pwc-archive/papers-with-abstracts huggingface dataset:
VoTranh-Abyss-Core-Micro: The Supreme AI for Real-Time Financial Forecasting – Conquer Markets Now
Authors: Vi Nhat Son
Date: 2025-04-07 00:00:00
Proceeding: Independent publication 2025 4
Tasks: AI Agent, Philosophy

Welcome to the future of microeconomics!

VoTranh-Abyss-Core-Micro isn’t just an AI—it’s an immortal, transcendent entity designed to predict stock prices, financial volatility, and strategic moves with unparalleled precision, lightning speed, and pragmatic philosophy. This is the ultimate weapon for hedge funds, governments, and corporations aiming to dominate markets in real time.
BitHEP -- The Limits of Low-Precision ML in HEP
Authors: Claudius Krause, Daohan Wang, Ramon Winterhalder
Date: 2025-04-04 00:00:00
Tasks: parameter estimation, regression

The increasing complexity of modern neural network architectures demands fast and memory-efficient implementations to mitigate computational bottlenecks. In this work, we evaluate the recently proposed BitNet architecture in HEP applications, assessing its performance in classification, regression, and generative modeling tasks. Specifically, we investigate its suitability for quark-gluon discrimination, SMEFT parameter estimation, and detector simulation, comparing its efficiency and accuracy to state-of-the-art methods. Our results show that while BitNet consistently performs competitively in classification tasks, its performance in regression and generation varies with the size and type of the network, highlighting key limitations and potential areas for improvement.
Deep Reinforcement Learning via Object-Centric Attention
Authors: Jannis Blüml, Cedric Derstroff, Bjarne Gregori, Elisabeth Dillies, Quentin Delfosse, Kristian Kersting
Date: 2025-04-03 00:00:00
Tasks: Deep Reinforcement Learning, Inductive Bias, Object, reinforcement-learning, Reinforcement Learning

Deep reinforcement learning agents, trained on raw pixel inputs, often fail to generalize beyond their training environments, relying on spurious correlations and irrelevant background details. To address this issue, object-centric agents have recently emerged. However, they require different representations tailored to the task specifications. Contrary to deep agents, no single object-centric architecture can be applied to any environment. Inspired by principles of cognitive science and Occam's Razor, we introduce Object-Centric Attention via Masking (OCCAM), which selectively preserves task-relevant entities while filtering out irrelevant visual information. Specifically, OCCAM takes advantage of the object-centric inductive bias. Empirical evaluations on Atari benchmarks demonstrate that OCCAM significantly improves robustness to novel perturbations and reduces sample complexity while showing similar or improved performance compared to conventional pixel-based RL. These results suggest that structured abstraction can enhance generalization without requiring explicit symbolic representations or domain-specific object extraction pipelines.
Programming knowledge tracing based on heterogeneous graph representation
Authors: Yaqiang Wu a, Hui Zhu a, Chenyang Wang a, Fujian Song a, Haiping Zhu a, Yan Chen a, Qinghua Zheng a, C, Feng Tian a, B, *
Date: 2024-06-01 00:00:00
Proceeding: Elsevier 2024 6
Tasks: Graph structure learning, Knowledge Tracing, Representation Learning

