pwc-archive/papers-with-abstracts huggingface dataset:
Effect of caffeine and energy drinks on sleep quality and brain activity in college students
Authors: Oviedo, N., Tito, K., Cigarán, A., Tello, B.
Date: 2024-07-02 00:00:00
Proceeding: Introducción a Señales Biomédicas UPCH 2024-I 2024 7
Tasks: EEG, Sleep Quality

This study investigates the impact of caffeine and energy drink consumption on sleep quality and brain activity among college students. Using EEG technology, the research identifies specific brain activity patterns associated with prolonged energy drink use and poor sleep quality. Participants were divided into groups based on their sleep habits and caffeine consumption levels. Results indicate a significant correlation between high caffeine intake and disrupted sleep patterns, evidenced by increased variability and energy metrics in EEG readings. Conversely, participants with low caffeine consumption demonstrated better sleep quality. The study highlights the challenges in EEG signal interpretation due to external factors and underscores the need for rigorous control in future research to enhance data reliability. These findings provide valuable insights for further studies on caffeine's effects on sleep and cognitive performance, aiming to develop comprehensive recommendations for managing caffeine intake among university students.
Early ECG Warning for Chagas Patients: Implementation of TinyML for Low-Resource Areas in Peru
Authors: Diego A. Taquiri, Erick A. Valdivia, Ana B. Mantilla, Armando A. Flórez
Date: 2024-07-02 00:00:00
Proceeding: UPCH 2024 7
Tasks: Electrocardiography (ECG)

Cardiovascular diseases (CVDs) are the leading cause of death globally, claiming approximately 17.9 million lives annually. These disorders, including coronary heart diseases, cerebrovascular diseases, and rheumatic heart diseases, predominantly affect low- and middle-income countries, where over 75% of deaths occur. In the Americas, CVDs are responsible for an estimated 2 million deaths per year. In Peru, cardiovascular diseases are the second leading cause of death. This study aims to develop an early warning system based on electrocardiography (ECG) for detecting high-risk Chagas disease patients, with potential applications to other cardiovascular conditions. We employ advanced signal processing and machine learning techniques, specifically wavelet transform feature extraction and Tiny Machine Learning (TinyML), to efficiently and cost-effectively identify specific patterns in the ECG signals of Chagas patients. This system aims to improve timely clinical intervention and reduce mortality rates associated with Chagas disease in low-resource settings.
MAX-AST: COMBINING CONVOLUTION, LOCAL AND GLOBAL SELF-ATTENTIONS FOR AUDIO EVENT CLASSIFICATION
Authors: Tony Alex, Sara Ahmed, Armin Mustafa, Muhammad Awais, Philip JB Jackson
Date: 2024-04-14 00:00:00
Proceeding: ICASSP 2024 4
Tasks: Audio Classification

In the domain of audio transformer architectures, prior research
has extensively investigated isotropic architectures that capture the
global context through full self-attention and hierarchical architectures that progressively transition from local to global context utilising hierarchical structures with convolutions or window-based attention. However, the idea of imbuing each individual block with
both local and global contexts, thereby creating a hybrid transformer
block, remains relatively under-explored in the field.
To facilitate this exploration, we introduce Multi Axis Audio
Spectrogram Transformer (Max-AST), an adaptation of MaxViT
to the audio domain. Our approach leverages convolution, local
window-attention, and global grid-attention in all the transformer
blocks. The proposed model excels in efficiency compared to prior
methods and consistently outperforms state-of-the-art techniques,
achieving significant gains of up to 2.6% on the AudioSet full set.
Further, we performed detailed ablations to analyse the impact of
each of these components on audio feature learning. The source
code is available at https://github.com/ta012/MaxAST.git
Home Study Environments: Assessment of Posture and Muscle Fatigue Using Superficial Electromyography in the Context of Virtual Education After the Pandemic
Authors: C. Boggio, M. Cama, J. Sandoval, A. Valle
Date: 2024-07-02 00:00:00
Proceeding: INTERCON 2024 7
Tasks: Electromyography (EMG)

