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18th International Conference on Computer Science and Education, ICCSE 2023

作者： Qiu, Yu Wang, Gang Zhang, Qianxi School of Computing and Data Engineering Ningbo Tech University Ningbo China School of Design University of Nottingham Nottingham United Kingdom Department of Architecture and Engineering University of Nottingham Ningbo China

ISBN: (纸本)9789819707904

This article evaluates the possibility of deploying virtual avatars on personal devices in online English instruction, as well as the impact on student-teacher interaction and tacit truancy issues. Based on the correlation analysis and linear regression analysis of the data acquired from the distributed questionnaires, it is possible to conclude that there may be no statistical association between avatars and student-teacher interaction. However, avatars have a positive effect on reducing truancy. Therefore, we consider that the utilization of virtual avatars in computer-assisted language learning provides an innovative and promising method for online instruction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Association reactions

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Enhancing EEG Analysis for Rapid Brain Activities Detection in Patients 1

Enhancing EEG Analysis for Rapid Brain Activities Detection ...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Yadav, Abhivyakti Ahmed, R Kaif Annamalai, R. Mercy, S.Sudha CSE Amrita School of Computing Chennai India R.M.K. Engineering College Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331518943

Electroencephalogram (EEG) analysis plays a critical role in diagnosing various neurological conditions by detecting abnormal brain activities. However, the complex nature of EEG signals poses challenges for traditional analysis methods, which often struggle to handle diverse and high-dimensional data effectively. This paper develops a new technique of EEG signal classification using machine learning and deep learning models. Specifically, attention has been given to the CatBoost, ResNet34D, EfficientNetB0, and EfficientNetB2 models. The proposed approach utilized a vast EEG dataset with 11,000 samples of spectrograms, and it conducted an in-depth comparison of these models. Data preprocessing included features developed with statistical measures and the formation of uniform EEG spectrograms. All these models were evaluated in terms of their KL divergence score, defined as a measure of the divergence between the predicted and target distributions. From all the models considered, CatBoost proved to be one with an excellent performance, having had a KL score value at 0.78 compared to that of deep learning models. This work demonstrates the potential of gradient boosting for EEG-activity classification tasks and can be of benefit as a source of insight for interested applications in real-time brain activity monitoring and diagnostics of neurological issues. © 2024 IEEE.

关键词： Electroencephalography

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Computer Science Responses to Abuse Toward Women and Children 1

Computer Science Responses to Abuse Toward Women and Childre...

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1st International Conference on Cognitive computing and engineering Education, ICCCEE 2023

作者： Bendale, Bhagyashree Pramod Shirke, Swati Dattatraya MIT Art Design and Technology University MIT School of Computing Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350332803

Worldwide public health concerns include violence towards women as well as children. One in three women globally, according to estimates, has been the victim of physical, emotional, or sexual abuse. Like adults, one in two children experiences some type of violence every year, including bullying and domestic abuse. Women and children are increasingly more susceptible to additional forms of abuse including cyberbullying or emotional harassment because of the extensive use of the Internet and social media. In recent years, computer science and associated technologies have been used to assist address this societal issue. Solutions to identify and stop violence against women and children have been developed using cloud computing, pattern recognition, mobile computing, ubiquitous artificial intelligence, as well as associated systems. This study presents a comprehensive evaluation of some of the initiatives that might assist in addressing the issue of violence against children and women. The present state of these contributions is discussed in this study along with trends, designs, technologies, and open issues. A review of the scholarly literature from 2010 to 2020 was used to produce the survey. The contributions were categorized into four application domains: education, safety, offline detection, and online detection. Digital simulation, the Internet of Things, and artificial intelligence were employed as additional categories to further divide these contributions. © 2023 IEEE.

