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Boli: A dataset for understanding stuttering experience and analyzing stuttered speech

Boli: A dataset for understanding stuttering experience and ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Batra, Ashita Narang, Mannas Sharma, Neeraj Kumar Das, Pradip K. Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati781039 India Mehta Family School of Data Science and AI Indian Institute of Technology Guwahati Guwahati781039 India

ISBN: (纸本)9798350368741

There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development. © 2025 IEEE.

关键词： Indian stuttered speech dataset Intelligibility assessment read speech spontaneous speech Stuttering event detection

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An Analysis of Privacy Attacks in Supervised Machine Learning Algorithms

An Analysis of Privacy Attacks in Supervised Machine Learnin...

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Computing, sciences and Communications (ICCSC), International Conference on

作者： Kavya Gupta Anurag Gupta Abha Kiran Raipoot Department ofCSE(AI&ML) KIET Group of Institutions Ghaziabad India Department of CSE(AI) KIET Group of Institutions Ghaziabad India School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With the widespread integration of machine learning (ml) into diverse applications, it has become paramount to scrutinize its implications for security and privacy. Despite the burgeoning body of work in privacy, there is a noticeable gap in research attention between the security and privacy aspects of ml, specifically supervised machine learning (S-ml). This paper bridges this gap by analyzing research papers focusing on privacy attacks against S-ml over the past years. This study offers a comprehensive overview of prevalent defense mechanisms and initiates a thoughtful discussion on unresolved challenges and prospective research directions. As ml continues to permeate various domains, the scientific community's interest has expanded beyond performance metrics to encompass broader concerns such as security, privacy, fairness, and explainability. This survey investigates state-of-the-art privacy-related attacks, elucidating common design patterns and distinctive characteristics. Through this exploration, we identify several open problems that demand further investigation, emphasizing the critical need to fortify privacy frameworks alongside the escalating ubiquity of ml technologies.

关键词： Surveys Measurement Privacy Data privacy Machine learning algorithms Quantum computing Organizations Machine learning Robustness Security

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Transfer Learning-based Emotion Detection System in Cultivating Workplace Harmony 20

Transfer Learning-based Emotion Detection System in Cultivat...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, aiSP 2024

作者： Shah, Premal P. Jadav, Nilesh Kumar Makwana, Vatsalkumar Trivedi, Harshal Bhatia, Jitendra Tanwar, Sudeep Shahinzadeh, Hossein Gujarat Technological University Adani Institute of Infrastructure Engineering Ahmedabad India Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India L. J. Institute of Engineering and Technology Department of Computer Engineering Ahmedabad India CEO Tushker AI Ahmedabad India Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

The technological advancements made the organizations achieve more business profits and attain new horizons that significantly impact on employee stress and mental-well well-being. Moreover, due to the advent of Artificial intelligence (ai), which offers automotive tasks and efficiency gains, the employee get fear of losing their jobs. Due to this, the employee suffers from excessive stress and mental health issues. Therefore, emotion detection is imperative in such a stressful workplace environment. Inspired by this, we proposed a transfer learning-based emotion detection system to improve the workplace environment. For that, a facial emotion dataset is utilized, which comprises grayscale images of employee faces having emotions such as fear, neutral, anger, sadness, happiness, surprise, and disgust. Then, we used transfer learning-based pre-trained models with the intention of reducing the computational overhead of ai training. We employed ResNet, Inception, MobileNet, and EfficientNet which offer an effective accuracy while detecting the emotions of the employee. This strategic use of pre-existing models not only optimizes efficiency but also enhances the overall effectiveness of our facial emotion analysis system, ensuring a robust and accurate representation of diverse emotional states in employees. From the result analysis, we found that the ResNet outperforms other existing pre-trained models in terms of training accuracy (95.23%), training loss (0.41), and training time (2803 sec). © 2024 IEEE.

关键词： Learning systems

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Graph-Based Machine Learning Approaches for Fraud Detection in Financial Networks 7

Graph-Based Machine Learning Approaches for Fraud Detection ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kotiyal, Arnav Hussein, Layth Deepak, A. Rana, Aryan Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI & Ml Telangana Hyderabad India

ISBN: (纸本)9798350350067

Fraud detection in financial networks presents a significant challenge due to the complexity and volume of transactions. Traditional detection methods often struggle with scalability and accuracy when faced with evolving fraudulent schemes. In this paper, we explore the application of graph-based machine learning techniques to enhance fraud detection in financial networks. By representing financial transactions as graph structures, we capture the relationships between entities, allowing for the identification of suspicious patterns that may indicate fraudulent behavior. We examine several graph-based algorithms, including Graph Neural Networks (GNNs) and Random Walk-based approaches, and compare their performance with conventional machine learning methods. The use of graph representations facilitates the extraction of features such as transaction flows and network centrality, which are critical in identifying anomalies. Furthermore, we propose an enhanced detection model that integrates community detection techniques with supervised learning for improved accuracy. Our experimental results, conducted on a real-world financial dataset, demonstrate that graph-based models outperform traditional models in detecting fraud with higher precision and recall. The study highlights the potential of these techniques to provide scalable and robust solutions for real-time fraud detection, contributing to a more secure financial ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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PEAC: unsupervised pre-training for cross-embodiment reinforcement learning 24

