pwc-archive/papers-with-abstracts huggingface dataset:
How social reinforcement learning can lead to metastable polarisation and the voter model
Authors: Benedikt V. Meylahn, Janusz M. Meylahn
Date: 2024-06-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Previous explanations for the persistence of polarization of opinions have typically included modelling assumptions that predispose the possibility of polarization (i.e., assumptions allowing a pair of agents to drift apart in their opinion such as repulsive interactions or bounded confidence). An exception is a recent simulation study showing that polarization is persistent when agents form their opinions using social reinforcement learning. Our goal is to highlight the usefulness of reinforcement learning in the context of modeling opinion dynamics, but that caution is required when selecting the tools used to study such a model. We show that the polarization observed in the model of the simulation study cannot persist indefinitely, and exhibits consensus asymptotically with probability one. By constructing a link between the reinforcement learning model and the voter model, we argue that the observed polarization is metastable. Finally, we show that a slight modification in the learning process of the agents changes the model from being non-ergodic to being ergodic. Our results show that reinforcement learning may be a powerful method for modelling polarization in opinion dynamics, but that the tools (objects to study such as the stationary distribution, or time to absorption for example) appropriate for analysing such models crucially depend on their properties (such as ergodicity, or transience). These properties are determined by the details of the learning process and may be difficult to identify based solely on simulations.
Bias-Corrected Joint Spectral Embedding for Multilayer Networks with Invariant Subspace: Entrywise Eigenvector Perturbation and Inference
Authors: Fangzheng Xie
Date: 2024-06-12 00:00:00
Tasks: Community Detection

In this paper, we propose to estimate the invariant subspace across heterogeneous multiple networks using a novel bias-corrected joint spectral embedding algorithm. The proposed algorithm recursively calibrates the diagonal bias of the sum of squared network adjacency matrices by leveraging the closed-form bias formula and iteratively updates the subspace estimator using the most recent estimated bias. Correspondingly, we establish a complete recipe for the entrywise subspace estimation theory for the proposed algorithm, including a sharp entrywise subspace perturbation bound and the entrywise eigenvector central limit theorem. Leveraging these results, we settle two multiple network inference problems: the exact community detection in multilayer stochastic block models and the hypothesis testing of the equality of membership profiles in multilayer mixed membership models. Our proof relies on delicate leave-one-out and leave-two-out analyses that are specifically tailored to block-wise symmetric random matrices and a martingale argument that is of fundamental interest for the entrywise eigenvector central limit theorem.
Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
Authors: Ting-Ji Huang, Jia-Qi Yang, Chunxu Shen, Kai-Qi Liu, De-Chuan Zhan, Han-Jia Ye
Date: 2024-06-12 00:00:00
Tasks: Memorization, Recommendation Systems

Characterizing users and items through vector representations is crucial for various tasks in recommender systems. Recent approaches attempt to apply Large Language Models (LLMs) in recommendation through a question and answer format, where real users and items (e.g., Item No.2024) are represented with in-vocabulary tokens (e.g., "item", "20", "24"). However, since LLMs are typically pretrained on natural language tasks, these in-vocabulary tokens lack the expressive power for distinctive users and items, thereby weakening the recommendation ability even after fine-tuning on recommendation tasks. In this paper, we explore how to effectively tokenize users and items in LLM-based recommender systems. We emphasize the role of out-of-vocabulary (OOV) tokens in addition to the in-vocabulary ones and claim the memorization of OOV tokens that capture correlations of users/items as well as diversity of OOV tokens. By clustering the learned representations from historical user-item interactions, we make the representations of user/item combinations share the same OOV tokens if they have similar properties. Furthermore, integrating these OOV tokens into the LLM's vocabulary allows for better distinction between users and items and enhanced capture of user-item relationships during fine-tuning on downstream tasks. Our proposed framework outperforms existing state-of-the-art methods across various downstream recommendation tasks.
Bridging the Gap: Unravelling Local Government Data Sharing Barriers in Estonia and Beyond
Authors: Katrin Rajamäe Soosaar, Anastasija Nikiforova
Date: 2024-06-12 00:00:00
Tasks: Decision Making

