pwc-archive/papers-with-abstracts huggingface dataset:
Mathematical Opportunities in Digital Twins (MATH-DT)
Authors: Harbir Antil
Date: 2024-02-15 00:00:00
Tasks: Math

The report describes the discussions from the Workshop on Mathematical Opportunities in Digital Twins (MATH-DT) from December 11-13, 2023, George Mason University. It illustrates that foundational Mathematical advances are required for Digital Twins (DTs) that are different from traditional approaches. A traditional model, in biology, physics, engineering or medicine, starts with a generic physical law (e.g., equations) and is often a simplification of reality. A DT starts with a specific ecosystem, object or person (e.g., personalized care) representing reality, requiring multi -scale, -physics modeling and coupling. Thus, these processes begin at opposite ends of the simulation and modeling pipeline, requiring different reliability criteria and uncertainty assessments. Additionally, unlike existing approaches, a DT assists humans to make decisions for the physical system, which (via sensors) in turn feeds data into the DT, and operates for the life of the physical system. While some of the foundational mathematical research can be done without a specific application context, one must also keep specific applications in mind for DTs. E.g., modeling a bridge or a biological system (a patient), or a socio-technical system (a city) is very different. The models range from differential equations (deterministic/uncertain) in engineering, to stochastic in biology, including agent-based. These are multi-scale hybrid models or large scale (multi-objective) optimization problems under uncertainty. There are no universal models or approaches. For e.g., Kalman filters for forecasting might work in engineering, but can fail in biomedical domain. Ad hoc studies, with limited systematic work, have shown that AI/ML methods can fail for simple engineering systems and can work well for biomedical problems. A list of `Mathematical Opportunities and Challenges' concludes the report.
Discovering and exploring cases of educational source code plagiarism with Dolos
Authors: Rien Maertens, Maarten Van Neyghem, Maxiem Geldhof, Charlotte Van Petegem, Niko Strijbol, Peter Dawyndt, Bart Mesuere
Date: 2024-02-16 00:00:00

Source code plagiarism is a significant issue in educational practice, and educators need user-friendly tools to cope with such academic dishonesty. This article introduces the latest version of Dolos, a state-of-the-art ecosystem of tools for detecting and preventing plagiarism in educational source code. In this new version, the primary focus has been on enhancing the user experience. Educators can now run the entire plagiarism detection pipeline from a new web app in their browser, eliminating the need for any installation or configuration. Completely redesigned analytics dashboards provide an instant assessment of whether a collection of source files contains suspected cases of plagiarism and how widespread plagiarism is within the collection. The dashboards support hierarchically structured navigation to facilitate zooming in and out of suspect cases. Clusters are an essential new component of the dashboard design, reflecting the observation that plagiarism can occur among larger groups of students. To meet various user needs, the Dolos software stack for source code plagiarism detections now includes a web interface, a JSON application programming interface (API), a command line interface (CLI), a JavaScript library and a preconfigured Docker container. Clear documentation and a free-to-use instance of the web app can be found at https://dolos.ugent.be. The source code is also available on GitHub.
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
Authors: Chen Xu, Jun Xu, Yiming Ding, Xiao Zhang, Qi Qi
Date: 2024-02-16 00:00:00
Tasks: Distributed Optimization, Fairness, Recommendation Systems, Retrieval

