Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2104.00488	Bayesian Graph Convolutional Network for Traffic Prediction	Recently, adaptive graph convolutional network based traffic prediction methods, learning a latent graph structure from traffic data via various attention-based mechanisms, have achieved impressive performance. However, they are still limited to find a better description of spatial relationships between traffic conditi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	228,016
2203.06555	Label-only Model Inversion Attack: The Attack that Requires the Least Information	In a model inversion attack, an adversary attempts to reconstruct the data records, used to train a target model, using only the model's output. In launching a contemporary model inversion attack, the strategies discussed are generally based on either predicted confidence score vectors, i.e., black-box attacks, or the ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	285,151
2112.00227	A Machine Learning Analysis of COVID-19 Mental Health Data	In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting disease COVID-19 were first identified in Wuhan China. The disease slipped through containment measures, with the first known case in the United States being identified on January 20th, 2020. In this paper, we utilize survey data from the Inter...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	269,062
1905.12334	Mixed Precision Training With 8-bit Floating Point	Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size. In recent years, deep learning training has largely migrated to 16-bit precision, with significant gains in performance and energy efficiency. However, attemp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,737
1110.3094	Syndromic classification of Twitter messages	Recent studies have shown strong correlation between social networking data and national influenza rates. We expanded upon this success to develop an automated text mining system that classifies Twitter messages in real time into six syndromic categories based on key terms from a public health ontology. 10-fold cross v...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	12,651
2010.07886	Compressive Summarization with Plausibility and Salience Modeling	Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be deleted, then learn a model of what to actually delete by optimizing for content selection (ROUGE). In this work, we propose to relax the rigid syntactic constraints on candi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	200,979
2501.03191	CLIX: Cross-Lingual Explanations of Idiomatic Expressions	Automated definition generation systems have been proposed to support vocabulary expansion for language learners. The main barrier to the success of these systems is that learners often struggle to understand definitions due to the presence of potentially unfamiliar words and grammar, particularly when non-standard lan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	522,791
2106.02845	Semi-Supervised Domain Adaptation via Adaptive and Progressive Feature Alignment	Contemporary domain adaptive semantic segmentation aims to address data annotation challenges by assuming that target domains are completely unannotated. However, annotating a few target samples is usually very manageable and worthwhile especially if it improves the adaptation performance substantially. This paper pres...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,056
1708.05891	Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM	Arabic word segmentation is essential for a variety of NLP applications such as machine translation and information retrieval. Segmentation entails breaking words into their constituent stems, affixes and clitics. In this paper, we compare two approaches for segmenting four major Arabic dialects using only several thou...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	79,220
2404.12278	DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era	In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for multimodal Data Fusion for Data Mining, integrating embeddings and the Cross-Industry Standard Process for Data Mining with the existing Da...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	447,814
1603.06098	Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation	We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with obje...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,441
1712.07705	Computing Optimal Repairs for Functional Dependencies	We investigate the complexity of computing an optimal repair of an inconsistent database, in the case where integrity constraints are Functional Dependencies (FDs). We focus on two types of repairs: an optimal subset repair (optimal S-repair) that is obtained by a minimum number of tuple deletions, and an optimal updat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	87,081
2001.11194	The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition	This paper presents a new approach for the recognition of elements in floor plan layouts. Besides of elements with common shapes, we aim to recognize elements with irregular shapes such as circular rooms and inclined walls. Furthermore, the reduction of noise in the semantic segmentation of the floor plan is on demand....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	162,008
2102.02922	Towards a Flexible System Architecture for Automated Knowledge Base Construction Frameworks	Although knowledge bases play an important role in many domains (including in archives, where they are sometimes used for entity extraction and semantic annotation tasks), it is challenging to build knowledge bases by hand. This is owing to a number of factors: Knowledge bases must be accurate, up-to-date, comprehensiv...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	218,565
2402.02826	SynthVision -- Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data	Rapid development of disease detection computer vision models is vital in response to urgent medical crises like epidemics or events of bioterrorism. However, traditional data gathering methods are too slow for these scenarios necessitating innovative approaches to generate reliable models quickly from minimal data. We...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	426,743
