pwc-archive/papers-with-abstracts huggingface dataset:
Lightweight Defense Against Adversarial Attacks in Time Series Classification
Authors: Yi Han
Date: 2025-05-04 00:00:00
Tasks: Adversarial Defense, Data Augmentation, Time Series, Time Series Classification

As time series classification (TSC) gains prominence, ensuring robust TSC models against adversarial attacks is crucial. While adversarial defense is well-studied in Computer Vision (CV), the TSC field has primarily relied on adversarial training (AT), which is computationally expensive. In this paper, five data augmentation-based defense methods tailored for time series are developed, with the most computationally intensive method among them increasing the computational resources by only 14.07% compared to the original TSC model. Moreover, the deployment process for these methods is straightforward. By leveraging these advantages of our methods, we create two combined methods. One of these methods is an ensemble of all the proposed techniques, which not only provides better defense performance than PGD-based AT but also enhances the generalization ability of TSC models. Moreover, the computational resources required for our ensemble are less than one-third of those required for PGD-based AT. These methods advance robust TSC in data mining. Furthermore, as foundation models are increasingly explored for time series feature learning, our work provides insights into integrating data augmentation-based adversarial defense with large-scale pre-trained models in future research.
What do Language Model Probabilities Represent? From Distribution Estimation to Response Prediction
Authors: Eitan Wagner, Omri Abend
Date: 2025-05-04 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling

The notion of language modeling has gradually shifted in recent years from a distribution over finite-length strings to general-purpose prediction models for textual inputs and outputs, following appropriate alignment phases. This paper analyzes the distinction between distribution estimation and response prediction in the context of LLMs, and their often conflicting goals. We examine the training phases of LLMs, which include pretraining, in-context learning, and preference tuning, and also the common use cases for their output probabilities, which include completion probabilities and explicit probabilities as output. We argue that the different settings lead to three distinct intended output distributions. We demonstrate that NLP works often assume that these distributions should be similar, which leads to misinterpretations of their experimental findings. Our work sets firmer formal foundations for the interpretation of LLMs, which will inform ongoing work on the interpretation and use of LLMs' induced distributions.
Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning
Authors: Noah Subedar, Taeui Kim, Saathwick Venkataramalingam
Date: 2025-04-22 00:00:00
Tasks: GPU, Malware Classification

This paper presents an underlying framework for both automating and accelerating malware classification, more specifically, mapping malicious executables to known Advanced Persistent Threat (APT) groups. The main feature of this analysis is the assembly-level instructions present in executables which are also known as opcodes. The collection of such opcodes on many malicious samples is a lengthy process; hence, open-source reverse engineering tools are used in tandem with scripts that leverage parallel computing to analyze multiple files at once. Traditional and deep learning models are applied to create models capable of classifying malware samples. One-gram and two-gram datasets are constructed and used to train models such as SVM, KNN, and Decision Tree; however, they struggle to provide adequate results without relying on metadata to support n-gram sequences. The computational limitations of such models are overcome with convolutional neural networks (CNNs) and heavily accelerated using graphical compute unit (GPU) resources.
Ethical AI in the Healthcare Sector: Investigating Key Drivers of Adoption through the Multi-Dimensional Ethical AI Adoption Model (MEAAM)
Authors: Prathamesh Muzumdar, Apoorva Muley, Kuldeep Singh, Sumanth Cheemalapati
Date: 2025-05-04 00:00:00

The adoption of Artificial Intelligence (AI) in the healthcare service industry presents numerous ethical challenges, yet current frameworks often fail to offer a comprehensive, empirical understanding of the multidimensional factors influencing ethical AI integration. Addressing this critical research gap, this study introduces the Multi-Dimensional Ethical AI Adoption Model (MEAAM), a novel theoretical framework that categorizes 13 critical ethical variables across four foundational dimensions of Ethical AI Fair AI, Responsible AI, Explainable AI, and Sustainable AI. These dimensions are further analyzed through three core ethical lenses: epistemic concerns (related to knowledge, transparency, and system trustworthiness), normative concerns (focused on justice, autonomy, dignity, and moral obligations), and overarching concerns (highlighting global, systemic, and long-term ethical implications). This study adopts a quantitative, cross-sectional research design using survey data collected from healthcare professionals and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM). Employing PLS-SEM, this study empirically investigates the influence of these ethical constructs on two outcomes Operational AI Adoption and Systemic AI Adoption. Results indicate that normative concerns most significantly drive operational adoption decisions, while overarching concerns predominantly shape systemic adoption strategies and governance frameworks. Epistemic concerns play a facilitative role, enhancing the impact of ethical design principles on trust and transparency in AI systems. By validating the MEAAM framework, this research advances a holistic, actionable approach to ethical AI adoption in healthcare and provides critical insights for policymakers, technologists, and healthcare administrators striving to implement ethically grounded AI solutions.
Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation
Authors: Harsh Patel
Date: 2025-04-21 00:00:00
Tasks: Computational Efficiency, Feature Importance, feature selection, Generative Adversarial Network