In pursuit of fairness and balanced development, recommender systems (RS) often prioritize group fairness, ensuring that specific groups maintain a minimum level of exposure over a given period. For example, RS platforms aim to ensure adequate exposure for new providers or specific categories of items according to their needs. Modern industry RS usually adopts a two-stage pipeline: stage-1 (retrieval stage) retrieves hundreds of candidates from millions of items distributed across various servers, and stage-2 (ranking stage) focuses on presenting a small-size but accurate selection from items chosen in stage-1. Existing efforts for ensuring amortized group exposures focus on stage-2, however, stage-1 is also critical for the task. Without a high-quality set of candidates, the stage-2 ranker cannot ensure the required exposure of groups. Previous fairness-aware works designed for stage-2 typically require accessing and traversing all items. In stage-1, however, millions of items are distributively stored in servers, making it infeasible to traverse all of them. How to ensure group exposures in the distributed retrieval process is a challenging question. To address this issue, we introduce a model named FairSync, which transforms the problem into a constrained distributed optimization problem. Specifically, FairSync resolves the issue by moving it to the dual space, where a central node aggregates historical fairness data into a vector and distributes it to all servers. To trade off the efficiency and accuracy, the gradient descent technique is used to periodically update the parameter of the dual vector. The experiment results on two public recommender retrieval datasets showcased that FairSync outperformed all the baselines, achieving the desired minimum level of exposures while maintaining a high level of retrieval accuracy.
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation
Authors: Lingzi Zhang, Xin Zhou, Zhiwei Zeng, Zhiqi Shen
Date: 2024-02-16 00:00:00
Tasks: Sequential Recommendation

Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance. Despite their effectiveness, the expressive power of text features in these models remains largely unexplored. While most existing models emphasize the importance of ID embeddings in recommendations, our study takes a step further by studying sequential recommendation models that only rely on text features and do not necessitate ID embeddings. Upon examining pretrained text embeddings experimentally, we discover that they reside in an anisotropic semantic space, with an average cosine similarity of over 0.8 between items. We also demonstrate that this anisotropic nature hinders recommendation models from effectively differentiating between item representations and leads to degenerated performance. To address this issue, we propose to employ a pre-processing step known as whitening transformation, which transforms the anisotropic text feature distribution into an isotropic Gaussian distribution. Our experiments show that whitening pre-trained text embeddings in the sequential model can significantly improve recommendation performance. However, the full whitening operation might break the potential manifold of items with similar text semantics. To preserve the original semantics while benefiting from the isotropy of the whitened text features, we introduce WhitenRec+, an ensemble approach that leverages both fully whitened and relaxed whitened item representations for effective recommendations. We further discuss and analyze the benefits of our design through experiments and proofs. Experimental results on three public benchmark datasets demonstrate that WhitenRec+ outperforms state-of-the-art methods for sequential recommendation.
Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism
Authors: Yujia Zhou, Qiannan Zhu, Jiajie Jin, Zhicheng Dou
Date: 2024-02-16 00:00:00

Traditional search engines usually provide identical search results for all users, overlooking individual preferences. To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs. Deep learning-based personalized search methods have shown promise, but they rely heavily on abundant training data, making them susceptible to data sparsity challenges. This paper proposes a Cognitive Personalized Search (CoPS) model, which integrates Large Language Models (LLMs) with a cognitive memory mechanism inspired by human cognition. CoPS employs LLMs to enhance user modeling and user search experience. The cognitive memory mechanism comprises sensory memory for quick sensory responses, working memory for sophisticated cognitive responses, and long-term memory for storing historical interactions. CoPS handles new queries using a three-step approach: identifying re-finding behaviors, constructing user profiles with relevant historical information, and ranking documents based on personalized query intent. Experiments show that CoPS outperforms baseline models in zero-shot scenarios.
Engraving Oriented Joint Estimation of Pitch Spelling and Local and Global Keys
Authors: Augustin Bouquillard, Florent Jacquemard
Date: 2024-02-15 00:00:00
Tasks: Music Transcription