The COVID-19 pandemic led to a rapid shift to virtual education, raising concerns about the ergonomics and posture of college students at home. This project aims to optimize home study environments by assessing posture and muscle fatigue using surface electromyography (EMG). It identifies and quantifies musculoskeletal problems caused by non-ergonomic home setups and analyzes the relationship between prolonged screen time, poor posture, and muscle fatigue. Using the BITalino (r)evolution EMG kit and Kinovea software, university students who studied virtually during the pandemic were assessed. The data is processed to propose ergonomic recommendations to improve posture, reduce muscle fatigue, and promote a healthier study environment for better academic performance.
Comparison of Means of Electrocardiographic Parameters in Peruvian University Students Based on Their Body Mass Index
Authors: A. Bravo, A. Palomino, L. Zuñiga
Date: 2024-07-02 00:00:00
Proceeding: INTERCON 2024 7

Several studies indicate that the body mass index is a
measure correlated with the incidence of cardiovascular diseases,
making it an important factor that doctors and nutritionists consider when analyzing a person’s physical condition. Additionally,
since multiple heart diseases, such as arrhythmias, can be directly
diagnosed through monitoring the patient’s electrocardiogram,
the evaluation between electrocardiogram characteristics and
body mass index is not trivial. In this context, this article aims to
develop a statistical comparison of means between the population
of many electrocardiogram characteristics among the population
of young undergraduate students at the Universidad Peruana
Cayetano Heredia who present a normal weight and overweight
according to their body mass index.
Effect of Prolonged Smartphone Use on Trapezius and Neck Extensor Muscle Activity, Using sEMG
Authors: R. Culque, A. Díaz, C. Aibar, N. Unsihuay
Date: 2024-07-03 00:00:00
Proceeding: . 2024 7

Intensive use of touchscreen mobile devices has increased musculoskeletal problems, particularly in the neck and shoulders, known as "text-neck." This study examines the effect of prolonged smartphone use on the activity of the trapezius and neck extensor muscles using surface electromyography (sEMG) in university students aged 20 to 25. The focus is on evaluating muscle activity, head and neck posture, and perceived discomfort in seated postures. The results provide quantitative data on muscle activity under typical smartphone usage conditions, which can be used to develop specific recommendations to reduce the risk of pain and musculoskeletal injuries. This study underscores the need for ergonomic interventions and preventive measures to address neck pain associated with prolonged smartphone use.
Surface Electromyographic Analysis of the Forearm in Forehand and Backhand Techniques in Table Tennis Players
Authors: María Zubiate, Christian Mucha, Jossef Tintaya, Gianni Di Trani
Date: 2024-07-02 00:00:00
Proceeding: IEEE EMBS 2024 2024 7

This study focuses on analyzing muscle fatigue in the
forearm muscles of table tennis players using surface electromyo-
graphy (sEMG). The primary objective is to detect the onset of
muscle fatigue during forehand and backhand strokes. sEMG
signals were acquired, preprocessed through normalization, and
analyzed using the Daubechies 4 (db4) wavelet transform for
noise reduction. Feature extraction was performed to identify
indicators of fatigue. A portable device was implemented to
facilitate data collection, but the main emphasis was on the
analysis and interpretation of the sEMG data. The results
demonstrate the ability to provide detailed feedback on muscle
activation and fatigue, potentially enhancing player performance
and injury prevention.
Classification of Phobia Levels in Patients with Arachnophobia Using BPM Analysis of ECG Signals
Authors: J.Arismendiz, G. Atencio, R.Gonzales, J.Rodriguez
Date: 2024-07-02 00:00:00
Proceeding: ResearchGate 2024 7
Tasks: Heart Rate Variability

This study proposes a method to accurately classify phobia levels in patients with arachnophobia using electrocardiogram (ECG) signal analysis. Key indicators such as heart rate (BPM) and heart rate variability (HRV) were utilized to objectively measure phobia responses. Data from 80 subjects exposed to anxiety-inducing video clips were analyzed, demonstrating the potential of ECG signals for quantitative assessment of phobia levels. Statistical analyses, including ANOVA and boxplots, revealed significant differences in BPM across varying phobia levels, highlighting the effectiveness of personalized therapeutic interventions.
Maze Discovery using Multiple Robots via Federated Learning
Authors: Kalpana Ranasinghe, H. P. Madushanka, Rafaela Scaciota, Sumudu Samarakoon, Mehdi Bennis
Date: 2024-06-25 00:00:00
Tasks: Classification, Federated Learning

