonstrate that this method consistently outperforms prior existing methods. | ['Zequn Zheng', 'Li Wang', 'Jiawang Nie'] | 2022-01-28 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.14141268e-01 -1.46899730e-01 -2.09389642e-01 -2.52210736e-01
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62c73593-adaf-43b2-a98c-e83f2de170bb | answer-type-prediction-for-visual-question | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Kafle_Answer-Type_Prediction_for_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Kafle_Answer-Type_Prediction_for_CVPR_2016_paper.pdf | Answer-Type Prediction for Visual Question Answering | Recently, algorithms for object recognition and related tasks have become sufficiently proficient that new vision tasks can now be pursued. In this paper, we build a system capable of answering open-ended text-based questions about images, which is known as Visual Question Answering (VQA). Our approach's key insight is that we can predict the form of the answer from the question. We formulate our solution in a Bayesian framework. When our approach is combined with a discriminative model, the combined model achieves state-of-the-art results on four benchmark datasets for open-ended VQA: DAQUAR, COCO-QA, The VQA Dataset, and Visual7W. | ['Kushal Kafle', 'Christopher Kanan'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['type-prediction'] | ['computer-code'] | [-1.34502456e-01 -6.44259006e-02 6.22013770e-02 -5.93463123e-01
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ebf6a5b6-c37a-4352-8692-0c8b318fe9d0 | matteformer-transformer-based-image-matting | 2203.15662 | null | https://arxiv.org/abs/2203.15662v1 | https://arxiv.org/pdf/2203.15662v1.pdf | MatteFormer: Transformer-Based Image Matting via Prior-Tokens | In this paper, we propose a transformer-based image matting model called MatteFormer, which takes full advantage of trimap information in the transformer block. Our method first introduces a prior-token which is a global representation of each trimap region (e.g. foreground, background and unknown). These prior-tokens are used as global priors and participate in the self-attention mechanism of each block. Each stage of the encoder is composed of PAST (Prior-Attentive Swin Transformer) block, which is based on the Swin Transformer block, but differs in a couple of aspects: 1) It has PA-WSA (Prior-Attentive Window Self-Attention) layer, performing self-attention not only with spatial-tokens but also with prior-tokens. 2) It has prior-memory which saves prior-tokens accumulatively from the previous blocks and transfers them to the next block. We evaluate our MatteFormer on the commonly used image matting datasets: Composition-1k and Distinctions-646. Experiment results show that our proposed method achieves state-of-the-art performance with a large margin. Our codes are available at https://github.com/webtoon/matteformer. | ['Nojun Kwak', 'SeHo Kim', 'Jaeyoung Yoo', 'Sungjoon Son', 'Gyutae Park'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Park_MatteFormer_Transformer-Based_Image_Matting_via_Prior-Tokens_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Park_MatteFormer_Transformer-Based_Image_Matting_via_Prior-Tokens_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-matting'] | ['computer-vision'] | [ 2.21886560e-01 4.07036357e-02 -1.36399716e-01 -2.91611135e-01
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b2cb5936-2237-4eb4-8d97-e4a64b21752a | theoretical-evaluation-of-feature-selection | 1609.06575 | null | http://arxiv.org/abs/1609.06575v2 | http://arxiv.org/pdf/1609.06575v2.pdf | Theoretical Evaluation of Feature Selection Methods based on Mutual Information | Feature selection methods are usually evaluated by wrapping specific
classifiers and datasets in the evaluation process, resulting very often in
unfair comparisons between methods. In this work, we develop a theoretical
framework that allows obtaining the true feature ordering of two-dimensional
sequential forward feature selection methods based on mutual information, which
is independent of entropy or mutual information estimation methods,
classifiers, or datasets, and leads to an undoubtful comparison of the methods.
Moreover, the theoretical framework unveils problems intrinsic to some methods
that are otherwise difficult to detect, namely inconsistencies in the
construction of the objective function used to select the candidate features,
due to various types of indeterminations and to the possibility of the entropy
of continuous random variables taking null and negative values. | ['António Pacheco', 'M. Rosário Oliveira', 'Cláudia Pascoal', 'Rui Valadas'] | 2016-09-21 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.60738844e-01 -1.10164657e-01 -1.22759357e-01 -5.64169586e-01
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c48584ba-00f3-4848-ab90-3cb8623b1289 | uncovering-the-evolution-of-non-stationary | 1510.07280 | null | http://arxiv.org/abs/1510.07280v1 | http://arxiv.org/pdf/1510.07280v1.pdf | Uncovering the evolution of non-stationary stochastic variables: the example of asset volume-price fluctuations | We present a framework for describing the evolution of stochastic observables
having a non-stationary distribution of values. The framework is applied to
empirical volume-prices from assets traded at the New York stock exchange.
