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6th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Nguyen-Mau, Trong-Hieu Phu-Thi, Kim-Trang Le-Dinh, Anh-Duy Tran, Minh-Triet Nguyen, Hai-Dang University of Science VNU-HCM Faculty of Information Technology and Software Engineering Lab Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

Recent advancements in ReID systems have primarily focused on individuals and four-wheeled vehicles to meet the rising demand for public safety and traffic management. However, the domain of motorcycle ReID has mainly been overlooked despite its significance in security applications. Extracting features from both motorcycles and their riders poses a unique challenge. In this paper, we present AANet, an innovative deep-learning model designed explicitly for motorcycle ReID. AANet incorporates multi-atrous convolution and self-attention mechanisms to extract local and global features, resulting in a robust embedding for the ReID task. We optimize the loss function and augmentations to address the specific challenges of motorcycle ReID. Our method achieves competitive accuracy and efficiency, surpassing other techniques, with a rank-l accuracy of 86.60 and a mean Average Precision of 88.32 on the Motorcycles ReIdentification dataset, the first extensive motorcycle ReID database collected from urban traffic cameras. The proposed AANet model enhances security measures through improved motorcycle surveillance and identification capabilities. © 2023 IEEE.

关键词： Motorcycles

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A Code Transformation Approach to Enhance Code Cardinality Understanding 23

A Code Transformation Approach to Enhance Code Cardinality U...

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23rd International Arab Conference on information technology, ACIT 2022

作者： Jebreen, Isam Faculty of Information Technology Zarqa University Software Engineering Department Zarqa Jordan

ISBN: (纸本)9798350320244

Small and Medium-sized Enterprises (SMEs) have been recognized as universal and they have a vital effect on developing countries economies. However, to develop or maintain software systems for such SMEs, special structures should be considered for each type. In addition, to maintain existing software systems, software engineers face a big challenge while trying to understand their code first. To overcome such difficulties, several researchers have developed tools to support software maintenance engineers in understanding software code. They have set up different code interpretation rules to understand code structure such as classes, methods, attributes, and the relationships between classes. However, these tools lack the focus on transforming code cardinality. This paper proposes a code transformation approach to enhance the understandability of code structure with a focus on code cardinality. Therefore, a semi-automated approach that relies on five main transformation rules is introduced. To evaluate the effectiveness of this approach, two case studies were conducted using different project codes. The results show that compared to traditional approaches the proposed approach has decreased the time and effort consumed by developers to understand the investigated code. © 2022 IEEE.

关键词： Developing countries

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Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

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International Journal of information technology (Singapore) 2023年 1-22页

作者： Salah, Abdelrahman Adel, Ghada Mohamed, Hussein Baghdady, Youssef Moussa, Sherin M. Department of Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo 11351 Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Laboratoire Interdisciplinaire de l’Université Française d’Égypte (UFEID Lab) Université Française d’Égypte Cairo 11837 Egypt

Considering recent smart home solutions that automate home appliances through voice commands, elderly people and/or with special needs prefer using their mother-tongue. However, most of current systems adopt the English language, making it harder for non-native English speakers, while personalization is not heavily considered. In this paper, we propose the Arabic Personalized Control of Things (APCoT) system that achieves inhabitant-tailored home automation through Arabic voice commands based on the user’s preferences to control a home device. A new dataset is created for a wider range of Arabic voice commands to interact with home appliances rather than just turning them on/off. Internet of Things, voice and speech recognition are utilized to attain personalization according to the inhabitants' voiceprints, while infrared is adopted to control non-smart devices supporting infrared signals. As a Proof-of-Concept, three home appliances are adopted: TV, Lights and Bluetooth speaker. Machine learning algorithms are customized for Arabic voice recognition, integrating face and hand gestures recognition to provide an additional facility for inhabitant’s identification and sending control commands respectively. The experimental results indicate that the best average accuracies obtained for Arabic voice recognition were 99.63% for a single speaker and 95.4% for multi-speakers, with average response times 0.141s and 14.29s respectively. For speech recognition, Google online API and a customized Vosk offline pretrained model achieved average accuracies of 95% and 90%, average response times 0.7553s and 0.3933s respectively. The face and hand gestures recognition achieved average accuracies of 90% and 98%, average response times 0.5934s and 15.62049s respectively. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Arabic commands Elderly people Home automation People with disabilities Smart home Speech and voice recognition

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Integrated Connectivity-based Stacking Ensemble Learning with GCNNs for EEG Representation

Integrated Connectivity-based Stacking Ensemble Learning wit...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Almohammadi, Abdullah Wang, Yu-Kai Australian Artificial Intelligence Institute University of Technology Cibci Lab Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9781665430654

This study proposes a novel approach that com-bines stacking ensemble learning with Graph Convolutional Neural Networks (GCNNs) to enhance the classification accuracy of Motor Imagery (MI) tasks in supporting individuals with injuries or impairments, enabling more effective rehabilitation and assistance. The method integrates both structural and functional connectivity information to leverage the benefits of GCNNs and ensemble learning techniques. The BCI Com-petition IV-2a dataset is used for evaluation. The approach employs a stacked ensemble model consisting of nine baseline models and six combining meta-models, including Logistic Re-gression, Neural Networks, Support Vector Machines, Random Forest, K-Nearest Neighbor, and Gradient Boosting Machine. By leveraging information from both structural and functional connectivity, the GCNNs extract meaningful features from MI data, leading to improved classification accuracy. The stacking ensemble learning technique combines multiple GCNN models trained on different connectivity aspects, resulting in a robust and accurate classifier. The fusion of structural connectivity (ADJ-CNNM) capturing anatomical connections and functional connectivity (PLV-CNNM) measuring brain activity synchronization enables a comprehensive analysis of MI data. The proposed approach effectively captures both local and global connectivity patterns, addressing the challenges associated with MI data analysis. By considering both types of connectivity, a holistic understanding of the dynamics of the underlying brain network during MI tasks is achieved. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 86.23% with K-Nearest Neighbor as the meta-model. Comparisons with state-of-the-art and baseline methods on the same dataset validate the approach's superiority, emphasizing the importance of GCNNs and stacking ensemble learning for accurate MI task classification. © 2023 IEEE.