This work presents a use case of federated learning (FL) applied to discovering a maze with LiDAR sensors-equipped robots. Goal here is to train classification models to accurately identify the shapes of grid areas within two different square mazes made up with irregular shaped walls. Due to the use of different shapes for the walls, a classification model trained in one maze that captures its structure does not generalize for the other. This issue is resolved by adopting FL framework between the robots that explore only one maze so that the collective knowledge allows them to operate accurately in the unseen maze. This illustrates the effectiveness of FL in real-world applications in terms of enhancing classification accuracy and robustness in maze discovery tasks.
Analysis of Ergonomic Risk in University Study Environments During Class Days
Authors: Jimena Alpiste, Jossymar León, Renato Cardoso
Date: 2024-07-02 00:00:00
Proceeding: Universidad Peruana Cayetano Heredia 2024 7

This study investigates the ergonomic impact of university seating on students’ musculoskeletal health, focusing on lumbar muscle fatigue during prolonged study sessions. Using surface electromyography (sEMG) with BiTalino devices, the research compared muscle activation in ergonomic and non-ergonomic chairs over three-hour periods. The methodology in-volved signal processing techniques including Wavelet Daubechies 6 filtering, normalization, and RMS value extraction. Results suggest that non-ergonomic seating may be associated with increased lumbar stress, while ergonomic chairs showed no signs of muscle fatigue. However, the study faced limitations due to interruptions and uncontrolled environmental factors. The findings highlight the potential benefits of ergonomic seating in academic settings and underscore the need for further research under more controlled conditions to definitively establish the relationship between seating ergonomics and musculoskeletal disorders among university students.
Policy Changes and Growth Slowdown: Assessing the Lost Decade of the Latin American Miracle
Authors: Emiliano Toni, Pablo Paniagua, Patricio Órdenes
Date: 2024-07-02 00:00:00

The Latin American region has suffered an economic slowdown since the end of the commodities boom. Within this context, Chile was the poster child of economic growth and development up until 2014. Since then, Chile has also been trapped in a decade of slow economic growth. Chile's sudden slowdown and recent growth path divergence have posed a puzzle for economic growth and development economics. This paper examines this slowdown from an empirical perspective and determines how much can be attributed to internal and external causes. Using a synthetic control approach and a structural time series Bayesian estimation, our findings suggest that at least two-thirds of the Chilean slowdown is attributable to internal causes driven by a policy regime change in 2014, with external factors playing a secondary role. The net effect of this set of internal reforms resulted in a nearly 10% reduction in real GDP per capita over five years and led to a 1.8% decline in average GDP growth rates from 2015 to 2019. Our results are consistent with the literature that establishes that external shocks can explain only a small fraction of the poor economic performance of developing countries, suggesting that internal factors are the primary source of subpar performance. This research sheds light on the potential effects of policy regime shifts in economic growth, thus providing valuable insights for development economics and, more specifically, emerging economies.
A Survey on Failure Analysis and Fault Injection in AI Systems
Authors: Guangba Yu, Gou Tan, Haojia Huang, Zhenyu Zhang, Pengfei Chen, Roberto Natella, Zibin Zheng
Date: 2024-06-28 00:00:00
Tasks: Fault Diagnosis, Survey

