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Computing (ICOCO), IEEE International Conference on

作者： Nik Azlina Nik Ahmad Anitawati Mohd Lokman Munaisyah Abdullah Ahmad Iqbal Hakim Suhaimi Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam Malaysia

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs.

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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IEEE International Conference on Big Data Computing Service and Applications (BigDataService)

作者： Donald Elmazi Dimitrios A. Karras Abdulsalam Alkholidi Klea Çapari Dept. of Electronics and Telecommunications Faculty of Engineering Canadian Institute of Technology (CIT) Tirana Albania Dept. S/W Engineering Engineering Faculty Canadian Institute of Technology (CIT) Tirana Albania Dept. General School of Science National and Kapodistrian University of Athens (NKUA) Athens Greece Canadian Institute of Technology (CIT) Tirana Albania Faculty of Engineering - Sana’a University Yemen Dept. of Computer Engineering Faculty of Engineering University of Rijeka Croatia Dept. of Software Engineering Faculty of Engineering CIT Tirana Albania

Presently, the global proliferation of Internet of Things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. The special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. The statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

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Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics

arXiv

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arXiv 2024年

作者： Qu, Mingcheng Wu, Yuncong Di, Donglin Su, Anyang Su, Tonghua Song, Yang Fan, Lei Faculty of Computing Harbin Institute of Technology Harbin China School of Astronautics Harbin Institute of Technology Harbin China Space AI Li Auto Beijing China College of Software Jilin University Changchun China Computer Science and Engineering UNSW Sydney Australia

Spatial transcriptomics (ST) has emerged as an advanced technology that provides spatial context to gene expression. Recently, deep learning-based methods have shown the capability to predict gene expression from WSI data using ST data. Existing approaches typically extract features from images and the neighboring regions using pretrained models, and then develop methods to fuse this information to generate the final output. However, these methods often fail to account for the cellular structure similarity, cellular density and the interactions within the microenvironment. In this paper, we propose a framework named BG-TRIPLEX, which leverages boundary information extracted from pathological images as guiding features to enhance gene expression prediction from WSIs. Specifically, our model consists of three branches: the spot, in-context and global branches. In the spot and in-context branches, boundary information, including edge and nuclei characteristics, is extracted using pretrained models. These boundary features guide the learning of cellular morphology and the characteristics of microenvironment through Multi-head Cross-Attention. Finally, these features are integrated with global features to predict the final output. Extensive experiments were conducted on three public ST datasets and the Visium dataset. The results demonstrate that our BG-TRIPLEX consistently outperforms existing methods in terms of Pearson Correlation Coefficient (PCC). This method highlights the crucial role of boundary features in understanding the complex interactions between WSI and gene expression, offering a promising direction for future research. Codes are available at: https://***/WcloudC0416/BG-TRIPLEX. Copyright © 2024, The Authors. All rights reserved.

关键词： Gene expression

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Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics

Boundary-Guided Learning for Gene Expression Prediction in S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingcheng Qu Yuncong Wu Donglin Di Anyang Su Tonghua Su Yang Song Lei Fan Faculty of Computing Harbin Institute of Technology Harbin China School of Astronautics Harbin Institute of Technology Harbin China Space AI Li Auto Beijing China College of Software Jilin University Changchun China Computer Science and Engineering UNSW Sydney Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Spatial transcriptomics (ST) has emerged as an advanced technology that provides spatial context to gene expression. Recently, deep learning-based methods have shown the capability to predict gene expression from WSI data using ST data. Existing approaches typically extract features from images and the neighboring regions using pretrained models, and then develop methods to fuse this information to generate the final output. However, these methods often fail to account for the cellular structure similarity, cellular density and the interactions within the *** this paper, we propose a framework named BG-TRIPLEX, which leverages boundary information extracted from pathological images as guiding features to enhance gene expression prediction from WSIs. Specifically, our model consists of three branches: the spot, in-context and global branches. In the spot and in-context branches, boundary information, including edge and nuclei characteristics, is extracted using pretrained models. These boundary features guide the learning of cellular morphology and the characteristics of microenvironment through Multi-Head Cross-Attention. Finally, these features are integrated with global features to predict the final *** experiments were conducted on three public ST datasets. The results demonstrate that our BG-TRIPLEX consistently outperforms existing methods in terms of Pearson Correlation Coefficient (PCC). This method highlights the crucial role of boundary features in understanding the complex interactions between WSI and gene expression, offering a promising direction for future research. Codes are available at: https://***/WcloudC0416/BG-TRIPLEX