We revisit the problems of pitch spelling and tonality guessing with a new algorithm for their joint estimation from a MIDI file including information about the measure boundaries. Our algorithm does not only identify a global key but also local ones all along the analyzed piece. It uses Dynamic Programming techniques to search for an optimal spelling in term, roughly, of the number of accidental symbols that would be displayed in the engraved score. The evaluation of this number is coupled with an estimation of the global key and some local keys, one for each measure. Each of the three informations is used for the estimation of the other, in a multi-steps procedure. An evaluation conducted on a monophonic and a piano dataset, comprising 216 464 notes in total, shows a high degree of accuracy, both for pitch spelling (99.5% on average on the Bach corpus and 98.2% on the whole dataset) and global key signature estimation (93.0% on average, 95.58% on the piano dataset). Designed originally as a backend tool in a music transcription framework, this method should also be useful in other tasks related to music notation processing.
Features Understanding in 3D CNNs for Actions Recognition in Video
Authors: Kazi Ahmed Asif Fuad, Pierre-Etienne Martin (1, 2), Romain Giot, Romain Bourqui, Jenny Benois-Pineau, Akka Zemmari
Date: 2020-10-01 00:00:00
Proceeding: Tenth International Conference on Image Processing Theory, Tools and Applications 2020 10
Tasks: Action Recognition, Decision Making, Optical Flow Estimation, Temporal Action Localization, Video Understanding

Human Action Recognition is one of the key tasks in video understanding. Deep Convolutional Neural Networks (CNN) are often used for this purpose. Although they usually perform impressively, their decision interpretation remains challenging. We propose a novel visual CNN features understanding technique. Its objective is to find salient features that played a key role in decision making of the network. The technique only uses the features from the last convolutional layer before the fully connected layers of a trained model and builds an importance map of features. The map is propagated to the original frame thus highlighting the regions in them that contribute to the final decision. The method is fast as it does not require gradient computation as many state-of-the-art methods do. Proposed technique is applied to the Twin Spatio-Temporal 3D Convolutional Neural Network (TSTCNN), designed for Table Tennis Actions recognition. Features visualization is performed at the RGB and Optical flow branches of the network. Obtained results are compared to other visualization techniques both in terms of human understanding and similarity metrics. The metrics show that generated maps are similar to those obtained with known Grad-CAM method, e.g. Pearson Correlation Coefficient between the maps generated of RGB data for Grad-CAM and our method is 0.7 ± 0.05 and 0.72 ± 0.06 on Optical Flow data
Diffusart: Enhancing Line Art Colorization with Conditional Diffusion Models
Authors: Hernan Carrillo, Michaël Clément, Aurélie Bugeau, Edgar Simo-Serra
Date: 2023-06-18 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2023 6
Tasks: Colorization, Line Art Colorization, SSIM

Colorization of line art drawings is an important task in illustration and animation workflows. However, this highly laborious process is mainly done manually, limiting the creative productivity. This paper presents a novel interactive approach for line art colorization using conditional Diffusion Probabilistic Models (DPMs). In our proposed approach, the user provides initial color strokes for colorizing the line art. The strokes are then integrated into the conditional DPM-based colorization process by means of a coupled implicit and explicit conditioning strategy to generates diverse and high-quality colorized images. We evaluate our proposal and show it outperforms existing state-of-the-art approaches using the FID, LPIPS and SSIM metrics.
Deep Learning Applications for Intrusion Detection in Network Traffic
Authors: Aleksandr Getman, Maxim Goryunov, Andrey Matskevich, Dmitry Rybolovlev, Anastasiya Nikolskaya
Date: 2024-01-13 00:00:00
Proceeding: Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS) 2024 1
Tasks: Deep Learning, Feature Engineering, Intrusion Detection, Network Intrusion Detection

The paper discusses the issues of applying deep learning methods for detecting computer attacks in network traffic. The results of the analysis of relevant studies and reviews of deep learning applications for intrusion detection are presented. The most used deep learning methods are discussed and compared. The classification system of deep learning methods for intrusion detection is proposed. Current trends and challenges of applying deep learning methods for detecting computer attacks in network traffic are identified. The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection. The synthesized neural network is compared to the previously developed model based on the use of the Random Forest classifier. The usage of the deep learning method enabled to simplify the feature engineering stage, and evaluation metrics of Random Forest and CNN-BiLSTM models are close. This confirms the prospects for the application of deep learning methods for intrusion detection.
Simple, unified analysis of Johnson-Lindenstrauss with applications
Authors: Yingru Li
Date: 2024-02-10 00:00:00
Tasks: Dimensionality Reduction, LEMMA