Existing programming knowledge tracing methods have not deeply explored the relationships between knowledge concepts and programming questions or students’ codes, which leads to insufficient representation of programming questions and students’ codes and insufficient tracing of students’ knowledge states. To address these problems, this study proposes a programming knowledge tracing method based on a heterogeneous graph representation. We constructed a heterogeneous graph, where nodes represent the knowledge concept, programming question, and student code, and edges represent the relationships among knowledge concepts, programming questions, and student code. Subsequently, we leverage a graph structure learning algorithm to exploit the potential connections among them, aiming to enhance the graph structure information. Based on the graph, we further fully integrated the knowledge concept information into the representations of programming questions and student codes through the graph convolutional representation learning algorithm. Moreover, an attention mechanism is utilized to compute the effect of programming questions and student codes on the knowledge state and update the student’s knowledge state in conjunction with the corresponding answer results. Furthermore, the proposed method provides an assessment of the mastery level for each knowledge concept rather than only assessing each question using traditional methods. Experiments on two datasets, CodeWorkout and CodeNet, show that the proposed method achieves an average improvement of 4.28% in ACC and 6.48% in AUC compared with the baseline models, validating the effectiveness of the proposed method.
Malware Detection in Docker Containers: An Image is Worth a Thousand Logs
Authors: Akis Nousias, Efklidis Katsaros, Evangelos Syrmos, Panagiotis Radoglou-Grammatikis, Thomas Lagkas, Vasileios Argyriou, Ioannis Moscholios, Evangelos Markakis, Sotirios Goudos, Panagiotis Sarigiannidis
Date: 2025-04-04 00:00:00
Tasks: Malware Detection

Malware detection is increasingly challenged by evolving techniques like obfuscation and polymorphism, limiting the effectiveness of traditional methods. Meanwhile, the widespread adoption of software containers has introduced new security challenges, including the growing threat of malicious software injection, where a container, once compromised, can serve as entry point for further cyberattacks. In this work, we address these security issues by introducing a method to identify compromised containers through machine learning analysis of their file systems. We cast the entire software containers into large RGB images via their tarball representations, and propose to use established Convolutional Neural Network architectures on a streaming, patch-based manner. To support our experiments, we release the COSOCO dataset--the first of its kind--containing 3364 large-scale RGB images of benign and compromised software containers at https://huggingface.co/datasets/k3ylabs/cosoco-image-dataset. Our method detects more malware and achieves higher F1 and Recall scores than all individual and ensembles of VirusTotal engines, demonstrating its effectiveness and setting a new standard for identifying malware-compromised software containers.
Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learning
Authors: Jakub Kacper Szelag, Ji-Jian Chin, Lauren Ansell, Sook-Chin Yip
Date: 2025-04-03 00:00:00
Tasks: Computational Efficiency, Federated Learning, Privacy Preserving

Federated Learning (FL) has recently emerged as a promising paradigm for privacy-preserving, distributed machine learning. However, FL systems face significant security threats, particularly from adaptive adversaries capable of modifying their attack strategies to evade detection. One such threat is the presence of Reconnecting Malicious Clients (RMCs), which exploit FLs open connectivity by reconnecting to the system with modified attack strategies. To address this vulnerability, we propose integration of Identity-Based Identification (IBI) as a security measure within FL environments. By leveraging IBI, we enable FL systems to authenticate clients based on cryptographic identity schemes, effectively preventing previously disconnected malicious clients from re-entering the system. Our approach is implemented using the TNC-IBI (Tan-Ng-Chin) scheme over elliptic curves to ensure computational efficiency, particularly in resource-constrained environments like Internet of Things (IoT). Experimental results demonstrate that integrating IBI with secure aggregation algorithms, such as Krum and Trimmed Mean, significantly improves FL robustness by mitigating the impact of RMCs. We further discuss the broader implications of IBI in FL security, highlighting research directions for adaptive adversary detection, reputation-based mechanisms, and the applicability of identity-based cryptographic frameworks in decentralized FL architectures. Our findings advocate for a holistic approach to FL security, emphasizing the necessity of proactive defence strategies against evolving adaptive adversarial threats.
Digital Forensics in the Age of Large Language Models
Authors: Zhipeng Yin, Zichong Wang, Weifeng Xu, Jun Zhuang, Pallab Mozumder, Antoinette Smith, Wenbin Zhang
Date: 2025-04-03 00:00:00