关键词： Learning systems Pathology Technological innovation Image edge detection Transcriptomics Morphology Predictive models Feature extraction Gene expression Data mining

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Design of a Lower Limb Exoskeleton Rehabilitation Robot System Based on Forward Dynamics Simulation

Design of a Lower Limb Exoskeleton Rehabilitation Robot Syst...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yudong Fang Yuefei Wang Liucun Zhu Zhen Liu Lei Shi Muxin Du Jianchu Lin School of Mechanical and Marine Engineering Beibu Gulf University Qinzhou China Sentinel Institute of Science and Technology Beibu Gulf University Qinzhou China Nagasaki Institute of Applied Science Nagasaki Japan Guangzhou Shiyuan Electronic Technology Company Limited Guangzhou China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Lower limb exoskeleton rehabilitation robots can effectively improve the rehabilitation of stroke patients and can substantially reduce the workload of rehabilitation therapists and improve work efficiency. In this paper, we propose a method to investigate the influence of muscle groups on the hip joint during the gait cycle through kinetic modeling simulation. The method is validated using electromyographic signals (EMG). The patients are treated according to their gait characteristics. Simulation with OpenSim; optimisation by Scone controller; joint motion change curves were analysed by Matlab simulation to verify the correctness of the mathematical model, and the simulation showed excellent following effect. The feasibility of the theory is verified, and a new scheme is proposed for the treatment of stroke patients.

关键词： Limbs Exoskeletons Stroke (medical condition) Muscles Assistive robots Mathematical models Kinetic theory MATLAB Robots Optimization

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Heterogeneous Deniable Authenticated Encryption Protocol 3rd

Heterogeneous Deniable Authenticated Encryption Protocol

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3rd International Conference on Frontiers in Cyber Security, FCS 2020

作者： Jin, Chunhua Kan, Ge Chen, Guanhua Yu, Changhui Xu, Chengjie Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an233003 China

ISBN: (纸本)9789811597381

Deniable authenticated encryption can achieve confidentiality and deniable authentication in a logical single step. Such a cryptographic primitive simplifies the design of cryptographic scheme and reduces the cost of computation and communication. In this paper, we propose a heterogeneous deniable authenticated encryption scheme called HDAE. The proposed scheme permits a sender in a public key infrastructure environment to transmit a message to a receiver in an identity-based environment. Our design utilizes tag-key encapsulation mechanism (tag-KEM) and data encapsulation mechanism (DEM) hybrid encryption methods, which is especially applicable in some privacy protection occasions. In addition, we give how to design an HDAE scheme utilizing a heterogeneous deniable authenticated tag-KEM (HDATK) and a DEM. We also construct an HDATK scheme and provide security proof in the random oracle model. Comprehensive analysis shows that our scheme is efficient and secure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Public key cryptography

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Low level Syntax Elements Study in Intra HEVC/H.265 Video Codec 7

Low level Syntax Elements Study in Intra HEVC/H.265 Video Co...

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7th International Conference on Image and Signal Processing and their Applications, ISPA 2022

作者： Menasri, Wahiba Meddah, Karim University of Sciecnes and Technology Houari Boumediene Usthb Laboratory of Image Processing and Radiation Algiers Algeria University Yahya Fares of Medea Nouveau Pôle Urbain Faculty of Technology Medea Algeria Polytechnique Montréal Motce Laboratory Department of Computer and Software Engineering MontrealQC Canada Usthb Laboratory of Instrumentation Department of Electrical Engineering Algiers Algeria

