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44,478 | 24 | Title: Hyperparameter Tuning and Model Evaluation in Causal Effect Estimation
Abstract: The performance of most causal effect estimators relies on accurate predictions of high-dimensional non-linear functions of the observed data. The remarkable flexibility of modern Machine Learning (ML) methods is perfectly suited to this task. However, data-driven hyperparameter tuning of ML methods requires effective model evaluation to avoid large errors in causal estimates, a task made more challenging because causal inference involves unavailable counterfactuals. Multiple performance-validation metrics have recently been proposed such that practitioners now not only have to make complex decisions about which causal estimators, ML learners and hyperparameters to choose, but also about which evaluation metric to use. This paper, motivated by unclear recommendations, investigates the interplay between the four different aspects of model evaluation for causal effect estimation. We develop a comprehensive experimental setup that involves many commonly used causal estimators, ML methods and evaluation approaches and apply it to four well-known causal inference benchmark datasets. Our results suggest that optimal hyperparameter tuning of ML learners is enough to reach state-of-the-art performance in effect estimation, regardless of estimators and learners. We conclude that most causal estimators are roughly equivalent in performance if tuned thoroughly enough. We also find hyperparameter tuning and model evaluation are much more important than causal estimators and ML methods. Finally, from the significant gap we find in estimation performance of popular evaluation metrics compared with optimal model selection choices, we call for more research into causal model evaluation to unlock the optimum performance not currently being delivered even by state-of-the-art procedures. | [] | Validation |
44,479 | 34 | Title: The Longest Subsequence-Repeated Subsequence Problem
Abstract: Motivated by computing duplication patterns in sequences, a new fundamental problem called the longest subsequence-repeated subsequence (LSRS) is proposed. Given a sequence $S$ of length $n$, a letter-repeated subsequence is a subsequence of $S$ in the form of $x_1^{d_1}x_2^{d_2}\cdots x_k^{d_k}$ with $x_i$ a subsequence of $S$, $x_j\neq x_{j+1}$ and $d_i\geq 2$ for all $i$ in $[k]$ and $j$ in $[k-1]$. We first present an $O(n^6)$ time algorithm to compute the longest cubic subsequences of all the $O(n^2)$ substrings of $S$, improving the trivial $O(n^7)$ bound. Then, an $O(n^6)$ time algorithm for computing the longest subsequence-repeated subsequence (LSRS) of $S$ is obtained. Finally we focus on two variants of this problem. We first consider the constrained version when $\Sigma$ is unbounded, each letter appears in $S$ at most $d$ times and all the letters in $\Sigma$ must appear in the solution. We show that the problem is NP-hard for $d=4$, via a reduction from a special version of SAT (which is obtained from 3-COLORING). We then show that when each letter appears in $S$ at most $d=3$ times, then the problem is solvable in $O(n^5)$ time. | [] | Validation |
44,480 | 24 | Title: Reinforcement Learning from Passive Data via Latent Intentions
Abstract: Passive observational data, such as human videos, is abundant and rich in information, yet remains largely untapped by current RL methods. Perhaps surprisingly, we show that passive data, despite not having reward or action labels, can still be used to learn features that accelerate downstream RL. Our approach learns from passive data by modeling intentions: measuring how the likelihood of future outcomes change when the agent acts to achieve a particular task. We propose a temporal difference learning objective to learn about intentions, resulting in an algorithm similar to conventional RL, but which learns entirely from passive data. When optimizing this objective, our agent simultaneously learns representations of states, of policies, and of possible outcomes in an environment, all from raw observational data. Both theoretically and empirically, this scheme learns features amenable for value prediction for downstream tasks, and our experiments demonstrate the ability to learn from many forms of passive data, including cross-embodiment video data and YouTube videos. | [
21062,
39479
] | Train |
44,481 | 24 | Title: Baseline Defenses for Adversarial Attacks Against Aligned Language Models
Abstract: As Large Language Models quickly become ubiquitous, it becomes critical to understand their security vulnerabilities. Recent work shows that text optimizers can produce jailbreaking prompts that bypass moderation and alignment. Drawing from the rich body of work on adversarial machine learning, we approach these attacks with three questions: What threat models are practically useful in this domain? How do baseline defense techniques perform in this new domain? How does LLM security differ from computer vision? We evaluate several baseline defense strategies against leading adversarial attacks on LLMs, discussing the various settings in which each is feasible and effective. Particularly, we look at three types of defenses: detection (perplexity based), input preprocessing (paraphrase and retokenization), and adversarial training. We discuss white-box and gray-box settings and discuss the robustness-performance trade-off for each of the defenses considered. We find that the weakness of existing discrete optimizers for text, combined with the relatively high costs of optimization, makes standard adaptive attacks more challenging for LLMs. Future research will be needed to uncover whether more powerful optimizers can be developed, or whether the strength of filtering and preprocessing defenses is greater in the LLMs domain than it has been in computer vision. | [
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11704,
61,
18764,
4565,
1369,
7648,
109,
41328
] | Train |
44,482 | 30 | Title: Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Abstract: This paper presents a systematic overview and comparison of parameter-efficient fine-tuning methods covering over 40 papers published between February 2019 and February 2023. These methods aim to resolve the infeasibility and impracticality of fine-tuning large language models by only training a small set of parameters. We provide a taxonomy that covers a broad range of methods and present a detailed method comparison with a specific focus on real-life efficiency and fine-tuning multibillion-scale language models. | [
3585,
13700,
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29076,
42901,
37919,
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36518,
18991,
40377,
36669,
20799,
841,
458,
33363,
39273,
32249
] | Test |
44,483 | 16 | Title: ITstyler: Image-optimized Text-based Style Transfer
Abstract: Text-based style transfer is a newly-emerging research topic that uses text information instead of style image to guide the transfer process, significantly extending the application scenario of style transfer. However, previous methods require extra time for optimization or text-image paired data, leading to limited effectiveness. In this work, we achieve a data-efficient text-based style transfer method that does not require optimization at the inference stage. Specifically, we convert text input to the style space of the pre-trained VGG network to realize a more effective style swap. We also leverage CLIP's multi-modal embedding space to learn the text-to-style mapping with the image dataset only. Our method can transfer arbitrary new styles of text input in real-time and synthesize high-quality artistic images. | [] | Train |
44,484 | 16 | Title: Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors
Abstract: Adversarial attacks on deep-learning models have been receiving increased attention in recent years. Work in this area has mostly focused on gradient-based techniques, so-called white-box attacks, wherein the attacker has access to the targeted model's internal parameters; such an assumption is usually unrealistic in the real world. Some attacks additionally use the entire pixel space to fool a given model, which is neither practical nor physical (i.e., real-world). On the contrary, we propose herein a gradient-free method that uses the learned image manifold of a pretrained generative adversarial network (GAN) to generate naturalistic physical adversarial patches for object detectors. We show that our proposed method works both digitally and physically. | [
7623,
24143,
11443,
12179,
39866
] | Validation |
44,485 | 30 | Title: Towards Explainable AI Writing Assistants for Non-native English Speakers
Abstract: We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text. Through an interview study with 15 non-native English speakers (NNESs) with varying levels of English proficiency, we observe that they face difficulties in assessing paraphrased texts generated by AI writing assistants, largely due to the lack of explanations accompanying the suggested paraphrases. Furthermore, we examine their strategies to assess AI-generated texts in the absence of such explanations. Drawing on the needs of NNESs identified in our interview, we propose four potential user interfaces to enhance the writing experience of NNESs using AI writing assistants. The proposed designs focus on incorporating explanations to better support NNESs in understanding and evaluating the AI-generated paraphrasing suggestions. | [] | Train |
44,486 | 30 | Title: External Reasoning: Towards Multi-Large-Language-Models Interchangeable Assistance with Human Feedback
Abstract: Memory is identified as a crucial human faculty that allows for the retention of visual and linguistic information within the hippocampus and neurons in the brain, which can subsequently be retrieved to address real-world challenges that arise through a lifetime of learning. The resolution of complex AI tasks through the application of acquired knowledge represents a stride toward the realization of artificial general intelligence. However, despite the prevalence of Large Language Models (LLMs) like GPT-3.5 and GPT-4 \cite{brown2020language, leiter2023chatgpt, zaitsu2023distinguishing, OpenAI2023GPT4TR} , which have displayed remarkable capabilities in language comprehension, generation, interaction, and reasoning, they are inhibited by constraints on context length that preclude the processing of extensive, continually evolving knowledge bases. This paper proposes that LLMs could be augmented through the selective integration of knowledge from external repositories, and in doing so, introduces a novel methodology for External Reasoning, exemplified by ChatPDF. Central to this approach is the establishment of a tiered policy for \textbf{External Reasoning based on Multiple LLM Interchange Assistance} in \cref{fig:overall}, where the level of support rendered is modulated across entry, intermediate, and advanced tiers based on the complexity of the query, with adjustments made in response to human feedback. A comprehensive evaluation of this methodology is conducted using multiple LLMs and the results indicate state-of-the-art performance in \cref{comparison} , surpassing existing solutions including ChatPDF.com. Moreover, the paper emphasizes that this approach is more efficient compared to the direct processing of full text by LLMs. The source code is publicly available at: \url{https://github.com/AkideLiu/ANLP}. | [
33220,
35785,
16827,
13700
] | Train |
44,487 | 26 | Title: Quantifying Collection Lag in European Modern and Contemporary Art Museums
Abstract: Museum collection strategies are governed by a variety of factors, including topical focus, acquisition funds, availability of works in the art market, donations and specific coincidental opportunities. Yet, it remains unclear if more fundamental collection patterns emerge, exist, and are shared between museums, which could for example allow an established artist to estimate when a contemporary art museum would acquire their works. Here we collect and analyze data from 12 European contemporary art museums, taking into account artwork creation dates, collection acquisition dates, and the associated artist age at both points in time. From this simple quantitative construct we are able to reveal a striking gradient of museum profiles at the aggregate level. This lag can function to constitute a macroeconomic index of"mean museum collection lag", ranging from 3 years in the most dynamic cases (Kiasma) to 33 years in the most established institutions (Reina Sofia). Meanwhile, on the granular level, plotting artist age over collection year, and using artist-age vs artwork-collection matrices, a detailed picture becomes evident, where individual museums are characterized by shared patterns and a rich heterogeneity of ideographic details. Regularities include continuous acquisitions, systematic acquisition of older materials over time, and brief bursts, where whole oeuvres of individual artists join specific collections. Hence, we are able to shed light on the detailed collection history of museums, transcending the anecdotal nature of art historical storytelling via the provision of a quantitative context. Our approach of cultural data analysis combines expertise in art, art history, computational social science, and computer science. Our joint perspective builds a bridge between and serves an audience of museum professionals, art market actors, collectors, and individual artists alike. | [] | Train |
44,488 | 30 | Title: Psychological Metrics for Dialog System Evaluation
Abstract: We present metrics for evaluating dialog systems through a psychologically-grounded"human"lens in which conversational agents express a diversity of both states (e.g., emotion) and traits (e.g., personality), just as people do. We present five interpretable metrics from established psychology that are fundamental to human communication and relationships: emotional entropy, linguistic style and emotion matching, agreeableness, and empathy. These metrics can be applied (1) across dialogs and (2) on turns within dialogs. The psychological metrics are compared against seven state-of-the-art traditional metrics (e.g., BARTScore and BLEURT) on seven standard dialog system data sets. We also introduce a novel data set, the Three Bot Dialog Evaluation Corpus, which consists of annotated conversations from ChatGPT, GPT-3, and BlenderBot. We demonstrate that our proposed metrics offer novel information; they are uncorrelated with traditional metrics, can be used to meaningfully compare dialog systems, and lead to increased accuracy (beyond existing traditional metrics) in predicting crowd-sourced dialog judgements. The interpretability and unique signal of our psychological metrics make them a valuable tool for evaluating and improving dialog systems. | [
