pwc-archive/papers-with-abstracts huggingface dataset:
A Portable Solution for Simultaneous Human Movement and Mobile EEG Acquisition: Readiness Potentials for Basketball Free-throw Shooting
Authors: Miguel Contreras-Altamirano, Melanie Klapprott, Nadine Jacobsen, Paul Maanen, Julius Welzel, Stefan Debener
Date: 2025-01-09 00:00:00
Tasks: EEG

Advances in wireless electroencephalography (EEG) technology promise to record brain-electrical activity in everyday situations. To better understand the relationship between brain activity and natural behavior, it is necessary to monitor human movement patterns. Here, we present a pocketable setup consisting of two smartphones to simultaneously capture human posture and EEG signals. We asked 26 basketball players to shoot 120 free throws each. First, we investigated whether our setup allows us to capture the readiness potential (RP) that precedes voluntary actions. Second, we investigated whether the RP differs between successful and unsuccessful free-throw attempts. The results confirmed the presence of the RP, but the amplitude of the RP was not related to shooting success. However, offline analysis of real-time human pose signals derived from a smartphone camera revealed pose differences between successful and unsuccessful shots for some individuals. We conclude that a highly portable, low-cost and lightweight acquisition setup, consisting of two smartphones and a head-mounted wireless EEG amplifier, is sufficient to monitor complex human movement patterns and associated brain dynamics outside the laboratory.
Evolution of Spots and Stripes in Cellular Automata
Authors: Peter Turney
Date: 2025-01-08 00:00:00

Cellular automata are computers, similar to Turing machines. The main difference is that Turing machines use a one-dimensional tape, whereas cellular automata use a two-dimensional grid. The best-known cellular automaton is the Game of Life, which is a universal computer. It belongs to a family of cellular automata with 262,144 members. Playing the Game of Life generally involves engineering; that is, assembling a device composed of various parts that are combined to achieve a specific intended result. Instead of engineering cellular automata, we propose evolving cellular automata. Evolution applies mutation and selection to a population of organisms. If a mutation increases the fitness of an organism, it may have many descendants, displacing the less fit organisms. Unlike engineering, evolution does not work towards an imagined goal. Evolution works towards increasing fitness, with no expectations about the specific form of the final result. Mutation, selection, and fitness yield structures that appear to be more organic and life-like than engineered structures. In our experiments, the patterns resulting from evolving cellular automata look much like the spots on leopards and the stripes on tigers.
Accelerated Diffusion Models via Speculative Sampling
Authors: Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet
Date: 2025-01-09 00:00:00

Speculative sampling is a popular technique for accelerating inference in Large Language Models by generating candidate tokens using a fast draft model and accepting or rejecting them based on the target model's distribution. While speculative sampling was previously limited to discrete sequences, we extend it to diffusion models, which generate samples via continuous, vector-valued Markov chains. In this context, the target model is a high-quality but computationally expensive diffusion model. We propose various drafting strategies, including a simple and effective approach that does not require training a draft model and is applicable out of the box to any diffusion model. Our experiments demonstrate significant generation speedup on various diffusion models, halving the number of function evaluations, while generating exact samples from the target model.
Learning convolution operators on compact Abelian groups
Authors: Emilia Magnani, Ernesto de Vito, Philipp Hennig, Lorenzo Rosasco
Date: 2025-01-09 00:00:00

We consider the problem of learning convolution operators associated to compact Abelian groups. We study a regularization-based approach and provide corresponding learning guarantees under natural regularity conditions on the convolution kernel. More precisely, we assume the convolution kernel is a function in a translation invariant Hilbert space and analyze a natural ridge regression (RR) estimator. Building on existing results for RR, we characterize the accuracy of the estimator in terms of finite sample bounds. Interestingly, regularity assumptions which are classical in the analysis of RR, have a novel and natural interpretation in terms of space/frequency localization. Theoretical results are illustrated by numerical simulations.
Robust Score Matching
Authors: Richard Schwank, Andrew McCormack, Mathias Drton
Date: 2025-01-09 00:00:00
Tasks: parameter estimation

Proposed in Hyv\"arinen (2005), score matching is a parameter estimation procedure that does not require computation of distributional normalizing constants. In this work we utilize the geometric median of means to develop a robust score matching procedure that yields consistent parameter estimates in settings where the observed data has been contaminated. A special appeal of the proposed method is that it retains convexity in exponential family models. The new method is therefore particularly attractive for non-Gaussian, exponential family graphical models where evaluation of normalizing constants is intractable. Support recovery guarantees for such models when contamination is present are provided. Additionally, support recovery is studied in numerical experiments and on a precipitation dataset. We demonstrate that the proposed robust score matching estimator performs comparably to the standard score matching estimator when no contamination is present but greatly outperforms this estimator in a setting with contamination.
Supervised Learning with Evolving Tasks and Performance Guarantees
Authors: Verónica Álvarez, Santiago Mazuelas, Jose A. Lozano
Date: 2025-01-09 00:00:00
Tasks: Continual Learning, Multi-Task Learning

