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5th International Conference on Advancements in computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Fuzzy Logic Expert System for Analyzing Student Performance 7

Fuzzy Logic Expert System for Analyzing Student Performance

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Aruna, P. Lokhesh, R.J. Shajith Rooban, G.G. Shakthivel, K. Tarun, S.R. Coimbatore Institute of Technology Department of Computing Coimbatore India Coimbatore Institute of Technology MSc Software Systems Coimbatore India

ISBN: (纸本)9798350341485

Assessing the effectiveness of academics at various tiers has grown progressively difficult when employing traditional approaches that predominantly depend on numerical ratings to gauge their teaching and research accomplishments. With the indexing of academic performance in international databases using impact indices at various scales, the demand for advanced computer models has arisen. In this study, we suggest employing expert systems that utilize fuzzy logic to tackle the evaluation of educators, even when confronted with imprecise data and uncertainties. As a novel contribution, this research develops a fuzzy logic model and implements an algorithm using Python and Anaconda, which allows for fuzzy logic rule inference and comprehensive analysis. The implementation of the system is done using Python and leveraging Anaconda's data science environment, providing a seamless and efficient development process. The results of this pilot study involve testing and validating the proposed model through a user-friendly graphical interface, providing comprehensive insights based on minimum criteria along with additional explanations. This approach opens up new possibilities for more accurate assessment of academic performance, particularly when dealing with ambiguous data and uncertain conditions. © 2023 IEEE.

关键词： Fuzzy logic

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Cas-FNE:Cascaded Face Normal Estimation

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2423-2434页

作者： Meng Wang Jiawan Zhang Jiayi Ma Xiaojie Guo the Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems Tianjin 300350 the School of Computer Science and Technology Tiangong UniversityTianjin 300387China IEEE the College of Intelligence and Computing Tianjin UniversityTianjin 300365China the Electronic Information School Wuhan UniversityWuhan 430072China

Capturing high-fidelity normals from single face images plays a core role in numerous computer vision and graphics *** significant progress has been made in recent years,how to effectively and efficiently explore normal priors remains *** existing approaches depend on the development of intricate network architectures and complex calculations for in-the-wild face *** overcome the above issue,we propose a simple yet effective cascaded neural network,called Cas-FNE,which progressively boosts the quality of predicted normals with marginal model parameters and computational ***,it can mitigate the imbalance issue between training data and real-world face images due to the progressive refinement mechanism,and thus boost the generalization ability of the ***,in the training phase,our model relies solely on a small amount of labeled *** earlier prediction serves as guidance for following *** addition,our shared-parameter cascaded block employs a recurrent mechanism,allowing it to be applied multiple times for optimization without increasing network *** and qualitative evaluations on benchmark datasets are conducted to show that our Cas-FNE can faithfully maintain facial details and reveal its superiority over *** code is available at https://***/AutoHDR/***.

关键词： Cascaded learning face normal progressive refinement shared-parameter

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OFDM-structure based waveform designs for integrated sensing and communication

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Science China(information Sciences) 2025年第5期68卷 267-284页

作者： Jiaqi LIU Ping YANG Ke JIANG Jiarong ZHAO Wei XIANG Jianping WEI Ruixiang DUAN Saviour ZAMMIT Tony Q.S.QUEK National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China School of Computing Engineering and Mathematical Sciences La Trobe University Meituan Academy of Robotics Department of Communications and Computer Engineering University of Malta Information Systems Technology and Design Pillar Singapore University of Technology and Design

The future sixth-generation(6G) paradigm aims to seamlessly integrate communication and environmental sensing capabilities into a single radio signal, promising improved efficiency and cost-effectiveness through simultaneous data communications and environmental perception. At the core of this evolution, orthogonal frequency division multiplexing(OFDM) and its advanced waveforms emerge as pivotal for integrated sensing and communications(ISAC). This study introduces a concise and unified ISAC waveform design framework based on orthogonal multicarriers. This framework supports versatile applications of OFDM and its derivative waveforms within a generalized ISAC system, marking a significant leap in integrating communication and sensing capabilities. A distinguishing feature of this framework is its adaptability,allowing users to intelligently select modulation strategies based on their specific environmental needs. This adaptability optimizes performance across diverse scenarios. Central to our innovations is the proposal of discrete Fourier transformspread OFDM with index modulation(DFT-S-OFDM-IM). This framework is paired with newly proposed signal processing methods for single-input single-output and multiple-input multiple-output(MIMO) systems. Extensive evaluations highlight DFT-S-OFDM-IM's superiority, including dramatically reduced peak-to-average power ratios(PAPRs), competitive communication performance, and exceptional sensing capabilities, striking an elegant balance between communication capacity and environmental sensing precision.

关键词： DFT-S-OFDM-IM index modulation integrated sensing and communication OFDM

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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A NEW HOPE: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation 33

A NEW HOPE: Contextual Privacy Policies for Mobile Applicati...

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33rd USENIX Security Symposium, USENIX Security 2024

作者： Pan, Shidong Tao, Zhen Hoang, Thong Zhang, Dawen Li, Tianshi Xing, Zhenchang Xu, Xiwei Staples, Mark Rakotoarivelo, Thierry Lo, David School of Computing Australian National University Australia Software and Computational Systems Research Program CSIRO's Data61 Khoury College of Computer Sciences Northeastern University United States School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9781939133441

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application's graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SEEPRIVACY could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://***/SeePrivacy/. © USENIX Security Symposium *** rights reserved.

