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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Liu, Han Xie, Xingrui Zhang, Zhuopu Cheng, Geyao Luo, Lailong Guo, Deke National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9798350330717

Storing files at the network edge has become a new paradigm of storage systems, which is promising to mitigate network congestion and reduce file retrieval latency. However, the traditional file storage scheme cannot effectively meet the requirements of rapid indexing and load balance when applied directly to the edge. Moreover, due to the dynamic nature of the edge environment where edge servers can join or leave at will, it is necessary for the storage scheme to adjust with minimal disruption. In this paper, we propose EdgeAnchor, a novel edge storage strategy that is composed of the two-layer hash mappings. The first layer, file-to-bucket mapping, adopts the pseudo-deletion algorithm to deal with the variations in file size, while the second layer utilizes the multiple bucket-to-server mapping to adapt to the heterogeneity in the servers' storage capacities. Furthermore, EdgeAnchor constructs a list of deleted or added working sets for each bucket and creates a dictionary for the mappings between buckets and edge servers. In the manner, EdgeAnchor ensures a rapid file index and balances server load at the dynamic network edge. We also attach the mathematical analyses to EdgeAnchor, which theoretically proves its logarithmic complexity of hash operations and memory accesses. The experiments conducted on real-world datasets demonstrate that EdgeAnchor achieves the file index throughput twice as high as that of Consistent Hashing, under the constraints of load balance. Additionally, it ensures a low and stable data migration volume, when adding or removing edge servers consecutively. © 2023 IEEE.

关键词： dynamic network edge file storage load balance rapid indexing

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Using an Evolutionary Algorithm Such as Genetic Algorithm (Ga) to Optimize Linear Equations 5

Using an Evolutionary Algorithm Such as Genetic Algorithm (G...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Wdaa, Abdul Sttar Ismail Al-Barzinji, Shokhan M. Tarish, Hiba A. College of Education for Pure Science University of Anbar Ramadi Iraq College of Computer Science and Information Technology University of Anbar Department of Computer Networks Systems Ramadi Iraq University of Technology Civil Engineering Departments Baghdad Iraq

ISBN: (纸本)9798350363739

An Evolutionary algorithm is a subset of evolutionary computations. Cloning, mutation, recombination, and selection are some of the methods used by the evolutionary algorithm that were inspired by biological evolution. The problem that was addressed in this study is how to find the coefficient values of the variables in linear equations that contain a set of variables, it must equal the right side. After reviewing previous research looking for optimization, genetic algorithm, which will use in this paper, so how are going to use this strategy to solve our problems? Select the best chromosomes from this population. Perform random chromosomal hybridization. Then, within the genes of our chromosomes, produce random mutations. Will send these chromosomes for selection again. The best chromosomes among them are selected again. It crosses and turns again, and the process is repeated until a certain number of generations have passed or find the best option. After applying the genetic algorithm through the Matlab program to the linear equation, which contains six variables, for example, the results were very excellent, as the parameter values were found in a very short time and with high accuracy, completely matching the right side of the equation. © 2024 IEEE.

关键词： Genetic algorithms

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Towards Optimization of Math Pedagogical Techniques and Behaviors Using Decision and Ensemble Trees 4

Towards Optimization of Math Pedagogical Techniques and Beha...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Fawy, Mohamed Hamza, Meer Aziz, Rasha Abd El Yafi, Eiad Technology and Maritime Transport College of Management and Technology Arab Academy for Science Alexandria Egypt Technology and Maritime Transport College of Computing and Information Technology Arab Academy for Science Alexandria Egypt Technology and Maritime Transport Arab Academy for Science Businese Information Systems Department Alexandria Egypt University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9798350348248

Professional development programs for educators are thought to be generally ineffective and inefficient in raising student enjoyment and performance. As a result, new educators often express that the first years in their career are challenging and lacking in appropriate and effective support. New educators may frequently experience stress and anxiety from those factors as well as the inherent challenges of teaching young learners. The objectives of this paper and further research are to derive the most effective teaching techniques and behaviors using decision and ensemble tree methods. Six different models were trained from data collected on Egyptian preparatory language school mathematics teachers and their respective students. Two decision trees, two random forests and two gradient boosted trees are included, one set for classification tasks and the other for regression. The Student Evaluation Index (SEI) employed and generated by the student survey responses in this paper are used to derive the importance rankings of teaching techniques and behaviors. The tree structures can also be used to help teachers create improvement programs that will increase the performance and enjoyment of their students. The results of SEI prediction utilizing our tree methods are found to be critically dependent on techniques like Visualization and the use of native Language. It stands to reason that preparatory language school teachers in Egypt should emphasize the use of visual aids while instructing students in their native language. Additional attributes of a popular or effective teachers include inspirational behavior and the usage of practice problem sets such as homework. © 2023 IEEE.