ISBN: (纸本)9781665480420

Ultra High Definition Television (UHDTV) imposes extremely high throughput requirement on video encoders based on High Efficiency Video Coding (H.265/HEVC). HEVC adopt many advanced developed techniques in order to compress and decompress the video sequence for ensuring the real time requirements with keeping the quality. The decoded video is obtained according to the syntax elements language specified by the HEVC standard. Consequently, HEVC adopt 74 syntax elements at the low level which are divided into four partitions: including quad tree partitioning, intra and inter prediction, transform level, quantization and loop filtering. This work propose a profound study of HEVC low level syntax elements. The four partitions of syntax elements are defined, detailed and specified (role). After that, the encoding process and order of each syntax element is given by diagram blocks followed by an examples of encoding process syntax elements for 4x4 and 8x8 transform blocks (references blocks) by using Matlab ***, performance comparison of different benchmarks is given in order to highlights the decoding efficiency of HEVC compared it predecessor AVC (Advenced Video Coding). This work give the only detailed study that demonstrate the main functionality of the all intra syntax elements encoding process in CABAC HEVC. © 2022 IEEE.

关键词： MATLAB

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Digital forensics for the socio-cyber world (DF-SCW): A novel framework for deepfake multimedia investigation on social media platforms

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Egyptian Informatics Journal 2024年 27卷

作者： Ayub Khan, Abdullah Chen, Yen-Lin Hajjej, Fahima Ahmed Shaikh, Aftab Yang, Jing Soon Ku, Chin Yee Por, Lip Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Inf. Sci. Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

Owing to the major development of social media platforms, the usage of technological adaptation increases by means of editing software tools. Posting media in social communication environments has become one of our common daily routines. Before posting, various editing generators are used to manipulate pixel values, such as for enhancing brightness and contrast. Undoubtedly, this software helps bring posting media from ordinary to outstanding. But such a type of editing crosses the line in terms of creating fakes—anything that comes from anywhere and does not retain its originality anyway. It poses a series of issues in the process of multimedia forensics investigation and chain of custody. In order to restrict the attempts at deep faking and make the investigation hierarchy more effective, efficient, and reliable in the socio-cyber space (SCS), this paper presents a novel framework called DF-SCW. A digital forensics-enabled socio-cyber world with artificial intelligence (AI), especially deep neural networks (DNNs), for detecting and analyzing deep fake media investigations on social media platforms. It compares pixels with their neighboring values in the same media (such as images and videos) to identify information about the original one. There is a media flag designed to filter out malicious and dangerous attempts, like a powerful leader declaring war. Putting flags on such fakes helps digital investigators resist sharing the posts. In addition, the other prospect of this research is to make the digital forensics ecosystem more appropriate to take qualitative judgments in real-time while media is uploaded on social media platforms. The simulation of the proposed DF-SCW is tested on three different platforms, such as Instagram, Facebook, and Twitter. Through the experiment, the DF-SCW outperformed in terms of detection, identification, and analysis of deepfake media by an increased rate of 3.77%. © 2024

关键词： Social networking (online)

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Study on collaborative filtering recommendation algorithm based on prediction for item rating

Study on collaborative filtering recommendation algorithm ba...

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2020 International Conference on engineering Management of Communication and technology, EMCTECH 2020

作者： Xiao, Shaozhang Ling, Haonan Lu, Yi Tang, Zhaoxia Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu China

ISBN: (数字)9781728196688

ISBN: (纸本)9781665404488

As the Internet is developing rapidly and people are becoming increasingly dependent on e-commerce, whether the users can be provided with accurate recommendation information has become the key to attracting users. Under this background, corresponding personalized recommendation systems have been proposed, and collaborative filtering algorithm is one of the algorithms commonly used in the recommendation systems. Collaborative filtering recommends the information in which the user is interested through analyzing the preferences of a group with similar interests and common experience. With the increasing number of users and items in the e-commerce system, the collaborative filtering algorithm is limited by data sparsity in operation, which will influence the quality of the recommendation results. This paper studied the collaborative filtering recommendation algorithm based on the prediction for item rating, and enhanced the accuracy of the recommendation algorithm by designing an improved similarity-based algorithm for predicting ratings based on the Pearson correlation coefficient. © 2020 IEEE.

关键词： Collaborative filtering

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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