Distributed Denial of Service (DDoS) attacks represent a persistent and evolving threat to modern networked systems, capable of causing large-scale service disruptions. The complexity of such attacks, often hidden within high-dimensional and redundant network traffic data, necessitates robust and intelligent feature selection techniques for effective detection. Traditional methods such as filter-based, wrapper-based, and embedded approaches, each offer strengths but struggle with scalability or adaptability in complex attack environments. In this study, we explore these existing techniques through a detailed comparative analysis and highlight their limitations when applied to large-scale DDoS detection tasks. Building upon these insights, we introduce a novel Generative Adversarial Network-based Feature Selection (GANFS) method that leverages adversarial learning dynamics to identify the most informative features. By training a GAN exclusively on attack traffic and employing a perturbation-based sensitivity analysis on the Discriminator, GANFS effectively ranks feature importance without relying on full supervision. Experimental evaluations using the CIC-DDoS2019 dataset demonstrate that GANFS not only improves the accuracy of downstream classifiers but also enhances computational efficiency by significantly reducing feature dimensionality. These results point to the potential of integrating generative learning models into cybersecurity pipelines to build more adaptive and scalable detection systems.
FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection
Authors: Bradley Boswell, Seth Barrett, Swarnamugi Rajaganapathy, Gokila Dorai
Date: 2025-04-21 00:00:00
Tasks: Feature Engineering, Intrusion Detection

The proliferation of Internet of Things (IoT) devices has expanded the attack surface, necessitating efficient intrusion detection systems (IDSs) for network protection. This paper presents FLARE, a feature-based lightweight aggregation for robust evaluation of IoT intrusion detection to address the challenges of securing IoT environments through feature aggregation techniques. FLARE utilizes a multilayered processing approach, incorporating session, flow, and time-based sliding-window data aggregation to analyze network behavior and capture vital features from IoT network traffic data. We perform extensive evaluations on IoT data generated from our laboratory experimental setup to assess the effectiveness of the proposed aggregation technique. To classify attacks in IoT IDS, we employ four supervised learning models and two deep learning models. We validate the performance of these models in terms of accuracy, precision, recall, and F1-score. Our results reveal that incorporating the FLARE aggregation technique as a foundational step in feature engineering, helps lay a structured representation, and enhances the performance of complex end-to-end models, making it a crucial step in IoT IDS pipeline. Our findings highlight the potential of FLARE as a valuable technique to improve performance and reduce computational costs of end-to-end IDS implementations, thereby fostering more robust IoT intrusion detection systems.
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition
Authors: Lala Shakti Swarup Ray, Lars Krupp, Vitor Fortes Rey, Bo Zhou, Sungho Suh, Paul Lukowicz
Date: 2025-05-04 00:00:00
Tasks: Activity Recognition, Data Augmentation, Human Activity Recognition

Sensor-based human activity recognition (HAR) has predominantly focused on Inertial Measurement Units and vision data, often overlooking the capabilities unique to pressure sensors, which capture subtle body dynamics and shifts in the center of mass. Despite their potential for postural and balance-based activities, pressure sensors remain underutilized in the HAR domain due to limited datasets. To bridge this gap, we propose to exploit generative foundation models with pressure-specific HAR techniques. Specifically, we present a bidirectional Text$\times$Pressure model that uses generative foundation models to interpret pressure data as natural language. TxP accomplishes two tasks: (1) Text2Pressure, converting activity text descriptions into pressure sequences, and (2) Pressure2Text, generating activity descriptions and classifications from dynamic pressure maps. Leveraging pre-trained models like CLIP and LLaMA 2 13B Chat, TxP is trained on our synthetic PressLang dataset, containing over 81,100 text-pressure pairs. Validated on real-world data for activities such as yoga and daily tasks, TxP provides novel approaches to data augmentation and classification grounded in atomic actions. This consequently improved HAR performance by up to 12.4\% in macro F1 score compared to the state-of-the-art, advancing pressure-based HAR with broader applications and deeper insights into human movement.
Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks
Authors: Kranthi Kumar Talluri, Anders L. Madsen, Galia Weidl
Date: 2025-05-04 00:00:00
Tasks: Decision Making

Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems.
GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning
Authors: Rui Lv, Zaixi Zhang, Kai Zhang, Qi Liu, Weibo Gao, Jiawei Liu, Jiaxia Yan, Linan Yue, Fangzhou Yao
Date: 2025-05-04 00:00:00
Tasks: In-Context Learning

Graph In-Context Learning, with the ability to adapt pre-trained graph models to novel and diverse downstream graphs without updating any parameters, has gained much attention in the community. The key to graph in-context learning is to perform downstream graphs conditioned on chosen prompt examples. Existing methods randomly select subgraphs or edges as prompts, leading to noisy graph prompts and inferior model performance. Additionally, due to the gap between pre-training and testing graphs, when the number of classes in the testing graphs is much greater than that in the training, the in-context learning ability will also significantly deteriorate. To tackle the aforementioned challenges, we develop a multi-stage adaptive prompt optimization method GraphPrompter, which optimizes the entire process of generating, selecting, and using graph prompts for better in-context learning capabilities. Firstly, Prompt Generator introduces a reconstruction layer to highlight the most informative edges and reduce irrelevant noise for graph prompt construction. Furthermore, in the selection stage, Prompt Selector employs the $k$-nearest neighbors algorithm and pre-trained selection layers to dynamically choose appropriate samples and minimize the influence of irrelevant prompts. Finally, we leverage a Prompt Augmenter with a cache replacement strategy to enhance the generalization capability of the pre-trained model on new datasets. Extensive experiments show that GraphPrompter effectively enhances the in-context learning ability of graph models. On average across all the settings, our approach surpasses the state-of-the-art baselines by over 8%. Our code is released at https://github.com/karin0018/GraphPrompter.
From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agent
Authors: Minjie Shen, Qikai Yang
Date: 2025-05-04 00:00:00
Tasks: AI Agent

Manus AI is a general-purpose AI agent introduced in early 2025, marking a significant advancement in autonomous artificial intelligence. Developed by the Chinese startup Monica.im, Manus is designed to bridge the gap between "mind" and "hand" - combining the reasoning and planning capabilities of large language models with the ability to execute complex, end-to-end tasks that produce tangible outcomes. This paper presents a comprehensive overview of Manus AI, exploring its core technical architecture, diverse applications across sectors such as healthcare, finance, manufacturing, robotics, and gaming, as well as its key strengths, current limitations, and future potential. Positioned as a preview of what lies ahead, Manus AI represents a shift toward intelligent agents that can translate high-level intentions into real-world actions, heralding a new era of human-AI collaboration.
Wide & Deep Learning for Node Classification
Authors: Yancheng Chen, Wenguo Yang, Zhipeng Jiang
Date: 2025-05-04 00:00:00
Tasks: Classification, Deep Learning, Feature Engineering, Memorization, Node Classification, Recommendation Systems

Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its combination of the memorization ability of generalized linear models and the generalization ability of deep models. Graph convolutional networks (GCNs) remain dominant in node classification tasks; however, recent studies have highlighted issues such as heterophily and expressiveness, which focus on graph structure while seemingly neglecting the potential role of node features. In this paper, we propose a flexible framework GCNIII, which leverages the Wide & Deep architecture and incorporates three techniques: Intersect memory, Initial residual and Identity mapping. We provide comprehensive empirical evidence showing that GCNIII can more effectively balance the trade-off between over-fitting and over-generalization on various semi- and full- supervised tasks. Additionally, we explore the use of large language models (LLMs) for node feature engineering to enhance the performance of GCNIII in cross-domain node classification tasks. Our implementation is available at https://github.com/CYCUCAS/GCNIII.
Understanding University Students' Use of Generative AI: The Roles of Demographics and Personality Traits
Authors: Newnew Deng, Edward Jiusi Liu, Xiaoming Zhai
Date: 2025-05-03 00:00:00