PEAC: unsupervised pre-training for cross-embodiment reinfor...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chengyang Ying Zhongkai Hao Xinning Zhou Xuezhou Xu Hang Su Xingxing Zhang Jun Zhu Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University and Pazhou Lab (Huangpu) Guangzhou China

ISBN: (纸本)9798331314385

Designing generalizable agents capable of adapting to diverse embodiments has achieved significant attention in Reinforcement Learning (RL), which is critical for deploying RL agents in various real-world applications. Previous Cross-Embodiment RL approaches have focused on transferring knowledge across embodiments within specific tasks. These methods often result in knowledge tightly coupled with those tasks and fail to adequately capture the distinct characteristics of different embodiments. To address this limitation, we introduce the notion of Cross-Embodiment Unsupervised RL (CEURL), which leverages unsupervised learning to enable agents to acquire embodiment-aware and task-agnostic knowledge through online interactions within reward-free environments. We formulate CEURL as a novel Controlled Embodiment Markov Decision Process (CE-MDP) and systematically analyze CEURL's pre-training objectives under CE-MDP. Based on these analyses, we develop a novel algorithm Pre-trained Embodiment-Aware Control (PEAC) for handling CEURL, incorporating an intrinsic reward function specifically designed for cross-embodiment pre-training. PEAC not only provides an intuitive optimization strategy for cross-embodiment pre-training but also can integrate flexibly with existing unsupervised RL methods, facilitating cross-embodiment exploration and skill discovery. Extensive experiments in both simulated (e.g., DMC and Robosuite) and real-world environments (e.g., legged locomotion) demonstrate that PEAC significantly improves adaptation performance and cross-embodiment generalization, demonstrating its effectiveness in overcoming the unique challenges of CEURL. The project page and code are in https://***/ceurl.

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Digital Evidence and Forensic Tools in Cyber Law: A Comprehensive Analysis 25

Digital Evidence and Forensic Tools in Cyber Law: A Comprehe...

引用

25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Sherideh, Ala'A Saeb Al-Zou'bi, Samah Suleiman Alshraiedeh, Fuad Elsoud, Esraa Abu Afaneh, Suha Al-Mousa, Mohammad Rasmi Alazaidah, Raed Khouj, Mohammed Samara, Ghassan Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Software Engineering Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Data Science and Ai Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The exponential rise in hacking, phishing, information theft, and electronic crime has become an actual concern with the increased use of digital devices and technology. By 2023 the number of IoT devices expectation will be 32.1 billion devices, leading to a significant increase in cybercrimes. These illegal behaviors are specifically connected to computers and communication technology. Consequently, there has been an urgent need for the development of cyber law which is a legal system closely related to digital evidence. This paper aim to provides delve into the significance and multifaceted applications of digital evidence within the context of cyber law, shedding light on the complexities and nuances inherent in its acceptance and reliability. The paper explores the importance and numerous uses of digital evidence in the context of cyber law, highlighting the challenges associated with its legitimacy and acceptance. Additionally, it focuses on the challenges that prevent digital evidence from being seamlessly incorporated into court processes and offers solutions and strategic advice to overcome these challenges. © 2024 IEEE.

关键词： Phishing

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BERT-based Models for Predicting Protein-Protein Interaction Sites

BERT-based Models for Predicting Protein-Protein Interaction...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Malviya, Bhupendra Alisher, Yigitaliyev Sharma, Mahima Rajalakshmi, G. Hussein, Layth Yaswitha, G. Ies College of Technology Department of Computer Science & Engineering M.P Bhopal India Fergana Medical Institute Of Public Health Department Of Urology And Oncology Doctoral Fergana Uzbekistan Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Saveetha Institute of Medical and Technical Sciences Department of Ai & Ml Tamilnadu Chennai India

ISBN: (纸本)9798350390759

In this work, a BERT-based model is proposed for predicting potential PPI sites with the aid of its remarkable contextual understanding feat. Thus, the model was adjusted to a set of positive PPI sites, containing 268 sites in total with the accuracy of 0. 89, precision of 0. 88, recall of 0. IL is 0. 78, accuracy of 0. 85, precision of 0. 83, and F1-score of 0. tested on face images achieving a performance of 87% which is higher than that of conventional models such as CNNs and SVMs. Only hyperparameter tuning and cross-validation have been used to make sure that the model is not overfitting to the data and it can produce reasonable results on unseen data as well. The results of this study also indicate that BERT-based models can be effectively utilized in the bioinformatics field, opening up myriad possibilities for future utilization of computational approaches in the prediction of PPIs. © 2024 IEEE.

