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Boosting speech recognition performance: a robust and accurate ensemble method based on HMMs

International Journal of Intelligent systems Technologies and Applications 2024年第1期22卷 41-76页

作者： Hazmoune, Samira Bougamouza, Fateh Mazouzi, Smaine Benmohammed, Mohamed Department of Computer Science Faculty of Sciences University of 20 Août 1955-Skikda Skikda Algeria Department of Software Technologies and Information Systems Faculty of New Technologies of Information and Communication University Constantine 2 Constantine Algeria

In this paper, we propose an ensemble method based on hidden Markov models (HMMs) for speech recognition. Our objective is to reduce the impact of the initial setting of training parameters on the final model while improving accuracy and robustness, particularly in speaker independent systems. The main idea is to exploit the sensitivity of HMMs to the initial setting of training parameters, thus creating diversity among the ensemble members. Additionally, we perform an experimental study to investigate the potential relationship between initial training parameters and ten diversity measures from literature. The proposed method is assessed on a standard dataset from the UCI machine-learning repository. Results demonstrate its effectiveness in terms of accuracy and robustness to intra-class variability, surpassing basic classifiers (HMM, KNN, NN, SVM) and some previous works in the literature including those using deep learning algorithms such as convolutional neural networks (CNNs) and long short-term memory (LSTM). Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Hidden Markov models

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Hierarchical Codebook Design Using Scale-Changeable Reconfigurable Holographic Surfaces in Near-Far Field communications

Hierarchical Codebook Design Using Scale-Changeable Reconfig...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Zhang, Shupei Di, Boya Zhang, Hongliang Poor, H. Vincent Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks Beijing China Princeton University Department of Electrical and Computer Engineering Princeton United States

ISBN: (纸本)9798350351255

Reconfigurable holographic surfaces (RHSs) have been proposed as a cost-effective and power-efficient solution for extremely large-scale arrays, where the amplitude of electromagnetic waves radiated at each element is controlled to achieve high directive gain. However, the complexity of acquiring real-time channel state information (CSI) required for beamforming is prohibitively high, especially when the near-field expansion brought by the large-scale RHS is considered. In this paper, we propose a codebook-based beam training scheme for a large-scale RHS-enabled communication system to bypass CSI estimation. Unlike traditional phase-controlled arrays, the amplitude-controlled property of the RHS implies that each RHS element can be selectively activated. This motivates an array reconfiguration method where a scale-changeable RHS array is constructed to generate gain-flat beams with different coverage in the angle-range domain. A hierarchical RHS codebook is then proposed where the coverage of the codewords in each layer is progressively refined. To address the substantial beam search overhead in the near-far field, a two-stage beam training scheme is performed in the proposed codebook, thereby reducing the overhead to a logarithmic level of the element number. The simulation results show that the proposed scheme performs better than phased arrays given the same input power in terms of sum rate, and it also approaches the upper bound achieved by the exhaustive search at a significantly reduced overhead. © 2024 IEEE.

关键词： Antenna phased arrays

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Distributional learning in multi-objective optimization of recommender systems

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 10849-10865页

作者： Candelieri, Antonio Ponti, Andrea Giordani, Ilaria Bosio, Anna Archetti, Francesco Department of Economics Management and Statistics University of Milano-Bicocca Milan Italy Department of Computer Science Systems and Communication University of Milano-Bicocca Milan Italy OAKS Srl Milan Italy

Metrics such as diversity and novelty have become important, beside accuracy, in the design of Recommender systems (RSs), in response the increasing users' heterogeneity. Therefore, the design of RSs is now increasingly modelled as a multi-objective optimization problem (MOP) for whose solution Multi-objective evolutionary algorithms (MOEAs) have been increasingly considered. In this paper we focus on the k-top recommendation problem in which a solution is encoded as a matrix whose rows correspond to customers and column to items. The value of accuracy, novelty, and coverage for each candidate list, is evaluated as a sample and can be represented as a 3-d histogram which encodes the knowledge obtained from function evaluations. This enables to map the solution space into a space, whose elements are histograms, structured by the Wasserstein (WST) distance between histograms. The similarity between 2 users in this probabilistic space is given by the Wasserstein distance between their histograms. This enables the construction of the WST graph whose nodes are the users and the weights of the edges are the WST distance between users. The clustering of users takes then place in the WST-graph. In the optimization phase the difference between two top-k lists can be encoded as the WST distance between their 3-dimensional histograms. This enables to derive new selection operators which provide a better diversification (exploration). The new algorithm Multi-objective evolutionary optimization/Wasserstein (MOEA/WST), compared with the benchmark NSGA-II, yields better hypervolume and coverage, in particular at low generation counts. © 2022, The Author(s).

关键词： Recommender systems

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CloudyPages: A Secure and Scalable Serverless Web-Based Blogging Application 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prakash, Vijay Jaiswal, Shrestha Purohit, Saumya Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MsidaMSD 2080 Malta

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

The CloudyPages app is a precise and trustworthy platform for bloggers to create and effectively manage blogs. The CloudyPages application provides bloggers a platform for creating and managing their blogs. The application can achieve remarkable accuracy, precision, and review rates by utilizing AWS services, assuring minimal errors and enhancing the discoverability and accessibility of bloggers’ content. Collectively, these metrics contribute to an optimized user experience. AWS WAF (Amazon Web Services Web Application Firewall) provides robust protection against common web application vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. By implementing this security layer, the application effectively protects the data of bloggers and readers from potential attacks. The AWS Web Application Firewall (WAF) protects against known attack patterns and continuously monitors and detects harmful activities to prevent unauthorized access. The application's serverless architecture lets bloggers focus on content creation and administration instead of server maintenance. The application's affordable price makes it an appealing, adaptable, and inclusive solution. The application's serverless design and AWS WAF and AWS Open make it flexible, affordable, and accessible for bloggers of all sizes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blogs

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Int-BEB: Enhanced Binary Exponential Backoff using Q-learning 5

Int-BEB: Enhanced Binary Exponential Backoff using Q-learnin...

