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Soft Real-time Emotion-aware System through Human Physiological Signals using Pervasive Devices

Soft Real-time Emotion-aware System through Human Physiologi...

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ieee international conference on Communication, Networks and Satellite (ComNetSat)

作者： Feri Setiawan Sunder Ali Khowaja Computer Science School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia School of Computing Faculty of Computing Digital and Data Technological University Dublin Dublin Ireland

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

The concept of connected things that involves emotional aspects has been raised as a new research issue known as “emotional IoT”. One of the challenges is to recognize the emotion in real-time without interfering the user’s task or activity. However, most of the existing works on emotion recognition deal with high sampling rate devices and thus need complex arrangement. In this study, we present a framework to recognize four emotions based on human physiological signals using pervasive devices with low sampling rate. The employment of low sampling rate devices may degrade the classification accuracy due to the data offsets or calibration errors. Our study proposes a set of energy deviation features to overcome the accuracy degradation caused by low sampling rate devices. The recursive feature elimination method is applied to avoid overfitting by selecting the most important features and reduce the computational overhead in real-world implementation. Experimental results show that the proposed approach achieves 92.01% and 91.75% accuracy in terms of valence and arousal, respectively. The realization of the proposed approach is done in an end-to-end soft real-time emotion recognition system using smartphone and smartwatch devices. The performance of our system was evaluated under laboratory setup, resulting in 46.3 milliseconds of response time on the average for recognizing emotions.

关键词： Performance evaluation Emotion recognition Wearable Health Monitoring Systems Accuracy Satellites Feature extraction Real-time systems Physiology Time factors Overfitting

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Multilingual Depression Detection Based on Speech Signals and Deep Learning

Multilingual Depression Detection Based on Speech Signals an...

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ieee international conference on Big data computing Service and Applications (BigdataService)

作者： Lidan Liu Florence Tydeman Wangqing Xie Yanzhong Wang Department of Population Health Faculty of Life Sciences & Medicine School of Life Course and Population Sciences King's College London London United Kingdom Department of Intelligent Medical Engineering School of Biomedical Engineering Anhui Medical University Hefei China

ISBN: (数字)9798350366389

ISBN: (纸本)9798350366396

Current assessments for depressive disorder are often influenced by cognitive function making them more susceptible to biases. Deep learning could provide more objective diagnoses with less access barriers for individuals who are unable to complete traditional assessments. In our study, we aim to explore the relations among speech, languages, and depression to demonstrate the feasibility of multi-lingual speech depression detection, and then build deep learning models using multi-lingual speech samples to support depression diagnosis. We first used a newly collected Chinese speech depression dataset to build a convolutional neural network (CNN) to conduct depression detection, and the accuracy of the test set reached 0.85. Besides, we tested the English depression speech dataset, DAIC-WOZ, using the same CNN model, and the accuracy of the test set was 0.73. While training the model using both Chinese and English speech samples and testing on mixture speeches, the accuracy achieved 0.74. We found that the CNN model can be applied across languages with a relatively stable performance of depression detection. This provides evidence that it is possible to develop a language-independent depression detection tool to support depression diagnostic and achieve worldwide long-term mental health monitoring.

关键词： Deep learning Training Accuracy Mental health Big data Depression Convolutional neural networks Monitoring Testing

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DaVE - A Curated database of Visualization Examples

DaVE - A Curated Database of Visualization Examples

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international conference on Visualisation, VIS

作者： Jens Koenen Marvin Petersen Christoph Garth Tim Gerrits RWTH Aachen University Germany RPTU Kaiserslautern-Landau Germany