关键词： Differential privacy

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Convolutional LSTM Network for Heart Disease Diagnosis on Electrocardiograms

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Computers, Materials & Continua 2023年第9期76卷 3745-3761页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Zeinel Momynkulov Aray Kassenkhan Saltanat Nuralykyzy Mereilim Iglikova Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmatyKazakhstan Department of Software Engineering Satbayev UniversityAlmatyKazakhstan

Heart disease is a leading cause ofmortality ***(ECG)play a crucial role in diagnosing heart ***,interpreting ECGsignals necessitates specialized knowledge and *** development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease *** research paper proposes a 3D Convolutional Long Short-Term Memory(Conv-LSTM)model for detecting heart disease using ECG *** proposed model combines the advantages of both convolutional neural networks(CNN)and long short-term memory(LSTM)*** considering both the spatial and temporal dependencies of ECG,the 3D Conv-LSTM model enables the detection of subtle changes in the signal over *** model is trained on a dataset of ECG recordings from patients with various heart conditions,including arrhythmia,myocardial infarction,and heart *** results show that the proposed 3D Conv-LSTM model outperforms traditional 2D CNN models in detecting heart disease,achieving an accuracy of 88%in the classification of five ***,themodel outperforms the other state-of-the-art deep learning models for ECG-based heart disease ***,the proposedConv-LSTMnetwork yields highly accurate outcomes in identifying abnormalities in specific ECG *** proposed 3D Conv-LSTM model holds promise as a valuable tool for automated heart disease detection and *** study underscores the significance of incorporating spatial and temporal dependencies in ECG-based heart disease *** highlights the potential of deep-learning models in enhancing the accuracy and efficiency of diagnosis.

关键词： Heart disease detection classification CNN LSTM Conv-LSTM

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Improving Data Transmission Efficiency in UAV Networks Through Adaptive Coding 7

Improving Data Transmission Efficiency in UAV Networks Throu...

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7th International Youth Conference on Radio Electronics, Electrical and Power Engineering, REEPE 2025

作者： Safadi, Majd M. Amoursky, Vladislav B. Mathematical and Software of Computing Systems Complexes and Computer Networks Faculty of Information Science and Control Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (纸本)9798331531836

In recent years, unmanned aerial vehicles (UAV) have become widely used in civil and military applications such as surveillance, data collection, and cargo delivery. With the increasing use of this technology, there is a need for improved communication systems to ensure reliable and efficient data transmission. UAV networks face unique challenges due to dynamic motion, environmental interference, and energy and bandwidth limitations. The objective of this study is to propose an adaptive coding scheme using Rate-Compatible Punctured Coding (RCPC) technology, which assigns different coding rates to different data depending on their importance and channel conditions. The proposed scheme includes a mechanism to dynamically adjust the coding rates based on the change in link quality. This study demonstrates that adaptive coding can improve resource efficiency, reduce latency, and enhance communication reliability. © 2025 IEEE.

关键词： adaptive coding dynamic movement RCPC signal-to-noise ratios (SNR) UAV

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Mining and Validating Belief-Based Agent Explanations 5th

Mining and Validating Belief-Based Agent Explanations

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Proceedings of the 5th International Workshop on EXTRAAMAS 2023

作者： Alelaimat, Ahmad Ghose, Aditya Dam, Hoa Khanh Decision Systems Lab School of Computing and Information Technology University of Wollongong Wollongong2522 Australia

ISBN: (纸本)9783031408779

Agent explanation generation is the task of justifying the decisions of an agent after observing its behaviour. Much of the previous explanation generation approaches can theoretically do so, but assuming the availability of explanation generation modules, reliable observations, and deterministic execution of plans. However, in real-life settings, explanation generation modules are not readily available, unreliable observations are frequently encountered, and plans are non-deterministic. We seek in this work to address these challenges. This work presents a data-driven approach to mining and validating explanations (and specifically belief-based explanations) of agent actions. Our approach leverages the historical data associated with agent system execution, which describes action execution events and external events (represented as beliefs). We present an empirical evaluation, which suggests that our approach to mining and validating belief-based explanations can be practical. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Dialog generation for Arabic chatbot

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International Journal of information technology (Singapore) 2024年第2期16卷 881-890页

作者： Youness, Farida Madkour, Mohamed Ashraf Elshenawy, Ayman Computers and Systems Department Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

Dialogue generation systems (DGS) is an important topic in the field of Natural Language Processing (NLP). It enables a wide range of real-world applications to interact with humans in various languages naturally and intelligently. Due to the complexity of the Arabic language, the development of an Arabic DGS is a challenge. In this paper, an Arabic DGS is developed using a sequence-to-sequence (Seq2Seq) framework comprised of Long-Short Term Memory (LSTM). A dataset of 12k conversations is translated from English to Arabic for the development process. The proposed DGS is analyzed, and its effectiveness is assessed using the Bilingual Evaluation Understudy (BLEU) score. The TensorFlow deep learning framework is used to test the proposed DGS. Also, the proposed DGS is tested and compared with a recently developed DGS. The Experiments revealed that the proposed Arabic DGS is effective in eliciting positive responses, with a BLEU score of 0.5988. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Artificial intelligence Chatbot Deep learning Dialogue generation Natural language preprocessing Seq2Seq

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