2409.00503	Non-negative Sparse Recovery at Minimal Sampling Rate	It is known that sparse recovery is possible if the number of measurements is in the order of the sparsity, but the corresponding decoders either lack polynomial decoding time or robustness to noise. Commonly, decoders that rely on a null space property are being used. These achieve polynomial time decoding and are rob...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	484,949
1505.06162	Design and Implementation of Real-time Algorithms for Eye Tracking and PERCLOS Measurement for on board Estimation of Alertness of Drivers	The alertness level of drivers can be estimated with the use of computer vision based methods. The level of fatigue can be found from the value of PERCLOS. It is the ratio of closed eye frames to the total frames processed. The main objective of the thesis is the design and implementation of real-time algorithms for me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,382
2112.00874	Neural Stochastic Dual Dynamic Programming	Stochastic dual dynamic programming (SDDP) is a state-of-the-art method for solving multi-stage stochastic optimization, widely used for modeling real-world process optimization tasks. Unfortunately, SDDP has a worst-case complexity that scales exponentially in the number of decision variables, which severely limits ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,275
2304.04590	LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search	Queries with similar information needs tend to have similar document clicks, especially in biomedical literature search engines where queries are generally short and top documents account for most of the total clicks. Motivated by this, we present a novel architecture for biomedical literature search, namely Log-Augmen...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	357,275
2405.18052	Algebraic Geometry Codes for Cross-Subspace Alignment in Private Information Retrieval	A new framework for interference alignment in secure and private information retrieval (PIR) from colluding servers is proposed, generalizing the original cross-subspace alignment (CSA) codes proposed by Jia, Sun, and Jafar. The general scheme is built on algebraic geometry codes and explicit constructions with replica...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	458,255
2202.04975	FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling	Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerable to poisoning attacks by malicious clients. Existing recommender system poisoning methods mainly focus on promoting the recommendation chan...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	279,729
2408.16233	PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator	Channel Pruning is one of the most widespread techniques used to compress deep neural networks while maintaining their performances. Currently, a typical pruning algorithm leverages neural architecture search to directly find networks with a configurable width, the key step of which is to identify representative subnet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,245
2111.07608	Property Inference Attacks Against GANs	While machine learning (ML) has made tremendous progress during the past decade, recent research has shown that ML models are vulnerable to various security and privacy attacks. So far, most of the attacks in this field focus on discriminative models, represented by classifiers. Meanwhile, little attention has been pai...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	266,428
2401.06868	Multicriteria decision support employing adaptive prediction in a tensor-based feature representation	Multicriteria decision analysis (MCDA) is a widely used tool to support decisions in which a set of alternatives should be ranked or classified based on multiple criteria. Recent studies in MCDA have shown the relevance of considering not only current evaluations of each criterion but also past data. Past-data-based ap...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	421,327
1502.04049	How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?	Electronic health records capture patient information using structured controlled vocabularies and unstructured narrative text. While structured data typically encodes lab values, encounters and medication lists, unstructured data captures the physician's interpretation of the patient's condition, prognosis, and respon...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	40,213
2006.00572	Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network	Due to the increasing amount of data on the internet, finding a highly-informative, low-dimensional representation for text is one of the main challenges for efficient natural language processing tasks including text classification. This representation should capture the semantic information of the text while retaining...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	179,508
2407.14730	FedDM: Enhancing Communication Efficiency and Handling Data Heterogeneity in Federated Diffusion Models	We introduce FedDM, a novel training framework designed for the federated training of diffusion models. Our theoretical analysis establishes the convergence of diffusion models when trained in a federated setting, presenting the specific conditions under which this convergence is guaranteed. We propose a suite of train...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	474,880
2412.04277	Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic	Large Language Models (LLMs) have shown impressive results in multiple domains of natural language processing (NLP) but are mainly focused on the English language. Recently, more LLMs have incorporated a larger proportion of multilingual text to represent low-resource languages. In Arabic NLP, several Arabic-centric LL...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	514,337