关键词： Internet of things

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SCFNet: A Spatial-Channel Features Network Based on Heterocentric Sample Loss for Visible-Infrared Person Re-identification 16th

SCFNet: A Spatial-Channel Features Network Based on Heteroc...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Su, Peng Liu, Rui Dong, Jing Yi, Pengfei Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

ISBN: (纸本)9783031262838

Cross-modality person re-identification between visible and infrared images has become a research hotspot in the image retrieval field due to its potential application scenarios. Existing research usually designs loss functions around samples or sample centers, mainly focusing on reducing cross-modality discrepancy and intra-modality variations. However, the sample-based loss function is susceptible to outliers, and the center-based loss function is not compact enough between features. To address the above issues, we propose a novel loss function called Heterocentric Sample Loss. It optimizes both the sample features and the center of the sample features in the batch. In addition, we also propose a network structure combining spatial and channel features and a random channel enhancement method, which improves feature discrimination and robustness to color changes. Finally, we conduct extensive experiments on the SYSU-MM01 and RegDB datasets to demonstrate the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Infrared imaging

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Semantic Analysis Using Deep Learning for Predicting Stock Trends 2

Semantic Analysis Using Deep Learning for Predicting Stock T...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Galphade, Manisha Nikam, V.B. Yedurkar, Dhanalekshmi Singh, Prabhishek Stephan, Thompson Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India Department of Computer Engineering & IT Veermata Jijabai Technological Institute Mumbai India Department of Computer Science & Engineering School of Computing MIT Art Design & Technology University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun India

Company Investors and financial professionals mostly rely on quarterly reports to help them decide the ways to invest in stocks and assess the company's current performance. Quarterly company reports offer an abstracted perspective of the company's overall past performance, as well as its present situation and the market value of its market share. Financial text streams in quarterly report are unstructured naturally, but they represent cooperative expressions that are of important in any financial decision for stake holder. It will be both daunting and necessary to procedure intelligence of unstructured textual data. In this study, we address important queries related with the explosion of interest in a method to extract useful information from unstructured data and the way to work out if such insight provides any hints regarding the trends of financial markets. There is a lack of availability in the labeled dataset for financial sentiment analysis applications. The pre-trained language model employs very little labeled parameters that is used for a variety of domain specific corpora including financial sentiment analysis. In this paper, FinBERT, a model built on the BERT framework, to address linguistics challenges in the financial domain. The proposed work uses twelve transformer layers and twelve attention layers with several million parameters. The design of encoder and decoder comprises of several attention layers along with RNN. This arrangement aids to recognize instances processing the strongest relation between the words within a particular sentence. The experimentation results shows that the presented method surpass the state-of-the-art methods for financial datasets. The results are also compared with other existing models using the same financial dataset. It is observed that the FinBERT attains an accuracy of 84.77% on quarterly reports despite using a lesser training set. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Automation of interaction—interaction design at the crossroads of user experience (UX) and artificial intelligence (AI)

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Personal and Ubiquitous computing 2023年第6期27卷 2281-2290页

作者： Wiberg, Mikael Stolterman Bergqvist, Erik Interaction Design Division Chalmers University of Technology Gothenburg Sweden Department of Informatics Umeå University Umeå Sweden Computing and Engineering Luddy School of Informatics Indiana University Bloomington United States

Interaction design/HCI seems to be at a crossroads. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human–machine interaction through the use of AI and other new technologies. In this paper, we explore this seemingly unavoidable gap. First, we discuss the fundamental design rationality underpinning interaction and automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we examine four examples from already published research on automation of interaction, including how different levels of automation of interaction affect or enable new practices, including coffee making, self-tracking, automated driving, and conversations with AI-based chatbots. Through an interaction analysis of these four examples, we show (1) how interaction and automation are combined in the design, (2) how interaction is dependent on a certain level of automation, and vice versa, and (3) how each example illustrates a different balance between, and integration of interaction and automation. Based on this analysis, we propose a two-dimensional design space as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed two-dimensional design space, discuss its theoretical implications, and suggest it as a useful tool—when designing for engaging user experiences (UX), with interaction and automation as two design materials. © 2023, The Author(s).

关键词： Automation

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Quantum Guard: Pioneering Quantum-Based Malware Defense for IoT Devices 7

Quantum Guard: Pioneering Quantum-Based Malware Defense for ...