We present a simplified and unified analysis of the Johnson-Lindenstrauss (JL) lemma, a cornerstone of dimensionality reduction for managing high-dimensional data. Our approach simplifies understanding and unifies various constructions under the JL framework, including spherical, binary-coin, sparse JL, Gaussian, and sub-Gaussian models. This unification preserves the intrinsic geometry of data, essential for applications from streaming algorithms to reinforcement learning. We provide the first rigorous proof of the spherical construction's effectiveness and introduce a general class of sub-Gaussian constructions within this simplified framework. Central to our contribution is an innovative extension of the Hanson-Wright inequality to high dimensions, complete with explicit constants. By using simple yet powerful probabilistic tools and analytical techniques, such as an enhanced diagonalization process, our analysis solidifies the theoretical foundation of the JL lemma by removing an independence assumption and extends its practical applicability to contemporary algorithms.
Data-Driven Abstractions for Control Systems via Random Exploration
Authors: Rudi Coppola, Andrea Peruffo, Manuel Mazo Jr
Date: 2024-02-16 00:00:00

At the intersection of dynamical systems, control theory, and formal methods lies the construction of symbolic abstractions: these typically represent simpler, finite-state models whose behavior mimics that of an underlying concrete system but are easier to analyse. Building an abstraction usually requires an accurate knowledge of the underlying model: this knowledge may be costly to gather, especially in real-life applications. We aim to bridge this gap by building abstractions based on sampling finite length trajectories. To refine a controller built for the abstraction to one for the concrete system, we newly define a notion of probabilistic alternating simulation, and provide Probably Approximately Correct (PAC) guarantees that the constructed abstraction includes all behaviors of the concrete system and that it is suitable for control design, for arbitrarily long time horizons, leveraging scenario theory. Our method is then tested on several numerical benchmarks.
Beamforming Optimization for Active RIS-Aided Multiuser Communications With Hardware Impairments
Authors: Zhangjie Peng, Zhibo Zhang, Cunhua Pan, Marco Di Renzo, Octavia A. Dobre, Jiangzhou Wang
Date: 2024-02-16 00:00:00

In this paper, we consider an active reconfigurable intelligent surface (RIS) to assist the multiuser downlink transmission in the presence of practical hardware impairments (HWIs), including the HWIs at the transceivers and the phase noise at the active RIS. The active RIS is deployed to amplify the incident signals to alleviate the multiplicative fading effect, which is a limitation in the conventional passive RIS-aided wireless systems. We aim to maximize the sum rate through jointly designing the transmit beamforming at the base station (BS), the amplification factors and the phase shifts at the active RIS. To tackle this challenging optimization problem effectively, we decouple it into two tractable subproblems. Subsequently, each subproblem is transformed into a second order cone programming problem. The block coordinate descent framework is applied to tackle them, where the transmit beamforming and the reflection coefficients are alternately designed. In addition, another efficient algorithm is presented to reduce the computational complexity. Specifically, by exploiting the majorization-minimization approach, each subproblem is reformulated into a tractable surrogate problem, whose closed-form solutions are obtained by Lagrange dual decomposition approach and element-wise alternating sequential optimization method. Simulation results validate the effectiveness of our developed algorithms, and reveal that the HWIs significantly limit the system performance of active RIS-empowered wireless communications. Furthermore, the active RIS noticeably boosts the sum rate under the same total power budget, compared with the passive RIS.
Human-Centric Goal Reasoning with Ripple-Down Rules
Authors: Kenji Brameld, Germán Castro, Claude Sammut, Mark Roberts, David W. Aha
Date: 2024-01-30 00:00:00