1608.05982	Social Networks Analysis in Discovering the Narrative Structure of Literary Fiction	In our paper we would like to make a cross-disciplinary leap and use the tools of network theory to understand and explore narrative structure in literary fiction, an approach that is still underestimated. However, the systems in fiction are sensitive to readers subjectivity and attention must to be paid to different m...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	60,049
2409.02531	Modular pipeline for small bodies gravity field modeling: an efficient representation of variable density spherical harmonics coefficients	Proximity operations to small bodies, such as asteroids and comets, demand high levels of autonomy to achieve cost-effective, safe, and reliable Guidance, Navigation and Control (GNC) solutions. Enabling autonomous GNC capabilities in the vicinity of these targets is thus vital for future space applications. However, t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	485,741
1805.06757	Matching Consecutive Subpatterns Over Streaming Time Series	Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching model, a pattern may contain multiple subpatterns representing differe...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	97,677
2107.14309	Distributed Identification of Contracting and/or Monotone Network Dynamics	This paper proposes methods for identification of large-scale networked systems with guarantees that the resulting model will be contracting -- a strong form of nonlinear stability -- and/or monotone, i.e. order relations between states are preserved. The main challenges that we address are: simultaneously searching fo...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	248,422
1909.00898	Average-based Robustness for Continuous-Time Signal Temporal Logic	We propose a new robustness score for continuous-time Signal Temporal Logic (STL) specifications. Instead of considering only the most severe point along the evolution of the signal, we use average scores to extract more information from the signal, emphasizing robust satisfaction of all the specifications' subformulae...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	143,747
2101.02082	Artificial Intelligence Methods in In-Cabin Use Cases: A Survey	As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles. In this context, the functionality inside the vehicle cabin plays a key role in ensuring a safe and pleasant journey for driver and passenger alike. At the same time, recent advances in th...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	214,521
2305.03515	GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent	Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difficult optimization problem, as it is non-convex and non-differentiable. Therefore, common approaches learn DTs using a greedy growth algorithm that minimizes the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	362,426
2403.04113	ZTRAN: Prototyping Zero Trust Security xApps for Open Radio Access Network Deployments	The open radio access network (O-RAN) offers new degrees of freedom for building and operating advanced cellular networks. Emphasizing on RAN disaggregation, open interfaces, multi-vendor support, and RAN intelligent controllers (RICs), O-RAN facilitates adaptation to new applications and technology trends. Yet, this a...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	true	435,456
2307.04212	Delay-Adaptive Control of First-order Hyperbolic PIDEs	We develop a delay-adaptive controller for a class of first-order hyperbolic partial integro-differential equations (PIDEs) with an unknown input delay. By employing a transport PDE to represent delayed actuator states, the system is transformed into a transport partial differential equation (PDE) with unknown propagat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	378,329
2305.00624	Diffusion Models for Time Series Applications: A Survey	Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research. With a distinguished performance in generating samples that resemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. In ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,403
2301.01110	Causal Discovery for Gene Regulatory Network Prediction	Biological systems and processes are networks of complex nonlinear regulatory interactions between nucleic acids, proteins, and metabolites. A natural way in which to represent these interaction networks is through the use of a graph. In this formulation, each node represents a nucleic acid, protein, or metabolite and ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	339,136
2104.00834	The Production and Consumption of Social Media	We model social media as collections of users producing and consuming content. Users value consuming content, but doing so uses up their scarce attention, and hence they prefer content produced by more able users. Users also value receiving attention, creating the incentive to attract an audience by producing valuable ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	228,137
2401.09198	Space and Time Continuous Physics Simulation From Partial Observations	Modern techniques for physical simulations rely on numerical schemes and mesh-refinement methods to address trade-offs between precision and complexity, but these handcrafted solutions are tedious and require high computational power. Data-driven methods based on large-scale machine learning promise high adaptivity by ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	422,180
2101.05084	This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models	Generative adversarial networks (GANs) are able to generate high resolution photo-realistic images of objects that "do not exist." These synthetic images are rather difficult to detect as fake. However, the manner in which these generative models are trained hints at a potential for information leakage from the supplie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,329
