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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Chellasamy, Natarajan Akram, Faiz Rajesh, Nichenametla Umamaheswari, R. Assosa University Department of Electrical and Computer Engineering Assosa Ethiopia Jimma University Jimma University Faculty of Computing and Informatics Jimma Ethiopia Department of Cse Andhra Pradesh Guntur India Gnanamani College of Technology Department of Cse Tamil Nadu Namakkal India

ISBN: (纸本)9798350323887

Serverless is a peer-to-peer system that is associated with the cloud architecture that aims to reduce the efforts required by the cloud manager to provide an efficient service. Blockchain enable collaborative distributed mechanism to enable a decentralized management of resource allocation. The resource allocation is been a major concern when blockchain is enabled in the serverless computing. In this paper, we develop a serverless computing model that is embedded with blockchain manner in a distributed fashion. The formulation is made in such a way that if fulfils the task allocation in a better way with increased makespan and reduced latency while a resource is allocated for required operation. © 2023 IEEE.

关键词： Cloud computing architecture

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xiuen Wu Tao Wang Yuanzheng Cai Lingyu Liang George Papageorgiou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians’ future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.

关键词： Deep learning Pedestrians Computational modeling Predictive models Prediction algorithms Vectors Inference algorithms

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Accelerating Feedback Control for QoE Fairness in Adaptive Video Streaming Over ICN

Accelerating Feedback Control for QoE Fairness in Adaptive V...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Rei Nakagawa Satoshi Ohzahata Ryo Yamamoto Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology Tokyo Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

Today, information centric networking enables adaptive video streaming clients to further improve QoE by applying flexible content-based control. However, an adaptive bitrate algorithm makes a client occupy the bottleneck link at excessively high bitrate, reducing the QoE fairness to other clients sharing the bottleneck link. Then, we propose fairAccel, a method of accelerating bitrate-based feedback control for achieving QoE fairness. fairAccel assigns more bandwidth to clients selecting the lower bitrate while suppressing content requests from clients selecting the highest bitrate on the bottleneck link. In addition, to further improve QoE fairness, fairAccel exploits the symmetric routing of ICN content request / response and applies bidirectional feedback control to the content request / response path. Thus, fairAccel accelerates feedback control by mitigating router queues under control of suppressing content requests before excessive traffic is delivered to the response path. Through simulation experiments, fairAccel improves the average bitrate and further improves QoE fairness for representative ABR algorithms.

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Concentric Blocking Techniques for Improved Feature Extraction in Local Binary Pattern (LBP) Systems

Concentric Blocking Techniques for Improved Feature Extracti...

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International Conference on Adaptive Science & technology (ICAST)

作者： Obed Appiah Ezekiel Mensah Martey Department of Computer Science and Informatics University of Energy and Natural Resources (UENR) Sunyani Ghana Department of Computer Science and Engineering University of Mines and Technology (UMaT) Tarkwa Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

The domain of image analysis has seen rapid advancements, particularly feature extraction techniques, driven by the increasing demand for accurate and efficient results. Among these advancements, Local Binary Patterns (LBP) have gained prominence due to their simplicity and computational efficiency. However, traditional LBP methods often need help with image orientation and scale variations, common in real-world scenarios. To address these challenges, this study explores the potential of concentric blocking methods as an alternative to conventional grid-based blocking techniques. Concentric blocking, which organizes image data into circular or spiral patterns, is hypothesized to enhance the robustness and accuracy of LBP-based systems by better accommodating geometric transformations. The study uses multiple LBP variants to compare traditional grid-based blocking with concentric blocking techniques across various image analysis tasks, including face recognition and texture classification. Experimental results demonstrate that the proposed ring-centric technique significantly outperforms traditional methods in precision and computational efficiency. Precision values for the proposed Ring Centric technique reach up to 90.73% for the basic LBP and 88.47% for Neighboring LBP with Distance (NLBP D ), indicating its superior performance in handling variations in orientation and scale. Additionally, the technique demonstrates lower running times, making it a highly efficient and accurate choice for image retrieval tasks. This study highlights the importance of image-blocking strategies in enhancing the performance of LBP-based systems and underscores the potential of concentric blocking methods in overcoming the limitations of traditional approaches. The findings contribute to developing more resilient and efficient image analysis systems, with significant implications for applications in computer vision, medical diagnostics, and security systems.