The rapid advancement of Artificial Intelligence (AI) has led to its integration into various areas, especially with Large Language Models (LLMs) significantly enhancing capabilities in Artificial Intelligence Generated Content (AIGC). However, the complexity of AI systems has also exposed their vulnerabilities, necessitating robust methods for failure analysis (FA) and fault injection (FI) to ensure resilience and reliability. Despite the importance of these techniques, there lacks a comprehensive review of FA and FI methodologies in AI systems. This study fills this gap by presenting a detailed survey of existing FA and FI approaches across six layers of AI systems. We systematically analyze 160 papers and repositories to answer three research questions including (1) what are the prevalent failures in AI systems, (2) what types of faults can current FI tools simulate, (3) what gaps exist between the simulated faults and real-world failures. Our findings reveal a taxonomy of AI system failures, assess the capabilities of existing FI tools, and highlight discrepancies between real-world and simulated failures. Moreover, this survey contributes to the field by providing a framework for fault diagnosis, evaluating the state-of-the-art in FI, and identifying areas for improvement in FI techniques to enhance the resilience of AI systems.
JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models
Authors: Haibo Jin, Leyang Hu, Xinuo Li, Peiyan Zhang, Chonghan Chen, Jun Zhuang, Haohan Wang
Date: 2024-06-26 00:00:00
Tasks: LLM Jailbreak, Survey

The rapid evolution of artificial intelligence (AI) through developments in Large Language Models (LLMs) and Vision-Language Models (VLMs) has brought significant advancements across various technological domains. While these models enhance capabilities in natural language processing and visual interactive tasks, their growing adoption raises critical concerns regarding security and ethical alignment. This survey provides an extensive review of the emerging field of jailbreaking--deliberately circumventing the ethical and operational boundaries of LLMs and VLMs--and the consequent development of defense mechanisms. Our study categorizes jailbreaks into seven distinct types and elaborates on defense strategies that address these vulnerabilities. Through this comprehensive examination, we identify research gaps and propose directions for future studies to enhance the security frameworks of LLMs and VLMs. Our findings underscore the necessity for a unified perspective that integrates both jailbreak strategies and defensive solutions to foster a robust, secure, and reliable environment for the next generation of language models. More details can be found on our website: \url{https://chonghan-chen.com/llm-jailbreak-zoo-survey/}.
A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound
Authors: Haiqiao Wang, Hong Wu, Zhuoyuan Wang, Peiyan Yue, Dong Ni, Pheng-Ann Heng, Yi Wang
Date: 2024-06-30 00:00:00
Tasks: Image Registration, Prognosis, Survey

Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) plays a vital role in the diagnosis and image-guided intervention of PCa.To facilitate physicians with more accurate and efficient computer-assisted diagnosis and interventions, many image processing algorithms in TRUS have been proposed and achieved state-of-the-art performance in several tasks, including prostate gland segmentation, prostate image registration, PCa classification and detection, and interventional needle detection. The rapid development of these algorithms over the past two decades necessitates a comprehensive summary. In consequence, this survey provides a \textcolor{blue}{narrative } analysis of this field, outlining the evolution of image processing methods in the context of TRUS image analysis and meanwhile highlighting their relevant contributions. Furthermore, this survey discusses current challenges and suggests future research directions to possibly advance this field further.
Characterization of Alzheimer's disease using EEG as a potential early diagnostic tool
Authors: M. Jaico, A. Woll, G. Armoa, S. Herrera
Date: 2024-07-03 00:00:00
Proceeding: 2024 2024 7
Tasks: Diagnostic, EEG

Alzheimer's disease (AD) is characterized by distinctive cytological, histological, and immunohistochemical changes that differentiate it from normal aging patterns. Although definitive diagnostic confirmation is anatomopathological, current diagnostic criteria combine clinical and biological evidence to formulate a probabilistic diagnosis. Given the high prevalence and socioeconomic impact of AD, there is an intensified search for early diagnostic tools. Electroencephalography (EEG) has emerged as a potential tool for early AD characterization and diagnosis, providing records of brain electrical activity that may reveal specific patterns of neuronal alteration associated with AD before clinical symptoms are evident. This study proposes using EEG to identify spectral power and complexity features to distinguish between healthy individuals and those with AD. The findings suggest that individuals with AD exhibit notable decreases in alpha (8-13 Hz) and beta (13-30 Hz) oscillations compared to healthy controls, which are associated with neuronal deterioration and loss of synaptic connectivity, hallmark characteristics of AD progression.
LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection
Authors: Yang Lu
Date: 2024-06-24 00:00:00
Proceeding: IEEE Transactions on Instrumentation and Measurement 2024 6
Tasks: Defect Detection, Insulator Defect Detection, Model Compression, object-detection, Object Detection