2409.17340	Koopman-driven grip force prediction through EMG sensing	Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities. Robotic rehabilitation provides tools to restore hand function, while novel methods based on surface electromyography (sEMG) enable the adaptation of the device's force output according to the user's condit...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	491,747
2110.09108	Asymmetric Modality Translation For Face Presentation Attack Detection	Face presentation attack detection (PAD) is an essential measure to protect face recognition systems from being spoofed by malicious users and has attracted great attention from both academia and industry. Although most of the existing methods can achieve desired performance to some extent, the generalization issue of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,680
2103.16709	Islanded Microgrid Restoration Studies with Graph-Based Analysis	The need to restore and keep the grid running or fast restoration during emergencies such as extreme weather conditions is quite apparent given the reliance of other infrastructure on electricity. One promising approach to electricity restoration is the use of locally available energy resources to restore the system to...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	227,676
2006.02879	Auto-decoding Graphs	We present an approach to synthesizing new graph structures from empirically specified distributions. The generative model is an auto-decoder that learns to synthesize graphs from latent codes. The graph synthesis model is learned jointly with an empirical distribution over the latent codes. Graphs are synthesized usin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,161
2305.18099	Writing user personas with Large Language Models: Testing phase 6 of a Thematic Analysis of semi-structured interviews	The goal of this paper is establishing if we can satisfactorily perform a Thematic Analysis (TA) of semi-structured interviews using a Large Language Model (more precisely GPT3.5-Turbo). Building on previous work by the author, which established an embryonal process for conducting a TA with the model, this paper will p...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	368,860
2412.06177	Quantum Algorithms for Optimal Power Flow	This paper explores the use of quantum computing, specifically the use of HHL and VQLS algorithms, to solve optimal power flow problem in electrical grids. We investigate the effectiveness of these quantum algorithms in comparison to classical methods. The simulation results presented here which substantially improve t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	515,131
1606.08366	Exploiting the Short-term to Long-term Plasticity Transition in Memristive Nanodevice Learning Architectures	Memristive nanodevices offer new frontiers for computing systems that unite arithmetic and memory operations on-chip. Here, we explore the integration of electrochemical metallization cell (ECM) nanodevices with tunable filamentary switching in nanoscale learning systems. Such devices offer a natural transition between...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	57,858
1903.08410	Some remarks on non projective Frobenius algebras and linear codes	With a small suitable modification, dropping the projectivity condition, we extend the notion of a Frobenius algebra to grant that a Frobenius algebra over a Frobenius commutative ring is itself a Frobenius ring. The modification introduced here also allows Frobenius finite rings to be precisely those rings which are F...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	124,826
1310.3174	Multi-Armed Bandits for Intelligent Tutoring Systems	We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them prog...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	27,728
1802.03101	Convolutional Hashing for Automated Scene Matching	We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately rel...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	89,897
2412.12799	RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection	In radar-camera 3D object detection, the radar point clouds are sparse and noisy, which causes difficulties in fusing camera and radar modalities. To solve this, we introduce a novel query-based detection method named Radar-Camera Transformer (RCTrans). Specifically, we first design a Radar Dense Encoder to enrich the ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	518,036
2406.07487	GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection	Diffusion models have shown superior performance on unsupervised anomaly detection tasks. Since trained with normal data only, diffusion models tend to reconstruct normal counterparts of test images with certain noises added. However, these methods treat all potential anomalies equally, which may cause two main problem...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	463,069
2302.09842	Codes Over Absorption Channels	In this paper, we present a novel communication channel, called the absorption channel, inspired by information transmission in neurons. Our motivation comes from in-vivo nano-machines, emerging medical applications, and brain-machine interfaces that communicate over the nervous system. Another motivation comes from vi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	346,592
0809.4882	Multi-Armed Bandits in Metric Spaces	In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a sequence of trials so as to maximize the total payoff of the chosen strategies. While the performance of bandit algorithms with a small finite strategy set is quite well understood, bandit problems with large strategy sets are st...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	2,420
2311.11656	Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images	Skin cancer is the most common type of cancer in the United States and is estimated to affect one in five Americans. Recent advances have demonstrated strong performance on skin cancer detection, as exemplified by state of the art performance in the SIIM-ISIC Melanoma Classification Challenge; however these solutions l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,033
