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TRANSIT-EEG - A Framework for Cross-Subject Classification with Subject Specific Adaptation

IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Ahuja, Chirag Sethia, Divyashikha Delhi Technology University Department of Computer Science and Engineering Delhi India Delhi Technology University Department of Software Engineering Delhi India

Electroencephalography (EEG) is pivotal in monitoring and analysing cerebral activity across diverse domains, including medical diagnostics, cognitive neuroscience, and brain-computer interfaces. However, the inherent intricacy of EEG signals and their subject-specific characteristics pose formidable challenges in devising robust and generalisable classification models. Traditional EEG signal classification paradigms rely on extensive subject-specific datasets. Also, the domain adaption for new subjects often leads to "catastrophic forgetting,"thereby diminishing the performance of model trained on prior subjects. This paper proposes a novel framework, Transfer and Robust Adaptation of New Subjects in EEG Technology (TRANSIT-EEG), designed to adapt adeptly to new subjects. TRANSIT-EEG demonstrates resilience to subject-specific artefacts by integrating synthetic data generation using the proposed subject-specific augmentation model - Individualised Diffusion Probabilistic Model (IDPM). Also, it employs a robust Self Organising Graph Attention Transformer (SOGAT) that dynamically constructs a graph for each subject, fostering a more accurate classification. Moreover, TRANSIT-EEG introduces adapter-based finetuning using Low-Rank Adaptation (LoRA) for new subjects, enriching the adaptation process. The TRANSIT-EEG framework presents a promising avenue for advancing the realm of EEG signal classification. Evaluation of widely studied datasets, specifically focusing on two significant tasks, SEED for Emotion Recognition and PhyAat for Auditory Activity Recognition, substantiates the efficacy and versatility of TRANSIT-EEG. This validation indicates a substantial stride toward achieving more generalisable and accurate EEG signal classification. © 2016 IEEE.

关键词： Graph neural networks

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Does Repeatedly Typing the Same Phrase Provide a Good Estimate of Expert Text Entry Performance?

Does Repeatedly Typing the Same Phrase Provide a Good Estima...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Mutasim, Aunnoy K. Batmaz, Anil Ufuk Hudhud Mughrabi, Moaaz Stuerzlinger, Wolfgang Simon Fraser University VancouverBC Canada Computer Science & Software Engineering Department Concordia University MontrealQC Canada Mechatronics Engineering Department Kadir Has University Istanbul Turkey

ISBN: (纸本)9781450394222

To identify if novel/unfamiliar keyboard layouts like OPTI can outperform QWERTY, lengthy training through longitudinal studies is typically required. To reduce this logistical bottleneck, a popular approach in the literature requires participants to type the same phrase repeatedly. However, it is still unknown whether this approach provides a good estimate of expert performance. To validate this method, we set up a study where participants were tasked with typing the same phrase 96 times for both OPTI and QWERTY. Results showed that this approach has the potential to estimate expert performance for novel/unfamiliar keyboards faster than the traditional approach with different phrases. Yet, we also found that accurate estimates still require training over several days and, therefore, do not eliminate the need for a longitudinal study. Our findings thus show the need for research on faster, easier, and more reliable empirical approaches to evaluate text entry systems. © 2023 Owner/Author.

关键词： Taps

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Improving students' cognitive abilities in online environment based on neurofeedback 14

Improving students' cognitive abilities in online environmen...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Jamil, Nuraini Lakas, Abderrahmane Belkacem, Abdelkader Nasreddine College of Information Technology United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates College of Information Technology United Arab Emirates University Department of Computer and Network Engineering United Arab Emirates

ISBN: (纸本)9798350399431

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style. © 2023 IEEE.

关键词： Brain

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GPT-Based Models Meet Simulation: How to Efficiently use Large-Scale Pre-Trained Language Models Across Simulation Tasks 23

GPT-Based Models Meet Simulation: How to Efficiently use Lar...

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Proceedings of the Winter Simulation Conference

作者： Philippe J. Giabbanelli Department of Computer Science & Software Engineering Miami University Oxford USA

ISBN: (纸本)9798350369663

The disruptive technology provided by large-scale pre-trained language models (LLMs) such as ChatGPT or GPT-4 has received significant attention in several application domains, often with an emphasis on high-level opportunities and concerns. This paper is the first examination regarding the use of LLMs for scientific simulations. We focus on four modeling and simulation tasks, each time assessing the expected benefits and limitations of LLMs while providing practical guidance for modelers regarding the steps involved. The first task is devoted to explaining the structure of a conceptual model to promote the engagement of participants in the modeling process. The second task focuses on summarizing simulation outputs, so that model users can identify a preferred scenario. The third task seeks to broaden accessibility to simulation platforms by conveying the insights of simulation visualizations via text. Finally, the last task evokes the possibility of explaining simulation errors and providing guidance to resolve them.

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An Approach for Localizing User Terminals in 6G Mobile Networks

An Approach for Localizing User Terminals in 6G Mobile Netwo...