ISBN: (数字)9798350354850

ISBN: (纸本)9798350354867

Visualization, from simple line plots to complex high-dimensional visual analysis systems, has established itself throughout numerous domains to explore, analyze, and evaluate data. Applying such visualizations in the context of simulation science where High-Performance computing (HPC) produces ever-growing amounts of data that is more complex, potentially multidimensional, and multimodal, takes up resources and a high level of technological experience often not available to domain experts. In this work, we present DaVE - a curated database of visualization examples, which aims to provide state-of-the-art and advanced visualization methods that arise in the context of HPC applications. Based on domain- or data-specific descriptors entered by the user, DaVE provides a list of appropriate visualization techniques, each accompanied by descriptions, examples, references, and resources. Sample code, adaptable container templates, and recipes for easy integration in HPC applications can be downloaded for easy access to high-fidelity visualizations. While the database is currently filled with a limited number of entries based on a broad evaluation of needs and challenges of current HPC users, DaVE is designed to be easily extended by experts from both the visualization and HPC communities.

关键词： Codes Visual analytics High performance computing data visualization Containers Visual databases

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Design and Optimization of Smart Contracts for Cross-Domain Sharing of Sensitive data

Design and Optimization of Smart Contracts for Cross-Domain ...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Manqing Zhu Lingxiao Wang Xiaohong Li College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

While studying the scenarios of sensitive data cross-domain sharing, we found there was no security assessment of smart contracts within existing data sharing schemes, which might lead to data leakage or unauthorized data access. To address this issue, we proposed a comprehensive security assessment of the smart contracts involved in existing data sharing solutions. In which, we extended the current smart contract tools to include detection capabilities for 7 additional types of vulnerabilities and implemented automatic repair functions for smart contracts. We also designed 3 types of smart contracts for the Hyperledger Fabric platform to facilitate sensitive data cross-domain sharing, specifically for data upload, data access, and data management functions to make sure that complex vulnerabilities could be automatically repaired. In our designation, the smart contracts for data upload and data access utilize the CP-ABE method and the data management smart contract includes various functions for secure management and tracing of data on the blockchain, which collectively ensure the security and comprehensive functionality of sensitive data cross-domain sharing scheme.

关键词： Distributed ledger Reviews Smart contracts Maintenance engineering Feature extraction Fabrics Blockchains Performance analysis Security Optimization

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A System Security Strategy Implementation for Secured data Communication System

A System Security Strategy Implementation for Secured Data C...

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computing, Power and Communication Technologies (IC2PCT), ieee international conference on

作者： V. S. Madhumala Pradeep Kumar Tripathi Priya Rana Nitin Kumar Sharma Nagendar Yamsani Shashikant Cyber Security Department Vignana Bharathi Institute of Technology Hyderabad India Department of Information Technology Ajay Kumar Garg Engineering College Ghaziabad India Department of Information Technology Raj Kumar Goel Institute of Technology Ghaziabad India Department of CSE NITRA Technical Campus Ghaziabad India School of Computer Science and Artificial Intelligence SR University Warangal India Electrical Engineering Department School of Engineering Babu Banarasi Das University Lucknow India

The digital age is one of omnipresent digital information and network access. As such, it is essential for any enterprise to make the security of their office networks a top priority. However, due to the rapid spread of sophisticated malware using increasingly varied technological hazardous methods and the looming possibility of data breaches, confidentiality and security of sensitive information in office settings is difficult in many cases. That’s why it has become increasingly important for organizations to implement robust, proactive, and cost-effective network security solutions. This revolutionary project was through the capability of ESP8266 Wi-Fi boards, called Innovative Workplace Network Security Approaches for Malware The process of detection, Security Mitigation, and data Protection It is IDS that meets the tailored need of office network scenarios. The Major themes in intrusion detection systems are malware detection, threat reduction, and data protection all in the manner of the IDS methodology applied in this project. Nowadays referred to as the heart of modern enterprises, the office network is a larger world where every kind of device and person connects with digital resources. To protect this complex ecosystem from cyber threats, we need to be on our guard every minute and respond quickly to any irregularity found in real-time. Our research offers a new answer because using ESP8266-based IDS can enhance the network security posture of office networks; only network security measures alone may not be sufficient in light of the steadily shifting threat landscape. In this system, both the focus and aim is the ESP8266-based Intrusion Detection System This system also provides a fine demonstration of its efficacy in shoring up office network security. To do this, we are combining the development of cutting-edge hardware with clever software design and careful threat data management. Consequently, our project represents a significant step forward in the s