Using Kullback-Leibler divergence we evaluate the best model out from four
biparametric models standardly used in the context of financial data analysis.
In our present data sets we conclude that the inverse $\Gamma$-distribution is
a good model, particularly for the distribution tail of the largest
volume-price fluctuations. Extracting the time-series of the corresponding
parameter values we show that they evolve in time as stochastic variables
themselves. For the particular case of the parameter controlling the
volume-price distribution tail we are able to extract an Ornstein-Uhlenbeck
equation which describes the fluctuations of the largest volume-prices observed
in the data. Finally, we discuss how to bridge from the stochastic evolution of
the distribution parameters to the stochastic evolution of the (non-stationary)
observable and put our conclusions into perspective for other applications in
geophysics and biology. | [] | 2015-10-25 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-3.47242057e-01 -3.69065434e-01 4.03982997e-01 -2.51689583e-01
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4559d732-5730-4f14-87bd-8d1fb1fead5d | hard-attention-control-by-mutual-information-1 | 2103.06371 | null | https://arxiv.org/abs/2103.06371v1 | https://arxiv.org/pdf/2103.06371v1.pdf | Hard Attention Control By Mutual Information Maximization | Biological agents have adopted the principle of attention to limit the rate of incoming information from the environment. One question that arises is if an artificial agent has access to only a limited view of its surroundings, how can it control its attention to effectively solve tasks? We propose an approach for learning how to control a hard attention window by maximizing the mutual information between the environment state and the attention location at each step. The agent employs an internal world model to make predictions about its state and focuses attention towards where the predictions may be wrong. Attention is trained jointly with a dynamic memory architecture that stores partial observations and keeps track of the unobserved state. We demonstrate that our approach is effective in predicting the full state from a sequence of partial observations. We also show that the agent's internal representation of the surroundings, a live mental map, can be used for control in two partially observable reinforcement learning tasks. Videos of the trained agent can be found at https://sites.google.com/view/hard-attention-control. | ['Charles Isbell', 'Himanshu Sahni'] | 2021-03-10 | hard-attention-control-by-mutual-information | https://openreview.net/forum?id=TV9INIrmtWN | https://openreview.net/pdf?id=TV9INIrmtWN | null | ['hard-attention'] | ['methodology'] | [ 3.02700460e-01 5.26199222e-01 2.49470230e-02 -1.82229467e-02
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7d5e85ec-6ea2-4fb6-ae37-901ceb547045 | meta-heuristic-based-deep-learning-model-for | null | null | https://link.springer.com/article/10.1007/s11063-022-10880-z#Abs1 | https://link.springer.com/content/pdf/10.1007/s11063-022-10880-z.pdf?pdf=button | Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection. | The automatic detection of leaf disease is necessary to improve the quality and quantity of agricultural production. This paper proposes a framework based on optimal deep neural network (ODNN) to detect the plant leaf disease using the leaf images of healthy and diseased plants. The proposed work uses Convolutional Neural Network for feature extraction and ODNN is used for detection. In ODNN, a two level of weight optimization is employed to boost the performance of the traditional Deep Neural Network. A two level of weight optimization is done by Improved Butterfly Optimization Algorithm. Here the traditional Butterfly Optimization Algorithm is improved using Genetic Algorithm; this double weight updation improves the convergence rate to a considerable amount. Sensitivity, Accuracy and Specificity metrics are used for evaluating the performance of the proposed method. Experimental evaluations were done with the help of MATLAB. The proposed disease detection methodology achieves an overall accuracy of 99 percent. The experimental results give an indication that the proposed method turned out to be a better technique when compared to the existing techniques. | ['P. Ramkumar', 'A. Meenakshi', 'R. Uma', 'J. Anitha Ruth'] | 2022-05-05 | null | null | null | neural-processing-letters-volume-54-2022-5 | ['specificity'] | ['natural-language-processing'] | [ 3.38906236e-02 -2.87510097e-01 -1.23724164e-02 -5.01880646e-02