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Wireless Telecommunications Symposium, WTS

作者： Caleb Ludinga Lodi Ronald Beaubrun Department of Computer Science and Software Engineering Laval University Quebec Canada

ISBN: (数字)9798350317886

ISBN: (纸本)9798350317893

Determining the position of a User Terminal (UT) with a high degree of accuracy is crucial for enabling a plethora of emerging applications, such as immersive remote presence, holographic teleportation, connected robotics, augmented reality (AR). Such accuracy will require the incorporation of Terahertz (THz) frequency bands into 6G mobile networks. In this context, we propose an approach which combines the Angle of Departure (AoD) technique with THz localization for localizing UTs in 6G mobile networks. The simulation environment is set up by using NYUSIM 4.0. Results are expressed in terms of localization errors, and show that the mean localization error is very small, indicating that the estimated positions from the simulation is close to the real positions for a given trajectory.

关键词： Location awareness 6G mobile communication Wireless communication Navigation Simulation Teleportation Public security

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Data Mining Management System Optimization using Swarm Intelligence 5

Data Mining Management System Optimization using Swarm Intel...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Hasan Al Mashhadani, Asraa Ahmed Inan, Timur Ahmed, Ali Saadoon Altinbas University Department of Information Technologies Istanbul Turkey Altinbas University Department of Software Engineering Istanbul Turkey AlMaarif University College Department of Computer Science AlAnbar Iraq

ISBN: (纸本)9798350337525

Because of a phenomenon known as the 'curs e of dimensionality,' standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases;however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation. © 2023 IEEE.

关键词： Swarm intelligence

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The role of library versions in Developer-ChatGPT conversations

The role of library versions in Developer-ChatGPT conversati...

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International Mining software Repositories, MSR

作者： Rachna Raj Diego Elias Costa Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

The latest breakthroughs in large language models (LLM) have empowered software development tools, such as ChatGPT, to aid developers in complex tasks. Developers use ChatGPT to write code, review code changes, and even debug their programs. In these interactions, ChatGPT often recommends code snippets that depend on external libraries. However, code from libraries changes over time, invalidating a once-correct code snippet and making it difficult to reuse recommended *** this study, we analyze DevGPT, a dataset of more than 4,000 Developer-ChatGPT interactions, to understand the role of library versions in code-related conversations. We quantify how often library version constraints are mentioned in code-related conversations and when ChatGPT recommends the installation of specific libraries. Our findings show that, albeit to constantly recommend and analyze code with external dependencies, library version constraints only appear in 9% of the conversations. In the majority of conversations, the version constraints are prompted by users (as opposed to being specified by ChatGPT) as a method for receiving better quality responses. Moreover, we study how library version constraints are used in the conversation through qualitative methods, identifying several potential problems that warrant further research.

关键词： Vocabulary Codes Reviews Oral communication Chatbots Libraries software

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CLPiDA: A Contrastive Learning Approach for Predicting Potential PiRNA-Disease Associations

CLPiDA: A Contrastive Learning Approach for Predicting Poten...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Xiaowen Zhang, Yuanpeng Zhang, Jiaxuan Deng, Lei Central South University School of Computer Science and Engineering Changsha China Xinjiang University School of Software Urumqi China University of California Department of Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9798350337488

Piwi-interacting RNAs (piRNAs) function as critical regulators, safeguarding genome stability through mechanisms like transposable element repression and gene stability maintenance, while also being associated with various disease pathways. Developing computationally efficient methods to predict piRNA-disease associations is vital for enhancing disease-specific drug discovery while managing costs. In this study, we present CLPiDA, a novel method for predicting potential piRNA-disease associations. CLPiDA begins by computing gaussian kernel similarities for piRNA-piRNA and disease-disease pairs to establish initial embeddings for piRNAs and diseases. Subsequently, it employs a parameter-sharing online and target network, along with data augmentation techniques, to create a contrastive learning framework. This facilitates the generation of embeddings for piRNAs and diseases using piRNA-disease association pairs. Furthermore, CLPiDA employs a cross-prediction approach to determine association scores for specific piRNAs and diseases. Notably, CLPiDA introduces a novel approach by excluding negative samples, thereby avoiding the introduction of false negatives and enhancing its reliability. Comparative experiments validate CLPiDA's superiority in terms of performance over existing methods. Case studies underscore CLPiDA's efficacy as a valuable tool for predicting piRNA-disease associations, providing valuable insights for biological experiments. The data and source code for CLPiDA are available at https://***/altriavin/CLPiDA. © 2023 IEEE.

关键词： contrastive learning graph convolutional network piRNA-disease association

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Understanding Blockchain Trilemma, Causes and Solutions

Understanding Blockchain Trilemma, Causes and Solutions

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Monu Chaudhary Suman Bhunia Department of Computer Science and Software Engineering Miami University Oxford Ohio

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

With extensive research and development in blockchain technology, the concern about scalability, security, and decentralization is still evident. Blockchain trilemma describes that it is realistically impossible to simultaneously achieve a high level of decentralization, security, and scalability. However, new developments in the sector of blockchain are making progress in simultaneous achievement of these three aspects. Layer 1 blockchain is the base level of blockchain network architecture. It includes blockchain networks like Bitcoin, Ethereum, etc. However, the Layer 1 blockchain network faces scalability issues. Layer 2 blockchain is a network built on top of Layer 1 blockchain to help eliminate scalability issues. This paper presents a comparative analysis of Layer 1 and Layer 2 blockchain networks and different approaches to solving the blockchain trilemma in Layer 1 and Layer 2 blockchain networks. From the study, we found that Layer 2 blockchain networks are easier and faster to implement.

关键词： Social computing Sharding Scalability Pressing Bitcoin Network architecture Blockchains Security Research and development Faces

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