2006.08723	Threats and Countermeasures of Cyber Security in Direct and Remote Vehicle Communication Systems	Traffic management, road safety, and environmental impact are important issues in the modern world. These challenges are addressed by the application of sensing, control and communication methods of intelligent transportation systems (ITS). A part of ITS is a vehicular ad-hoc network (VANET) which means a wireless netw...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	182,277
2501.06650	SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning	Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN is partitioned in SL, with most layers residing on the server and a few initial...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	524,064
2011.15082	Parity-Checked Strassen Algorithm	To multiply astronomic matrices using parallel workers subject to straggling, we recommend interleaving checksums with some fast matrix multiplication algorithms. Nesting the parity-checked algorithms, we weave a product code flavor protection. Two demonstrative configurations are as follows: (A) $9$ workers multiply...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	208,976
2106.09474	Optimising simulations for diphoton production at hadron colliders using amplitude neural networks	Machine learning technology has the potential to dramatically optimise event generation and simulations. We continue to investigate the use of neural networks to approximate matrix elements for high-multiplicity scattering processes. We focus on the case of loop-induced diphoton production through gluon fusion and deve...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	241,674
2109.11386	Energy efficient distributed analytics at the edge of the network for IoT environments	Due to the pervasive diffusion of personal mobile and IoT devices, many "smart environments" (e.g., smart cities and smart factories) will be, generators of huge amounts of data. Currently, analysis of this data is typically achieved through centralised cloud-based services. However, according to many studies, this app...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	256,927
q-bio/0610040	Metric learning pairwise kernel for graph inference	Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly character...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	540,857
1812.05138	Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks	Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigate the role the logic...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	116,351
1807.04631	Optimal Network Topology for Effective Collective Response	Natural, social, and artificial multi-agent systems usually operate in dynamic environments, where the ability to respond to changing circumstances is a crucial feature. An effective collective response requires suitable information transfer among agents, and thus is critically dependent on the agents' interaction netw...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	102,756
1804.10601	Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-sum Objectives	Partially-observable Markov decision processes (POMDPs) with discounted-sum payoff are a standard framework to model a wide range of problems related to decision making under uncertainty. Traditionally, the goal has been to obtain policies that optimize the expectation of the discounted-sum payoff. A key drawback of th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	96,185
1103.2651	Efficient Continual Top-$k$ Keyword Search in Relational Databases	Keyword search in relational databases has been widely studied in recent years because it does not require users neither to master a certain structured query language nor to know the complex underlying data schemas. Most of existing methods focus on answering snapshot keyword queries in static databases. In practice, h...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	9,605
2407.15329	Efficient Multi-disparity Transformer for Light Field Image Super-resolution	This paper presents the Multi-scale Disparity Transformer (MDT), a novel Transformer tailored for light field image super-resolution (LFSR) that addresses the issues of computational redundancy and disparity entanglement caused by the indiscriminate processing of sub-aperture images inherent in conventional methods. MD...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,135
1803.08103	A Unified Framework for Multi-View Multi-Class Object Pose Estimation	One core challenge in object pose estimation is to ensure accurate and robust performance for large numbers of diverse foreground objects amidst complex background clutter. In this work, we present a scalable framework for accurately inferring six Degree-of-Freedom (6-DoF) pose for a large number of object classes from...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,196
2103.04985	Significance tests of feature relevance for a black-box learner	An exciting recent development is the uptake of deep neural networks in many scientific fields, where the main objective is outcome prediction with the black-box nature. Significance testing is promising to address the black-box issue and explore novel scientific insights and interpretation of the decision-making proce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,824
2304.02488	SCB-dataset: A Dataset for Detecting Student Classroom Behavior	Using deep learning methods to detect the classroom behaviors of both students and teachers is an effective way to automatically analyze classroom performance and enhance teaching effectiveness. Then, there is still a scarcity of publicly available high-quality datasets on student-teacher behaviors. Based on the SCB-Da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,450
2201.08024	UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation	In online advertising, conventional post-click conversion rate (CVR) estimation models are trained using clicked samples. However, during online serving the models need to estimate for all impression ads, leading to the sample selection bias (SSB) issue. Intuitively, providing reliable supervision signals for unclicked...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	276,207