Open Government Data (OGD) plays a crucial role in transforming smart cities into sustainable and intelligent entities by providing data for analytics, real-time monitoring, and informed decision-making. This data is increasingly used in urban digital twins, enhancing city management through stakeholder collaboration. However, local administrative data remains underutilized even in digitally advanced countries like Estonia. This study explores barriers preventing Estonian municipalities from sharing OGD, using a qualitative approach through interviews with Estonian municipalities and drawing on the OGD-adapted Innovation Resistance Theory model (IRT). Interviews with local government officials highlight ongoing is-sues in data provision and quality. By addressing overlooked weaknesses in the Estonian open data ecosystem and providing actionable recommendations, this research contributes to a more resilient and sustainable open data ecosystem. Additionally, by validating the OGD-adapted Innovation Resistance Theory model and proposing a revised version tailored for local government contexts, the study advances theoretical frameworks for understanding data sharing resistance. Ultimately, this study serves as a call to action for policymakers and practitioners to prioritize local OGD initiatives, ensuring the full utilization of OGD in smart city development.
Wiki Entity Summarization Benchmark
Authors: Saeedeh Javadi, Atefeh Moradan, Mohammad Sorkhpar, Klim Zaporojets, Davide Mottin, Ira Assent
Date: 2024-06-12 00:00:00
Tasks: Knowledge Graphs

Entity summarization aims to compute concise summaries for entities in knowledge graphs. Existing datasets and benchmarks are often limited to a few hundred entities and discard graph structure in source knowledge graphs. This limitation is particularly pronounced when it comes to ground-truth summaries, where there exist only a few labeled summaries for evaluation and training. We propose WikES, a comprehensive benchmark comprising of entities, their summaries, and their connections. Additionally, WikES features a dataset generator to test entity summarization algorithms in different areas of the knowledge graph. Importantly, our approach combines graph algorithms and NLP models as well as different data sources such that WikES does not require human annotation, rendering the approach cost-effective and generalizable to multiple domains. Finally, WikES is scalable and capable of capturing the complexities of knowledge graphs in terms of topology and semantics. WikES features existing datasets for comparison. Empirical studies of entity summarization methods confirm the usefulness of our benchmark. Data, code, and models are available at: https://github.com/msorkhpar/wiki-entity-summarization.
It is Never Too Late to Mend: Separate Learning for Multimedia Recommendation
Authors: Zhuangzhuang He, Zihan Wang, Yonghui Yang, Haoyue Bai, Le Wu
Date: 2024-06-12 00:00:00
Tasks: cross-modal alignment, Multimedia recommendation, Self-Supervised Learning

Multimedia recommendation, which incorporates various modalities (e.g., images, texts, etc.) into user or item representation to improve recommendation quality, and self-supervised learning carries multimedia recommendation to a plateau of performance, because of its superior performance in aligning different modalities. However, more and more research finds that aligning all modal representations is suboptimal because it damages the unique attributes of each modal. These studies use subtraction and orthogonal constraints in geometric space to learn unique parts. However, our rigorous analysis reveals the flaws in this approach, such as that subtraction does not necessarily yield the desired modal-unique and that orthogonal constraints are ineffective in user and item high-dimensional representation spaces. To make up for the previous weaknesses, we propose Separate Learning (SEA) for multimedia recommendation, which mainly includes mutual information view of modal-unique and -generic learning. Specifically, we first use GNN to learn the representations of users and items in different modalities and split each modal representation into generic and unique parts. We employ contrastive log-ratio upper bound to minimize the mutual information between the general and unique parts within the same modality, to distance their representations, thus learning modal-unique features. Then, we design Solosimloss to maximize the lower bound of mutual information, to align the general parts of different modalities, thus learning more high-quality modal-generic features. Finally, extensive experiments on three datasets demonstrate the effectiveness and generalization of our proposed framework. The code is available at SEA and the full training record of the main experiment.
Graph Bottlenecked Social Recommendation
Authors: Yonghui Yang, Le Wu, Zihan Wang, Zhuangzhuang He, Richang Hong, Meng Wang
Date: 2024-06-12 00:00:00
Tasks: Denoising

With the emergence of social networks, social recommendation has become an essential technique for personalized services. Recently, graph-based social recommendations have shown promising results by capturing the high-order social influence. Most empirical studies of graph-based social recommendations directly take the observed social networks into formulation, and produce user preferences based on social homogeneity. Despite the effectiveness, we argue that social networks in the real-world are inevitably noisy~(existing redundant social relations), which may obstruct precise user preference characterization. Nevertheless, identifying and removing redundant social relations is challenging due to a lack of labels. In this paper, we focus on learning the denoised social structure to facilitate recommendation tasks from an information bottleneck perspective. Specifically, we propose a novel Graph Bottlenecked Social Recommendation (GBSR) framework to tackle the social noise issue.GBSR is a model-agnostic social denoising framework, that aims to maximize the mutual information between the denoised social graph and recommendation labels, meanwhile minimizing it between the denoised social graph and the original one. This enables GBSR to learn the minimal yet sufficient social structure, effectively reducing redundant social relations and enhancing social recommendations. Technically, GBSR consists of two elaborate components, preference-guided social graph refinement, and HSIC-based bottleneck learning. Extensive experimental results demonstrate the superiority of the proposed GBSR, including high performances and good generality combined with various backbones. Our code is available at: https://github.com/yimutianyang/KDD24-GBSR.
Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
Authors: Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, Jun Xu, Ji-Rong Wen
Date: 2024-06-12 00:00:00
Tasks: counterfactual, Recommendation Systems