Multiple supervised learning scenarios are composed by a sequence of classification tasks. For instance, multi-task learning and continual learning aim to learn a sequence of tasks that is either fixed or grows over time. Existing techniques for learning tasks that are in a sequence are tailored to specific scenarios, lacking adaptability to others. In addition, most of existing techniques consider situations in which the order of the tasks in the sequence is not relevant. However, it is common that tasks in a sequence are evolving in the sense that consecutive tasks often have a higher similarity. This paper presents a learning methodology that is applicable to multiple supervised learning scenarios and adapts to evolving tasks. Differently from existing techniques, we provide computable tight performance guarantees and analytically characterize the increase in the effective sample size. Experiments on benchmark datasets show the performance improvement of the proposed methodology in multiple scenarios and the reliability of the presented performance guarantees.
Unraveling the Impact of Visual Complexity on Search as Learning
Authors: Wolfgang Gritz, Anett Hoppe, Ralph Ewerth
Date: 2025-01-09 00:00:00
Tasks: Information Retrieval

Information search has become essential for learning and knowledge acquisition, offering broad access to information and learning resources. The visual complexity of web pages is known to influence search behavior, with previous work suggesting that searchers make evaluative judgments within the first second on a page. However, there is a significant gap in our understanding of how visual complexity impacts searches specifically conducted with a learning intent. This gap is particularly relevant for the development of optimized information retrieval (IR) systems that effectively support educational objectives. To address this research need, we model visual complexity and aesthetics via a diverse set of features, investigating their relationship with search behavior during learning-oriented web sessions. Our study utilizes a publicly available dataset from a lab study where participants learned about thunderstorm formation. Our findings reveal that while content relevance is the most significant predictor for knowledge gain, sessions with less visually complex pages are associated with higher learning success. This observation applies to features associated with the layout of web pages rather than to simpler features (e.g., number of images). The reported results shed light on the impact of visual complexity on learning-oriented searches, informing the design of more effective IR systems for educational contexts. To foster reproducibility, we release our source code (https://github.com/TIBHannover/sal_visual_complexity).
De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems
Authors: Robin Burke, Gediminas Adomavicius, Toine Bogers, Tommaso Di Noia, Dominik Kowald, Julia Neidhardt, Özlem Özgöbek, Maria Soledad Pera, Nava Tintarev, Jürgen Ziegler
Date: 2025-01-09 00:00:00
Tasks: Recommendation Systems

Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved -- from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.
Detection of Body Packs in Abdominal CT scans Through Artificial Intelligence
Authors: Seyed Ali Mohtarami, Shahin Shadnia, Mitra Rahimi, Peyman Erfan Talab Evini, Babak Mostafazadeh, Azadeh Memarian, Elmira Heidarli
Date: 2024-12-26 00:00:00
Proceeding: Archives of Academic Emergency Medicine 2024 12
Tasks: Computed Tomography (CT), Diagnostic, object-detection, Object Detection

Abstract
Introduction: Identifying the people who try to hide illegal substances in the body for smuggling is of considerable importance in forensic medicine and poisoning. This study aimed to develop a new diagnostic method using artificial intelligence to detect body packs in real-time Abdominal computed tomography (CT) scans.

Methods: In this cross-sectional study, abdominal CT scan images were employed to create a machine learning-based model for detecting body packs. A single-step object detection called RetinaNet using a modified neck (Proposed Model) was performed to achieve the best results. Also, an angled Bbox (oriented bounding box) in the training dataset played an important role in improving the results.

Results: A total of 888 abdominal CT scan images were studied. Our proposed Body Packs Detection (BPD) model achieved a mean average precision (mAP) value of 86.6% when the intersection over union (IoU) was 0.5, and a mAP value of 45.6% at different IoU thresholds (from 0.5 to 0.95 in steps of 0.05). It also obtained a Recall value of 58.5%, which was the best result among the standard object detection methods such as the standard RetinaNet.

Conclusion: This study employed a deep learning network to identify body packs in abdominal CT scans, highlighting the importance of incorporating object shape and variability when leveraging artificial intelligence in healthcare to aid medical practitioners. Nonetheless, the development of a tailored dataset for object detection, like body packs, requires careful curation by subject matter specialists to ensure successful training.