36097,
846
] | Train |
44,489 | 16 | Title: Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Study
Abstract: The back-propagation algorithm has long been the de-facto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep learning models. Its widespread adoption in fields like natural language processing, computer vision, and remote sensing has revolutionized automation in various tasks. The popularity of back-propagation stems from its ability to achieve outstanding performance in tasks such as classification, detection, and segmentation. Nevertheless, back-propagation is not without its limitations, encompassing sensitivity to initial conditions, vanishing gradients, overfitting, and computational complexity. The recent introduction of a forward-forward algorithm (FFA), which computes local goodness functions to optimize network parameters, alleviates the dependence on substantial computational resources and the constant need for architectural scaling. This study investigates the application of FFA for hyperspectral image classification. Experimental results and comparative analysis are provided with the use of the traditional back-propagation algorithm. Preliminary results show the potential behind FFA and its promises. | [] | Train |
44,490 | 10 | Title: AAAI 2022 Fall Symposium: System-1 and System-2 realized within the Common Model of Cognition
Abstract: Attempts to import dual-system descriptions of System-1 and System-2 into AI have been hindered by a lack of clarity over their distinction. We address this and other issues by situating System-1 and System-2 within the Common Model of Cognition. Results show that what are thought to be distinctive characteristics of System-1 and 2 instead form a spectrum of cognitive properties. The Common Model provides a comprehensive vision of the computational units involved in System-1 and System-2, their underlying mechanisms, and the implications for learning, metacognition, and emotion. | [
7818
] | Train |
44,491 | 26 | Title: Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees
Abstract: The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this article, we organize a claim post in circulation as an ad hoc event tree, extract event elements, and convert it into bipartite ad hoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite ad hoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods. | [
39680
] | Test |
44,492 | 21 | Title: Classification of Sequential Circuits as Causal Functions
Abstract: In sequential circuits, the current output may depend on both past and current inputs. However, certain kinds of sequential circuits do not refer to all of the past inputs to generate the current output; they only refer to a subset of past inputs. This paper investigates which subset of past inputs a sequential circuit refers to, and proposes a new classification of sequential circuits based on this criterion. The conventional classification of sequential circuits distinguishes between synchronous and asynchronous circuits. In contrast, the new classification consolidates synchronous circuits and multiple clock domain circuits into the same category. | [] | Train |
44,493 | 23 | Title: dump1030: Open-Source Plug-and-Play Demodulator/Decoder for 1030 MHz Uplink
Abstract: Automatic Dependent Surveillance, Automatic Dependent Surveillance–Broadcast, secondary surveillance radars, and Mode S are key air surveillance technologies representing a critical component of next-generation air transportation systems. However, compared to 1090-MHz demodulators and decoders, which have plenty of implementations, the 1030-MHz uplink receivers are, in general, scarcely, if at all, represented. In this article, we present the development and evaluation of dump1030—a cross-platform plug-and-play open-source implementation for decoding 1030-MHz uplink Mode A/C/S interrogations. We demonstrate and detail an agile development process of building dump1030 by adapting a state-of-the-art dump1090 design and implementation. In our repeated experiments, dump1030 achieves a high detection accuracy of 1030-MHz interrogation signals based on lab evaluation using synthetically generated interrogation signals. We also discuss practical use cases where dump1030 can find immediate application and implementation, both in research and industrial settings. | [
33784
] | Test |
44,494 | 28 | Title: Joint Identification and Sensing for Discrete Memoryless Channels
Abstract: In the identification (ID) scheme proposed by Ahlswede and Dueck, the receiver only checks whether a message of special interest to him has been sent or not. In contrast to Shannon transmission codes, the size of ID codes for a Discrete Memoryless Channel (DMC) grows doubly exponentially fast with the blocklength, if randomized encoding is used. This groundbreaking result makes the ID paradigm more efficient than the classical Shannon transmission in terms of necessary energy and hardware components. Further gains can be achieved by taking advantage of additional resources such as feedback. We study the problem of joint ID and channel state estimation over a DMC with independent and identically distributed (i.i.d.) state sequences. The sender simultaneously sends an ID message over the DMC with a random state and estimates the channel state via a strictly causal channel output. The random channel state is available to neither the sender nor the receiver. For the proposed system model, we establish a lower bound on the ID capacity-distortion function. | [
11013
] | Train |
44,495 | 4 | Title: TorKameleon: Improving Tor's Censorship Resistance With K-anonimization and Media-based Covert Channels
Abstract: Anonymity networks like Tor greatly improve online privacy but are susceptible to correlation attacks from state-level adversaries and Internet censors. To enhance privacy, covert channels encapsulated in media protocols, particularly WebRTC-based encapsulation, have shown promise against passive traffic correlation attacks. However, their effectiveness against active correlation attacks has not been explored, and compatibility with Tor remains limited. This paper introduces TorKameleon, a censorship evasion solution that protects Tor users from passive and active correlation attacks. It incorporates K-anonymization techniques to fragment and reroute traffic through multiple paths formed by multiple proxies and uses covert WebRTC-based channels or TLS tunnels to encapsulate user traffic. The developed prototype has undergone extensive validation for performance and resilience against correlation attacks, showcasing its effectiveness. | [] | Train |
44,496 | 16 | Title: Query - Dependent Video Representation for Moment Retrieval and Highlight Detection
Abstract: Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transformer-based models brought some advances, we found that these methods do not fully exploit the information of a given query. For example, the relevance between text query and video contents is sometimes neglected when predicting the moment and its saliency. To tackle this issue, we introduce Query-Dependent DETR (QD-DETR), a detection transformer tailored for MR/HD. As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation. Then, to enhance the model's capability of exploiting the query information, we manipulate the video-query pairs to produce irrelevant pairs. Such negative (irrelevant) video-query pairs are trained to yield low saliency scores, which in turn, encourages the model to estimate precise accordance between query-video pairs. Lastly, we present an input-adaptive saliency predictor which adaptively defines the criterion of saliency scores for the given video-query pairs. Our extensive studies verify the importance of building the query-dependent representation for MR/HD. Specifically, QD-DETR outperforms state-of-the-art methods on QVHighlights, TVSum, and Charades-STA datasets. Codes are available at github.com/wjun0830IQD-DETR. | [
30690,
20010,
3076,
27374
] | Test |
44,497 | 16 | Title: Transformer-based Image Generation from Scene Graphs
Abstract: Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination of graph convolutional networks and adversarial methods for layout prediction and image generation, respectively. In this work, we show how employing multi-head attention to encode the graph information, as well as using a transformer-based model in the latent space for image generation can improve the quality of the sampled data, without the need to employ adversarial models with the subsequent advantage in terms of training stability. The proposed approach, specifically, is entirely based on transformer architectures both for encoding scene graphs into intermediate object layouts and for decoding these layouts into images, passing through a lower dimensional space learned by a vector-quantized variational autoencoder. Our approach shows an improved image quality with respect to state-of-the-art methods as well as a higher degree of diversity among multiple generations from the same scene graph. We evaluate our approach on three public datasets: Visual Genome, COCO, and CLEVR. We achieve an Inception Score of 13.7 and 12.8, and an FID of 52.3 and 60.3, on COCO and Visual Genome, respectively. We perform ablation studies on our contributions to assess the impact of each component. Code is available at https://github.com/perceivelab/trf-sg2im | [
41677
] | Test |
44,498 | 22 | Title: Local Reasoning about Probabilistic Behaviour for Classical-Quantum Programs
Abstract: Verifying the functional correctness of programs with both classical and quantum constructs is a challenging task. The presence of probabilistic behaviour entailed by quantum measurements and unbounded while loops complicate the verification task greatly. We propose a new quantum Hoare logic for local reasoning about probabilistic behaviour by introducing distribution formulas to specify probabilistic properties. We show that the proof rules in the logic are sound with respect to a denotational semantics. To demonstrate the effectiveness of the logic, we formally verify the correctness of non-trivial quantum algorithms including the HHL and Shor's algorithms. | [] | Test |
44,499 | 26 | Title: Extending adjacency matrices to 3D with triangles
Abstract: Social networks are the fabric of society and the subject of frequent visual analysis. Closed triads represent triangular relationships between three people in a social network and are significant for understanding inherent interconnections and influence within the network. The most common methods for representing social networks (node-link diagrams and adjacency matrices) are not optimal for understanding triangles. We propose extending the adjacency matrix form to 3D for better visualization of network triads. We design a 3D matrix reordering technique and implement an immersive interactive system to assist in visualizing and analyzing closed triads in social networks. A user study and usage scenarios demonstrate that our method provides substantial added value over node-link diagrams in improving the efficiency and accuracy of manipulating and understanding the social network triads. | [] | Train |
44,500 | 35 | Title: Joint Time-and Event-Triggered Scheduling in the Linux Kernel
Abstract: There is increasing interest in using Linux in the real-time domain due to the emergence of cloud and edge computing, the need to decrease costs, and the growing number of complex functional and non-functional requirements of real-time applications. Linux presents a valuable opportunity as it has rich hardware support, an open-source development model, a well-established programming environment, and avoids vendor lock-in. Although Linux was initially developed as a general-purpose operating system, some real-time capabilities have been added to the kernel over many years to increase its predictability and reduce its scheduling latency. Unfortunately, Linux currently has no support for time-triggered (TT) scheduling, which is widely used in the safety-critical domain for its determinism, low run-time scheduling latency, and strong isolation properties. We present an enhancement of the Linux scheduler as a new low-overhead TT scheduling class to support offline table-driven scheduling of tasks on multicore Linux nodes. Inspired by the Slot shifting algorithm, we complement the new scheduling class with a low overhead slot shifting manager running on a non-time-triggered core to provide guaranteed execution time to real-time aperiodic tasks by using the slack of the time-triggered tasks and avoiding high-overhead table regeneration for adding new periodic tasks. Furthermore, we evaluate our implementation on server-grade hardware with Intel Xeon Scalable Processor. | [] | Train |
44,501 | 16 | Title: Generalizable Neural Voxels for Fast Human Radiance Fields
Abstract: Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem. The information is too sparse to model complicated human body structures and motions from both view and pose dimensions. Neural radiance fields (NeRF) have shown great power in novel view synthesis and have been applied to human body rendering. However, most current NeRF-based methods bear huge costs for both training and rendering, which impedes the wide applications in real-life scenarios. In this paper, we propose a rendering framework that can learn moving human body structures extremely quickly from a monocular video. The framework is built by integrating both neural fields and neural voxels. Especially, a set of generalizable neural voxels are constructed. With pretrained on various human bodies, these general voxels represent a basic skeleton and can provide strong geometric priors. For the fine-tuning process, individual voxels are constructed for learning differential textures, complementary to general voxels. Thus learning a novel body can be further accelerated, taking only a few minutes. Our method shows significantly higher training efficiency compared with previous methods, while maintaining similar rendering quality. The project page is at https://taoranyi.com/gneuvox . | [