Insulator defect detection is of great significance to ensure the normal operation of power transmission and distribution networks. In response to the problems of low speed, low accuracy, and difficulty in deploying to embedded terminals in existing insulator defect detection, this paper proposes a lightweight insulator defect detection model based on an improved YOLOv5s, named LiteYOLO-ID. Firstly, to significantly reduce the model parameters while maintaining detection accuracy, we design a new lightweight convolution module called EGC (ECA-GhostNet-C2f). Secondly, based on the EGC module, we construct the EGC-CSPGhostNet backbone network, which optimizes the feature extraction process and achieves model compression. Additionally, we design a lightweight neck network, EGC-PANet, to further reduce the parameter count and achieve efficient feature fusion. Experimental results show that on the IDID-Plus dataset, compared to the original YOLOv5s model, not only does LiteYOLO-ID reduce the model parameters by 47.13\%, but it also improves the mAP(0.5) by 1\%. Furthermore, the generalization of the model is validated on the Pascal VOC dataset and the SFID dataset. Importantly, after TensorRT optimization, the inference speed of the LiteYOLO-ID algorithm on the Jetson TX2 NX reaches 20.2 FPS, meeting the real-time detection requirements of insulator defects. Our code, weight models, and datasets can be obtained at the following URL: https://github.com/LuYang-2023/Insulator-defect-detection.
VisionAD, a software package of performant anomaly detection algorithms, and Proportion Localised, an interpretable metric
Authors: Alexander D. J. Taylor, Phillip Tregidgo, Jonathan James Morrison, Neill D. F. Campbell
Date: 2024-06-07 00:00:00
Proceeding: Transactions of Machine Learning Research 2024 6
Tasks: Anomaly Detection, Benchmarking

We release VisionAD, an anomaly detection library in the domain of images. The library forms the largest and most performant collection of such algorithms to date. Each algorithm is written through a standardised API, for ease of use. The library has a focus on fair benchmarking intended to mitigate the issue of cherry-picked results. It enables rapid experimentation and straightforward integration of new algorithms. In addition, we propose a new metric, Proportion Localised (PL). This reports the proportion of anomalies that are sufficiently localised via classifying each discrete anomaly as localised or not. The metric is far more intuitive as it has a real physical relation, meaning it is attractive to industry-based professionals. We also release the VisionADIndustrial (VADI) benchmark, a thorough benchmarking of the top anomaly detection algorithms. This benchmark calculates the mean across the pooled classes of the MVTec and VisA datasets. We are committed to hosting an updated version of this leaderboard online, and encourage researchers to add, tweak and improve algorithms to climb this leaderboard. VisionAD code is found at https://github.com/alext1995/VisionAD, and Proportion Localised code is found at https://github.com/alext1995/proportion_localised.
Processing and Feature Extraction of ECG Signals for Arrhythmia Detection in AI Models
Authors: John M. De Moura, Ana L. Espinoza, Maria A. Flores, Juan A. Zavaleta
Date: 2024-07-02 00:00:00
Proceeding: - 2024 7
Tasks: Arrhythmia Detection

Arrhythmias, such as tachycardia and bradycardia, are prevalent in postoperative patients, especially within the first week after surgery. These conditions can lead to significant health risks, particularly in settings with inadequate monitoring resources, such as rural areas in Peru. This paper proposes an advanced approach to arrhythmia detection using ECG signal processing and feature extraction to feed artificial intelligence (AI) models. By optimizing training data and comparing ECG signal features, this method aims to enhance the accuracy and efficiency of arrhythmia identification, thereby improving patient monitoring and reducing associated health risks. Postoperative patients are at high risk of developing heart pathologies, which generally manifest as persistent arrhythmias, especially after the first week after surgery; conditions that might lead to significant health risks. Modern heart monitoring systems should prevent patients and their family when arrhythmias are present, thus they attend to medical assistance. This paper proposes an advanced approach to arrhythmia detection using ECG signal processing and feature extraction to feed artificial intelligence models. This method aims to enhance the accuracy and efficiency of arrhythmia detection, and thereby improving patients’ monitoring systems, by optimizing training data.