关键词： Image analysis Accuracy Spirals Face recognition Image retrieval Feature extraction Robustness Computational efficiency Security Medical diagnosis

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Enhanced Redundant Weighted Clustered Scheme for Damaged S-UAV

Enhanced Redundant Weighted Clustered Scheme for Damaged S-U...

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IEEE International Conference on Communications (ICC)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Evangelos K. Markakis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Cyprus and Department of Electrical & Electronic Engineering Science University of Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Electrical and Computer Engineering Hellenic Mediteranean University Estavromenos Heraklion Greece

A spontaneous group of unmanned aerial vehicles (UAVs) is denoted as a swarm of UAVs (S-UAVs). The UAVs communicate wirelessly and cooperate to accomplish tasks. In crisis scenarios such as flooding or earthquakes, all UAVs are at risk of getting damaged and thus non-functional. A non- functional UAV will result in a disconnected network, especially if that UAV is highly responsible for packet forwarding. S- UAV s are dynamic networks; clustering is one of the most adopted routing schemes in S-UAVs. The clustering scheme groups the UAVs into clusters where each cluster is formed of a cluster head (CH) and cluster members (CMs). Only the CH can handle inter-cluster communication. Due to the crucial role played by the CH, its selection is a continuous field of research. This paper proposes an enhanced clustered weighted scheme with redundancy to ensure end-to-end communication. The proposed scheme is based on a weighted formula for the primary CH, redundant CH, and CMs selection. The weighted formula calculates a cluster index based on the distance, the speed, and the reward index. A new component is added to the reward index which is performance. The redundant CH is selected to automatically replace the primary CH whenever it is damaged. If the redundant CH becomes inoperable, the second redundant CH will take over. Each cluster is formed of n CMs and will have n-2 redundant CHs. The results obtained from the conducted simulation experiments concluded that this promising scheme decreases data loss in a crisis case scenario.

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Artificial neural networks in supply chain management, a review

Journal of Economy and Technology

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Journal of Economy and technology 2023年 1卷 179-196页

作者： Mohsen Soori Behrooz Arezoo Roza Dastres Department of Aeronautical Engineering University of Kyrenia Via Mersin 10 Kyrenia North Cyprus Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875-4413 Iran Department of Computer Engineering Cyprus International University Via Mersin 10 North Cyprus Turkey

Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired by the structure and function of the human brain. In the context of supply chain management, ANNs can be used for demand forecasting, inventory optimization, logistics planning, and anomaly detection. ANNs help companies to optimize their inventory levels, production schedules and procurement activities in terms of productivity enhancement of part production. By considering multiple variables and constraints, ANNs can identify the most efficient routes, allocate resources effectively, and reduce costs. Furthermore, ANNs can identify anomalies as well as abnormalities in supply chain data, such as unexpected demand patterns, quality issues and disruptions in logistics operations in order to minimize their impact on the supply chain. ANNs can also analyze supplier performance data, including quality, delivery times and pricing in order to assess the reliability and effectiveness of suppliers. This information can support decision-making processes in supplier evaluation and selection processes. Moreover, ANNs can continuously monitor supplier performance, raising alerts for deviations from predefined criteria to provide safe and secure supply chain in part production processes. By analyzing various data sources, including weather conditions, and political instability, ANNs can identify and mitigate risks in terms of safety enhancement of supply chain processes. Artificial neural networks in supply chain management is studied in the research work to analyze and enhance performances of supply chain management in process of part manufacturing. New ideas and concepts of future research works are presented by reviewing and analyzing of recent achievements in applications of artificial neural networks in supply chain management. Thus, productivity of part manufacturing can be enhanced by promoting the supply chain management using the artificial neural networks.

关键词： Artificial neural networks Supply chain management

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Microservice API Implementation for E-Government Service Interoperability

Microservice API Implementation for E-Government Service Int...

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2019 International Conference of Science and Information technology in Smart Administration, ICSINTeSA 2019

作者： Puspitasari, N. Budiman, E. Sulaiman, Y.N. Firdaus, M.B. Department of Informatics Engineering Faculty of Computer Science and Information Technology Mulawarman University Indonesia

To improve e-government services released by Communication and Information technology Office of Samarinda City, each system needs to be able to interoperate even when developed by different developers. Interoperation can be achieved by using one data source which is API (Application Programming Interface) for general data objects such as announcements. Given this condition, API built by using microservice can support further enhancement even if API is developed by developers who use different programming languages. The result shows that microservice API can be used to interoperate in relaying data between e-government services and can be developed using more than one programming language and base codes. Further development of this API can be done by adding more data objects, using AWS Cognito as authorization management, adding AWS Elasticsearch to load and filter data, and by showing data objects in real-time on the front end. © Published under licence by IOP Publishing Ltd.