3672,
43561,
43296,
38368
] | Train |
44,502 | 30 | Title: Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts
Abstract: This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data. We demonstrate that multi-pass T5-based text-to-text generative models exhibit better generalization across exam modalities compared to approaches that employ BERT-based task-specific classification layers. We then develop methods that reduce the inference cost of the model, making large-scale corpus processing more feasible for clinical applications. Specifically, we introduce a generative technique that decomposes complex tasks into smaller subtask blocks, which improves a single-pass model when combined with multitask training. In addition, we leverage target-domain contexts during inference to enhance domain adaptation, enabling use of smaller models. Analyses offer insights into the benefits of different cost reduction strategies. | [
42908,
9518
] | Validation |
44,503 | 16 | Title: FULLER: Unified Multi-modality Multi-task 3D Perception via Multi-level Gradient Calibration
Abstract: Multi-modality fusion and multi-task learning are becoming trendy in 3D autonomous driving scenario, considering robust prediction and computation budget. However, naively extending the existing framework to the domain of multi-modality multi-task learning remains ineffective and even poisonous due to the notorious modality bias and task conflict. Previous works manually coordinate the learning framework with empirical knowledge, which may lead to sub-optima. To mitigate the issue, we propose a novel yet simple multi-level gradient calibration learning framework across tasks and modalities during optimization. Specifically, the gradients, produced by the task heads and used to update the shared backbone, will be calibrated at the backbone's last layer to alleviate the task conflict. Before the calibrated gradients are further propagated to the modality branches of the backbone, their magnitudes will be calibrated again to the same level, ensuring the downstream tasks pay balanced attention to different modalities. Experiments on large-scale benchmark nuScenes demonstrate the effectiveness of the proposed method, eg, an absolute 14.4% mIoU improvement on map segmentation and 1.4% mAP improvement on 3D detection, advancing the application of 3D autonomous driving in the domain of multi-modality fusion and multi-task learning. We also discuss the links between modalities and tasks. | [] | Test |
44,504 | 24 | Title: Reinforcement Learning by Guided Safe Exploration
Abstract: Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them in the real world. However, the real-world target task might be unknown prior to deployment. Reward-free RL trains an agent without the reward to adapt quickly once the reward is revealed. We consider the constrained reward-free setting, where an agent (the guide) learns to explore safely without the reward signal. This agent is trained in a controlled environment, which allows unsafe interactions and still provides the safety signal. After the target task is revealed, safety violations are not allowed anymore. Thus, the guide is leveraged to compose a safe behaviour policy. Drawing from transfer learning, we also regularize a target policy (the student) towards the guide while the student is unreliable and gradually eliminate the influence of the guide as training progresses. The empirical analysis shows that this method can achieve safe transfer learning and helps the student solve the target task faster. | [] | Train |
44,505 | 26 | Title: Predicting Tweet Engagement with Graph Neural Networks
Abstract: Social Networks represent one of the most important online sources to share content across a world-scale audience. In this context, predicting whether a post will have any impact in terms of engagement is of crucial importance to drive the profitable exploitation of these media. In the literature, several studies address this issue by leveraging direct features of the posts, typically related to the textual content and the user publishing it. In this paper, we argue that the rise of engagement is also related to another key component, which is the semantic connection among posts published by users in social media. Hence, we propose TweetGage, a Graph Neural Network solution to predict the user engagement based on a novel graph-based model that represents the relationships among posts. To validate our proposal, we focus on the Twitter platform and perform a thorough experimental campaign providing evidence of its quality. | [] | Test |
44,506 | 23 | Title: Software Vulnerability Prediction Knowledge Transferring Between Programming Languages
Abstract: Developing automated and smart software vulnerability detection models has been receiving great attention from both research and development communities. One of the biggest challenges in this area is the lack of code samples for all different programming languages. In this study, we address this issue by proposing a transfer learning technique to leverage available datasets and generate a model to detect common vulnerabilities in different programming languages. We use C source code samples to train a Convolutional Neural Network (CNN) model, then, we use Java source code samples to adopt and evaluate the learned model. We use code samples from two benchmark datasets: NIST Software Assurance Reference Dataset (SARD) and Draper VDISC dataset. The results show that proposed model detects vulnerabilities in both C and Java codes with average recall of 72\%. Additionally, we employ explainable AI to investigate how much each feature contributes to the knowledge transfer mechanisms between C and Java in the proposed model. | [] | Train |
44,507 | 16 | Title: Fusion of Radio and Camera Sensor Data for Accurate Indoor Positioning
Abstract: Indoor positioning systems have received a lot of attention recently due to their importance for many location-based services, e.g. indoor navigation and smart buildings. Lightweight solutions based on WiFi and inertial sensing have gained popularity, but are not fit for demanding applications, such as expert museum guides and industrial settings, which typically require sub-meter location information. In this paper, we propose a novel positioning system, RAVEL (Radio And Vision Enhanced Localization), which fuses anonymous visual detections captured by widely available camera infrastructure, with radio readings (e.g. WiFi radio data). Although visual trackers can provide excellent positioning accuracy, they are plagued by issues such as occlusions and people entering/exiting the scene, preventing their use as a robust tracking solution. By incorporating radio measurements, visually ambiguous or missing data can be resolved through multi-hypothesis tracking. We evaluate our system in a complex museum environment with dim lighting and multiple people moving around in a space cluttered with exhibit stands. Our experiments show that although the WiFi measurements are not by themselves sufficiently accurate, when they are fused with camera data, they become a catalyst for pulling together ambiguous, fragmented, and anonymous visual tracklets into accurate and continuous paths, yielding typical errors below 1 meter. | [
15872
] | Train |
44,508 | 16 | Title: Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer
Abstract: There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the distributionally shifted unlabeled target domain, and simultaneously iii) detect Open Set samples, i.e., unseen novel categories not present in the training source domain. This paper proposes a highly practical setting by addressing the abovementioned challenges in one fell swoop, i.e., source-free Open Set domain adaptation (SF-OSDA), which addresses the situation where a model pre-trained on the inaccessible source dataset can be adapted on the unlabeled target dataset containing Open Set samples. The central tenet of our proposed method is distilling knowledge from a self-supervised vision transformer trained in the target domain. We propose a novel style-based data augmentation used as hard positives for self-training a vision transformer in the target domain, yielding strongly contextualized embedding. Subsequently, semantically similar target images are clustered while the source model provides their corresponding weak pseudo-labels with unreliable confidence. Furthermore, we propose cluster relative maximum logit score (CRMLS) to rectify the confidence of the weak pseudo-labels and compute weighted class prototypes in the contextualized embedding space that are utilized for adapting the source model on the target domain. Our method significantly outperforms the previous methods, including open set detection, test-time adaptation, and SF-OSDA methods, setting the new state-of-the-art on three public histopathological datasets of colorectal cancer (CRC) assessment- Kather-16, Kather-19, and CRCTP. Our code is available at https://github.com/LTS5/Proto-SF-OSDA. | [] | Train |
44,509 | 39 | Title: On the Giant Component of Geometric Inhomogeneous Random Graphs
Abstract: In this paper we study the threshold model of \emph{geometric inhomogeneous random graphs} (GIRGs); a generative random graph model that is closely related to \emph{hyperbolic random graphs} (HRGs). These models have been observed to capture complex real-world networks well with respect to the structural and algorithmic properties. Following comprehensive studies regarding their \emph{connectivity}, i.e., which parts of the graphs are connected, we have a good understanding under which circumstances a \emph{giant} component (containing a constant fraction of the graph) emerges. While previous results are rather technical and challenging to work with, the goal of this paper is to provide more accessible proofs. At the same time we significantly improve the previously known probabilistic guarantees, showing that GIRGs contain a giant component with probability $1 - \exp(-\Omega(n^{(3-\tau)/2}))$ for graph size $n$ and a degree distribution with power-law exponent $\tau \in (2, 3)$. Based on that we additionally derive insights about the connectivity of certain induced subgraphs of GIRGs. | [] | Test |
44,510 | 24 | Title: Defect detection using weakly supervised learning
Abstract: In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly supervised classifier to its fully supervised counterpart is compared on the task of defect detection. Experiments are conducted on a dataset of images containing defects, and evaluate the two classifiers based on their accuracy, precision, and recall. Our results show that the weakly supervised classifier achieves comparable performance to the supervised classifier, while requiring significantly less labeled data. | [] | Train |
44,511 | 16 | Title: When ChatGPT for Computer Vision Will Come? From 2D to 3D
Abstract: ChatGPT and its improved variant GPT4 have revolutionized the NLP field with a single model solving almost all text related tasks. However, such a model for computer vision does not exist, especially for 3D vision. This article first provides a brief view on the progress of deep learning in text, image and 3D fields from the model perspective. Moreover, this work further discusses how AIGC evolves from the data perspective. On top of that, this work presents an outlook on the development of AIGC in 3D from the data perspective. | [
27866,
3066,
1899
] | Train |
44,512 | 8 | Title: Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage
Abstract: This paper proposes a Time Division Multiple Access (TDMA) MAC slot allocation protocol with efficient bandwidth usage in wireless sensor networks and Internet of Things (IoTs). The developed protocol has two primary components: a Multi-Armed Bandits (MAB)-based slot allocation mechanism for collision free transmission, and a Decentralized Defragmented Slot Backshift (DDSB) operation for improving bandwidth usage efficiency. The proposed framework is decentralized in that each node finds its transmission schedule independently without the control of any centralized arbitrator. The developed mechanism is suitable for networks with or without time synchronization, thus, making it suitable for low-complexity wireless transceivers for wireless sensor and IoT nodes. This framework is able to manage the trade-off between learning convergence time and bandwidth. In addition, it allows the nodes to adapt to topological changes while maintaining efficient bandwidth usage. The developed logic is tested for both fully-connected and arbitrary mesh networks with extensive simulation experiments. It is shown how the nodes can learn to select collision-free transmission slots using MAB. Moreover, the nodes learn to self-adjust their transmission schedules using a novel DDSB framework in order to reduce bandwidth usage. | [] | Test |
44,513 | 24 | Title: PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Abstract: Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance. However, existing methods, effective as they are, suffer from suboptimal performance, limited adaptability, and unsatisfactory computational efficiency. We propose a novel framework, PROTO, which overcomes the aforementioned limitations by augmenting the standard RL objective with an iteratively evolving regularization term. Performing a trust-region-style update, PROTO yields stable initial finetuning and optimal final performance by gradually evolving the regularization term to relax the constraint strength. By adjusting only a few lines of code, PROTO can bridge any offline policy pretraining and standard off-policy RL finetuning to form a powerful offline-to-online RL pathway, birthing great adaptability to diverse methods. Simple yet elegant, PROTO imposes minimal additional computation and enables highly efficient online finetuning. Extensive experiments demonstrate that PROTO achieves superior performance over SOTA baselines, offering an adaptable and efficient offline-to-online RL framework. | [
