pwc-archive/papers-with-abstracts huggingface dataset:
Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport
Authors: Mahela Pandukabhaya, Tharaka Fonseka, Madhumini Kulathunge, Roshan Godaliyadda, Parakrama Ekanayake, Chanaka Senanayake, Vijitha Herath
Date: 2024-11-25 00:00:00
Tasks: Benchmarking

Mastering psychomotor skills, such as those essential in sports, rehabilitation, and professional training, often requires a precise understanding of motion patterns and performance metrics. This study proposes a versatile framework for optimizing psychomotor learning through human motion analysis. Utilizing a wearable IMU sensor system, the motion trajectories of a given psychomotor task are acquired and then linked to points in a performance space using a predefined set of quality metrics specific to the psychomotor skill. This enables the identification of a benchmark cluster in the performance space, which represents a group of reference points that define optimal performance across multiple criteria, allowing correspondences to be established between the performance clusters and sets of trajectories in the motion space. As a result, common or specific deviations in the performance space can be identified, enabling remedial actions in the motion space to optimize performance. A thorough validation of the proposed framework is done in this paper using a Table Tennis forehand stroke as a case study. The resulting quantitative and visual representation of performance empowers individuals to optimize their skills and achieve peak performance.
Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance
Authors: Lilian Gasser, Christoph Schür, Fernando Perez-Cruz, Kristin Schirmer, Marco Baity-Jesi
Date: 2024-06-03 00:00:00
Proceeding: Environmental Science: Advances 2024 6
Tasks: regression

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired through in vivo testing. The recent effort to find alternatives based on machine learning, however, has not focused on guaranteeing transparency, comparability and reproducibility, which makes it difficult to assess advantages and disadvantages of these methods. Also, comparable baseline performances are needed. In this study, we trained regression models on the ADORE “t-F2F” challenge proposed in [Schür et al., Nature Scientific data, 2023] to predict acute mortality, measured as LC50 (lethal concentration 50), of organic compounds on fishes. We trained LASSO, random forest (RF), XGBoost, Gaussian process (GP) regression models, and found a series of aspects that are stable across models: (i) using mass or molar concentrations does not affect performances; (ii) the performances are only weakly dependent on the molecular representations of the chemicals, but (iii) strongly on how the data is split. Overall, the tree-based models RF and XGBoost performed best and we were able to predict the log10-transformed LC50 with a root mean square error of 0.90, which corresponds to an order of magnitude on the original LC50 scale. On a local level, on the other hand, the models are not able to consistently predict the toxicity of individual chemicals accurately enough. Predictions for single chemicals are mostly influenced by a few chemical properties while taxonomic traits are not captured sufficiently by the models. We discuss technical and conceptual improvements for these challenges to enhance the suitability of in silico methods to environmental hazard assessment. Accordingly, this work showcases state-of-the-art models and contributes to the ongoing discussion on regulatory integration.
Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks
Authors: Youze Wang, Shengsheng Qian, Jun Hu1, Quan Fang, Changsheng Xu
Date: 2020-06-08 00:00:00
Proceeding: ICMR 2020 6
Tasks: Fake News Detection, News Recommendation, World Knowledge

Nowadays, with the rapid development of social media, there is a
great deal of news produced every day. How to detect fake news
automatically from a large of multimedia posts has become very
important for people, the government and news recommendation
sites. However, most of the existing approaches either extract features from the text of the post which is a single modality or simply
concatenate the visual features and textual features of a post to get
a multimodal feature and detect fake news. Most of them ignore
the background knowledge hidden in the text content of the post
which facilitates fake news detection. To address these issues, we
propose a novel Knowledge-driven Multimodal Graph Convolutional Network (KMGCN) to model the semantic representations by
jointly modeling the textual information, knowledge concepts and
visual information into a unified framework for fake news detection. Instead of viewing text content as word sequences normally,
we convert them into a graph, which can model non-consecutive
phrases for better obtaining the composition of semantics. Besides,
we not only convert visual information as nodes of graphs but
also retrieve external knowledge from real-world knowledge graph
as nodes of graphs to provide complementary semantics information to improve fake news detection. We utilize a well-designed
graph convolutional network to extract the semantic representation
of these graphs. Extensive experiments on two public real-world
datasets illustrate the validation of our approach.
Learning-Enabled Adaptive Voltage Protection Against Load Alteration Attacks On Smart Grids
Authors: Anjana B., Suman Maiti, Sunandan Adhikary, Soumyajit Dey, Ashish R. Hota
Date: 2024-11-21 00:00:00
Tasks: Deep Reinforcement Learning