In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time. However, watch time prediction suffers from duration bias, hindering its ability to reflect users' interests accurately. Existing label-correction approaches attempt to uncover user interests through grouping and normalizing observed watch time according to video duration. Although effective to some extent, we found that these approaches regard completely played records (i.e., a user watches the entire video) as equally high interest, which deviates from what we observed on real datasets: users have varied explicit feedback proportion when completely playing videos. In this paper, we introduce the counterfactual watch time(CWT), the potential watch time a user would spend on the video if its duration is sufficiently long. Analysis shows that the duration bias is caused by the truncation of CWT due to the video duration limitation, which usually occurs on those completely played records. Besides, a Counterfactual Watch Model (CWM) is proposed, revealing that CWT equals the time users get the maximum benefit from video recommender systems. Moreover, a cost-based transform function is defined to transform the CWT into the estimation of user interest, and the model can be learned by optimizing a counterfactual likelihood function defined over observed user watch times. Extensive experiments on three real video recommendation datasets and online A/B testing demonstrated that CWM effectively enhanced video recommendation accuracy and counteracted the duration bias.
"It answers questions that I didn't know I had": Ph.D. Students' Evaluation of an Information Sharing Knowledge Graph
Authors: Stanislava Gardasevic, Manika Lamba
Date: 2024-06-11 00:00:00
Tasks: Decision Making

Interdisciplinary PhD programs can be challenging as the vital information needed by students may not be readily available, it is scattered across university's websites, while tacit knowledge can be obtained only by interacting with people. Hence, there is a need to develop a knowledge management model to create, query, and maintain a knowledge repository for interdisciplinary students. We propose a knowledge graph containing information on critical categories and their relationships, extracted from multiple sources, essential for interdisciplinary PhD students. This study evaluates the usability of a participatory designed knowledge graph intended to facilitate information exchange and decision-making. The usability findings demonstrate that interaction with this knowledge graph benefits PhD students by notably reducing uncertainty and academic stress, particularly among newcomers. Knowledge graph supported them in decision making, especially when choosing collaborators in an interdisciplinary setting. Key helpful features are related to exploring student faculty networks, milestones tracking, rapid access to aggregated data, and insights into crowdsourced fellow students' activities. The knowledge graph provides a solution to meet the personalized needs of doctoral researchers and has the potential to improve the information discovery and decision-making process substantially. It also includes the tacit knowledge exchange support missing from most current approaches, which is critical for this population and establishing interdisciplinary collaborations. This approach can be applied to other interdisciplinary programs and domains globally.
Familiar biological, chemical and physical events credibly evolve the Standard Genetic Code
Authors: Michael Yarus
Date: 2024-06-12 00:00:00
Tasks: Unity

The genetic code is profoundly shaped by an origin in ancient RNA-mediated interactions, needing an extended development to reach the Standard Genetic Code (SGC). That development can serially use RNA specificities, a ribonucleopeptide transition (RNPT), finally code escape and diaspora. An index of evolutionary plausibility based on least selection takes simultaneous account of speed and accuracy of evolution, identifying favored evolutions. Combining RNA world specificities allowed convergence of early coding to SGC assignments. Secondly, this was sufficient to launch a post-RNA-world RNPT. The RNPT allowed biosynthesis of complex amino acids, depending heavily on late code fusions between coexisting independent codes. Thirdly, escape from fluctuating, but highly-evolved codes of the RNPT applied a near-ideal selection for fastest-evolving and most accurate/useful genetic codes. Concurrently, a code and its microbial carrier suited to a free-living existence necessarily evolved. The established unity of life on Earth likely traces to SGC ascendancy during escape from the RNPT, and code diaspora.
Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion Detection
Authors: Kahraman Kostas, Mike Just, Michael A. Lones
Date: 2024-06-07 00:00:00
Tasks: Intrusion Detection

Machine learning is increasingly used for intrusion detection in IoT networks. This paper explores the effectiveness of using individual packet features (IPF), which are attributes extracted from a single network packet, such as timing, size, and source-destination information. Through literature review and experiments, we identify the limitations of IPF, showing they can produce misleadingly high detection rates. Our findings emphasize the need for approaches that consider packet interactions for robust intrusion detection. Additionally, we demonstrate that models based on IPF often fail to generalize across datasets, compromising their reliability in diverse IoT environments.
ABANet: Attention boundary-aware network for image segmentation
Authors: Sadjad Rezvani, Mansoor Fateh, Hossein Khosravi
Date: 2024-05-17 00:00:00
Proceeding: Expert Systems 2024 5
Tasks: Face Recognition, Facial Expression Recognition, Facial Inpainting, Image Segmentation, Segmentation, Semantic Segmentation