15201,
17631,
14475,
816,
23859,
28534,
16407,
44566,
7039
] | Validation |
44,514 | 6 | Title: "Nice to meet you!": Expressing Emotions with Movement Gestures and Textual Content in Automatic Handwriting Robots
Abstract: Text-writing robots have been used in assistive writing and drawing applications. However, robots do not convey emotional tones in the writing process due to the lack of behaviors humans typically adopt. To examine how people interpret designed robotic expressions of emotion through both movements and textual output, we used a pen-plotting robot to generate texts by performing human-like behaviors like stop-and-go, speed, and pressure variation. We examined how people convey emotion in the writing process by observing how they wrote in different emotional contexts. We then mapped these human expressions during writing to the handwriting robot and measured how well other participants understood the robot's affective expression. We found that textual output was the strongest determinant of participants' ability to perceive the robot's emotions, whereas parameters of gestural movements of the robots like speed, fluency, pressure, size, and acceleration could be useful for understanding the context of the writing expression. | [] | Train |
44,515 | 28 | Title: On Minimax Detection of Gaussian Stochastic Sequences with Imprecisely Known Means and Covariance Matrices
Abstract: nan | [
37664
] | Train |
44,516 | 24 | Title: Stop overkilling simple tasks with black-box models and use transparent models instead
Abstract: In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process, which is generally considered to be both error-prone and tedious. Moreover, deep learning strategies often outperform traditional models in terms of accuracy. | [] | Validation |
44,517 | 1 | Title: Scene Graph Lossless Compression with Adaptive Prediction for Objects and Relations
Abstract: The scene graph is a new data structure describing objects and their pairwise relationship within image scenes. As the size of scene graph in vision applications grows, how to losslessly and efficiently store such data on disks or transmit over the network becomes an inevitable problem. However, the compression of scene graph is seldom studied before because of the complicated data structures and distributions. Existing solutions usually involve general-purpose compressors or graph structure compression methods, which is weak at reducing redundancy for scene graph data. This paper introduces a new lossless compression framework with adaptive predictors for joint compression of objects and relations in scene graph data. The proposed framework consists of a unified prior extractor and specialized element predictors to adapt for different data elements. Furthermore, to exploit the context information within and between graph elements, Graph Context Convolution is proposed to support different graph context modeling schemes for different graph elements. Finally, a learned distribution model is devised to predict numerical data under complicated conditional constraints. Experiments conducted on labeled or generated scene graphs proves the effectiveness of the proposed framework in scene graph lossless compression task. | [] | Train |
44,518 | 34 | Title: Approximation algorithms for the square min-sum bin packing problem
Abstract: In this work, we study the square min-sum bin packing problem (SMSBPP), where a list of square items has to be packed into indexed square bins of dimensions $1 \times 1$ with no overlap between the areas of the items. The bins are indexed and the cost of packing each item is equal to the index of the bin in which it is placed in. The objective is to minimize the total cost of packing all items, which is equivalent to minimizing the average cost of items. The problem has applications in minimizing the average time of logistic operations such as cutting stock and delivery of products. We prove that classic algorithms for two-dimensional bin packing that order items in non-increasing order of size, such as Next Fit Decreasing Height or Any Fit Decreasing Height heuristics, can have an arbitrarily bad performance for SMSBPP. We, then, present a $\frac{53}{22}$-approximation and a PTAS for the problem. | [] | Train |
44,519 | 31 | Title: Cross-Modal Content Inference and Feature Enrichment for Cold-Start Recommendation
Abstract: Multimedia recommendation aims to fuse the multi-modal information of items for feature enrichment to improve the recommendation performance. However, existing methods typically introduce multi-modal information based on collaborative information to improve the overall recommendation precision, while failing to explore its cold-start recommendation performance. Meanwhile, these above methods are only applicable when such multi-modal data is available. To address this problem, this paper proposes a recommendation framework, named Cross-modal Content Inference and Feature Enrichment Recommendation (CIERec), which exploits the multi-modal information to improve its cold-start recommendation performance. Specifically, CIERec first introduces image annotation as the privileged information to help guide the mapping of unified features from the visual space to the semantic space in the training phase. And then CIERec enriches the content representation with the fusion of collaborative, visual, and cross-modal inferred representations, so as to improve its cold-start recommendation performance. Experimental results on two real-world datasets show that the content representations learned by CIERec are able to achieve superior cold-start recommendation performance over existing visually-aware recommendation algorithms. More importantly, CIERec can consistently achieve significant improvements with different conventional visually-aware backbones, which verifies its universality and effectiveness. | [
39731,
27422,
45343
] | Train |
44,520 | 16 | Title: Dynamic Event-based Optical Identification and Communication
Abstract: Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking. We propose a solution with light-emitting beacons that improves this trade-off by exploiting fast event-based cameras and, for tracking, sparse neuromorphic optical flow computed with spiking neurons. The system is embedded in a simulated drone and evaluated in an asset monitoring use case. It is robust to relative movements and enables simultaneous communication with, and tracking of, multiple moving beacons. Finally, in a hardware lab prototype, we demonstrate for the first time beacon tracking performed simultaneously with state-of-the-art frequency communication in the kHz range. | [
1718
] | Train |
44,521 | 16 | Title: AutoKary2022: A Large-Scale Densely Annotated Dataset for Chromosome Instance Segmentation
Abstract: Automated chromosome instance segmentation from metaphase cell microscopic images is critical for the diagnosis of chromosomal disorders (i.e., karyotype analysis). However, it is still a challenging task due to lacking of densely annotated datasets and the complicated morphologies of chromosomes, e.g., dense distribution, arbitrary orientations, and wide range of lengths. To facilitate the development of this area, we take a big step forward and manually construct a large-scale densely annotated dataset named AutoKary2022, which contains over 27,000 chromosome instances in 612 microscopic images from 50 patients. Specifically, each instance is annotated with a polygonal mask and a class label to assist in precise chromosome detection and segmentation. On top of it, we systematically investigate representative methods on this dataset and obtain a number of interesting findings, which helps us have a deeper understanding of the fundamental problems in chromosome instance segmentation. We hope this dataset could advance research towards medical understanding. The dataset can be available at:https://github.com/wangjuncongyu/chromosome-instance-segmentation-dataset. | [] | Train |
44,522 | 16 | Title: Towards Local Visual Modeling for Image Captioning
Abstract: nan | [
24622
] | Validation |
44,523 | 10 | Title: RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization
Abstract: Visual Reinforcement Learning (Visual RL), coupled with high-dimensional observations, has consistently confronted the long-standing challenge of out-of-distribution generalization. Despite the focus on algorithms aimed at resolving visual generalization problems, we argue that the devil is in the existing benchmarks as they are restricted to isolated tasks and generalization categories, undermining a comprehensive evaluation of agents' visual generalization capabilities. To bridge this gap, we introduce RL-ViGen: a novel Reinforcement Learning Benchmark for Visual Generalization, which contains diverse tasks and a wide spectrum of generalization types, thereby facilitating the derivation of more reliable conclusions. Furthermore, RL-ViGen incorporates the latest generalization visual RL algorithms into a unified framework, under which the experiment results indicate that no single existing algorithm has prevailed universally across tasks. Our aspiration is that RL-ViGen will serve as a catalyst in this area, and lay a foundation for the future creation of universal visual generalization RL agents suitable for real-world scenarios. Access to our code and implemented algorithms is provided at https://gemcollector.github.io/RL-ViGen/. | [
43473,
8983
] | Validation |
44,524 | 26 | Title: Node Attribute Prediction on Multilayer Networks with Weighted and Directed Edges
Abstract: With the rapid development of digital platforms, users can now interact in endless ways from writing business reviews and comments to sharing information with their friends and followers. As a result, organizations have numerous digital social networks available for graph learning problems with little guidance on how to select the right graph or how to combine multiple edge types. In this paper, we first describe the types of user-to-user networks available across the Facebook (FB) and Instagram (IG) platforms. We observe minimal edge overlap between these networks, indicating users are exhibiting different behaviors and interaction patterns between platforms. We then compare predictive performance metrics across various node attribute prediction tasks for an ads click prediction task on Facebook and for a publicly available dataset from the Open Graph Benchmark. We adapt an existing node attribute prediction method for binary prediction, LINK-Naive Bayes, to account for both edge direction and weights on single-layer networks. We observe meaningful predictive performance gains when incorporating edge direction and weight. We then introduce an approach called MultiLayerLINK-NaiveBayes that can combine multiple network layers during training and observe superior performance over the single-layer results. Ultimately, whether edge direction, edge weights, and multi-layers are practically useful will depend on the particular setting. Our approach enables practitioners to quickly combine multiple layers and additional edge information such as direction or weight. | [] | Train |
44,525 | 37 | Title: A Primer on the Data Cleaning Pipeline
Abstract: The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this expansion, the statistical and methodological questions around data integration, or rather merging multiple data sources, has also grown. Specifically, the science of the ``data cleaning pipeline'' contains four stages that allow an analyst to perform downstream tasks, predictive analyses, or statistical analyses on ``cleaned data.'' This article provides a review of this emerging field, introducing technical terminology and commonly used methods. | [] | Validation |
44,526 | 30 | Title: Trillion Dollar Words: A New Financial Dataset, Task & Market Analysis
Abstract: Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major driver of financial market returns. We construct the largest tokenized and annotated dataset of FOMC speeches, meeting minutes, and press conference transcripts in order to understand how monetary policy influences financial markets. In this study, we develop a novel task of hawkish-dovish classification and benchmark various pre-trained language models on the proposed dataset. Using the best-performing model (RoBERTa-large), we construct a measure of monetary policy stance for the FOMC document release days. To evaluate the constructed measure, we study its impact on the treasury market, stock market, and macroeconomic indicators. Our dataset, models, and code are publicly available on Huggingface and GitHub under CC BY-NC 4.0 license. | [
37397
] | Train |
44,527 | 5 | Title: Breadth-First Depth-Next: Optimal Collaborative Exploration of Trees with Low Diameter
Abstract: We consider the problem of collaborative tree exploration posed by Fraigniaud, Gasieniec, Kowalski, and Pelc [Fraigniaud et al., 2006] where a team of k agents is tasked to collectively go through all the edges of an unknown tree as fast as possible. Denoting by n the total number of nodes and by D the tree depth, the O ( n/ log( k ) + D ) algorithm of Fraigniaud et al. [2006] achieves the best-known competitive ratio with respect to the cost of offline exploration which is Θ(max { 2 n/k, 2 D } ) . Brass, Cabrera-Mora, Gasparri, and Xiao Brass et al. [2011] consider an alternative performance criterion, namely the additive overhead with respect to 2 n/k , and obtain a 2 n/k + O (( D + k ) k ) runtime guarantee. In this paper, we introduce ‘Breadth-First Depth-Next’ (BFDN), a novel and simple algorithm that performs collaborative tree exploration in time 2 n/k + O ( D 2 log( k )) , thus outperforming Brass et al. [2011] for all values of ( n, D ) and being order-optimal for all trees with depth D = o k ( √ n ) . Moreover, a recent result from Disser et al. [2017] implies that no exploration algorithm can achieve a 2 n/k + O ( D 2 − (cid:15) ) runtime guarantee. The dependency in D 2 of our bound is in this sense optimal. The proof of our result crucially relies on the analysis of an associated two-player game. We extend the guarantees of BFDN to: scenarios with limited memory and communication, adversarial setups where robots can be blocked, and exploration of classes of non-tree graphs. Finally, we provide a recursive version of BFDN with a runtime of O (cid:96) ( n/k 1 /(cid:96) | [