2305.01011	Deception Detection with Feature-Augmentation by soft Domain Transfer	In this era of information explosion, deceivers use different domains or mediums of information to exploit the users, such as News, Emails, and Tweets. Although numerous research has been done to detect deception in all these domains, information shortage in a new event necessitates these domains to associate with each...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	361,523
2208.04415	Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey	With the future striving toward data-centric decision-making, seamless access to databases is of utmost importance. There is extensive research on creating an efficient text-to-sql (TEXT2SQL) model to access data from the database. Using a Natural language is one of the best interfaces that can bridge the gap between t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	312,100
2110.04745	Reinforcement Learning for Systematic FX Trading	We explore online inductive transfer learning, with a feature representation transfer from a radial basis function network formed of Gaussian mixture model hidden processing units to a direct, recurrent reinforcement learning agent. This agent is put to work in an experiment, trading the major spot market currency pair...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,025
2311.12391	From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation	Addressing the challenge of adapting pre-trained vision-language models for generating insightful explanations for visual reasoning tasks with limited annotations, we present ReVisE: a $\textbf{Re}$cursive $\textbf{Vis}$ual $\textbf{E}$xplanation algorithm. Our method iteratively computes visual features (conditioned o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,315
2501.04735	Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data	Deep Anterior Lamellar Keratoplasty (DALK) is a partial-thickness corneal transplant procedure used to treat corneal stromal diseases. A crucial step in this procedure is the precise separation of the deep stroma from Descemet's membrane (DM) using the Big Bubble technique. To simplify the tasks of needle insertion and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	523,326
2001.03728	Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks	Modeling and recognition of surgical activities poses an interesting research problem. Although a number of recent works studied automatic recognition of surgical activities, generalizability of these works across different tasks and different datasets remains a challenge. We introduce a modality that is robust to scen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,045
2404.13978	Pour une interop{\'e}rabilit{\'e} s{\'e}mantique en {\'e}ducation : les mod{\`e}les normatifs de l'ISO/IEC JTC1 SC36	The semantics of content is one of the essential constituents of models of innovative educational systems. It is gradually built based on normative efforts carried out by different actors in the fields of the technological industry, telecommunications, IT, linguistic engineering, information sciences documentation, etc...	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	448,521
1807.01763	Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text	We present an end-to-end approach that takes unstructured textual input and generates structured output compliant with a given vocabulary. Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder frame...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	102,126
1307.5708	Vertex-Frequency Analysis on Graphs	One of the key challenges in the area of signal processing on graphs is to design dictionaries and transform methods to identify and exploit structure in signals on weighted graphs. To do so, we need to account for the intrinsic geometric structure of the underlying graph data domain. In this paper, we generalize one o...	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,973
2111.10813	Experience-Enhanced Learning: One Size Still does not Fit All in Automatic Database	Recent years, the database committee has attempted to develop automatic database management systems. Although some researches show that the applying AI to data management is a significant and promising direction, there still exists many problems in implementing these techniques to real applications (long training time,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	267,456
2012.09856	Reconstructing Hand-Object Interactions in the Wild	In this work we explore reconstructing hand-object interactions in the wild. The core challenge of this problem is the lack of appropriate 3D labeled data. To overcome this issue, we propose an optimization-based procedure which does not require direct 3D supervision. The general strategy we adopt is to exploit all ava...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,194
2110.00196	What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence	In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main theme of wireless system design up until 5G was the data rate maximization. In his theory, the semantic aspect and meaning of messages were t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	258,312
2303.06275	A Systematic Study of Joint Representation Learning on Protein Sequences and Structures	Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,771
2207.00939	An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics	Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An automatic summarization system that can effectively condense long documents into short and concise texts to encapsulate the most important ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	305,963