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7th IEEE Conference on Dependable and Secure computing, DSC 2024

作者： Khan, Mansoor Ali Aman, Muhammad Naveed Sikdar, Biplab College of Design and Engineering National University of Singapore Department of Electrical and Computer Engineering 117575 Singapore School of Computing College of Engineering University of Nebraska-Lincoln LincolnNE68588-0115 United States

ISBN: (纸本)9798331540289

In the burgeoning landscape of the Internet of Things (IoT), ensuring the integrity and security of embedded devices is paramount. With the increasing frequency of cyberattacks targeting these typically low-powered devices, there is an urgent need for improved defensive infrastructure. Traditional malware detection methods such as signature-based, heuristic-based, and specification-based techniques are hampered by limitations in scanning speed, microprocessor capabilities, and energy efficiency. These classical approaches encompassing static, dynamic, and hybrid analyses struggle to keep pace with the demands of modern resource-constrained IoT devices. Addressing these challenges, this article introduces 'Quantum Guard,' a trans-formative quantum-based malware detection framework that utilizes a hybrid approach. Here, the IoT device handles initial data probing by inspecting potential threats, while the quantum processor runs Grover's algorithm with a quantum oracle for intensive search tasks to accurately identify and mark specific malware classes. This innovative approach significantly enhances the speed and efficiency of malware detection across diverse types by employing quantum superposition to enable simultaneous scanning of multiple memory locations. The integration of a quantum oracle facilitates precise targeting of malware signatures, while the amplitude amplification algorithm improves the probability of detecting and accurately classifying signatures within unstructured datasets. Performance analysis shows that 'Quantum Guard' significantly outperforms traditional methods, offering a scalable and efficient solution with a space complexity of O(log N) and a time complexity of O(VN). This prototype not only allows for effective detection and protection against various malware types, including elusive polymorphic and metamorphic variants, but also integrates quantum cryptographic techniques to introduce a unique layer of security inherently resistant to eme

关键词： Quantum computers

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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Computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Bridging the Modality Gap: Generative Adversarial Networks for T1-T2 MRI Image Translation 15

Bridging the Modality Gap: Generative Adversarial Networks f...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Mayank Kunda, Naga Sai Shreya Pranave, K.C. Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

CycleGAN, a significant deep learning model, has transformed medical imaging by providing a remedy for translating MRI images between different modalities. Its ability to transform T1-weighted to T2-weighted images is crucial in healthcare environments where obtaining multiple scans can be time-consuming and burdensome for patients. This can be achieved by utilizing CycleGAN, which generates images that contains vital information that will help the healthcare professionals to diagnose the patient without exposing them to further radiations. This unleashes an effective and safer way of diagnosing patients. The adaptability of CycleGAN also allows scalability and the ability to customize it for different types of medical images, where scanning can be achieved with less exposure to radiation, without loosing or compromising any important information. © 2024 IEEE.

关键词： Discriminator GAN Generator Magnetic Resonance Image T1 T2

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Stationary wavelet transform and SpinalNet trained light spectrum Tasmanian devil optimization enabled DR detection using fundus images

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Multimedia Tools and Applications 2025年第4期84卷 1597-1626页

作者： Tamboli, Mubin Shoukat Dhumane, Amol Prasad, Rajesh Prasad, Jayashree Rajesh Ranjan, Nihar M. Computer Engineering Pimpri Chinchwad College of Engineering Pradhikaran Maharashtra Pune411044 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Diabetes is a common disease that causes complications in the eyes known as Diabetic Retinopathy (DR). It aids in discovering the DR and tends to be the main cause behind people’s blindness amidst the previous decades. For prevent the complications of DR and to control the level of blood Glucose, the timely and crucial diagnosis is essential. A model is provided for detecting the DR. Here, Light Spectrum Tasmanian Devil Optimization (LSTDO) is utilized with SpinalNet for DR detection. The method undergoes an adaptive median filter and Region of interest mining process to generate a pre-processed image. The Sparse Fuzzy C-Means (FCM) is adapted for segmenting the optic disks followed by feature extraction. Then, the Hough line transformation with Tyler Coye algorithm (HLTCA) is used for blood vessel segmentation. Concurrently, the Stationary Wavelet Transform (SWT) is adapted to input image to overwhelm the deficiency of translation-invariance issue and then the feature mining is done. The feature vector is generated by unifying the outputs of the three-feature extraction phases and is employed to detect DR. The accuracy, sensitivity, and specificity obtained by the LSTDO-based SpinalNet are 91.6%, 89.9% and 92.1%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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