24031
] | Train |
44,528 | 4 | Title: Majority Voting Approach to Ransomware Detection
Abstract: Crypto-ransomware remains a significant threat to governments and companies alike, with high-profile cyber security incidents regularly making headlines. Many different detection systems have been proposed as solutions to the ever-changing dynamic landscape of ransomware detection. In the majority of cases, these described systems propose a method based on the result of a single test performed on either the executable code, the process under investigation, its behaviour, or its output. In a small subset of ransomware detection systems, the concept of a scorecard is employed where multiple tests are performed on various aspects of a process under investigation and their results are then analysed using machine learning. The purpose of this paper is to propose a new majority voting approach to ransomware detection by developing a method that uses a cumulative score derived from discrete tests based on calculations using algorithmic rather than heuristic techniques. The paper describes 23 candidate tests, as well as 9 Windows API tests which are validated to determine both their accuracy and viability for use within a ransomware detection system. Using a cumulative score calculation approach to ransomware detection has several benefits, such as the immunity to the occasional inaccuracy of individual tests when making its final classification. The system can also leverage multiple tests that can be both comprehensive and complimentary in an attempt to achieve a broader, deeper, and more robust analysis of the program under investigation. Additionally, the use of multiple collaborative tests also significantly hinders ransomware from masking or modifying its behaviour in an attempt to bypass detection. | [] | Train |
44,529 | 24 | Title: Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Abstract: Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs. To this end, we propose a Neighborhood Aggregation Graph Transformer (NAGphormer) that treats each node as a sequence containing a series of tokens constructed by our proposed Hop2Token module. For each node, Hop2Token aggregates the neighborhood features from different hops into different representations, producing a sequence of token vectors as one input. In this way, NAGphormer could be trained in a mini-batch manner and thus could scale to large graphs. Moreover, we mathematically show that compared to a category of advanced Graph Neural Networks (GNNs), called decoupled Graph Convolutional Networks, NAGphormer could learn more informative node representations from multi-hop neighborhoods. In addition, we propose a new data augmentation method called Neighborhood Augmentation (NrAug) based on the output of Hop2Token that augments simultaneously the features of neighborhoods from global as well as local views to strengthen the training effect of NAGphormer. Extensive experiments on benchmark datasets from small to large demonstrate the superiority of NAGphormer against existing graph Transformers and mainstream GNNs, and the effectiveness of NrAug for further boosting NAGphormer. | [
44836
] | Train |
44,530 | 24 | Title: Causal Theories and Structural Data Representations for Improving Out-of-Distribution Classification
Abstract: We consider how human-centered causal theories and tools from the dynamical systems literature can be deployed to guide the representation of data when training neural networks for complex classification tasks. Specifically, we use simulated data to show that training a neural network with a data representation that makes explicit the invariant structural causal features of the data generating process of an epidemic system improves out-of-distribution (OOD) generalization performance on a classification task as compared to a more naive approach to data representation. We take these results to demonstrate that using human-generated causal knowledge to reduce the epistemic uncertainty of ML developers can lead to more well-specified ML pipelines. This, in turn, points to the utility of a dynamical systems approach to the broader effort aimed at improving the robustness and safety of machine learning systems via improved ML system development practices. | [
13349
] | Train |
44,531 | 30 | Title: Personality Understanding of Fictional Characters during Book Reading
Abstract: Comprehending characters’ personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived. This leads to a natural problem of situated and fine-grained personality understanding. The problem has not been studied in the NLP field, primarily due to the lack of appropriate datasets mimicking the process of book reading. We present the first labeled dataset PersoNet for this problem. Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books. Experiments and human studies indicate that our dataset construction is both efficient and accurate; and our task heavily relies on long-term context to achieve accurate predictions for both machines and humans. | [
13700
] | Train |
44,532 | 30 | Title: Shared Lexical Items as Triggers of Code Switching
Abstract: Why do bilingual speakers code-switch (mix their two languages)? Among the several theories that attempt to explain this natural and ubiquitous phenomenon, the Triggering Hypothesis relates code-switching to the presence of lexical triggers, specifically cognates and proper names, adjacent to the switch point. We provide a fuller, more nuanced and refined exploration of the triggering hypothesis, based on five large datasets in three language pairs, reflecting both spoken and written bilingual interactions. Our results show that words that are assumed to reside in a mental lexicon shared by both languages indeed trigger code-switching; that the tendency to switch depends on the distance of the trigger from the switch point; and on whether the trigger precedes or succeeds the switch; but not on the etymology of the trigger words. We thus provide strong, robust, evidence-based confirmation to several hypotheses on the relationships between lexical triggers and code-switching. | [] | Train |
44,533 | 10 | Title: ITHACA. A TOOL FOR INTEGRATING FUZZY LOGIC IN UNITY
Abstract: Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C# framework and an Application Programming Interface (API) for writing inference systems, as well as a set of tools for graphic development and debugging. Additionally, a Fuzzy Control Language (FCL) parser is provided in order to import systems previously defined using this standard. | [] | Train |
44,534 | 24 | Title: Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation
Abstract: An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-Balanced Re-Weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-Balanced Re-Weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 and V6 show the performances and generality of the SCR with the traditional SGG models. | [
22819,
20940,
31575
] | Train |
44,535 | 11 | Title: Differentiable Arbitrating in Zero-sum Markov Games
Abstract: We initiate the study of how to perturb the reward in a zero-sum Markov game with two players to induce a desirable Nash equilibrium, namely arbitrating. Such a problem admits a bi-level optimization formulation. The lower level requires solving the Nash equilibrium under a given reward function, which makes the overall problem challenging to optimize in an end-to-end way. We propose a backpropagation scheme that differentiates through the Nash equilibrium, which provides the gradient feedback for the upper level. In particular, our method only requires a black-box solver for the (regularized) Nash equilibrium (NE). We develop the convergence analysis for the proposed framework with proper black-box NE solvers and demonstrate the empirical successes in two multi-agent reinforcement learning (MARL) environments. | [] | Train |
44,536 | 30 | Title: Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks
Abstract: Information extraction(IE) is a crucial subfield within natural language processing. However, for the traditionally segmented approach to sentence classification and Named Entity Recognition, the intricate interactions between these individual subtasks remain largely uninvestigated. In this study, we propose an integrative analysis, converging sentence classification with Named Entity Recognition, with the objective to unveil and comprehend the mutual reinforcement effect within these two information extraction subtasks. To achieve this, we introduce a Sentence Classification and Named Entity Recognition Multi-task (SCNM) approach that combines Sentence Classification (SC) and Named Entity Recognition (NER). We develop a Sentence-to-Label Generation (SLG) framework for SCNM and construct a Wikipedia dataset containing both SC and NER. Using a format converter, we unify input formats and employ a generative model to generate SC-labels, NER-labels, and associated text segments. We propose a Constraint Mechanism (CM) to improve generated format accuracy. Our results show SC accuracy increased by 1.13 points and NER by 1.06 points in SCNM compared to standalone tasks, with CM raising format accuracy from 63.61 to 100. The findings indicate mutual reinforcement effects between SC and NER, and integration enhances both tasks' performance. We additionally implemented the SLG framework on single SC task. It yielded superior accuracies compared to the baseline on two distinct Japanese SC datasets. Notably, in the experiment of few-shot learning, SLG framework shows much better performance than fine-tune method. These empirical findings contribute additional evidence to affirm the efficacy of the SLG framework. | [
11024
] | Train |
44,537 | 24 | Title: Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Abstract: Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further align the network with the intended usage. Yet the imperfections in the proxy reward may hinder the training and lead to suboptimal results; the diversity of objectives in real-world tasks and human opinions exacerbate the issue. This paper proposes embracing the heterogeneity of diverse rewards by following a multi-policy strategy. Rather than focusing on a single a priori reward, we aim for Pareto-optimal generalization across the entire space of preferences. To this end, we propose rewarded soup, first specializing multiple networks independently (one for each proxy reward) and then interpolating their weights linearly. This succeeds empirically because we show that the weights remain linearly connected when fine-tuned on diverse rewards from a shared pre-trained initialization. We demonstrate the effectiveness of our approach for text-to-text (summarization, Q&A, helpful assistant, review), text-image (image captioning, text-to-image generation, visual grounding, VQA), and control (locomotion) tasks. We hope to enhance the alignment of deep models, and how they interact with the world in all its diversity. | [
17153,
28674,
13700,
37252,
32263,
4111,
45074,
45848,
37152,
42786,
39464,
41514,
25772,
7085,
4658,
22201,
29755,
15809,
846,
36179,
45923,
20709,
33638,
28905,
12142,
36084,
24316,
17789
] | Train |
44,538 | 24 | Title: Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity
Abstract: Understanding how external stimuli are encoded in distributed neural activity is of significant interest in clinical and basic neuroscience. To address this need, it is essential to develop analytical tools capable of handling limited data and the intrinsic stochasticity present in neural data. In this study, we propose a straightforward Bayesian time series classifier (BTsC) model that tackles these challenges whilst maintaining a high level of interpretability. We demonstrate the classification capabilities of this approach by utilizing neural data to decode colors in a visual task. The model exhibits consistent and reliable average performance of 75.55% on 4 patients' dataset, improving upon state-of-the-art machine learning techniques by about 3.0 percent. In addition to its high classification accuracy, the proposed BTsC model provides interpretable results, making the technique a valuable tool to study neural activity in various tasks and categories. The proposed solution can be applied to neural data recorded in various tasks, where there is a need for interpretable results and accurate classification accuracy. | [] | Train |
44,539 | 16 | Title: Active Learning with Contrastive Pre-training for Facial Expression Recognition
Abstract: Deep learning has played a significant role in the success of facial expression recognition (FER), thanks to large models and vast amounts of labelled data. However, obtaining labelled data requires a tremendous amount of human effort, time, and financial resources. Even though some prior works have focused on reducing the need for large amounts of labelled data using different unsupervised methods, another promising approach called active learning is barely explored in the context of FER. This approach involves selecting and labelling the most representative samples from an unlabelled set to make the best use of a limited 'labelling budget'. In this paper, we implement and study 8 recent active learning methods on three public FER datasets, FER13, RAF-DB, and KDEF. Our findings show that existing active learning methods do not perform well in the context of FER, likely suffering from a phenomenon called 'Cold Start', which occurs when the initial set of labelled samples is not well representative of the entire dataset. To address this issue, we propose contrastive self-supervised pre-training, which first learns the underlying representations based on the entire unlabelled dataset. We then follow this with the active learning methods and observe that our 2-step approach shows up to 9.2% improvement over random sampling and up to 6.7% improvement over the best existing active learning baseline without the pre-training. We will make the code for this study public upon publication at: github.com/ShuvenduRoy/ActiveFER. | [] | Validation |
