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作者： Chiba, Tairi Ohmura, Ren Nakamura, Junya Department of Computer Science and Engineering Toyohashi University of Technology Aichi Japan Information and Media Center Toyohashi University of Technology Aichi Japan

This paper proposes a new state transfer method for geographic state machine replication (SMR) that dynamically allocates the state to be transferred among replicas according to changes in communication bandwidths. SMR improves fault tolerance by replicating a service to multiple replicas. When a replica is newly added or recovered from a failure, the other replicas transfer the current state of the service to it. However, in geographic SMR, the communication bandwidths of replicas are different and constantly changing. Therefore, existing state transfer methods cannot fully utilize the available bandwidth, and their state transfer time increases. To overcome this problem, our method divides the state into multiple chunks and assigns them to replicas based on each replica's bandwidth so that the broader a replica's bandwidth is, the more chunks it transfers. The proposed method also updates the chunk assignment of each replica dynamically based on the currently estimated bandwidth. The performance evaluation on Amazon EC2 shows that the proposed method reduces the state transfer time by up to 47% compared to the existing one. In addition, we apply the proposed method to dynamic replacement of replicas, which can mitigate latency degradation caused by network trouble, and evaluate how fast the method can relocate a replica. © 2022 John Wiley & Sons, Ltd.

关键词： Bandwidth

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Exploring ChatGPT in Network Management and Monitoring: Benefits and Challenges 2

Exploring ChatGPT in Network Management and Monitoring: Bene...

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2nd International Conference on information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Islam, Muhammad Nazrul Fahim, F.M. Shafiullah Akash, Pritidhrita Paul Arifeen, Kazi Afsana Nahian, Naila Baki, Ramisha Fariha Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Bangladesh University of Professionals Department of Information Communication Technology Dhaka Bangladesh

ISBN: (纸本)9798350358667

ChatGPT is an AI-based natural language model that has made a significant impact across various technological domains, including software development, network management, and the like. Network monitoring is one of the critical tasks in network management to ensure the proper and safe functioning of the entire network. The primary objective of this research is to explore the potential benefits and challenges associated with using ChatGPT in network management and monitoring. As an outcome, this research has highlighted that ChatGPT offers diverse functionalities in networking, including automation of operations and easy code generation, enabling instant feedback to enhance the quality and accessibility of network management tasks. However, the study also acknowledges the challenges inherent in using ChatGPT. Biased output is identified as a concern that could potentially impact response accuracy and fairness. Additionally, the possibility of errors in the generated output can undermine system reliability and effectiveness. While ChatGPT demonstrates the ability to simulate human-like conversations, it is important to address these challenges promptly for responsible and effective utilization in the domain of computer network. © 2023 IEEE.

关键词： Network management

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Intrusion Detection in the Internet of Things Using Fusion of GRU-LSTM Deep Learning Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 2279-2290页

作者： Mohammad S.Al-kahtani Zahid Mehmood Tariq Sadad Islam Zada Gauhar Ali Mohammed ElAffendi Department of Computer Engineering Prince Sattam Bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan The FAMLIR Group The University of LahoreLahore54000Pakistan Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan Faculty of Computing International Islamic UniversityIslamabad44000Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.

关键词： Cyber security deep learning intrusion detection PSO-GA CICIDS-2017 intelligent system security and privacy IoT

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Ia-Mstm: Improved Adaptive Moment Estimation Enabled Mixed Attention-Based Long Short-Term Memory For Crop Yield Prediction 3

Ia-Mstm: Improved Adaptive Moment Estimation Enabled Mixed A...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vani, B.V. Guruprakash, C.D. Sri Siddhartha Academy of Higher Education Sri Siddhartha Institute of Technology Saraswathipuram Information Science and Engineering Karnataka Tumakuru572105 India Sri Siddhartha Institute of Technology Department Computer Science and Engineering Karnataka Tumakuru572105 India

ISBN: (纸本)9798350364569

Crop yield prediction has sought tremendous attention in recent years several approaches were developed for crop yield prediction. However, these techniques created certain limitations in real-time applications including a reduction in relative errors, and low prediction accuracy. Therefore, this research proposed a deep learning technique known as improved Adaptive Moment estimation enabled mixed attention-based long short-term memory (IA-MSTM) to predict crop yields. The mixed attention techniques combine the advantages of channel attention with the hard attention mechanism (HAM), which can capture long-term dependencies that are crucial for understanding crop growth dynamics over time. Additionally, the improved repeated Loss-based Adam optimizer adapts the learning rate during training, leading to quicker convergence compared to traditional methods. The performance of the IA-MSTM achieves a minimal error value in terms of Mean Absolute Error (MAE) of 2.00 for TP 90. © 2024 IEEE.

关键词： Prediction models

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Design Principles for Data-Driven Decision Evaluation

Design Principles for Data-Driven Decision Evaluation

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2023 International Conference on ENTERprise information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care information Systems and Technologies, HCist 2023

作者： Elgendy, Nada Paivarinta, Tero Elragal, Ahmed Hannula, Karoliina Puolitaival, Kaisa M3S Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Antell Oulu Finland

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems. © 2024 The Author(s).

