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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 IEEE.

关键词： Feature Selection

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Self-Optimizing Memory Controllers: Proposing Request-level Scheduling 2

Self-Optimizing Memory Controllers: Proposing Request-level ...

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2nd International Conference on computer Science, engineering and Applications, ICCSEA 2022

作者： Shawky, Alaa Watheq El-Kharashi, M. Safar, Mona Dessouky, Mohamed Ain Shams University Faculty of Engineering Electronics and Communications Dept. Cairo Egypt Siemens Digital Industries Software Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Dept. Cairo Egypt

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Self-Optimizing Memory Controllers present a great potential in the future of memory controllers. As they alleviate the burden of designing an optimal memory scheduling policy, while providing adaptability to different workloads and systems. Previous state-of-art controllers scheduled memory accesses on the command level. This causes the agent to lose sight of the relation between commands and can lead to the issuing of unuseful sequences of precharge/activate of rows or closing banks that should have been left open, and vice versa. In this work we propose a bank-aware self-optimizing memory controller that schedules memory references on the request-level, presenting to the agent the actual problem it needs to tackle. This simplifies the evaluation of system attributes and eliminates the need for correction mechanisms that was needed in previous controllers. With the proposed algorithm a maximum of 18% increase in the data bus utilization is achieved compared to the commonly used FR-FCFS algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Ridho Hendra Yoga Perdana Toan-Van Nguyen Yushintia Pramitarini Duy H. N. Nguyen Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea Department of Electrical and Computer Engineering San Diego State University San Diego CA USA Dept. of Software and Communications Engineering Hongik University Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance.

关键词： Array signal processing Precoding Simulation Quality of service Programming Search problems Prediction algorithms Energy efficiency Real-time systems Optimization

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ATL Model Checking for Analysis of Ethereum Smart Contracts

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Transactions of the Korean Institute of Electrical Engineers 2021年第12期70卷 2006-2014页

作者： Nam, Wonhong Kil, Hyunyoung Dept. of Software Korea Aerospace University Korea Republic of Dept. of Computer Science and Engineering Konkuk University Korea Republic of

A blockchain is a growing list of cryptographically secured blocks to maintain shared data on decentralized systems, in order to archive transactions between untrusted participants. Smart contracts are computer programs that automatically execute legal events according to the terms of contracts. Although the Ethereum blockchain has been successfully applied to a number of interesting applications, there have been several events that subtle flaws in smart contracts induce a huge amount of financial loss such as the DAO attack. Accordingly, to analyze smart contracts, we propose a novel formal verification technique to employ ATL (Alternating-time Temporal Logic) model checking. Our methodology represents the interaction between users and smart contracts with a two-player game and verify game properties by using MCMAS that is an efficient ATL model checker. © The Korean Institute of Electrical Engineers

关键词： Smart contract

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GAN vs. LSTM: Network State Prediction 17

GAN vs. LSTM: Network State Prediction

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Byun, Gyurin Raza, Syed M. Yang, Huigyu Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic. © 2023 IEEE.

关键词： Generative adversarial networks

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Leveraging a Hierarchical Selection of Communities in Social Networks to Maximize Diffusion 9

Leveraging a Hierarchical Selection of Communities in Social...

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9th International Conference on Web Research, ICWR 2023

作者： Heidari, Mohsen Fasahat, Mohamad Ali Afsharian, Sanaz Artishdar, Parinaz Esfarayen University Dept. of Electrical Computer and Industrial Engineering North Khorasan Iran Mehr Alborz Higher Education Institute Dept. of Computer Science Tehran Iran Islamic Azad University Damavand Branch English Department Tehran Iran Islamic Azad University Yadegar Emam Khomeini Branch Dept. of Software Engineering Tehran Iran

ISBN: (纸本)9798350399691

For businesses, discovering key nodes in social networks has a high computational overhead and cost. Because of this, in the problem of influence maximization, at least key nodes with the most influence in social networks are chosen at the lowest possible cost. Numerous algorithms have been proposed to find key nodes in social networks using the technique of influence maximization under the characteristics of submodularity and monotonicity;however, these algorithms struggle with the issue of optimal influence spread because some graph characteristics are ignored when considering diffusion and influence spread in the social network. The HSMD method, which gives special weight to the structure of communities and selects key nodes hierarchically utilizing the structure of communities, has been presented as a solution to this challenge. Based on the characteristics and strategic placement of the nodes within each community, weight is given to each community in this algorithm. Seed nodes are then chosen hierarchically using BFS traversal. This method enhanced the influence spread rate and execution time of recently introduced algorithms such as CTIM, IMBC, RNR, HEDVGreedy, and k-shell. © 2023 IEEE.