44,540 | 16 | Title: EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection
Abstract: In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. Although deep convolutional neural network has significantly promoted the research on face detection recently, current deep face detectors rarely take into account low-resolution faces and are still vulnerable to the real-world scenarios where massive amount of low-resolution faces exist. Consequently, they usually achieve degraded performance for low-resolution face detection. In order to alleviate this problem, we develop an efficient detector termed EfficientSRFace by introducing a feature-level super-resolution reconstruction network for enhancing the feature representation capability of the model. This module plays an auxiliary role in the training process, and can be removed during the inference without increasing the inference time. Extensive experiments on public benchmarking datasets, such as FDDB and WIDER Face, show that the embedded image super-resolution module can significantly improve the detection accuracy at the cost of a small amount of additional parameters and computational overhead, while helping our model achieve competitive performance compared with the state-of-the-arts methods. | [
23360
] | Train |
44,541 | 4 | Title: Decentralized Finance (DeFi): A Survey
Abstract: Decentralized Finance (DeFi) is a new paradigm in the creation, distribution, and utilization of financial services via the integration of blockchain technology. Our research conducts a comprehensive introduction and meticulous classification of various DeFi applications. Beyond that, we thoroughly analyze these risks from both technical and economic perspectives, spanning multiple layers. Lastly, we point out research directions in DeFi, encompassing areas of technological advancements, innovative economics, and privacy optimization. | [
43959
] | Train |
44,542 | 24 | Title: Imprecise Bayesian Neural Networks
Abstract: Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of uncertainty are indistinguishable. We present Imprecise Bayesian Neural Networks (IBNNs); they generalize and overcome some of the drawbacks of standard BNNs. These latter are trained using a single prior and likelihood distributions, whereas IBNNs are trained using credal prior and likelihood sets. They allow to distinguish between aleatoric and epistemic uncertainties, and to quantify them. In addition, IBNNs are more robust than BNNs to prior and likelihood misspecification, and to distribution shift. They can also be used to compute sets of outcomes that enjoy probabilistic guarantees. We apply IBNNs to two case studies. One, for motion prediction in autonomous driving scenarios, and two, to model blood glucose and insulin dynamics for artificial pancreas control. We show that IBNNs performs better when compared to an ensemble of BNNs benchmark. | [
13553,
36894,
4647
] | Train |
44,543 | 16 | Title: Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-Based Multiple Instance Learning
Abstract: 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. | [] | Train |
44,544 | 39 | Title: Simpler and faster algorithms for detours in planar digraphs
Abstract: In the directed detour problem one is given a digraph $G$ and a pair of vertices $s$ and~$t$, and the task is to decide whether there is a directed simple path from $s$ to $t$ in $G$ whose length is larger than $\mathsf{dist}_{G}(s,t)$. The more general parameterized variant, directed long detour, asks for a simple $s$-to-$t$ path of length at least $\mathsf{dist}_{G}(s,t)+k$, for a given parameter $k$. Surprisingly, it is still unknown whether directed detour is polynomial-time solvable on general digraphs. However, for planar digraphs, Wu and Wang~[Networks, '15] proposed an $\mathcal{O}(n^3)$-time algorithm for directed detour, while Fomin et al.~[STACS 2022] gave a $2^{\mathcal{O}(k)}\cdot n^{\mathcal{O}(1)}$-time fpt algorithm for directed long detour. The algorithm of Wu and Wang relies on a nontrivial analysis of how short detours may look like in a plane embedding, while the algorithm of Fomin et al.~is based on a reduction to the ${\S}$-disjoint paths problem on planar digraphs. This latter problem is solvable in polynomial time using the algebraic machinery of Schrijver~[SIAM~J.~Comp.,~'94], but the degree of the obtained polynomial factor is huge. In this paper we propose two simple algorithms: we show how to solve, in planar digraphs, directed detour in time $\mathcal{O}(n^2)$ and directed long detour in time $2^{\mathcal{O}(k)}\cdot n^4 \log n$. In both cases, the idea is to reduce to the $2$-disjoint paths problem in a planar digraph, and to observe that the obtained instances of this problem have a certain topological structure that makes them amenable to a direct greedy strategy. | [
11584,
26314,
707
] | Validation |
44,545 | 30 | Title: Inflected Forms Are Redundant in Question Generation Models
Abstract: Neural models with an encoder-decoder framework provide a feasible solution to Question Generation (QG). However, after analyzing the model vocabulary we find that current models (both RNN-based and pre-training based) have more than 23\% inflected forms. As a result, the encoder will generate separate embeddings for the inflected forms, leading to a waste of training data and parameters. Even worse, in decoding these models are vulnerable to irrelevant noise and they suffer from high computational costs. In this paper, we propose an approach to enhance the performance of QG by fusing word transformation. Firstly, we identify the inflected forms of words from the input of encoder, and replace them with the root words, letting the encoder pay more attention to the repetitive root words. Secondly, we propose to adapt QG as a combination of the following actions in the encode-decoder framework: generating a question word, copying a word from the source sequence or generating a word transformation type. Such extension can greatly decrease the size of predicted words in the decoder as well as noise. We apply our approach to a typical RNN-based model and \textsc{UniLM} to get the improved versions. We conduct extensive experiments on SQuAD and MS MARCO datasets. The experimental results show that the improved versions can significantly outperform the corresponding baselines in terms of BLEU, ROUGE-L and METEOR as well as time cost. | [] | Train |
44,546 | 16 | Title: InCrowdFormer: On-Ground Pedestrian World Model From Egocentric Views
Abstract: We introduce an on-ground Pedestrian World Model, a computational model that can predict how pedestrians move around an observer in the crowd on the ground plane, but from just the egocentric-views of the observer. Our model, InCrowdFormer, fully leverages the Transformer architecture by modeling pedestrian interaction and egocentric to top-down view transformation with attention, and autoregressively predicts on-ground positions of a variable number of people with an encoder-decoder architecture. We encode the uncertainties arising from unknown pedestrian heights with latent codes to predict the posterior distributions of pedestrian positions. We validate the effectiveness of InCrowdFormer on a novel prediction benchmark of real movements. The results show that InCrowdFormer accurately predicts the future coordination of pedestrians. To the best of our knowledge, InCrowdFormer is the first-of-its-kind pedestrian world model which we believe will benefit a wide range of egocentric-view applications including crowd navigation, tracking, and synthesis. | [] | Train |
44,547 | 32 | Title: PNet: A Python Library for Petri Net Modeling and Simulation
Abstract: Petri Net is a formalism to describe changes between 2 or more states across discrete time and has been used to model many systems. We present PNet – a pure Python library for Petri Net modeling and simulation in Python programming language. The design of PNet focuses on reducing the learning curve needed to define a Petri Net by using a text-based language rather than programming constructs to define transition rules. Complex transition rules can be refined as regular Python functions. To demonstrate the simplicity of PNet, we present 2 examples – bread baking, and epidemiological models. | [] | Test |
44,548 | 24 | Title: Achieving Stable Training of Reinforcement Learning Agents in Bimodal Environments through Batch Learning
Abstract: Bimodal, stochastic environments present a challenge to typical Reinforcement Learning problems. This problem is one that is surprisingly common in real world applications, being particularly applicable to pricing problems. In this paper we present a novel learning approach to the tabular Q-learning algorithm, tailored to tackling these specific challenges by using batch updates. A simulation of pricing problem is used as a testbed to compare a typically updated agent with a batch learning agent. The batch learning agents are shown to be both more effective than the typically-trained agents, and to be more resilient to the fluctuations in a large stochastic environment. This work has a significant potential to enable practical, industrial deployment of Reinforcement Learning in the context of pricing and others. | [] | Test |
44,549 | 16 | Title: Edit Temporal-Consistent Videos with Image Diffusion Model
Abstract: Large-scale text-to-image (T2I) diffusion models have been extended for text-guided video editing, yielding impressive zero-shot video editing performance. Nonetheless, the generated videos usually show spatial irregularities and temporal inconsistencies as the temporal characteristics of videos have not been faithfully modeled. In this paper, we propose an elegant yet effective Temporal-Consistent Video Editing (TCVE) method, to mitigate the temporal inconsistency challenge for robust text-guided video editing. In addition to the utilization of a pretrained 2D Unet for spatial content manipulation, we establish a dedicated temporal Unet architecture to faithfully capture the temporal coherence of the input video sequences. Furthermore, to establish coherence and interrelation between the spatial-focused and temporal-focused components, a cohesive joint spatial-temporal modeling unit is formulated. This unit effectively interconnects the temporal Unet with the pretrained 2D Unet, thereby enhancing the temporal consistency of the generated video output while simultaneously preserving the capacity for video content manipulation. Quantitative experimental results and visualization results demonstrate that TCVE achieves state-of-the-art performance in both video temporal consistency and video editing capability, surpassing existing benchmarks in the field. | [
20960,
6145,
42272,
12841,
23563
] | Test |
44,550 | 9 | Title: The Sharp Power Law of Local Search on Expanders
Abstract: Local search is a powerful heuristic in optimization and computer science, the complexity of which was studied in the white box and black box models. In the black box model, we are given a graph $G = (V,E)$ and oracle access to a function $f : V \to \mathbb{R}$. The local search problem is to find a vertex $v$ that is a local minimum, i.e. with $f(v) \leq f(u)$ for all $(u,v) \in E$, using as few queries as possible. The query complexity is well understood on the grid and the hypercube, but much less is known beyond. We show the query complexity of local search on $d$-regular expanders with constant degree is $\Omega\left(\frac{\sqrt{n}}{\log{n}}\right)$, where $n$ is the number of vertices. This matches within a logarithmic factor the upper bound of $O(\sqrt{n})$ for constant degree graphs from Aldous (1983), implying that steepest descent with a warm start is an essentially optimal algorithm for expanders. The best lower bound known from prior work was $\Omega\left(\frac{\sqrt[8]{n}}{\log{n}}\right)$, shown by Santha and Szegedy (2004) for quantum and randomized algorithms. We obtain this result by considering a broader framework of graph features such as vertex congestion and separation number. We show that for each graph, the randomized query complexity of local search is $\Omega\left(\frac{n^{1.5}}{g}\right)$, where $g$ is the vertex congestion of the graph; and $\Omega\left(\sqrt[4]{\frac{s}{\Delta}}\right)$, where $s$ is the separation number and $\Delta$ is the maximum degree. For separation number the previous bound was $\Omega\left(\sqrt[8]{\frac{s}{\Delta}} /\log{n}\right)$, given by Santha and Szegedy for quantum and randomized algorithms. We also show a variant of the relational adversary method from Aaronson (2006), which is asymptotically at least as strong as the version in Aaronson (2006) for all randomized algorithms and strictly stronger for some problems. | [] | Test |
44,551 | 31 | Title: A Self-Correcting Sequential Recommender
Abstract: Sequential recommendations aim to capture users’ preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user’s historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction. We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are ‘keep’, ‘delete’ and ‘insert.’ In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences. | [] | Train |
44,552 | 26 | Title: BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
Abstract: Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, we adopt a bootstrapped training strategy that eliminates the need for negative sampling, enabling BOURNE to handle large graphs efficiently. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies. | [] | Train |
44,553 | 23 | Title: LmPa: Improving Decompilation by Synergy of Large Language Model and Program Analysis
Abstract: Decompilation aims to recover the source code form of a binary executable. It has many applications in security and software engineering such as malware analysis, vulnerability detection and code reuse. A prominent challenge in decompilation is to recover variable names. We propose a novel method that leverages the synergy of large language model (LLM) and program analysis. Language models encode rich multi-modal knowledge, but its limited input size prevents providing sufficient global context for name recovery. We propose to divide the task to many LLM queries and use program analysis to correlate and propagate the query results, which in turn improves the performance of LLM by providing additional contextual information. Our results show that 75% of the recovered names are considered good by users and our technique outperforms the state-of-the-art technique by 16.5% and 20.23% in precision and recall, respectively. | [