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7a913563-9629-4626-a6ae-89996f7db363 | transfer-reinforcement-learning-for-differing | 2202.02442 | null | https://arxiv.org/abs/2202.02442v3 | https://arxiv.org/pdf/2202.02442v3.pdf | Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations | Transfer learning approaches in reinforcement learning aim to assist agents in learning their target domains by leveraging the knowledge learned from other agents that have been trained on similar source domains. For example, recent research focus within this space has been placed on knowledge transfer between tasks that have different transition dynamics and reward functions; however, little focus has been placed on knowledge transfer between tasks that have different action spaces. In this paper, we approach the task of transfer learning between domains that differ in action spaces. We present a reward shaping method based on source embedding similarity that is applicable to domains with both discrete and continuous action spaces. The efficacy of our approach is evaluated on transfer to restricted action spaces in the Acrobot-v1 and Pendulum-v0 domains. A comparison with two baselines shows that our method does not outperform these baselines in these continuous action spaces but does show an improvement in these discrete action spaces. We conclude our analysis with future directions for this work. | ['Hieu Tran', 'Abhiramon Rajasekharan', 'Nathan Beck'] | 2022-02-05 | null | null | null | null | ['transfer-reinforcement-learning', 'acrobot'] | ['methodology', 'playing-games'] | [ 5.38048567e-03 2.71752980e-02 -3.31814557e-01 -1.72520310e-01
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c2d638cd-c12c-471d-9afe-601b8ba72545 | hmc-at-semeval-2016-task-11-identifying | null | null | https://aclanthology.org/S16-1161 | https://aclanthology.org/S16-1161.pdf | HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees | null | ['Maury Quijada', 'Julie Medero'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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5533e0f8-907b-4573-aeae-140a45e2acea | attention-convolutional-binary-neural-tree | 1909.11378 | null | https://arxiv.org/abs/1909.11378v2 | https://arxiv.org/pdf/1909.11378v2.pdf | Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization | Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, illumination, etc. An attention convolutional binary neural tree architecture is presented to address those problems for weakly supervised FGVC. Specifically, we incorporate convolutional operations along edges of the tree structure, and use the routing functions in each node to determine the root-to-leaf computational paths within the tree. The final decision is computed as the summation of the predictions from leaf nodes. The deep convolutional operations learn to capture the representations of objects, and the tree structure characterizes the coarse-to-fine hierarchical feature learning process. In addition, we use the attention transformer module to enforce the network to capture discriminative features. The negative log-likelihood loss is used to train the entire network in an end-to-end fashion by SGD with back-propagation. Several experiments on the CUB-200-2011, Stanford Cars and Aircraft datasets demonstrate that the proposed method performs favorably against the state-of-the-arts. | ['Yanjun Wu', 'Xianglong Liu', 'Longyin Wen', 'Ruyi Ji', 'Libo Zhang', 'Dawei Du', 'Chen Zhao', 'Feiyue Huang'] | 2019-09-25 | attention-convolutional-binary-neural-tree-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Attention_Convolutional_Binary_Neural_Tree_for_Fine-Grained_Visual_Categorization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ji_Attention_Convolutional_Binary_Neural_Tree_for_Fine-Grained_Visual_Categorization_CVPR_2020_paper.pdf | cvpr-2020-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-1.10057574e-02 -8.91709477e-02 -3.84908319e-02 -8.14566910e-01
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a9a91199-e254-439e-893a-1bc96b746cde | leveraging-users-social-network-embeddings | 2211.10672 | null | https://arxiv.org/abs/2211.10672v1 | https://arxiv.org/pdf/2211.10672v1.pdf | Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter | Social networks (SNs) are increasingly important sources of news for many people. The online connections made by users allows information to spread more easily than traditional news media (e.g., newspaper, television). However, they also make the spread of fake news easier than in traditional media, especially through the users' social network connections. In this paper, we focus on investigating if the SNs' users connection structure can aid fake news detection on Twitter. In particular, we propose to embed users based on their follower or friendship networks on the Twitter platform, so as to identify the groups that users form. Indeed, by applying unsupervised graph embedding methods on the graphs from the Twitter users' social network connections, we observe that users engaged with fake news are more tightly clustered together than users only engaged in factual news. Thus, we hypothesise that the embedded user's network can help detect fake news effectively. Through extensive experiments using a publicly available Twitter dataset, our results show that applying graph embedding methods on SNs, using the user connections as network information, can indeed classify fake news more effectively than most language-based approaches. Specifically, we observe a significant improvement over using only the textual information (i.e., TF.IDF or a BERT language model), as well as over models that deploy both advanced textual features (i.e., stance detection) and complex network features (e.g., users network, publishers cross citations). We conclude that the Twitter users' friendship and followers network information can significantly outperform language-based approaches, as well as the existing state-of-the-art fake news detection models that use a more sophisticated network structure, in classifying fake news on Twitter. | ['Iadh Ounis', 'Craig Macdonald', 'Ting Su'] | 2022-11-19 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-4.56407040e-01 4.38449472e-01 -8.97493243e-01 2.34670475e-01