关键词： Prediction models

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Plant Leaf Disease Detection and Classification Using MobileNetV3 and Wavelet Kernel Extreme Learning Machine 3

Plant Leaf Disease Detection and Classification Using Mobile...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Shruthi, B.S. Narasimha Murthy, M.S. Prakash, Chidurala Sai Riad Al-Fatlawy, Ramy Sundaram, S. Meenakshi Malnad College Of Engineering Department Of Information Science Hassan India Bms Institute Of Technology And Management Department Of Computer Science And Engineering Bangalore India Sr University Department Of Electrical And Electronics Engineering Warangal India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India

ISBN: (纸本)9798350318609

Detection of plant disease is the most important aspect in agriculture, aimed at identifying and diagnosing disease that affect plants. If the plant disease detected earlier, that helps to avoid risk from damaging plants and the soil. However, there is a difficulty to differentiate the diseased and healthy leaves due to similarity of disease symptoms. To solve this problem, an effective Wavelet Kernel Extreme Learning Machine (WKELM) classifier is proposed for distinguishing diseased and healthy plants. Images from the PlantVillage dataset are preprocessed using Bilateral filter and then segmented by Adaptive Gaussian Threshold (AGT) to separate the diseased portion from leaf. MobileNetV3 a Convolutional Neural Network (CNN) used for effective feature extraction with the hyper parameters which are tuned using effective Emperor Penguin Optimizer (EPO) and classification is done by the WKELM for accurate results of plant disease. The experimental results of the proposed model performed by the evaluation metrics achieved accuracy of 99.7%, recall of 99.6%, precision of 99.6%, F1-score of 99.7% which is greater than other existing deep learning models like CNN, Transfer learning with Deep CNN models, CNN - Visual Geometry Group (VGG). © 2024 IEEE.

关键词： Convolutional neural networks

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Demystifying Cyber terrorism: Causes, Costs & its impact on individuals' psyche and public confidence

Demystifying Cyber terrorism: Causes, Costs & its impact on ...

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2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

作者： Zahra, Syed Rameem Saleem, Tausifa Jan Sonia Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India Indian Institute of Technology Delhi Department of Electrical Engineering Delhi India Shoolini University Yogananda School of Ai Computer and Data Science H.P Solan India

ISBN: (纸本)9781665493826

Just like the game for Information Technology (IT) security was turned topsy-turvy by the arrival of mobile devices a few decades ago, the Internet of Things (IoT) today is changing it again a big time. Data breaches, loss of productivity and profitability were quite a commonplace when mobile security became an issue. Today as IoT tries to connect everything to the internet, it intensifies the problem (more privacy violations, phishing/skimming attacks, security incidents) by creating a vast attack surface for the rogue players implying that important national (ports, airports, bridges, power-pants etc.) and cyber infrastructure could now be attacked. Moreover, if the network of your company has a weak point, it can easily be compromised to set off an attack. These Cyber terrorism attacks instigate identical responses from individuals as those for conventional terrorism attacks e.g. they heighten anxiety and stress, worsen feelings of vulnerability and harden the political attitudes of people. But this dimension is often overlooked by the policy makers. However, the increase in cyber terrorism cases has increased the research interest in the domain. Researchers are focusing on improving the security of internet and IoT devices. The field is still in its infancy. A lot needs to be done to mitigate these attacks. Motivated to demystify the repercussions of the cyber terrorism, this paper analyzes the distinctive characteristics of the most significant cyberattacks, investigates the expenditures incurred by the industries as a result of these cyber events, and then examines the impact of these attacks on the psychological health of the populace and the trust in public institutions. © 2023 IEEE.

关键词： Internet of things

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air Quality Control using Machine Learning

Air Quality Control using Machine Learning

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International Conference on Inventive Computation Technologies (ICICT)

作者： K. RamaKrishna Reddy D. Vidya Shree N. Vasu N. Muthukumaran K. Saketh Patel N. Vamshi Karthik Rao Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In recent times, the world has witnessed remarkable advancements driven by various technologies such as advanced computing, self-driving vehicles, and robot-assisted surgeries, among others. However, these technological strides have come with a significant environmental cost, contributing to the pollution of vital natural resources such as air and water. The contamination of air and water worsens health issues, particularly for individuals with respiratory ailments such as asthma and lung infections, posing risks to the overall population’s well-being. This research study has carried out a qualitative research and presents the challenges that demand careful navigation and interpretation in the near future.

关键词： Support vector machines Atmospheric modeling Data visualization Surgery Robot sensing systems Water pollution Decision trees

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