ActorSim is a goal reasoning framework developed at the Naval Research Laboratory. Originally, all goal reasoning rules were hand-crafted. This work extends ActorSim with the capability of learning by demonstration, that is, when a human trainer disagrees with a decision made by the system, the trainer can take over and show the system the correct decision. The learning component uses Ripple-Down Rules (RDR) to build new decision rules to correctly handle similar cases in the future. The system is demonstrated using the RoboCup Rescue Agent Simulation, which simulates a city-wide disaster, requiring emergency services, including fire, ambulance and police, to be dispatched to different sites to evacuate civilians from dangerous situations. The RDRs are implemented in a scripting language, FrameScript, which is used to mediate between ActorSim and the agent simulator. Using Ripple-Down Rules, ActorSim can scale to an order of magnitude more goals than the previous version.
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate
Authors: Chenhao Tong, Maria A. Rodriguez, Richard O. Sinnott
Date: 2024-01-28 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance, Deep Reinforcement Learning, Multi-agent Reinforcement Learning

Autonomous vehicles are suited for continuous area patrolling problems. Finding an optimal patrolling strategy can be challenging due to unknown environmental factors, such as wind or landscape; or autonomous vehicles' constraints, such as limited battery life or hardware failures. Importantly, patrolling large areas often requires multiple agents to collectively coordinate their actions. However, an optimal coordination strategy is often non-trivial to be manually defined due to the complex nature of patrolling environments. In this paper, we consider a patrolling problem with environmental factors, agent limitations, and three typical cooperation problems -- collision avoidance, congestion avoidance, and patrolling target negotiation. We propose a multi-agent reinforcement learning solution based on a reinforced inter-agent learning (RIAL) method. With this approach, agents are trained to develop their own communication protocol to cooperate during patrolling where faults can and do occur. The solution is validated through simulation experiments and is compared with several state-of-the-art patrolling solutions from different perspectives, including the overall patrol performance, the collision avoidance performance, the efficiency of battery recharging strategies, and the overall fault tolerance.
WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing
Authors: Shuokang Huang, Kaihan Li, Di You, Yichong Chen, Arvin Lin, Siying Liu, Xiaohui Li, Julie A. McCann
Date: 2024-01-24 00:00:00
Tasks: Activity Recognition

WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare. However, most previous works focus on single-user sensing, which has limited practicability in scenarios involving multiple users. Although recent studies have begun to investigate WiFi-based multi-user sensing, there remains a lack of benchmark datasets to facilitate reproducible and comparable research. To bridge this gap, we present WiMANS, to our knowledge, the first dataset for multi-user sensing based on WiFi. WiMANS contains over 9.4 hours of dual-band WiFi Channel State Information (CSI), as well as synchronized videos, monitoring simultaneous activities of multiple users. We exploit WiMANS to benchmark the performance of state-of-the-art WiFi-based human sensing models and video-based models, posing new challenges and opportunities for future work. We believe WiMANS can push the boundaries of current studies and catalyze the research on WiFi-based multi-user sensing.
Personalized Federated Learning for Statistical Heterogeneity
Authors: Muhammad Firdaus, Kyung-Hyune Rhee
Date: 2024-02-07 00:00:00
Tasks: Federated Learning, Personalized Federated Learning

The popularity of federated learning (FL) is on the rise, along with growing concerns about data privacy in artificial intelligence applications. FL facilitates collaborative multi-party model learning while simultaneously ensuring the preservation of data confidentiality. Nevertheless, the problem of statistical heterogeneity caused by the presence of diverse client data distributions gives rise to certain challenges, such as inadequate personalization and slow convergence. In order to address the above issues, this paper offers a brief summary of the current research progress in the field of personalized federated learning (PFL). It outlines the PFL concept, examines related techniques, and highlights current endeavors. Furthermore, this paper also discusses potential further research and obstacles associated with PFL.