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6b82a71c-dc27-4487-990c-64617cf4b0e5 | multi-target-domain-adaptation-with | 2106.03418 | null | https://arxiv.org/abs/2106.03418v1 | https://arxiv.org/pdf/2106.03418v1.pdf | Multi-Target Domain Adaptation with Collaborative Consistency Learning | Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple target domains. In this work, we propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation. An unsupervised domain adaptation expert model is first trained for each source-target pair and is further encouraged to collaborate with each other through a bridge built between different target domains. These expert models are further improved by adding the regularization of making the consistent pixel-wise prediction for each sample with the same structured context. To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights. Extensive experiments demonstrate that the proposed method can effectively exploit rich structured information contained in both labeled source domain and multiple unlabeled target domains. Not only does it perform well across multiple target domains but also performs favorably against state-of-the-art unsupervised domain adaptation methods specially trained on a single source-target pair | ['Shengjin Wang', 'Huchuan Lu', 'Jianzhuang Liu', 'Yongjie Shi', 'Jianzhong He', 'Shuaijun Chen', 'Xu Jia', 'Takashi Isobe'] | 2021-06-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 6.00056469e-01 7.81623498e-02 -3.73478383e-01 -6.09746695e-01
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65ac7889-22a4-462c-b759-13eb946fe0c0 | enhancing-speech-to-speech-translation-with | 2304.04618 | null | https://arxiv.org/abs/2304.04618v1 | https://arxiv.org/pdf/2304.04618v1.pdf | Enhancing Speech-to-Speech Translation with Multiple TTS Targets | It has been known that direct speech-to-speech translation (S2ST) models usually suffer from the data scarcity issue because of the limited existing parallel materials for both source and target speech. Therefore to train a direct S2ST system, previous works usually utilize text-to-speech (TTS) systems to generate samples in the target language by augmenting the data from speech-to-text translation (S2TT). However, there is a limited investigation into how the synthesized target speech would affect the S2ST models. In this work, we analyze the effect of changing synthesized target speech for direct S2ST models. We find that simply combining the target speech from different TTS systems can potentially improve the S2ST performances. Following that, we also propose a multi-task framework that jointly optimizes the S2ST system with multiple targets from different TTS systems. Extensive experiments demonstrate that our proposed framework achieves consistent improvements (2.8 BLEU) over the baselines on the Fisher Spanish-English dataset. | ['Shinji Watanabe', 'Juan Pino', 'Changhan Wang', 'Hirofumi Inaguma', 'Ann Lee', 'Yun Tang', 'Jiatong Shi'] | 2023-04-10 | null | null | null | null | ['speech-to-text-translation', 'speech-to-speech-translation'] | ['natural-language-processing', 'speech'] | [ 4.54530627e-01 5.26318373e-03 -1.46765247e-01 -5.81539214e-01
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62e1e081-59cb-4efa-8a19-95610ce2ab28 | sdr-gain-a-high-real-time-occluded-pedestrian | 2306.03538 | null | https://arxiv.org/abs/2306.03538v3 | https://arxiv.org/pdf/2306.03538v3.pdf | A Work Based on GAN | This work will enter the submission stage, so specific information will be temporarily hidden, also hide the title. | ['Honghao Fu'] | 2023-06-06 | null | null | null | null | ['pedestrian-detection', 'imputation', 'dimensionality-reduction', 'imputation', 'imputation'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous', 'time-series'] | [-8.66713747e-02 2.30293274e-01 -6.09110594e-01 -2.36244366e-01
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-9.74796772e-01 -2.67482996e-01 -1.10996890e+00 5.91362834e-01
3.93463433e-01 4.59326953e-01 -1.98846623e-01 -1.97406352e-01
1.84902295e-01 1.83524117e-01 -2.22027563e-02 -5.61309874e-01
-9.85296071e-01 -6.27556294e-02 1.25434920e-01 2.67299175e-01
6.89652488e-02 -6.88312054e-01 -1.09016645e+00 -9.17121232e-01
-2.94589758e-01 1.06380153e+00 8.89331222e-01 5.38293242e-01
5.60689151e-01 4.19841558e-01 7.25150645e-01 -1.18287109e-01
-2.34058708e-01 -1.07118058e+00 -4.48463738e-01 -3.65593880e-01
4.54142451e-01 -7.57111669e-01 -4.19424832e-01 2.58484542e-01] | [9.450182914733887, 8.391992568969727] |
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