关键词： Man machine systems

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Speech Emotion Recognition based on Semi-Supervised Adversarial Variational Autoencoder 10

Speech Emotion Recognition based on Semi-Supervised Adversar...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xiao, Yufeng Bo, Yuqin Zheng, Zhiling Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Industry Polytechnic Information Center Changsha China

ISBN: (纸本)9798350312461

The application of semi-supervised learning in the field of speech emotion recognition can alleviate the problem of dependence on the amount of labeled data. Its core assumption is that the labeled and unlabeled data have the same feature representations, which determine the performance of the models. Therefore, this paper proposes a novel speech emotion recognition model based on semi-supervised adversarial variational autoencoding (SSAVAE), which reaps the advantages of generative adversarial network(GAN) and variational autoencoding(VAE) to learn the distribution of input data in the feature space. On the one hand, it can overcome the disturbance of the input data through learning the distribution of the input data in the feature space. On the other hand, it can approximate arbitrary feature distribution by introducing GAN. Concretely, SSAVAE considers the unlabeled data as the problem of the lack of emotional label attributes. The labeled data share the category information with the unlabeled data in the feature space. Several experiments are conducted on the FAU Aibo dataset to evaluate the effectiveness of the algorithm. The results show that the proposed method is superior to other benchmark algorithms and has strong feature learning capabilities. © 2023 IEEE.

关键词： Emotion Recognition

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A Leaky-Wave Beam Scanning Antenna with PIN Diodes 7

A Leaky-Wave Beam Scanning Antenna with PIN Diodes

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7th IEEE International Conference on Electronic information and Communication technology, ICEICT 2024

作者： Xiao, Gengbo Feng, Botao Ding, Xiao Deng, Li Chung, Kwok L. Gu, Wenzhe Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou516007 China Huizhou University School of Computer Science and Engineering Huizhou516007 China

ISBN: (纸本)9798350384437

In this paper, a reconfigurable digital modulation leaky-wave antenna (LWA) is proposed. This antenna does not require additional ADC/DAC modules and mixing circuits, which simplifies the antenna structure design. By introducing diodes, the proposed antenna realizes digital beam scanning and digital modulation of the leaky-wave. By inputting different digital codes, the multi-beam switching functionality is achieved, thereby realizing the purpose of beam reconfigurability. A low-cost and easy-to-manufacture metasurface radiator is fabricated, which can achieve the leaky-wave radiation effect. In summary, the proposed antenna is a promising candidate for realizing beam reconfigurability in future intelligent wireless communications. © 2024 IEEE.

关键词： Scanning antennas

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Robotic Process Automation (RPA): A software bot for healthcare sector

Robotic Process Automation (RPA): A software bot for healthc...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Bindiya, A.J. Anitha, N. Indumathi, S. East Point College of Engineering and Technology Dept of Information Science & Engg Bengaluru India BNM Institute of Technology Dept of Computer Science & Engg Bengaluru India

ISBN: (纸本)9781665462631

Nowadays different corporate sectors in the world want all digital works done by human to be automated and done very quickly. Here comes Robotic Process Automation (RPA) technology which helps in building software robots called digital bots which mimics and integrates works of human that he does sitting with digital system. So in the current paper we are presenting tasks performed in health care division using software bots. One use case is claiming insurance, here our software robot will open web form, bot fills all needed insurance details collected and stored in electronic media (excel sheet) already, submitting the form once data entry done, repeat the same task for another patient. Second use case is collecting day-wise critical patient information which are received through chat-bots and processing patients details which are found critical. Work finishes within fractions of seconds without human intervention. © 2023 IEEE.

关键词： Robotics

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Automatic Detection of Some Tajweed Rules 20

Automatic Detection of Some Tajweed Rules

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20th International Learning and technology Conference, L and T 2023

作者： Omran, Dahlia Fawzi, Sahar Kandil, Ahmed Cairo University Systems and Biomedical Engineering Faculty of Engineering Giza Egypt Nile University Information Technology & Computer Science Giza Egypt

ISBN: (纸本)9798350300307

correct understanding of the Holy Quran is an essential duty for all Muslims. Tajweed rules guide the reciter to perform Holy Quran reading exactly as it was uttered by Prophet Muhammad peace be upon him. This work focused on the recognition of one Quranic recitation rule. Qalqalah rule is applied to five letters of the Arabic Alphabet (Baa/Daal/Jeem/Qaaf/Taa) having sukun vowelization. The proposed system used the Mel Frequency Cepstral Coefficients (MFCC) as the feature extraction technique, and the Convolutional Neural Networks (CNN) model was used for recognition. The available dataset consists of 3322 audio samples from different surahs of the Quran for four professional readers (Sheihk) AlHussary, AlMinshawy, Abdel Baset, and Ayman Swayed. The best results were gained using Ayman Swayed audio samples with a validation accuracy of 90.8%. © 2023 IEEE.

关键词： Convolutional neural networks

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Advanced Analytics for Proactive Cybersecurity Breach Prediction and Prevention 4

Advanced Analytics for Proactive Cybersecurity Breach Predic...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Pathmanaban, J. Sangeetha, S. Harini, A. Pavithra, S. Hemasri, P. Sri Sairam Institute of Technology Dept of Computer Science and Engineering Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Dept of Information Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

As technology advances, so do the potential risks and security threats associated with it. It's crucial to prioritize and invest in cybersecurity measures to safeguard these advancements. Balancing innovation with robust security measures is essential for staying proactive and implementing security protocols by this we can mitigate the risks and ensure a safer digital future. This project focuses on the development of a predictive model for cyber-attack detection on servers. Leveraging advanced analytics and machine learning techniques, the system aims to proactively identify potential security breaches before they manifest. This involves the analysis of server logs, network traffic, and anomaly behaviour to create a robust predictive model. Various types of Cyberattacks which include DDOS, PROBE, U2R, and R2L attacks. In this, we use the RNN algorithm for classification and training in order to improve the efficiency of IDS detection. © 2024 IEEE.

关键词： Cyber attacks

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