Smart grids are designed to efficiently handle variable power demands, especially for large loads, by real-time monitoring, distributed generation and distribution of electricity. However, the grid's distributed nature and the internet connectivity of large loads like Heating Ventilation, and Air Conditioning (HVAC) systems introduce vulnerabilities in the system that cyber-attackers can exploit, potentially leading to grid instability and blackouts. Traditional protection strategies, primarily designed to handle transmission line faults are often inadequate against such threats, emphasising the need for enhanced grid security. In this work, we propose a Deep Reinforcement Learning (DRL)-based protection system that learns to differentiate any stealthy load alterations from normal grid operations and adaptively adjusts activation thresholds of the protection schemes. We train this adaptive protection scheme against an optimal and stealthy load alteration attack model that manipulates the power demands of HVACs at the most unstable grid buses to induce blackouts. We theoretically prove that the adaptive protection system trained in this competitive game setting can effectively mitigate any stealthy load alteration-based attack. To corroborate this, we also demonstrate the method's success in several real-world grid scenarios by implementing it in a hardware-in-loop setup.
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Authors: Yong Xie, Weijie Zheng, Hanxun Huang, Guangnan Ye, Xingjun Ma
Date: 2024-11-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Adversarial Robustness, image-classification, Image Classification

As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their reliability and trustworthiness. Over the past decade, a large number of white-box adversarial robustness evaluation methods (i.e., attacks) have been proposed, ranging from single-step to multi-step methods and from individual to ensemble methods. Despite these advances, challenges remain in conducting meaningful and comprehensive robustness evaluations, particularly when it comes to large-scale testing and ensuring evaluations reflect real-world adversarial risks. In this work, we focus on image classification models and propose a novel individual attack method, Probability Margin Attack (PMA), which defines the adversarial margin in the probability space rather than the logits space. We analyze the relationship between PMA and existing cross-entropy or logits-margin-based attacks, and show that PMA can outperform the current state-of-the-art individual methods. Building on PMA, we propose two types of ensemble attacks that balance effectiveness and efficiency. Furthermore, we create a million-scale dataset, CC1M, derived from the existing CC3M dataset, and use it to conduct the first million-scale white-box adversarial robustness evaluation of adversarially-trained ImageNet models. Our findings provide valuable insights into the robustness gaps between individual versus ensemble attacks and small-scale versus million-scale evaluations.
Transforming Triple-Entry Accounting with Machine Learning: A Path to Enhanced Transparency Through Analytics
Authors: Abraham Itzhak Weinberg, Alessio Faccia
Date: 2024-11-19 00:00:00

Triple Entry (TE) is an accounting method that utilizes three accounts or 'entries' to record each transaction, rather than the conventional double-entry bookkeeping system. Existing studies have found that TE accounting, with its additional layer of verification and disclosure of inter-organizational relationships, could help improve transparency in complex financial and supply chain transactions such as blockchain. Machine learning (ML) presents a promising avenue to augment the transparency advantages of TE accounting. By automating some of the data collection and analysis needed for TE bookkeeping, ML techniques have the potential to make this more transparent accounting method scalable for large organizations with complex international supply chains, further enhancing the visibility and trustworthiness of financial reporting. By leveraging ML algorithms, anomalies within distributed ledger data can be swiftly identified, flagging potential instances of fraud or errors. Furthermore, by delving into transaction relationships over time, ML can untangle intricate webs of transactions, shedding light on obscured dealings and adding an investigative dimension. This paper aims to demonstrate the interaction between TE and ML and how they can leverage transparency levels.
Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes
Authors: Mohammed Saqr, Sonsoles López-Pernas, Tiina Törmänen, Rogers Kaliisa, Kamila Misiejuk, Santtu Tikka
Date: 2024-11-23 00:00:00
Tasks: Community Detection

This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a single lens offers capabilities beyond either framework, including centralities to capture important learning events, community detection to identify behavior patterns, and clustering to reveal temporal patterns. Furthermore, TNA introduces several significance tests that go beyond either method and add rigor to the analysis. Here, we introduce the theoretical and mathematical foundations of TNA and we demonstrate the functionalities of TNA with a case study where students (n=191) engaged in small-group collaboration to map patterns of group dynamics using the theories of co-regulation and socially-shared regulated learning. The analysis revealed that TNA can map the regulatory processes as well as identify important events, patterns, and clusters. Bootstrap validation established the significant transitions and eliminated spurious transitions. As such, TNA can capture learning dynamics and provide a robust framework for investigating the temporal evolution of learning processes. Future directions include -- inter alia -- expanding estimation methods, reliability assessment, and building longitudinal TNA.
Suspected Undeclared Use of Artificial Intelligence in the Academic Literature: An Analysis of the Academ-AI Dataset
Authors: Alex Glynn
Date: 2024-11-20 00:00:00
Tasks: Large Language Model