关键词： Functional Connectivity GCNNs Motor Imagery(MI) Stacking Ensemble Learning Structural Connectivity

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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Computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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A New Chaotic Based Cryptographically Secure Pseudo Random Number Generator 21

A New Chaotic Based Cryptographically Secure Pseudo Random N...

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21st International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Senouci, Sarra Madoune, Sid Ali Senouci, Mohammed Raouf Senouci, Abdelkader Zhangchuan, Tang School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Northumbria University Faculty of Engineering and Environment Newcastle United Kingdom

ISBN: (纸本)9798331519254

This paper introduces a novel chaotic-based pseudo-random number generator (PRNG) architecture utilizing the Lorenz system. We examine an enhanced model, employing techniques such as Euler integration, bifurcation analysis, and autocorrelation functions. Our enhanced model, featuring a feedback mechanism, demonstrates superior output distribution, complexity, and randomness, confirmed through successful testing using the NIST SP800-22 suite. Based on these results, we regard this improved model as a cryptographically secure pseudo-random number generator (CSPRNG). © 2024 IEEE.

关键词： Random number generation

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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Frontiers of Computer Science 2024年第1期18卷 33-48页

作者： Yanbin JIANG Huifang MA Xiaohui ZHANG Zhixin LI Liang CHANG College of Computer Science and Engineering Northwest Normal UniversityLanzhou 730070China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin 541001China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Heterogeneous information network(HIN)has recently been widely adopted to describe complex graph structure in recommendation systems,proving its effectiveness in modeling complex graph *** existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths,they have the following major *** works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths,which are not expressive enough to capture more complicated dependency relationships involved on the ***,the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately *** tackle these limitations,we propose a novel social recommendation model MPISR,which models MetaPath Interaction for Social Recommendation on heterogeneous information ***,our model first learns the initial node representation through a pretraining module,and then identifies potential social friends and item relations based on their similarity to construct a unified *** then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different *** experiments on five real datasets demonstrate the effectiveness of our method.

关键词： heterogeneous information network social reco-mmender system metapath interaction attention mechanism

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Provision for Energy: A Resource Allocation Problem in Federated Learning for Edge Systems

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷

作者： Liu, Mingyue Rajamanickam, Leelavathi Parthasarathy, Rajamohan SEGi University Centre for Software Engineering Faculty of Engineering Built Environment Information Technology Malaysia SEGi University Centre for Software Engineering Centre for Network Security and IoT Faculty of Engineering Built Environment Information Technology Malaysia

The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their limited local computing resources and the data they have collected. These local models are then transmitted to a base station, where they are aggregated and broadcast back to all users. The level of accuracy in learning, as well as computation and communication latency, are determined by the exchange of models between users and the base station. Throughout the FL process, energy consumption for both local computation and transmission must be taken into account. Given the limited energy resources of wireless users, the communication problem is formulated as an optimization problem with the goal of minimizing overall system energy consumption while meeting a latency requirement. To address this problem, we propose an iterative algorithm that takes into account factors such as bandwidth, power, and computational resources. Results from numerical simulations demonstrate that the proposed algorithm can reduce energy consumption compared to traditional FL methods up to 51% reduction. © 2024 M. Liu et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Base stations

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FedQP: Towards Accurate Federated Learning using Quadratic Programming Guided Mutation 36

FedQP: Towards Accurate Federated Learning using Quadratic P...

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36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Weng, Jiawen Xia, Zeke Li, Ran Hu, Ming Chen, Mingsong Software/Hardware Co-Design Engineering Research Center Ministry of Education East China Normal University Shanghai China School of Software Nanjing University of Information Science and Technology Nanjing China Research Lab for Intelligent Software Engineering Singapore Management University Singapore

ISBN: (纸本)1891706594

Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference performance caused by data heterogeneity. Specifically, due to heterogeneous data, the optimization directions of different local models vary greatly, making it difficult for the traditional FL method to get a generalized global model that performs well on all clients. As one of the state-of-the-art FL methods, the mutation-based FL method attempts to adopt a stochastic mutation strategy to guide the model training towards a well-generalized area (i.e., flat area in the loss landscape). Specifically, mutation allows the model to shift within the solution space, providing an opportunity to escape areas with poor generalization (i.e., sharp area). However, the stochastic mutation strategy easily results in diverse optimal directions of mutated models, which limits the performance of the existing mutation-based FL method. To achieve higher performance, this paper proposes a novel mutation-based FL approach named FedQP, utilizing a quadratic programming strategy to regulate the mutation directions wisely. By biasing the model mutation towards the direction of gradient update rather than traditional random mutation, FedQP can effectively guide the model to optimize towards a well-generalized area (i.e., flat area). Experiments on multiple well-known datasets show that our quadratic programming-guided mutation strategy effectively improves the inference accuracy of the global model in various heterogeneous data scenarios. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Federated learning

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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore software Maintenance

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Computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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