关键词： Students

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A Memristive Chaotic System: Attractor Self-Reproducing, Amplitude Control and Offset Boosting

A Memristive Chaotic System: Attractor Self-Reproducing, Amp...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Li, Yongxin Li, Chunbiao Gao, Yikai Zhang, Xin Volos, Christos Yu, Wanning Sch. of Electronic and Information Engineering Nanjing University of Information Science Technology Nanjing China School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing China Aristotle University of Thessaloniki Laboratory of Nonlinear Systems Circuits & Compleixty Greece

ISBN: (纸本)9798350316230

Two flux-controlled memristors sharing the same internal variable and the same resistor function are introduced into the simplest variable boostable system VB5 for constructing the 3-D memristive chaotic oscillator. The offset booster is well inherited from the original chaotic system for attractor boosting, which makes the corresponding oscillation controlled on different positions by a single offset constant. Meanwhile, the periodic resistor function makes attractors self-reproducing and produces infinitely many attractors under different initial conditions. Furthermore, the property of rotational symmetry implies an extra partial amplitude control. The amplitude of the system variables x and z signal can be modified by a single non-bifurcation parameter. Analog circuit simulation based on Pspice shows agreement with the theoretical and numerical analysis. © 2023 IEEE.

关键词： Chaotic systems

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Autonomous Search and Rescue Agent Using Deep Reinforcement Learning

Autonomous Search and Rescue Agent Using Deep Reinforcement ...

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2023 International Conference on Recent Advances in science and engineering technology, ICRASET 2023

作者： Rampriya, R.S. Alam, Mohammed Junaid Jayan, Sri Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Madras Institute of Technology Department of Computer Technology Tamilnadu Chennai India

ISBN: (纸本)9798350306927

Disaster management is one of the areas of paramount importance for any government or organization. This process gets very tedious and tricky for some specific type of disasters. Deploying manpower for mitigating the disaster and saving the victims is a risky decision because the life of the people in the disaster management team is put to risk, which is a very unfortunate thing. So instead of using manpower, we can deploy intelligent machine agents, which are equivalent or sometimes more efficient than human beings for such highly risky tasks and avert the loss of lives. In our case, we have specifically considered the disaster wherein fire breaks out in a building and people get trapped inside due to the dangerous situation. In this case, where deploying manpower to rescue them requires a high quality protective gear and can even put their lives on risk, we have proposed to deploy intelligent machine agents that are trained to figure out the path on their own and find their way to the victims after which a rescue operation can be performed by safely escorting the victims to a nearby exit in the building. The agent in the proposed application has been evaluated by the metrics such as the time taken to reach the victims and come back to the exit, the total rewards gathered by the agent during its exploration in the environment and the number of correct decisions it made during its journey. The agent takes an average time of 145.6 seconds to complete its operation in the real-time environment. It gathered an average peak cumulative reward value of -0.2 in the real time environment. The agent on an average, records 67.3% chance of making a correct decision (that gets a positive reward) given a state vector. © 2023 IEEE.

关键词： Autonomous agents

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Enhancing Agile Development in Tech Companies: Backlog Management, Tool Integration, and Stakeholder Collaboration 6

Enhancing Agile Development in Tech Companies: Backlog Manag...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Suganya, R. Banerjee, Dhiimayyi Mishra, Aarohi Subbulakshmi, T. Subramanian, Girish Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Computer Science and Engineering Chennai India Penn State University Information Systems State College United States

ISBN: (纸本)9798350394702

This paper investigates the Agile development processes in tech companies, focusing on strategies for effective backlog management, tool utilization for progress tracking, Agile methodology integration, stakeholder involvement, and handling challenges in the tech domain. The study reveals the adaptability of Agile methodologies to diverse organizational needs and objectives, emphasizing their significance in the technical industry. Companies prioritize backlog tasks through subtask breakdown, Story Points, and priority tags, aligning with Agile principles. The integration of Agile involves Jira and Scrum, enhancing development efficiency. Stakeholders and team members engage through planning and design sessions, ensuring collaboration. Challenges are addressed through regular meetings, technical discussions, and transparency. Knowledge transfer sessions keep teams updated. Data security is maintained through NDA agreements and robust security measures. Metrics like story points and KPIs are tracked for Agile success evaluation. The paper concludes by highlighting the importance of aligning process models with organizational requirements. © 2023 IEEE.