36617,
11190
] | Train |
44,554 | 10 | Title: Goal Alignment: A Human-Aware Account of Value Alignment Problem
Abstract: Value alignment problems arise in scenarios where the specified objectives of an AI agent don't match the true underlying objective of its users. The problem has been widely argued to be one of the central safety problems in AI. Unfortunately, most existing works in value alignment tend to focus on issues that are primarily related to the fact that reward functions are an unintuitive mechanism to specify objectives. However, the complexity of the objective specification mechanism is just one of many reasons why the user may have misspecified their objective. A foundational cause for misalignment that is being overlooked by these works is the inherent asymmetry in human expectations about the agent's behavior and the behavior generated by the agent for the specified objective. To address this lacuna, we propose a novel formulation for the value alignment problem, named goal alignment that focuses on a few central challenges related to value alignment. In doing so, we bridge the currently disparate research areas of value alignment and human-aware planning. Additionally, we propose a first-of-its-kind interactive algorithm that is capable of using information generated under incorrect beliefs about the agent, to determine the true underlying goal of the user. | [] | Train |
44,555 | 4 | Title: Defending against the nothing-at-stake problem in multi-threaded blockchains
Abstract: In blockchain systems, the scarcity of a resource is used as a Sybil protection mechanism. In Proof-of-Work blockchains, that resource is computing power. In the event of a fork, the scarcity of this resource theoretically prevents miners from producing blocks on both branches of a fork. In Proof-of-Stake blockchains, because that resource is token stake, the computational cost of creating a block is negligible. In the event of a fork, and if no specific measures have been taken, rational block producers should extend both branches of the fork. In blockchains with sequential block production, a punishment mechanism known as slashing is often cited as a protection against the nothing-at-stake problem. However, in the context of a blockchain with parallel block production, it seems that slashing is not sufficient against the numerous divergence opportunities. In this paper, we propose a novel protection against the nothing-at-stake problem that takes the most out of BFT and Nakamoto-based consensus. By combining those approaches, we wish to scale up blockchains by allowing parallel block production without reconciliation. | [] | Train |
44,556 | 38 | Title: Every Author as First Author
Abstract: We propose a new standard for writing author names on papers and in bibliographies, which places every author as a first author -- superimposed. This approach enables authors to write papers as true equals, without any advantage given to whoever's name happens to come first alphabetically (for example). We develop the technology for implementing this standard in LaTeX, BibTeX, and HTML; show several examples; and discuss further advantages. | [] | Test |
44,557 | 24 | Title: Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
Abstract: The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means of a high-level probabilistic-based heuristic. This research introduces a novel population-based Bayesian hyper-heuristic (BHH) that is used to train feedforward neural networks (FFNNs). The performance of the BHH is compared to that of ten popular low-level heuristics, each with different search behaviours. The chosen heuristic pool consists of classic gradient-based heuristics as well as meta-heuristics (MHs). The empirical process is executed on fourteen datasets consisting of classification and regression problems with varying characteristics. The BHH is shown to be able to train FFNNs well and provide an automated method for finding the best heuristic to train the FFNNs at various stages of the training process. | [] | Train |
44,558 | 13 | Title: Experience-Based Evolutionary Algorithms for Expensive Optimization
Abstract: Optimization algorithms are very different from human optimizers. A human being would gain more experiences through problem-solving, which helps her/him in solving a new unseen problem. Yet an optimization algorithm never gains any experiences by solving more problems. In recent years, efforts have been made towards endowing optimization algorithms with some abilities of experience learning, which is regarded as experience-based optimization. In this paper, we argue that hard optimization problems could be tackled efficiently by making better use of experiences gained in related problems. We demonstrate our ideas in the context of expensive optimization, where we aim to find a near-optimal solution to an expensive optimization problem with as few fitness evaluations as possible. To achieve this, we propose an experience-based surrogate-assisted evolutionary algorithm (SAEA) framework to enhance the optimization efficiency of expensive problems, where experiences are gained across related expensive tasks via a novel meta-learning method. These experiences serve as the task-independent parameters of a deep kernel learning surrogate, then the solutions sampled from the target task are used to adapt task-specific parameters for the surrogate. With the help of experience learning, competitive regression-based surrogates can be initialized using only 1$d$ solutions from the target task ($d$ is the dimension of the decision space). Our experimental results on expensive multi-objective and constrained optimization problems demonstrate that experiences gained from related tasks are beneficial for the saving of evaluation budgets on the target problem. | [
3286
] | Validation |
44,559 | 4 | Title: Time Moves Faster When There is Nothing You Anticipate: The Role of Time in MEV Rewards
Abstract: This study explores the intricacies of waiting games, a novel dynamic that emerged with Ethereum's transition to a Proof-of-Stake (PoS)-based block proposer selection protocol. Within this PoS framework, validators acquire a distinct monopoly position during their assigned slots, given that block proposal rights are set deterministically, contrasting with Proof-of-Work (PoW) protocols. Consequently, validators have the power to delay block proposals, stepping outside the honest validator specs, optimizing potential returns through MEV payments. Nonetheless, this strategic behaviour introduces the risk of orphaning if attestors fail to observe and vote on the block timely. Our quantitative analysis of this waiting phenomenon and its associated risks reveals an opportunity for enhanced MEV extraction, exceeding standard protocol rewards, and providing sufficient incentives for validators to play the game. Notably, our findings indicate that delayed proposals do not always result in orphaning and orphaned blocks are not consistently proposed later than non-orphaned ones. To further examine consensus stability under varying network conditions, we adopt an agent-based simulation model tailored for PoS-Ethereum, illustrating that consensus disruption will not be observed unless significant delay strategies are adopted. Ultimately, this research offers valuable insights into the advent of waiting games on Ethereum, providing a comprehensive understanding of trade-offs and potential profits for validators within the blockchain ecosystem. | [
209,
14772,
37065
] | Test |
44,560 | 24 | Title: Cyclophobic Reinforcement Learning
Abstract: In environments with sparse rewards, finding a good inductive bias for exploration is crucial to the agent's success. However, there are two competing goals: novelty search and systematic exploration. While existing approaches such as curiosity-driven exploration find novelty, they sometimes do not systematically explore the whole state space, akin to depth-first-search vs breadth-first-search. In this paper, we propose a new intrinsic reward that is cyclophobic, i.e., it does not reward novelty, but punishes redundancy by avoiding cycles. Augmenting the cyclophobic intrinsic reward with a sequence of hierarchical representations based on the agent's cropped observations we are able to achieve excellent results in the MiniGrid and MiniHack environments. Both are particularly hard, as they require complex interactions with different objects in order to be solved. Detailed comparisons with previous approaches and thorough ablation studies show that our newly proposed cyclophobic reinforcement learning is more sample efficient than other state of the art methods in a variety of tasks. | [] | Test |
44,561 | 16 | Title: TextDiffuser: Diffusion Models as Text Painters
Abstract: Diffusion models have gained increasing attention for their impressive generation abilities but currently struggle with rendering accurate and coherent text. To address this issue, we introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. TextDiffuser consists of two stages: first, a Transformer model generates the layout of keywords extracted from text prompts, and then diffusion models generate images conditioned on the text prompt and the generated layout. Additionally, we contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs with text recognition, detection, and character-level segmentation annotations. We further collect the MARIO-Eval benchmark to serve as a comprehensive tool for evaluating text rendering quality. Through experiments and user studies, we show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text. The code, model, and dataset will be available at \url{https://aka.ms/textdiffuser}. | [
15811,
30243,
30503,
7915,
8210,
20306,
28532,
34074
] | Train |
44,562 | 4 | Title: P-CFT: A Privacy-preserving and Crash Fault Tolerant Consensus Algorithm for Permissioned Blockchains
Abstract: Consensus algorithms play a critical role in blockchains and directly impact their performance. During consensus processing, nodes need to validate and order the pending transactions into a new block, which requires verifying the application-specific data encapsulated within a transaction. This exposes the underlying data to the consensus nodes, presenting privacy concerns. Existing consensus algorithms focus on realizing application security and performance goals, but lack privacy-by-design properties or are resource-heavy and intended for securing permissionless blockchain networks. In this paper, we propose P-CFT, a zero-knowledge and crash fault tolerant consensus algorithm for permissioned blockchains. The proposed consensus algorithm provides inherent data privacy directly to the consensus layer, while still providing guarantees of crash fault tolerance. We conduct experiments using the Hyperledger Ursa cryptographic library, and the results show promise for integrating P-CFT into existing permissioned blockchain systems requiring privacy-preserving and crash fault tolerant features. | [
11402,
14075,
1668
] | Test |
44,563 | 22 | Title: Reasoning about Choreographic Programs
Abstract: Choreographic programming is a paradigm where a concurrent or distributed system is developed in a top-down fashion. Programs, called choreographies, detail the desired interactions between processes, and can be compiled to distributed implementations based on message passing. Choreographic languages usually guarantee deadlock-freedom and provide an operational correspondence between choreographies and their compiled implementations, but until now little work has been done on verifying other properties. This paper presents a Hoare-style logic for reasoning about the behaviour of choreographies, and illustrate its usage in representative examples. We show that this logic is sound and complete, and discuss decidability of its judgements. Using existing results from choreographic programming, we show that any functional correctness property proven for a choreography also holds for its compiled implementation. | [] | Validation |
44,564 | 22 | Title: PEak: A Single Source of Truth for Hardware Design and Verification
Abstract: Domain-specific languages for hardware can significantly enhance designer productivity, but sometimes at the cost of ease of verification. On the other hand, ISA specification languages are too static to be used during early stage design space exploration. We present PEak, an open-source hardware design and specification language, which aims to improve both design productivity and verification capability. PEak does this by providing a single source of truth for functional models, formal specifications, and RTL. PEak has been used in several academic projects, and PEak-generated RTL has been included in three fabricated hardware accelerators. In these projects, the formal capabilities of PEak were crucial for enabling both novel design space exploration techniques and automated compiler synthesis. | [] | Train |
44,565 | 24 | Title: Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences
Abstract: Accelerated magnetic resonance imaging resorts to either Fourier-domain subsampling or better reconstruction algorithms to deal with fewer measurements while still generating medical images of high quality. Determining the optimal sampling strategy given a fixed reconstruction protocol often has combinatorial complexity. In this work, we apply double deep Q-learning and REINFORCE algorithms to learn the sampling strategy for dynamic image reconstruction. We consider the data in the format of time series, and the reconstruction method is a pre-trained autoencoder-typed neural network. We present a proof of concept that reinforcement learning algorithms are effective to discover the optimal sampling pattern which underlies the pre-trained reconstructor network (i.e., the dynamics in the environment). The code for replicating experiments can be found at https://github.com/zhishenhuang/RLsamp. | [] | Test |