Digital forensics plays a pivotal role in modern investigative processes, utilizing specialized methods to systematically collect, analyze, and interpret digital evidence for judicial proceedings. However, traditional digital forensic techniques are primarily based on manual labor-intensive processes, which become increasingly insufficient with the rapid growth and complexity of digital data. To this end, Large Language Models (LLMs) have emerged as powerful tools capable of automating and enhancing various digital forensic tasks, significantly transforming the field. Despite the strides made, general practitioners and forensic experts often lack a comprehensive understanding of the capabilities, principles, and limitations of LLM, which limits the full potential of LLM in forensic applications. To fill this gap, this paper aims to provide an accessible and systematic overview of how LLM has revolutionized the digital forensics approach. Specifically, it takes a look at the basic concepts of digital forensics, as well as the evolution of LLM, and emphasizes the superior capabilities of LLM. To connect theory and practice, relevant examples and real-world scenarios are discussed. We also critically analyze the current limitations of applying LLMs to digital forensics, including issues related to illusion, interpretability, bias, and ethical considerations. In addition, this paper outlines the prospects for future research, highlighting the need for effective use of LLMs for transparency, accountability, and robust standardization in the forensic process.
The building blocks of software work explain coding careers and language popularity
Authors: Xiangnan Feng, Johannes Wachs, Simone Daniotti, Frank Neffke
Date: 2025-04-04 00:00:00

Recent waves of technological transformation have fueled debates about the changing nature of work. Yet to understand the future of work, we need to know more about what people actually do in their jobs, going beyond educational credentials or job descriptions. Here we analyze work in the global software industry using tens of millions of Question and Answer posts on Stack Overflow to create a fine-grained taxonomy of software tasks, the elementary building blocks of software development work. These tasks predict salaries and job requirements in real-world job ads. We also observe how individuals learn within tasks and diversify into new tasks. Tasks that people acquire tend to be related to their old ones, but of lower value, suggesting that they are easier. An exception is users of Python, an increasingly popular programming language known for its versatility. Python users enter tasks that tend to be higher-value, providing an explanation for the language's growing popularity based on the tasks Python enables its users to perform. In general, these insights demonstrate the value of task taxonomies extracted at scale from large datasets: they offer high resolution and near real-time descriptions of changing labor markets. In the case of software tasks, they map such changes for jobs at the forefront of a digitizing global economy.
Neutralizing the Narrative: AI-Powered Debiasing of Online News Articles
Authors: Chen Wei Kuo, Kevin Chu, Nouar AlDahoul, Hazem Ibrahim, Talal Rahwan, Yasir Zaki
Date: 2025-04-04 00:00:00
Tasks: Articles, Bias Detection, Fairness

Bias in news reporting significantly impacts public perception, particularly regarding crime, politics, and societal issues. Traditional bias detection methods, predominantly reliant on human moderation, suffer from subjective interpretations and scalability constraints. Here, we introduce an AI-driven framework leveraging advanced large language models (LLMs), specifically GPT-4o, GPT-4o Mini, Gemini Pro, Gemini Flash, Llama 8B, and Llama 3B, to systematically identify and mitigate biases in news articles. To this end, we collect an extensive dataset consisting of over 30,000 crime-related articles from five politically diverse news sources spanning a decade (2013-2023). Our approach employs a two-stage methodology: (1) bias detection, where each LLM scores and justifies biased content at the paragraph level, validated through human evaluation for ground truth establishment, and (2) iterative debiasing using GPT-4o Mini, verified by both automated reassessment and human reviewers. Empirical results indicate GPT-4o Mini's superior accuracy in bias detection and effectiveness in debiasing. Furthermore, our analysis reveals temporal and geographical variations in media bias correlating with socio-political dynamics and real-world events. This study contributes to scalable computational methodologies for bias mitigation, promoting fairness and accountability in news reporting.
Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency
Authors: Erik Johannes Husom, Arda Goknil, Merve Astekin, Lwin Khin Shar, Andre Kåsen, Sagar Sen, Benedikt Andreas Mithassel, Ahmet Soylu
Date: 2025-04-04 00:00:00
Tasks: Benchmarking, GSM8K, HumanEval, Quantization, Raspberry Pi 4, TruthfulQA