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Trustworthy Analysis of Drain3-based Cold Storage Behavior in Judicial Depository Scenarios

Trustworthy Analysis of Drain3-based Cold Storage Behavior i...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Xiangyu Meng Xuejun Yu College of Computer Science Beijing University of Technology Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In the judicial deposit scenario, the storage device needs to ensure the long-term trustworthy storage of electronic evidence. In order to reduce the energy consumption of the storage device in the storage process, the judicial authority introduces the cold storage device as the storage device for electronic evidence. Cold storage equipment as a core component in the judicial depository scene, the trustworthiness of its cold storage behavior affects the trustworthiness of the entire scene of electronic evidence, judicial institutions and so on. Since the success of storage depends on many complex factors, and there are many cold storage devices in the archiving system, the managers can not quickly locate the abnormal problem information, and in the long run, there are many inconveniences in the abnormal management of the devices. Therefore, this research will study the trustworthiness of cold storage behavior in judicial depository scenarios. This paper mainly focuses on the description of the trustworthiness of cold storage behavior and the collection of data related to behavioral trustworthiness, and the trustworthiness assessment of cold storage behavior will be carried out at a later stage. In this paper, a new trusted attribute classification method is proposed to analyze the trusted attributes for the observer, operation ontology and operation object of deeds. In addition, the cold storage logs are obtained based on the operations of constructing trusted behavior declarations and simulating the operating environment, and the logs are analyzed according to the Drain3 algorithm to extract the real behavioral data of cold storage, which provides theoretical and data support for the next step of cold storage behavioral trustworthiness metrics.

关键词： Measurement Energy consumption data privacy Focusing Ontologies Observers Software Classification algorithms Security Business

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InfoTech Assistant: A Multimodal Conversational Agent for InfoTechnology Web Portal Queries

InfoTech Assistant: A Multimodal Conversational Agent for In...

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ieee international conference on Big data

作者： Sai Surya Gadiraju Duoduo Liao Akhila Kudupudi Santosh Kasula Charitha Chalasani School of Computing George Mason University Fairfax VA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This pilot study presents the development of the InfoTech Assistant, a domain-specific, multimodal chatbot engineered to address queries in bridge evaluation and infrastructure technology. By integrating web data scraping, large language models (LLMs), and Retrieval-Augmented Generation (RAG), the InfoTech Assistant provides accurate and contextually relevant responses. data, including textual descriptions and images of 41 bridge technologies, are sourced from publicly available documents on the InfoTechnology website and organized in JSON format to facilitate efficient querying. The architecture of the system includes an HTML-based interface and a Flask back end connected to the Llama 3.1 model via LLM Studio. Evaluation results show approximately 95 percent accuracy on domain-specific tasks, with high similarity scores confirming the quality of response matching. This RAG-enhanced setup enables the InfoTech Assistant to handle complex, multimodal queries, offering both textual and visual information in its responses. The InfoTech Assistant demonstrates strong potential as a dependable tool for infrastructure professionals, delivering high accuracy and relevance in its domain-specific outputs.

关键词： Bridges Measurement Visualization Accuracy Large language models Retrieval augmented generation data models Reliability Time factors Context modeling

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Efficient DDoS Detection and Mitigation in Cloud data Centers Using eBPF and XDP