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5th International Conference on Pattern Analysis and Intelligent systems, PAIS 2023

作者： Zerguine, N. Ferhat Abbas Setif 1 University Networks and Distributed Systems Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798350381450

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 Distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection;this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy. © 2023 IEEE.

关键词： Reinforcement learning

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An energy efficient Dantzig Wolfe Decomposition based parallel optimization for software-defined industrial IoT

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Journal of Intelligent and Fuzzy systems 2023年第2期44卷 2441-2454页

作者： Sathish, S. Kavitha, K. Poongodi, J. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electronics and Communication Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Tamil Nadu Coimbatore India

The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. © 2023 - IOS Press. All

关键词： Internet of things

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Towards 6G for Connected Autonomous Vehicles: A Trial Facility Analysis 29

Towards 6G for Connected Autonomous Vehicles: A Trial Facili...

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29th IEEE International Workshop on computer Aided Modeling and Design of communication Links and networks, CAMAD 2024

作者： Chou, Shih-Kai Hribarl, Jernej Dusparic, Ivana Fortuna, Carolina Jozef Stefan Institute Department of Communication Systems SI-1000 Slovenia School of Computer Science and Statistics Trinity College Dublin Ireland

ISBN: (纸本)9798350377644

Artificial Intelligence (AI) techniques for traffic management and optimization have the potential to funda-mentally change the way transportation systems operate. In particular, Deep Reinforcement Learning (DRL) has been shown to achieve significant improvements in numerous traffic-optimization approaches, allowing transportation systems to become safer, more efficient, and environmentally friendly. How-ever, most approaches proposed for such traffic management have so far been validated only within simulated environments as real-world test facilities tend to be limited in availability and functionality. In this paper we elaborate on Deep Reinforcement Learning (DRL)-based smart traffic management applications for future Connected Autonomous Vehicles (CAV s) and give insights on their vertical integration into emerging 6G infrastructures in view of enabling realistic experimentation. We propose a layered architecture in which the experimenter develops a software as a service (SaaS) service layer on top of platform as a service (PaaS) layers provided by the trial facility. We also present a qualitative analysis of existing trial facilities for smart traffic management and address the potential challenges within these trial facilities. © 2024 IEEE.

关键词： Software as a service (SaaS)

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Covert Channel Detection and Generation Techniques: A Survey 3

Covert Channel Detection and Generation Techniques: A Survey

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Khulaidi, Nesmah A. Zahary, Ammar T. Hazaa, Muneer A.S Nasser, Adel A. Yemen Academy for Graduate Studies Department of Computer Networks Sana'a Yemen University of Science and Technology Faculty of Computing and It Department of Computer Science Sana'a Yemen Thamar University Faculty of Computer and Information System Department of Computer Science Thamar Yemen Sa'adah University Faculty of Sciences Department of Information Systems and Computer Science Sa'adah Yemen

ISBN: (纸本)9798350305333

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. System security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment. © 2023 IEEE.

关键词： Intrusion detection

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Generic Simulated Evolution and Stochastic Evolution

Generic Simulated Evolution and Stochastic Evolution

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2024 International Conference on computer and Applications, ICCA 2024

作者： Kallab, Chadi Haddad, Samir Shakir, Wafaa M.R. Merhej, Joseph Turkey, Nisrine Sayah, Jinane Lebanese American University Department of Computer Science and Mathematics Jbeil Lebanon University of Balamand Computer Science Department Koura Lebanon Al-Furat Al-Awsat Technical University Department of Computer Systems Babil Iraq Lebanese University LaRRIS Laboratory Computer Science Department Fanar Lebanon Lebanese American University Jbeil Lebanon University of Balamand Telecom and Networks Department Koura Lebanon

ISBN: (纸本)9798350367560

The problem of Multiple Sequence Alignment is regarded as one of the many issues that are categorized as NP-Hard. Firstly, it necessitates, like any other such system, a specific encoding scheme and precise design of the key characteristics of the heuristic algorithm being created and implemented. This paper aims to clarify our suggested universal interpretation of the stochastic evolution (Stoc-E) and Simulated Evolution (SimE) algorithms. Undoubtedly, heuristic search methods such as Sim-E and Stoc-E are more effective than the simplified local search methods observed in Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS), which we have already analyzed, for tackling these kinds of problems. Although other works in the literature have examined the use of refined methodology or a restricted set of heuristics, we have steadfastly adhered to our original objective of aligning as many heuristics as feasible, even if these two algorithms are better suited for the placement category of algorithms. Hence, we first developed a thorough encoding scheme that is compatible with all the above techniques. Consistent with our prior study, we are simultaneously presenting several pseudo-code options for these algorithms, together with the capability to trace functions, which will provide further examination for the purpose of adjusting starting parameters. © 2024 IEEE.

关键词： Encoding (symbols)

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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computer Modeling in Engineering & sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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