44,566 | 24 | Title: Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Abstract: A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also ensuring that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and define it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that a conservative offline RL algorithm that also learns a calibrated value function leads to effective online fine-tuning, enabling us to take the benefits of offline initializations in online fine-tuning. In practice, Cal-QL can be implemented on top of the conservative Q learning (CQL) for offline RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11 fine-tuning benchmark tasks that we study in this paper. Code and video are available at https://nakamotoo.github.io/projects/Cal-QL | [
15201,
4002,
44513,
31960,
1196,
11089,
16273,
30200,
16347,
7039
] | Test |
44,567 | 24 | Title: Information Geometrically Generalized Covariate Shift Adaptation
Abstract: Abstract Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is often violated. In particular, the marginal distribution of the data changes, called covariate shift, is one of the most important research topics in machine learning. We show that the well-known family of covariate shift adaptation methods is unified in the framework of information geometry. Furthermore, we show that parameter search for a geometrically generalized covariate shift adaptation method can be achieved efficiently. Numerical experiments show that our generalization can achieve better performance than the existing methods it encompasses. | [] | Test |
44,568 | 26 | Title: Effective Community Search on Large Attributed Bipartite Graphs
Abstract: Community search over bipartite graphs has attracted significant interest recently. In many applications such as user-item bipartite graph in E-commerce, customer-movie bipartite graph in movie rating website, nodes tend to have attributes, while previous community search algorithm on bipartite graphs ignore attributes, which makes the returned results with poor cohesion with respect to their node attributes. In this paper, we study the community search problem on attributed bipartite graphs. Given a query vertex q, we aim to find attributed $\left(\alpha,\beta\right)$-communities of $G$, where the structure cohesiveness of the community is described by an $\left(\alpha,\beta\right)$-core model, and the attribute similarity of two groups of nodes in the subgraph is maximized. In order to retrieve attributed communities from bipartite graphs, we first propose a basic algorithm composed of two steps: the generation and verification of candidate keyword sets, and then two improved query algorithms Inc and Dec are proposed. Inc is proposed considering the anti-monotonity property of attributed bipartite graphs, then we adopt different generating method and verifying order of candidate keyword sets and propose the Dec algorithm. After evaluating our solutions on eight large graphs, the experimental results demonstrate that our methods are effective and efficient in querying the attributed communities on bipartite graphs. | [] | Train |
44,569 | 37 | Title: Pengembangan Domain Specific Language Untuk Pengelolaan Data Warehouse
Abstract: Muhamad Taufan. 92308041 PENGEMBANGAN DOMAIN SPECIFIC LANGUAGE UNTUK PENGELOLAAN DATA WAREHOUSE. Tesis, Fakultas Pasca Sarjana, Jurusan Perangkat Lunak Sistem Informasi, Universitas Gunadarma, 2012. Kata Kunci : DSL, Retensi data, Kompiler, Data warehouse (14+ 66+ lampiran) Upaya peningkatan kinerja pelayanan atas transaksi pada suatu bank dapat dilakukan dengan cara retensi data, mengurangi volume data di dalam database produksi dengan cara memotong data histori sesuai dengan aturan pada suatu bank ke data warehouse. Perancangan dan implementasi aplikasi Domain Specific Language (DSL) untuk penanganan data transfer pada data warehouse dibagi menjadi analisis leksikal, analisis sintaks, analisis semantik dan penghasil kode. Tiap bagian memiliki karakteristik yang berbeda untuk menghasilkan suatu perintah eksekusi. Telah dikembangkan suatu aplikasi dengan metode DSL, yang bermanfaat mengurangi kesalahan penulisan perintah bagi user biasa (non-teknis) dalam melakukan pemindahan data. Dari pengujian menghasilkan keputusan metode transfer Oracle sesuai dengan ukuran skala data tertentu. | [] | Train |
44,570 | 5 | Title: EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy
Abstract: In the post-Moore's Law era, relying solely on hardware advancements for automatic performance gains is no longer feasible without increased energy consumption, due to the end of Dennard scaling. Consequently, computing accounts for an increasing amount of global energy usage, contradicting the objective of sustainable computing. The lack of hardware support and the absence of a standardized, software-centric method for the precise tracing of energy provenance exacerbates the issue. Aiming to overcome this challenge, we argue that fine-grained software energy attribution is attainable, even with limited hardware support. To support our position, we present a thread-level, NUMA-aware energy attribution method for CPU and DRAM in multi-tenant environments. The evaluation of our prototype implementation, EnergAt, demonstrates the validity, effectiveness, and robustness of our theoretical model, even in the presence of the noisy-neighbor effect. We envisage a sustainable cloud environment and emphasize the importance of collective efforts to improve software energy efficiency. | [
9610
] | Train |
44,571 | 23 | Title: Do RESTful API Design Rules Have an Impact on the Understandability of Web APIs? A Web-Based Experiment with API Descriptions
Abstract: Context: Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking. Objective: We therefore wanted to study 1) the impact of RESTful API design rules on understandability, 2) if rule violations are also perceived as more difficult to understand, and 3) if demographic attributes like REST-related experience have an influence on this. Method: We conducted a controlled Web-based experiment with 105 participants, from both industry and academia and with different levels of experience. Based on a hybrid between a crossover and a between-subjects design, we studied 12 design rules using API snippets in two complementary versions: one that adhered to a"rule"and one that was a"violation"of this rule. Participants answered comprehension questions and rated the perceived difficulty. Results: For 11 of the 12 rules, we found that"violation"performed significantly worse than"rule"for the comprehension tasks. Regarding the subjective ratings, we found significant differences for 9 of the 12 rules, meaning that most violations were subjectively rated as more difficult to understand. Demographics played no role in the comprehension performance for"violation". Conclusions: Our results provide first empirical evidence for the importance of following design rules to improve the understandability of Web APIs, which is important for researchers, practitioners, and educators. | [] | Train |
44,572 | 31 | Title: Form-NLU: Dataset for the Form Natural Language Understanding
Abstract: Compared to general document analysis tasks, form document structure understanding and retrieval are challenging. Form documents are typically made by two types of authors; A form designer, who develops the form structure and keys, and a form user, who fills out form values based on the provided keys. Hence, the form values may not be aligned with the form designer's intention (structure and keys) if a form user gets confused. In this paper, we introduce Form-NLU, the first novel dataset for form structure understanding and its key and value information extraction, interpreting the form designer's intent and the alignment of user-written value on it. It consists of 857 form images, 6k form keys and values, and 4k table keys and values. Our dataset also includes three form types: digital, printed, and handwritten, which cover diverse form appearances and layouts. We propose a robust positional and logical relation-based form key-value information extraction framework. Using this dataset, Form-NLU, we first examine strong object detection models for the form layout understanding, then evaluate the key information extraction task on the dataset, providing fine-grained results for different types of forms and keys. Furthermore, we examine it with the off-the-shelf pdf layout extraction tool and prove its feasibility in real-world cases. | [] | Test |
44,573 | 16 | Title: Multi-modal Prompting for Low-Shot Temporal Action Localization
Abstract: In this paper, we consider the problem of temporal action localization under low-shot (zero-shot&few-shot) scenario, with the goal of detecting and classifying the action instances from arbitrary categories within some untrimmed videos, even not seen at training time. We adopt a Transformer-based two-stage action localization architecture with class-agnostic action proposal, followed by open-vocabulary classification. We make the following contributions. First, to compensate image-text foundation models with temporal motions, we improve category-agnostic action proposal by explicitly aligning embeddings of optical flows, RGB and texts, which has largely been ignored in existing low-shot methods. Second, to improve open-vocabulary action classification, we construct classifiers with strong discriminative power, i.e., avoid lexical ambiguities. To be specific, we propose to prompt the pre-trained CLIP text encoder either with detailed action descriptions (acquired from large-scale language models), or visually-conditioned instance-specific prompt vectors. Third, we conduct thorough experiments and ablation studies on THUMOS14 and ActivityNet1.3, demonstrating the superior performance of our proposed model, outperforming existing state-of-the-art approaches by one significant margin. | [
19497,
13310,
40497
] | Train |
44,574 | 16 | Title: Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Abstract: Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception. | [
1155,
21508,
25989,
17286,
21784,
10265,
37915,
1953,
43169,
22955,
27436,
31028,
21687,
7416,
36549,
1606,
10329,
19675,
34017,
15864,
35581
] | Test |
44,575 | 30 | Title: Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning
Abstract: Unsupported and unfalsifiable claims we encounter in our daily lives can influence our view of the world. Characterizing, summarizing, and -- more generally -- making sense of such claims, however, can be challenging. In this work, we focus on fine-grained debate topics and formulate a new task of distilling, from such claims, a countable set of narratives. We present a crowdsourced dataset of 12 controversial topics, comprising more than 120k arguments, claims, and comments from heterogeneous sources, each annotated with a narrative label. We further investigate how large language models (LLMs) can be used to synthesise claims using In-Context Learning. We find that generated claims with supported evidence can be used to improve the performance of narrative classification models and, additionally, that the same model can infer the stance and aspect using a few training examples. Such a model can be useful in applications which rely on narratives , e.g. fact-checking. | [] | Train |
44,576 | 4 | Title: Privacy-preserving Blockchain-enabled Parametric Insurance via Remote Sensing and IoT
Abstract: Traditional Insurance, a popular approach of financial risk management, has suffered from the issues of high operational costs, opaqueness, inefficiency and a lack of trust. Recently, blockchain-enabled"parametric insurance"through authorized data sources (e.g., remote sensing and IoT) aims to overcome these issues by automating the underwriting and claim processes of insurance policies on a blockchain. However, the openness of blockchain platforms raises a concern of user privacy, as the private user data in insurance claims on a blockchain may be exposed to outsiders. In this paper, we propose a privacy-preserving parametric insurance framework based on succinct zero-knowledge proofs (zk-SNARKs), whereby an insuree submits a zero-knowledge proof (without revealing any private data) for the validity of an insurance claim and the authenticity of its data sources to a blockchain for transparent verification. Moreover, we extend the recent zk-SNARKs to support robust privacy protection for multiple heterogeneous data sources and improve its efficiency to cut the incurred gas cost by 80%. As a proof-of-concept, we implemented a working prototype of bushfire parametric insurance on real-world blockchain platform Ethereum, and present extensive empirical evaluations. | [
6051
] | Test |
44,577 | 10 | Title: Multi-Grained Multimodal Interaction Network for Entity Linking
Abstract: Multimodal entity linking (MEL) task, which aims at resolving ambiguous mentions to a multimodal knowledge graph, has attracted wide attention in recent years. Though large efforts have been made to explore the complementary effect among multiple modalities, however, they may fail to fully absorb the comprehensive expression of abbreviated textual context and implicit visual indication. Even worse, the inevitable noisy data may cause inconsistency of different modalities during the learning process, which severely degenerates the performance. To address the above issues, in this paper, we propose a novel Multi-GraIned Multimodal InteraCtion Network (MIMIC) framework for solving the MEL task. Specifically, the unified inputs of mentions and entities are first encoded by textual/visual encoders separately, to extract global descriptive features and local detailed features. Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions. In particular, three modules, namely the Text-based Global-Local interaction Unit (TGLU), Vision-based DuaL interaction Unit (VDLU) and Cross-Modal Fusion-based interaction Unit (CMFU) are designed to capture and integrate the fine-grained representation lying in abbreviated text and implicit visual cues. Afterwards, we introduce a unit-consistency objective function via contrastive learning to avoid inconsistency and model degradation. Experimental results on three public benchmark datasets demonstrate that our solution outperforms various state-of-the-art baselines, and ablation studies verify the effectiveness of designed modules. | [] | Train |
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