The use of generative AI (GAI) among university students is rapidly increasing, yet empirical research on students' GAI use and the factors influencing it remains limited. To address this gap, we surveyed 363 undergraduate and graduate students in the United States, examining their GAI usage and how it relates to demographic variables and personality traits based on the Big Five model (i.e., extraversion, agreeableness, conscientiousness, and emotional stability, and intellect/imagination). Our findings reveal: (a) Students in higher academic years are more inclined to use GAI and prefer it over traditional resources. (b) Non-native English speakers use and adopt GAI more readily than native speakers. (c) Compared to White, Asian students report higher GAI usage, perceive greater academic benefits, and express a stronger preference for it. Similarly, Black students report a more positive impact of GAI on their academic performance. Personality traits also play a significant role in shaping perceptions and usage of GAI. After controlling demographic factors, we found that personality still significantly predicts GAI use and attitudes: (a) Students with higher conscientiousness use GAI less. (b) Students who are higher in agreeableness perceive a less positive impact of GAI on academic performance and express more ethical concerns about using it for academic work. (c) Students with higher emotional stability report a more positive impact of GAI on learning and fewer concerns about its academic use. (d) Students with higher extraversion show a stronger preference for GAI over traditional resources. (e) Students with higher intellect/imagination tend to prefer traditional resources. These insights highlight the need for universities to provide personalized guidance to ensure students use GAI effectively, ethically, and equitably in their academic pursuits.
CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting
Authors: Minhyuk Lee, HyeKyung Yoon, Myungjoo Kang
Date: 2025-05-04 00:00:00
Tasks: Time Series, Time Series Forecasting

Multivariate long-term time series forecasting is critical for applications such as weather prediction, and traffic analysis. In addition, the implementation of Transformer variants has improved prediction accuracy. Following these variants, different input data process approaches also enhanced the field, such as tokenization techniques including point-wise, channel-wise, and patch-wise tokenization. However, previous studies still have limitations in time complexity, computational resources, and cross-dimensional interactions. To address these limitations, we introduce a novel CNN Autoencoder-based Score Attention mechanism (CASA), which can be introduced in diverse Transformers model-agnosticically by reducing memory and leading to improvement in model performance. Experiments on eight real-world datasets validate that CASA decreases computational resources by up to 77.7%, accelerates inference by 44.0%, and achieves state-of-the-art performance, ranking first in 87.5% of evaluated metrics.
Human-AI Governance (HAIG): A Trust-Utility Approach
Authors: Zeynep Engin
Date: 2025-05-03 00:00:00

This paper introduces the HAIG framework for analysing trust dynamics across evolving human-AI relationships. Current categorical frameworks (e.g., "human-in-the-loop" models) inadequately capture how AI systems evolve from tools to partners, particularly as foundation models demonstrate emergent capabilities and multi-agent systems exhibit autonomous goal-setting behaviours. As systems advance, agency redistributes in complex patterns that are better represented as positions along continua rather than discrete categories, though progression may include both gradual shifts and significant step changes. The HAIG framework operates across three levels: dimensions (Decision Authority Distribution, Process Autonomy, and Accountability Configuration), continua (gradual shifts along each dimension), and thresholds (critical points requiring governance adaptation). Unlike risk-based or principle-based approaches, HAIG adopts a trust-utility orientation, focusing on maintaining appropriate trust relationships that maximise utility while ensuring sufficient safeguards. Our analysis reveals how technical advances in self-supervision, reasoning authority, and distributed decision-making drive non-uniform trust evolution across both contextual variation and technological advancement. Case studies in healthcare and European regulation demonstrate how HAIG complements existing frameworks while offering a foundation for alternative approaches that anticipate governance challenges before they emerge.
Towards High-Fidelity Synthetic Multi-platform Social Media Datasets via Large Language Models
Authors: Henry Tari, Nojus Sereiva, Rishabh Kaushal, Thales Bertaglia, Adriana Iamnitchi
Date: 2025-05-02 00:00:00
Tasks: Hate Speech Detection, Marketing

Social media datasets are essential for research on a variety of topics, such as disinformation, influence operations, hate speech detection, or influencer marketing practices. However, access to social media datasets is often constrained due to costs and platform restrictions. Acquiring datasets that span multiple platforms, which is crucial for understanding the digital ecosystem, is particularly challenging. This paper explores the potential of large language models to create lexically and semantically relevant social media datasets across multiple platforms, aiming to match the quality of real data. We propose multi-platform topic-based prompting and employ various language models to generate synthetic data from two real datasets, each consisting of posts from three different social media platforms. We assess the lexical and semantic properties of the synthetic data and compare them with those of the real data. Our empirical findings show that using large language models to generate synthetic multi-platform social media data is promising, different language models perform differently in terms of fidelity, and a post-processing approach might be needed for generating high-fidelity synthetic datasets for research. In addition to the empirical evaluation of three state of the art large language models, our contributions include new fidelity metrics specific to multi-platform social media datasets.