Keywords: Artificial intelligence CT-scan Body packer Object detection Oriented Bounding Box
oschman-2007-can-electrons-act-as-antioxidants-a-review-and-commentary
Authors: James L. Oschman.
Date: 2007-11-30 00:00:00
Proceeding: ResearchGate 2007 11
Tasks: Rhythm

A previous study demonstrated that connecting the human body to the earth during sleep (earthing) normalizes the daily cortisol rhythm and improves sleep. A variety of other benefits were reported, including reductions in pain and inflammation. Subsequent studies have confirmed these earlier findings and documented virtually immediate physiologic and clinical effects of grounding or earthing the body. It is well established, though not widely known, that the surface of the earth possesses a limitless and continuously renewed supply of free or mobile electrons as a consequence of a global atmospheric electron circuit. Wearing shoes with insulating soles and/or sleeping in beds that are isolated from the electrical ground plane of the earth have disconnected most people from the earth's electrical rhythms and free electrons. The most reasonable hypothesis to explain the beneficial effects of earthing is that a direct earth connection enables both diurnal electrical rhythms and free electrons to flow from the earth to the body. It is proposed that the earth's diurnal electrical rhythms set the biological clocks for hormones that regulate sleep and activity. It is also suggested that free electrons from the earth neutralize the positively charged free radicals that are the hallmark of chronic inflammation. A relationship between cortisol and inflammation was established in the pioneering work of H. Selye published in the 1950s. Current biomedical research has led to an inflammation hypothesis that is establishing chronic inflammation as the culprit behind almost every modern chronic illness. The research summarized here and in subsequent reports provides a basis for a number of earthing technologies that restore and maintain natural electrical contact between the human body and the earth throughout the day and night in situations where going barefoot on the earth is impractical. It is proposed that free or mobile electrons from the earth can resolve chronic inflammation by serving as natural antioxidants.
African Gender Classification Using Clothing Identification Via Deep Learning
Authors: Samuel Ozechi
Date: 2024-01-12 00:00:00
Proceeding: None 2024 1
Tasks: Attribute, Classification, Deep Learning, Gender Classification

Human attributes identification and classification are popular aspects of computer vision which have been utilized in building relevant innovative systems in recent years. Most of these systems heavily rely on detection and recognition of facial attributes to  perform efficiently. This work explores the use of an alternative approach to gender attribute classification of Africans by identifying traditional attires.
Traditional attire is very popular among African societies and reflects attributes such as ethnic background, social status, and gender. 
The gender attributes of Africans reflected by the visual information of African traditional attires are explored in this work for gender classification.
The AFRIFASHION1600 dataset was used to train a deep learning model, which achieved 87%  accuracy on the test set.
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion Detection
Authors: Hamza Kaddour, Shaibal Das; Rishikesh Bajgai; Amairanni Sanchez; Jason Sanchez; Steve C. Chiu
Date: 2024-10-02 00:00:00
Proceeding: IEEE Opportunity Research Scholars Symposium (ORSS) 2024 10
Tasks: Intrusion Detection

As the Internet of Things (IoT) continues to expand its footprint across various sectors, including healthcare, industrial automation, and smart homes, the security of these interconnected devices becomes paramount. With the proliferation of IoT devices, the attack surface for potential cybersecurity threats has significantly increased, necessitating the development of efficient Intrusion Detection Systems (IDS). This study embarks on a comprehensive examination of several machine learning algorithms aimed at enhancing the prediction accuracy of IDS within IoT networks. Leveraging the ToN-IoT dataset, we implement and compare the effectiveness of models. The findings reveal that ensemble methods, particularly Random Forest and XGBoost, exhibit superior performance, underscoring their potential for deployment in safeguarding IoT ecosystems against malicious intrusions.
Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data
Authors: Joshua Glasser, Brian Lindauer
Date: 2013-07-22 00:00:00
Proceeding: IEEE Security and Privacy Workshops 2013 7

The threat of malicious insider activity continues to be of paramount concern in both the public and private sectors. Though there is great interest in advancing the state of the art in predicting and stopping these threats, the difficulty of obtaining suitable data for research, development, and testing remains a significant hinderance. We outline the use of synthetic data to enable progress in one research program, while discussing the benefits and limitations of synthetic insider threat data, the meaning of realism in this context, as well as future research directions.
Attention to Patterns is all you need for Insider threat detection
Authors: Priya Tiwary, Akshayraj Madhubalan, Amit Gautam, Raj Darji
Date: 2024-10-26 00:00:00
Proceeding: International Conference on Artificial Intelligence, Metaverse and Cybersecurity (ICAMAC) 2024 10
Tasks: All, Anomaly Detection, Classification