关键词： Interoperability

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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Improved RC6 Block Cipher Based on Data Dependent Rotations

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computers, Materials & Continua 2022年第1期70卷 1921-1934页

作者： Osama S.Faragallah Ibrahim F.Elashry Ahmed AlGhamdi Walid El-Shafai S.El-Rabaie Fathi E.Abd El-Samie Hala S.El-sayed Mohamed A.Elaskily Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electrical Communications Faculty of EngineeringKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom32511Egypt Department of Informatics Electronic Research Institute(ERI)CairoEgypt

This paper introduces an Improved RC6(IRC6)cipher for data encryption based on data-dependent *** proposed scheme is designed with the potential of meeting the needs of the Advanced Encryption Standard(AES).Four parameters are used to characterize the proposed *** parameters are the size of the word(w)in bits,the number of rounds(r),the length of the secret key(b)in bytes,and the size of the block(L)in *** main feature of IRC6 is the variable number of working registers instead of just four registers as in RC6,resulting in a variable block size for plaintext and *** IRC6 cipher is designed to improve the robustness against attacks by increasing the diffusion for each round and providing greater security with fewer *** effectiveness of the proposed IRC6 scheme is verified against theoretical *** proposed IRC6 scheme depends on full diffusion and confusion mechanisms regardless of the utilized block *** proposed IRC6 scheme saves 70%of the encryption time and 64%of the decryption time of *** simulation results prove that the IRC6 achieves a better encryption/decryption time compared to the ***,the proposed IRC6 is anticipated to fulfill the market needs and system security requirements.

关键词： Cryptography block cipher RC6,IRC6,AES

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AI-powered blockchain technology in industry 4.0, a review

Journal of Economy and Technology

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Journal of Economy and technology 2023年 1卷 222-241页

作者： Mohsen Soori Roza Dastres Behrooz Arezoo Department of Aeronautical Engineering University of Kyrenia Via Mersin 10 Kyrenia North Cyprus Turkey Department of Computer Engineering Cyprus International University Via Mersin 10 North Cyprus Turkey CAD/CAPP/CAM Research Center Department of Mechanical Engineering Amirkabir University of Technology (Tehran Polytechnic) 424 Hafez Avenue Tehran 15875–4413 Iran

Artificial Intelligence (AI) algorithms can be employed to enhance the security of the blockchain networks in the era of industry 4.0. Smart contracts, powered by blockchain, can be developed by using the AI capabilities. These contracts can execute themselves based on predefined conditions, automating various processes and reducing the need for intermediaries. Blockchain provides a transparent and immutable ledger for supply chain transactions. AI can be integrated to analyze this data, providing insights into the entire supply chain. This helps in tracking and tracing products, ensuring authenticity, and optimizing the supply chain based on data-driven decisions. AI algorithms can be employed to analyze data from the manufacturing process recorded on the blockchain. This allows for predictive maintenance and quality control, helping to identify and address issues before they result in defects or downtime. AI and blockchain can work together to secure the massive amount of data generated by IoT devices in an Industry 4.0 setting. Blockchain ensures the integrity and immutability of IoT data, while AI can analyze this data for insights and optimization. AI and blockchain can facilitate the creation of decentralized energy grids where AI algorithms optimize energy distribution and blockchain ensures transparent and secure energy trading through smart contracts. AI and blockchain can support decentralized and collaborative manufacturing networks where different entities can securely and efficiently collaborate on production processes. Blockchain enables individuals or organizations to securely monetize their data. AI can help analyze and determine the value of data, ensuring fair compensation in tokenized ecosystems. In this paper, applications of AI-Powered Blockchain technology in Industry 4.0 is reviewed and discussed and future research works are also suggested. As a result, efficiency, transparency, and security across various industrial processes can be enhanced

关键词： Artificial Intelligence Blockchain AI-Powered Blockchain Industry 4.0

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