Deep learning techniques have attained substantial progress in various face-related tasks, such as face recognition, face inpainting, and facial expression recognition. To prevent infection or the spread of the virus, wearing of masks in public places has been mandated following the COVID-19 epidemic, which has led to face occlusion and posed significant challenges for face recognition systems. Most prominent masked face recognition solutions rely on mask segmentation tasks. Therefore, segmentation can be used to mitigate the negative impacts of wearing a mask and improve recognition accuracy. Mask region segmentation suffers from two main problems: there is no standard type of masks that people wear, they come in different colours and designs, and there is no publicly available masked face dataset with appropriate ground truth for the mask region. In order to address these issues, we propose an encoder–decoder framework that utilizes a boundary-aware attention network combined with a new hybrid loss to provide a map, patch, and pixel-level supervision. We also introduce a dataset called MFSD, with 11,601 images and 12,758 masked faces for masked face segmentation. Furthermore, we compare the performance of different cutting-edge deep learning semantic segmentation models on the presented dataset. Experimental results on the MSFD dataset reveal that the suggested approach outperforms state-of-the-art, algorithms with 97.623% accuracy, 93.814% IoU, and 96.817% F1-score rate. Our dataset of masked faces with mask region labels and source code will be available online.
GAPses: Versatile smart glasses for comfortable and fully-dry acquisition and parallel ultra-low-power processing of EEG and EOG
Authors: Sebastian Frey, Mattia Alberto Lucchini, Victor Kartsch, Thorir Mar Ingolfsson, Andrea Helga Bernardi, Michael Segessenmann, Jakub Osieleniec, Simone Benatti, Luca Benini, Andrea Cossettini
Date: 2024-06-12 00:00:00
Tasks: EEG, Specificity

Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusiveness, comfort, and data privacy. To address these challenges, this paper presents GAPSES, a novel smart glasses platform designed for unobtrusive, comfortable, and secure acquisition and processing of electroencephalography (EEG) and electrooculography (EOG) signals. We introduce a direct electrode-electronics interface with custom fully dry soft electrodes to enhance comfort for long wear. An integrated parallel ultra-low-power RISC-V processor (GAP9, Greenwaves Technologies) processes data at the edge, thereby eliminating the need for continuous data streaming through a wireless link, enhancing privacy, and increasing system reliability in adverse channel conditions. We demonstrate the broad applicability of the designed prototype through validation in a number of EEG-based interaction tasks, including alpha waves, steady-state visual evoked potential analysis, and motor movement classification. Furthermore, we demonstrate an EEG-based biometric subject recognition task, where we reach a sensitivity and specificity of 98.87% and 99.86% respectively, with only 8 EEG channels and an energy consumption per inference on the edge as low as 121 uJ. Moreover, in an EOG-based eye movement classification task, we reach an accuracy of 96.68% on 11 classes, resulting in an information transfer rate of 94.78 bit/min, which can be further increased to 161.43 bit/min by reducing the accuracy to 81.43%. The deployed implementation has an energy consumption of 24 uJ per inference and a total system power of only 16.28 mW, allowing for continuous operation of more than 12 h with a small 75 mAh battery.
Mediapi-RGB: Enabling Technological Breakthroughs in French Sign Language (LSF) Research through an Extensive Video-Text Corpus
Authors: Yanis Ouakrim, Hannah Bull, Michèle Gouiffès, Denis Beautemps, Thomas Hueber, Annelies Braffort
Date: 2024-02-29 00:00:00
Proceeding: VISAPP 2024 2
Tasks: Machine Translation, Sign Language Translation, Translation

We introduce Mediapi-RGB, a new dataset of French Sign Language (LSF) along with the first LSF-to-French machine translation model. With 86 hours of video, it the largest LSF corpora with translation. The corpus consists of original content in French Sign Language produced by deaf journalists, and has subtitles in written French aligned to the signing. The current release of Mediapi-RGB is available at the Ortolang corpus repository, and can be used for academic research purposes. The test and validation sets contain 13 and 7 hours of video respectively. The training set contains 66 hours of video that will be released progressively until December 2024. Additionally, the current release contains skeleton keypoints, sign temporal segmentation, spatio-temporal features and subtitles for all the videos in the train, validation and test sets, as well as a suggested vocabulary of nouns for evaluation purposes. In addition, we present the results obtained on this corpus with the first LSF-to-French translation baseline to give an overview of the possibilities offered by this corpus of unprecedented caliber for LSF. Finally, we suggest potential technological and linguistic applications for this new video-text dataset.