Insider threats pose a significant and often underestimated risk to organizations. Traditional anomaly detection methods relying on simplistic patterns and lacking temporal awareness struggle to capture the nuances of user behavior, leading to missed detections and false alarms. This research proposes a novel approach that leverages the power of deep learning models to capture complex, hierarchical patterns in user behavior, enabling the early detection of malicious insider activity. The proposed approach introduces two distinct architectures: Time-Distributed Deep Learning Architecture (TD-CNN-LSTM) and Contextually Aware Attention-Based Architecture (TD-CNN-Attention). These architectures combine CNNs with LSTMs or attention mechanisms to extract both spatial and temporal features from user access data, capturing intricate patterns across different timescales. Additionally, they incorporate user information such as psychometrics and organizational data, providing a holistic view of user behavior and context. Through extensive evaluation, both architectures demonstrate significant improvements in accuracy and F1 score compared to existing insider threat detection solutions. The attention-based model in particular emerges as a state-of-the-art approach with superior performance capabilities. This research marks a significant step forward in the field of insider threat detection, paving the way for organizations to better secure their critical assets and safeguard their future in the ever-changing cybersecurity landscape.
Spatio-temporal collaborative multiple-stream transformer network for liver lesion classification on multiple-sequence magnetic resonance imaging
Authors: Shuangping Huang, Zinan Hong, Bianzhe Wu, Jinglin Liang, Qinghua Huang
Date: 2025-02-15 00:00:00
Proceeding: Engineering Applications of Artificial Intelligence 2025 2
Tasks: Diagnostic, Lesion Classification

Accurate identification of focal liver lesions is essential for determining the appropriate therapeutic approach in clinical practice. Magnetic resonance imaging (MRI) is a valuable technology for precise classification, revealing diverse physical and biological details of lesions. However, due to the wide variety and morphological variability of the lesions, unsystematic mixing analysis of multiple-sequences MRI may cause aliasing of lesion information, obscuring the inter-tissue relationships between various imaging sequences and impeding a comprehensive diagnosis. In this paper, we proposed a Spatio-Temporal Collaborative Multiple-Stream Transformer Network that simultaneously considers spatial contrast and temporal variations to obtain detailed information on anatomical structures and tissue dynamics, effectively organizing and utilizing multiple-sequence MRI for analysis. Specifically, multiple-sequence MRI is first grouped into multiple streams based on MRI diagnostic characteristics. To reduce the interference of redundancy across multiple streams, we design a bottleneck bridge structure for spatial information aggregation. Additionally, we adopt a bidirectional Long Short-Term Memory to simulate radiologists observing the vascular morphology and hemodynamics in lesion sites from contrast-enhanced sequences. Experiments conducted on public MRI dataset, which includes seven categories of focal liver lesions from 498 patients, demonstrate that our framework achieves state-of-the-art performance, with an accuracy of 85.6%, a precision of 87.4%, a recall of 84.2%, an F1-score of 85.3%, and an Area Under the Curve of 97.1%. The experimental results indicate that the network performs well in predicting focal liver lesions, advancing the application of precision medicine.
LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection
Authors: Woochang Hyun, Insoo Lee, Bongwon Suh
Date: 2024-10-21 00:00:00
Proceeding: ACM International Conference on Information and Knowledge Management (CIKM) 2024 10
Tasks: Fraud Detection, Graph Neural Network, Node Classification

Graph-based fraud detection faces significant challenges, such as severe class imbalance, inconsistent connections due to the scarcity of fraudulent nodes, and the camouflage of these nodes appearing like benign nodes. Existing studies often adopt the approach of filtering similar nodes to strengthen the homophily assumption of graph neural networks. However, to effectively address these issues, it is important to distinguish and adaptively utilize the labels of neighboring nodes. In this study, we propose the Label-Exploring Graph Neural Network (LEX-GNN), designed to enhance fraud detection by actively leveraging labeled node information. The core idea is that the manner of message passing and reception should vary depending on the node types. Specifically, we first predict the labels of nodes based on their original or previous representations. Subsequently, each node transmits differently processed messages according to its probability of being fraudulent. Finally, target nodes also receive the messages differently depending on their pre-predicted probability. Extensive experimental results on real-world benchmarks demonstrate that LEX-GNN outperforms existing state-of-the-art baselines. Our code is available at https://github.com/wdhyun/LEX-GNN.