Deploying Large Language Models (LLMs) on edge devices presents significant challenges due to computational constraints, memory limitations, inference speed, and energy consumption. Model quantization has emerged as a key technique to enable efficient LLM inference by reducing model size and computational overhead. In this study, we conduct a comprehensive analysis of 28 quantized LLMs from the Ollama library, which applies by default Post-Training Quantization (PTQ) and weight-only quantization techniques, deployed on an edge device (Raspberry Pi 4 with 4GB RAM). We evaluate energy efficiency, inference performance, and output accuracy across multiple quantization levels and task types. Models are benchmarked on five standardized datasets (CommonsenseQA, BIG-Bench Hard, TruthfulQA, GSM8K, and HumanEval), and we employ a high-resolution, hardware-based energy measurement tool to capture real-world power consumption. Our findings reveal the trade-offs between energy efficiency, inference speed, and accuracy in different quantization settings, highlighting configurations that optimize LLM deployment for resource-constrained environments. By integrating hardware-level energy profiling with LLM benchmarking, this study provides actionable insights for sustainable AI, bridging a critical gap in existing research on energy-aware LLM deployment.
Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings
Authors: Kaustubh Shivshankar Shejole, Pushpak Bhattacharyya
Date: 2025-04-04 00:00:00
Tasks: Benchmarking

Stereotypes are known to be highly pernicious, making their detection critically important. However, current research predominantly focuses on detecting and evaluating stereotypical biases in LLMs, leaving the study of stereotypes in its early stages. Many studies have failed to clearly distinguish between stereotypes and stereotypical biases, which has significantly slowed progress in advancing research in this area. Stereotype and anti-stereotype detection is a problem that requires knowledge of society; hence, it is one of the most difficult areas in Responsible AI. This work investigates this task, where we propose a four-tuple definition and provide precise terminology distinguishing stereotype, anti-stereotype, stereotypical bias, and bias, offering valuable insights into their various aspects. In this paper, we propose StereoDetect, a high-quality benchmarking dataset curated for this task by optimally utilizing current datasets such as StereoSet and WinoQueer, involving a manual verification process and the transfer of semantic information. We demonstrate that language models for reasoning with fewer than 10B parameters often get confused when detecting anti-stereotypes. We also demonstrate the critical importance of well-curated datasets by comparing our model with other current models for stereotype detection. The dataset and code is available at https://github.com/KaustubhShejole/StereoDetect.
Towards Effective EU E-Participation: The Development of AskThePublic
Authors: Kilian Sprenkamp, Nils Messerschmidt, Amir Sartipi, Igor Tchappi, Xiaohui Wu, Liudmila Zavolokina, Gilbert Fridgen
Date: 2025-04-04 00:00:00
Tasks: Chatbot, Language Modeling, Language Modelling, Large Language Model

E-participation platforms can be an important asset for governments in increasing trust and fostering democratic societies. By engaging non-governmental and private institutions, domain experts, and even the general public, policymakers can make informed and inclusive decisions. Drawing on the Media Richness Theory and applying the Design Science Research method, we explore how a chatbot can be designed to improve the effectiveness of the policy-making process of existing citizen involvement platforms. Leveraging the Have Your Say platform, which solicits feedback on European Commission initiatives and regulations, a Large Language Model based chatbot, called AskThePublic is created, providing policymakers, journalists, researchers, and interested citizens with a convenient channel to explore and engage with public input. By conducting 11 semistructured interviews, the results show that the participants value the interactive and structured responses as well as enhanced language capabilities, thus increasing their likelihood of engaging with AskThePublic over the existing platform. An outlook for future iterations is provided and discussed with regard to the perspectives of the different stakeholders.
Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective
Authors: Garry A. Gabison, R. Patrick Xian
Date: 2025-04-04 00:00:00