Efficient DDoS Detection and Mitigation in Cloud Data Center...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Ziyue Chen He Kong Shuai Ding Quanfeng Lv Guo Wei Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread adoption of cloud-native architectures, data centers are increasingly facing severe security challenges, particularly in defending against distributed denial-of-service (DDoS) attacks. The complex interdependencies between microservices allow the impact of a DDoS attack to spread rapidly, significantly degrading overall server performance. Traditional DDoS defense strategies often struggle to effectively counter attacks within data centers, particularly those targeting east-west traffic. To address this issue, this paper proposes a fast and efficient DDoS detection and mitigation mechanism specifically tailored for data center environments using extended Berkeley Packet Filter (eBPF) and eXpress data Path (XDP) technologies. By leveraging eBPF, we achieve kernel-level, nonintrusive data collection while maintaining flow state tracking, thereby reducing communication overhead. Additionally, we trained an XGBoost classification model to create DDoS traffic filtering rules specifically designed for data center scenarios. Combined with XDP technology, we implemented low-latency malicious traffic detection at the entry point of the network protocol stack, allowing for the immediate filtering of attack traffic and thereby mitigating the impact of DDoS attacks. Experimental results demonstrate that, compared to traditional tools, our approach not only achieves high accuracy but also significantly enhances system performance.

关键词： data centers Target tracking Filtering Prevention and mitigation Telecommunication traffic Machine learning Denial-of-service attack Blocklists Security Computer crime

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data-Driven Model for Improving MEG Epileptic Spike Detection

Data-Driven Model for Improving MEG Epileptic Spike Detectio...

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computing and Machine Intelligence (ICMI), international conference on

作者： Antora Dev Mostafa M. Fouda Zubair Md Fadlullah Electrical and Computer Engineering Idaho State University Pocatello ID USA Computer Science Western University London ON Canada

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Epilepsy is a neurological disorder characterized by spontaneous recurrent seizures, affecting over 50 million people worldwide. The prompt and accurate detection of epileptic events is crucial for effective treatment and management. Traditional methods such as Electroencephalogram (EEG) have been complemented by Magnetoencephalography (MEG), which offers superior spatial resolution due to its insensitivity to the distortive effects of the skull and scalp. This study advances the analysis of MEG data using complex Continuous Morlet Wavelet Transform (CCMWT) for feature extraction, coupled with innovative machine learning architectures for classifying epileptic versus non-epileptic signal segments. We developed and compared a 3D Convolutional Neural Network (3D-CNN) and a VGG16 model with the Transfer learning strategy, in terms of accuracy, computational efficiency, and error rates. Our results demonstrate the VGG16 model with the Transfer learning strategy's superior performance in all cases except the computation times.

关键词： Accuracy Continuous wavelet transforms Computational modeling Biological system modeling Transfer learning Computer architecture Brain modeling

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Leveraging BERT and Network-Based Attention Analysis for Identifying Treatment Milestones in EHRs

Leveraging BERT and Network-Based Attention Analysis for Ide...

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ieee international conference on Big data

作者： Minsu Kim Sol Kim Byung Park Comp. Sci. & Math. Division Oak Ridge National Laboratory Oak Ridge TN USA College of Engineering and Computing Rice University Houston TX USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study introduces a sophisticated data-driven framework for analyzing Electronic Health Records (EHRs) using transformer-based models to identify and disentangle overlapping treatment contexts. The framework leverages a preprocessing pipeline that transforms structured procedural codes into semantically enriched descriptive text, enabling the use of attention mechanisms to cluster medical events into treatment milestones—cohesive and distinct components of care processes. The methodology is rigorously validated using synthetic datasets derived from the MIMIC-III database, designed to simulate the heterogeneity and overlapping procedural contexts characteristic of real-world EHR scenarios. Quantitative evaluation highlights the framework’s robustness in disentangling concurrent care pathways, with attention metrics and unsupervised clustering approaches demonstrating the ability to preserve intra-context relationships while distinguishing inter-context dependencies. By addressing challenges inherent in data heterogeneity, this approach provides a foundation for uncovering complex treatment patterns, advancing clinical decision-making, and optimizing resource allocation in diverse healthcare environments.

关键词： Analytical models Adaptation models Attention mechanisms Decision making Medical services Transforms Transformers data models Electronic medical records Testing

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