Since generative artificial intelligence (AI) tools such as OpenAI's ChatGPT became widely available, researchers have used them in the writing process. The consensus of the academic publishing community is that such usage must be declared in the published article. Academ-AI documents examples of suspected undeclared AI usage in the academic literature, discernible primarily due to the appearance in research papers of idiosyncratic verbiage characteristic of large language model (LLM)-based chatbots. This analysis of the first 500 examples collected reveals that the problem is widespread, penetrating the journals and conference proceedings of highly respected publishers. Undeclared AI seems to appear in journals with higher citation metrics and higher article processing charges (APCs), precisely those outlets that should theoretically have the resources and expertise to avoid such oversights. An extremely small minority of cases are corrected post publication, and the corrections are often insufficient to rectify the problem. The 500 examples analyzed here likely represent a small fraction of the undeclared AI present in the academic literature, much of which may be undetectable. Publishers must enforce their policies against undeclared AI usage in cases that are detectable; this is the best defense currently available to the academic publishing community against the proliferation of undisclosed AI.
Data Processing Efficiency Aware User Association and Resource Allocation in Blockchain Enabled Metaverse over Wireless Communications
Authors: Liangxin Qian, Jun Zhao
Date: 2024-11-25 00:00:00

In the rapidly evolving landscape of the Metaverse, enhanced by blockchain technology, the efficient processing of data has emerged as a critical challenge, especially in wireless communication systems. Addressing this need, our paper introduces the innovative concept of data processing efficiency (DPE), aiming to maximize processed bits per unit of resource consumption in blockchain-empowered Metaverse environments. To achieve this, we propose the DPE-Aware User Association and Resource Allocation (DAUR) algorithm, a tailored solution for these complex systems. The DAUR algorithm transforms the challenging task of optimizing the sum of DPE ratios into a solvable convex optimization problem. It uniquely alternates the optimization of key variables like user association, work offloading ratios, task-specific computing resource distribution, bandwidth allocation, user power usage ratios, and server computing resource allocation ratios. Our extensive numerical results demonstrate the DAUR algorithm's effectiveness in DPE.
Homeopathic Modernization and the Middle Science Trap: conceptual context of ergonomics, econometrics and logic of some national scientific case
Authors: Eldar Knar
Date: 2024-11-24 00:00:00
Tasks: Econometrics

This article analyses the structural and institutional barriers hindering the development of scientific systems in transition economies, such as Kazakhstan. The main focus is on the concept of the "middle science trap," which is characterized by steady growth in quantitative indicators (publications, grants) but a lack of qualitative advancement. Excessive bureaucracy, weak integration into the international scientific community, and ineffective science management are key factors limiting development. This paper proposes an approach of "homeopathic modernization," which focuses on minimal yet strategically significant changes aimed at reducing bureaucratic barriers and enhancing the effectiveness of the scientific ecosystem. A comparative analysis of international experience (China, India, and the European Union) is provided, demonstrating how targeted reforms in the scientific sector can lead to significant results. Social and cultural aspects, including the influence of mentality and institutional structure, are also examined, and practical recommendations for reforming the scientific system in Kazakhstan and Central Asia are offered. The conclusions of the article could be useful for developing national science modernization programs, particularly in countries with high levels of bureaucracy and conservatism.
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language Model
Authors: Yifan Wu, Min Zeng, Yang Li, Yang Zhang, Min Li
Date: 2024-11-23 00:00:00
Tasks: Language Modeling, Language Modelling, Property Prediction, Text Generation

Most current molecular language models transfer the masked language model or image-text generation model from natural language processing to molecular field. However, molecules are not solely characterized by atom/bond symbols; they encapsulate important physical/chemical properties. Moreover, normal language models bring grammar rules that are irrelevant for understanding molecules. In this study, we propose a novel physicochemical knowledge-guided molecular meta language framework MolMetaLM. We design a molecule-specialized meta language paradigm, formatted as multiple <S,P,O> (subject, predicate, object) knowledge triples sharing the same S (i.e., molecule) to enhance learning the semantic relationships between physicochemical knowledge and molecules. By introducing different molecular knowledge and noises, the meta language paradigm generates tens of thousands of pretraining tasks. By recovering the token/sequence/order-level noises, MolMetaLM exhibits proficiency in large-scale benchmark evaluations involving property prediction, molecule generation, conformation inference, and molecular optimization. Through MolMetaLM, we offer a new insight for designing language models.
DAGSM: Disentangled Avatar Generation with GS-enhanced Mesh
Authors: Jingyu Zhuang, Di Kang, Linchao Bao, Liang Lin, Guanbin Li
Date: 2024-11-20 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising

Text-driven avatar generation has gained significant attention owing to its convenience. However, existing methods typically model the human body with all garments as a single 3D model, limiting its usability, such as clothing replacement, and reducing user control over the generation process. To overcome the limitations above, we propose DAGSM, a novel pipeline that generates disentangled human bodies and garments from the given text prompts. Specifically, we model each part (e.g., body, upper/lower clothes) of the clothed human as one GS-enhanced mesh (GSM), which is a traditional mesh attached with 2D Gaussians to better handle complicated textures (e.g., woolen, translucent clothes) and produce realistic cloth animations. During the generation, we first create the unclothed body, followed by a sequence of individual cloth generation based on the body, where we introduce a semantic-based algorithm to achieve better human-cloth and garment-garment separation. To improve texture quality, we propose a view-consistent texture refinement module, including a cross-view attention mechanism for texture style consistency and an incident-angle-weighted denoising (IAW-DE) strategy to update the appearance. Extensive experiments have demonstrated that DAGSM generates high-quality disentangled avatars, supports clothing replacement and realistic animation, and outperforms the baselines in visual quality.
Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
Authors: Baptiste Chatelier, José Miguel Mateos-Ramos, Vincent Corlay, Christian Häger, Matthieu Crussière, Henk Wymeersch, Luc Le Magoarou
Date: 2024-11-06 00:00:00
Tasks: Integrated sensing and communication

Direction of arrival (DoA) estimation is a common sensing problem in radar, sonar, audio, and wireless communication systems. It has gained renewed importance with the advent of the integrated sensing and communication paradigm. To fully exploit the potential of such sensing systems, it is crucial to take into account potential hardware impairments that can negatively impact the obtained performance. This study introduces a joint DoA estimation and hardware impairment learning scheme following a model-based approach. Specifically, a differentiable version of the multiple signal classification (MUSIC) algorithm is derived, allowing efficient learning of the considered impairments. The proposed approach supports both supervised and unsupervised learning strategies, showcasing its practical potential. Simulation results indicate that the proposed method successfully learns significant inaccuracies in both antenna locations and complex gains. Additionally, the proposed method outperforms the classical MUSIC algorithm in the DoA estimation task.
Numerical investigation of the effect of macro control measures on epidemics transport via a coupled PDE crowd flow - epidemics spreading dynamics model
Authors: A. I. Delis, N. Bekiaris-Liberis
Date: 2024-11-25 00:00:00

This work aims to provide an approach to the macroscopic modeling and simulation of pedestrian flow, coupled with contagion spreading, towards numerical investigation of the effect of certain, macro-control measures on epidemics transport dynamics. To model the dynamics of the pedestrians, a second-order macroscopic model, coupled with an Eikonal equation, is used. This model is coupled with a macroscopic Susceptible-Exposed-Infected-Susceptible-Vaccinated (SEISV) contagion model, where the force-of-infection $\beta$ coefficient is modeled via a drift-diffusion equation, which is affected by the air-flow dynamics due to the ventilation. The air-flow dynamics are obtained assuming a potential flow that can imitate the existence of ventilation in the computational domain. Numerical approximations are considered for the coupled model along with numerical tests and results. In particular, we investigate the effect of employment of different, epidemics transport control measures, which may be implemented through real-time manipulation of i) ventilation rate and direction, ii) maximum speed of pedestrians, and iii) average distances between pedestrians, and through iv) incorporation in the crowd of masked or vaccinated individuals. Such simulations of disease spreading in a moving crowd can potentially provide valuable information about the risks of infection in relevant situations and support the design of systematic intervention/control measures.
Urban Region Embeddings from Service-Specific Mobile Traffic Data
Authors: Giulio Loddi, Chiara Pugliese, Francesco Lettich, Fabio Pinelli, Chiara Renso
Date: 2024-11-20 00:00:00

With the advent of advanced 4G/5G mobile networks, mobile phone data collected by operators now includes detailed, service-specific traffic information with high spatio-temporal resolution. In this paper, we leverage this type of data to explore its potential for generating high-quality representations of urban regions. To achieve this, we present a methodology for creating urban region embeddings from service-specific mobile traffic data, employing a temporal convolutional network-based autoencoder, transformers, and learnable weighted sum models to capture key urban features. In the extensive experimental evaluation conducted using a real-world dataset, we demonstrate that the embeddings generated by our methodology effectively capture urban characteristics. Specifically, our embeddings are compared against those of a state-of-the-art competitor across two downstream tasks. Additionally, through clustering techniques, we investigate how well the embeddings produced by our methodology capture the temporal dynamics and characteristics of the underlying urban regions. Overall, this work highlights the potential of service-specific mobile traffic data for urban research and emphasizes the importance of making such data accessible to support public innovation.