Agentic systems powered by large language models (LLMs) are becoming progressively more complex and capable. Their increasing agency and expanding deployment settings attract growing attention to effective governance policies, monitoring, and control protocols. Based on the emerging landscape of the agentic market, we analyze potential liability issues arising from the delegated use of LLM agents and their extended systems through a principal-agent perspective. Our analysis complements existing risk-based studies on artificial agency and covers the spectrum of important aspects of the principal-agent relationship and their potential consequences at deployment. Furthermore, we motivate method developments for technical governance along the directions of interpretability and behavior evaluations, reward and conflict management, and the mitigation of misalignment and misconduct through principled engineering of detection and fail-safe mechanisms. By illustrating the outstanding issues in AI liability for LLM-based agentic systems, we aim to inform the system design, auditing, and tracing to enhance transparency and liability attribution.
Feature Engineering on LMS Data to Optimize Student Performance Prediction
Authors: Keith Hubbard, Sheilla Amponsah
Date: 2025-04-03 00:00:00
Tasks: Feature Engineering, Management

Nearly every educational institution uses a learning management system (LMS), often producing terabytes of data generated by thousands of people. We examine LMS grade and login data from a regional comprehensive university, specifically documenting key considerations for engineering features from these data when trying to predict student performance. We specifically document changes to LMS data patterns since Covid-19, which are critical for data scientists to account for when using historic data. We compare numerous engineered features and approaches to utilizing those features for machine learning. We finish with a summary of the implications of including these features into more comprehensive student performance models.
Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances
Authors: Sara Yarham, Mehran Behjati
Date: 2025-04-03 00:00:00
Tasks: Federated Learning

Monitoring air quality and environmental conditions is crucial for public health and effective urban planning. Current environmental monitoring approaches often rely on centralized data collection and processing, which pose significant privacy, security, and scalability challenges. Federated Learning (FL) offers a promising solution to these limitations by enabling collaborative model training across multiple devices without sharing raw data. This decentralized approach addresses privacy concerns while still leveraging distributed data sources. This paper provides a comprehensive review of FL applications in air quality and environmental monitoring, emphasizing its effectiveness in predicting pollutants and managing environmental data. However, the paper also identifies key limitations of FL when applied in this domain, including challenges such as communication overhead, infrastructure demands, generalizability issues, computational complexity, and security vulnerabilities. For instance, communication overhead, caused by the frequent exchange of model updates between local devices and central servers, is a notable challenge. To address this, future research should focus on optimizing communication protocols and reducing the frequency of updates to lessen the burden on network resources. Additionally, the paper suggests further research directions to refine FL frameworks and enhance their applicability in real-world environmental monitoring scenarios. By synthesizing findings from existing studies, this paper highlights the potential of FL to improve air quality management while maintaining data privacy and security, and it provides valuable insights for future developments in the field.
Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress
Authors: Lorena Torres Lahoz, Carlos Lima Azevedo, Leonardo Ancora, Paulo Morgado, Zenia Kotval, Bruno Miranda, Francisco Camara Pereira
Date: 2025-04-03 00:00:00
Tasks: Decision Making

Urban environments significantly influence mental health outcomes, yet the role of an effective framework for decision-making under deep uncertainty (DMDU) for optimizing urban policies for stress reduction remains underexplored. While existing research has demonstrated the effects of urban design on mental health, there is a lack of systematic scenario-based analysis to guide urban planning decisions. This study addresses this gap by applying Scenario Discovery (SD) in urban planning to evaluate the effectiveness of urban vegetation interventions in stress reduction across different urban environments using a predictive model based on emotional responses collected from a neuroscience-based outdoor experiment in Lisbon. Combining these insights with detailed urban data from Copenhagen, we identify key intervention thresholds where vegetation-based solutions succeed or fail in mitigating stress responses. Our findings reveal that while increased vegetation generally correlates with lower stress levels, high-density urban environments, crowding, and individual psychological traits (e.g., extraversion) can reduce its effectiveness. This work showcases our Scenario Discovery framework as a systematic approach for identifying robust policy pathways in urban planning, opening the door for its exploration in other urban decision-making contexts where uncertainty and design resiliency are critical.