1909.10289	Towards Practical Private Information Retrieval from MDS Array Codes	Private information retrieval (PIR) is the problem of privately retrieving one out of $M$ original files from $N$ severs, i.e., each individual server learns nothing about the file that the user is requesting. Usually, the $M$ files are replicated or encoded by a maximum distance separable (MDS) code and then stored ac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	146,504
2106.05519	Consistent Instance False Positive Improves Fairness in Face Recognition	Demographic bias is a significant challenge in practical face recognition systems. Existing methods heavily rely on accurate demographic annotations. However, such annotations are usually unavailable in real scenarios. Moreover, these methods are typically designed for a specific demographic group and are not general e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	240,125
2112.06410	How Good are Low-Rank Approximations in Gaussian Process Regression?	We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on truncating the kernel's Mercer expansion. In particular, we bound the Kullback-Leibler divergence between an exact GP and one resulting from one...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,172
2412.19804	Universal MIMO Jammer Mitigation	Multi-antenna processing enables jammer mitigation through spatial filtering, provided that the receiver knows the spatial signature of the jammer interference. Estimating this signature is easy for barrage jammers that transmit continuously and with static signature, but difficult for more sophisticated jammers. Smart...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	520,971
2412.12005	Codes from $A_m$-invariant polynomials	Let $q$ be a prime power. This paper provides a new class of linear codes that arises from the action of the alternating group on $\mathbb F_q[x_1,\dots,x_m]$ combined with the ideas in (M. Datta and T. Johnsen, 2022). Compared with Generalized Reed-Muller codes with similar parameters, our codes have the same asymptot...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	517,681
2405.18570	It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap	Multi-modal contrastive models such as CLIP achieve state-of-the-art performance in zero-shot classification by embedding input images and texts on a joint representational space. Recently, a modality gap has been reported in two-encoder contrastive models like CLIP, meaning that the image and text embeddings reside in...	false	false	false	false	false	true	true	false	true	false	false	true	false	false	false	false	false	false	458,480
2311.02122	Lost Your Style? Navigating with Semantic-Level Approach for Text-to-Outfit Retrieval	Fashion stylists have historically bridged the gap between consumers' desires and perfect outfits, which involve intricate combinations of colors, patterns, and materials. Although recent advancements in fashion recommendation systems have made strides in outfit compatibility prediction and complementary item retrieval...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	405,310
2409.18676	Toward Universal and Interpretable World Models for Open-ended Learning Agents	We introduce a generic, compositional and interpretable class of generative world models that supports open-ended learning agents. This is a sparse class of Bayesian networks capable of approximating a broad range of stochastic processes, which provide agents with the ability to learn world models in a manner that may ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	492,356
2012.11448	The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective	Training datasets for machine learning often have some form of missingness. For example, to learn a model for deciding whom to give a loan, the available training data includes individuals who were given a loan in the past, but not those who were not. This missingness, if ignored, nullifies any fairness guarantee of th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	212,645
2203.06953	Forward Compatible Few-Shot Class-Incremental Learning	Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without forgetting old ones. This scenario becomes more challenging when new class instances are insufficient, which is called few-shot class-incremen...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	285,282
2501.16743	Hierarchical Trajectory (Re)Planning for a Large Scale Swarm	We consider the trajectory replanning problem for a large-scale swarm in a cluttered environment. Our path planner replans for robots by utilizing a hierarchical approach, dividing the workspace, and computing collision-free paths for robots within each cell in parallel. Distributed trajectory optimization generates a ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	528,091
2204.09556	De-biasing facial detection system using VAE	Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems may negatively impact society. There are many reasons behind a system being biased. The bias can be due to the algorithm we are using for our problem or may be due to the dataset we are using, having some features over-represented in it. In t...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	292,472
2302.12100	Parameter-free shape optimization: various shape updates for engineering applications	In the last decade, parameter-free approaches to shape optimization problems have matured to a state where they provide a versatile tool for complex engineering applications. However, sensitivity distributions obtained from shape derivatives in this context cannot be directly used as a shape update in gradient-based op...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	347,437
2211.13282	Voice-preserving Zero-shot Multiple Accent Conversion	Most people who have tried to learn a foreign language would have experienced difficulties understanding or speaking with a native speaker's accent. For native speakers, understanding or speaking a new accent is likewise a difficult task. An accent conversion system that changes a speaker's accent but preserves that sp...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	332,414

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