关键词： Knowledge management

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High-density MDM-WDM silicon photonic transmitter chiplet based on MRMs and dual-mode GC for 4×56 Gbps 3D co-packaged optical interconnects

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Chinese Optics Letters 2024年第12期22卷 1-6页

作者： Xinyi Wang Jiangbing Du Weihong Shen Ke Xu Zuyuan He State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong UniversityShanghai 200240China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Department of Electronic and Information Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

In this Letter,a four-channel silicon photonic mode-division-multiplexing(MDM)and wavelength-division multiplexing(WDM)transmitter chiplet is proposed over a 1.65 mm~2 footprint,utilizing add-drop micro-ring modulators to simultaneously achieve electro-optic modulation and two-wavelength multiplexing.A dual-mode grating coupler with a side-distributed Bragg reflector for equalized two-mode coupling is realized with high chip-to-fiber coupling efficiency,so as to support the MDM optical fiber interface.A high data rate of up to 4×56 Gbps signaling is experimentally demonstrated,featuring applications like 200G quad small form-factor pluggable(QSFP)transceivers and indicating significant potential for highdensity and large-capacity 3D co-packaged optical interconnects through flip-chip-based electronic-photonic packaging.

关键词： high density silicon photonic MDM-WDM transmitter add-drop micro-ring modulator dual-mode grating coupler

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MuLAD: Multimodal Aggression Detection from Social Media Memes Exploiting Visual and Textual Features 27th

MuLAD: Multimodal Aggression Detection from Social Media Me...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hasan, Md. Maruf Ahsan, Shawly Hoque, Mohammed Moshiul Dewan, M. Ali Akber Chittagong University of Engineering and Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada

ISBN: (纸本)9783031781186

Aggression detection from memes is challenging due to their region-specific interpretation and multimodal nature. Detecting or classifying aggressive memes is complicated in low-resource languages (including Bengali) because benchmark datasets and primary language processing software are needed. This paper proposes an innovative meme classification technique that harnesses deep learning (DL) approaches to leverage memes’ visual and textual features in Bengali. Various DL frameworks, such as VGG16, VGG19, ResNet50, CNN, BiLSTM, and BiLSTM+CNN, extract visual and textual features from memes. A novel corpus named the Bengali Meme Dataset (AMemD) is also introduced, comprising a substantial amount of multimodal data, including text and image components. Experimental results on AMemD demonstrate the effectiveness of the proposed approach. The CNN combined with VGG16 obtained the highest f1-score of 0.738 among all multimodal techniques tested. This pioneering research offers valuable insights into the complex task of aggression detection from memes in Bengali and provides a foundation for future studies in this area. The dataset is available at https://***/Maruf089/Multimodal-Aggression-Detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Maximum Entropy Deep Reinforcement Learning Based Power Allocation for NOMA Maritime Network 14th

Maximum Entropy Deep Reinforcement Learning Based Power Allo...

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14th EAI International Conference on Wireless and Satellite systems, WiSATS 2024

作者： He, Jiayi Zhang, Yakai Liu, Zhiyong School of Information Science and Engineering of HIT Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Weihai264209 China Key Laboratory of Cross Domain Synergy and Comprehensive Support for Unmanned Marine Systems of Ministry of Industry and Information Technology Weihai264209 China

ISBN: (纸本)9783031862021

In order to address the challenges of high propagation delays and limited service capabilities in maritime satellite communications, unmanned aerial vehicles have been proposed as an airborne backhaul solution to enhance communications between satellites and maritime base stations. The non-orthogonal multiple access (NOMA) framework can solve the user sparsity problem in maritime networks. In this paper, a deep reinforcement learning algorithm is used to solve the nonconvex power allocation problem under NOMA. In order to mitigate the risk of overestimation of Q values and local optimal convergence of Deep Q Network (DQN) algorithm, we propose an algorithm called Soft Agent Critical Ocean Satellite Communication Power Allocation (SAC-OSCPA) based on the idea of maximum entropy and compare it with the traditional DQN algorithm. The main goal of this research is to maximize network throughput in scenarios with randomly distributed users. Simulation results show that the average system throughput is improved by 13.18% with the SAC-OSCPA algorithm, and the average throughput of the worst performing user is significantly improved by 41.59%. These results demonstrate the efficacy of the proposed algorithm in optimizing the communication performance of maritime satellite networks. © ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Deep reinforcement learning

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