关键词： Diffusion

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A Task Scheduler for Mobile Edge Computing Using Priority-based Reinforcement Learning

A Task Scheduler for Mobile Edge Computing Using Priority-ba...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Avan, Amin Kheiri, Farnaz Mahmoud, Qusay H. Azim, Akramul Makrehchi, Masoud Rahnamayan, Shahryar Computer and Software Engineering Ontario Tech University Dept. of Electrical OshawaONL1G 0C5 Canada Brock University Dept. of Engineering St. CatharinesONL2S 3A1 Canada

ISBN: (纸本)9781665430654

Edge computing offers cloud-like services closer to users and IoT devices, providing high speed and accessibility for network users. Edge computing, often called Mobile Edge Computing (MEC), is a distributed paradigm that utilizes heterogeneous computational and storage resources with well-provisioned capabilities rather than relying on the ample resources of the cloud. In addition, edge users usually refer to portable and mobile devices that connect to and disconnect from the network at will. Therefore, scheduling tasks at the appropriate time and allocating the right resources can be modeled as a multi-objective optimization problem in MEC. Moreover, each task has specific requirements, further adding to the complexity of the optimization problem. In this study, we formulate the scheduling problem as a Markov Decision Process (MDP) to schedule the tasks. The learning time of the task scheduler is minimized when it faces new users and edge servers. Subsequently, we employ the Q-learning (QL) algorithm from the Reinforcement Learning (RL) paradigm to address the optimization problem and effectively adapt the proposed scheduler to the dynamic nature of MEC. Accordingly, we designed the valid state space, action space, and reward function with appropriate conditions and proper rewards for the proposed QL-based technique. We conducted comprehensive experiments to validate the results of the proposed solution, taking into account the inherent randomness of the QL-based technique. The experimental results demonstrate that the proposed technique achieves the lowest learning time compared to Deep learning-based and Deep RL-based approaches. Furthermore, on average, the proposed technique obtains a 72% faster runtime compared to previous works, using 58% fewer computation cycles and 50% less memory. These improvements make the proposed approach an efficient and lightweight task scheduler for MEC. © 2023 IEEE.

关键词： Edge Computing Machine Learning Markov Decision Process Mobile Edge Computing Reinforcement Learning Task Scheduling

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Federated Learning-Enhanced QoS Multicast Routing to Support RIS and Edge Computing in IoT-Enabled MANETs with CF-mMIMO

Federated Learning-Enhanced QoS Multicast Routing to Support...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Amalia Amalia Yushintia Pramitarini Ridho Hendra Yoga Perdana Kyusung Shim Beongku An Dept. of Software and Communications Engineering in Graduate School Hongik University Republic of Korea School of Computer Engineering and Applied Mathematics Hankyong National University Republic of Korea Dept. of Software and Communications Engineering Hongik University Republic of Korea

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

In this paper, we propose a novel federated learning (FL)-enhanced quality of service (QoS) multicast routing, called FLQMR protocol, in IoT-enabled mobile ad-hoc networks (MANETs) with cell-free massive multiple input multiple output (CF -mMIMO). The main contributions of this paper can be summarized as follows. First, we consider the integration of cross-layer design, reconfigurable intelligent surfaces (RIS), FL, and edge computing to enhance network performance. Second, we design the FL framework to optimize routing decisions by selecting the best paths from the source node to multiple destinations. Third, we employ a cross-layer design that combines the physical layer information (i.e., mobility, position, SE) with the network layer information (i.e., route information) to establish a stable multicast tree from the source node to multiple destinations. The simulation results show that the proposed FLQMR protocol achieves a high packet delivery ratio, low routing delay, and low control overhead.

关键词： Cross layer design Simulation Quality of service Reconfigurable intelligent surfaces Routing Physical layer Ad hoc networks

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Measuring Influencing Factors of API Usability

Measuring Influencing Factors of API Usability

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Tian, Jeff Alanazy, Sultan Bokhary, Abdullah Alharthi, Sultan Ghanem, Sakher Southern Methodist University Dept of Computer Science DallasTX United States College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia College of Computer Science and Engineering University of Jeddah Dept of Software Engineering Jeddah Saudi Arabia College of Computer Science and Engineering University of Jeddah Dept of Computer Science and Artificial Intelligence Jeddah Saudi Arabia

ISBN: (纸本)9798350320282

In this paper, we identify the influencing factors of API usability by extending a comprehensive framework for measuring API usability we proposed earlier, through a systematic examination of entities involved and artifacts produced in API development and usage. Existing research in software metrics are adapted to define metrics capturing relevant metrics dimensions and at the appropriate levels. Our current set of identified influencing factors includes metrics defined on API documentation and other entities/artifacts, in addition to the code metrics. Some preliminary results from actual measurement of such influencing factors are included to demonstrate the viability of our approach. © 2022 IEEE.

关键词： Application programs

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Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-Free Massive MIMO

Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-F...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Tran, Thong-Nhat Interdonato, Giovanni Da Costa, Daniel Benevides An, Beongku Hongik University Dept. of Electronics and Computer Engineering Graduate School Korea Republic of Italy Digital Science Research Center Technology Innovation Institute Abu Dhabi Masdar City United Arab Emirates Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350370218

This paper evaluates the uplink spectral efficiency (SE) achieved by a reconfigurable intelligent surface (RIS)-aided cell-free massive MIMO system with max-min fairness (MMF) power allocation. We provide an exact closed-form expression for an achievable SE expression which assumes maximum ratio receive combining at the access points (APs) and centralized large-scale fading decoding at the central processing unit. The derived SE expression also accounts for imperfect channel estimation and spatially correlated Ricean fading channels for both direct (APs-users) and cascaded (APs-RISs-users) links. Alongside this, we provide a generalized SE expression and evaluate the performance achieved by local partial minimum mean-squared error combining. Finally, we optimally solve the RIS phase-shift selection problem in order to maximize the aggregate channel gains, and derive a closed-form solution to the MMF power allocation problem. Simulation results validate the effectiveness of the proposed framework in enhancing the unlink SE with resnect to various benchmarks. © 2023 IEEE.

关键词： Cell-free massive MIMO max-min fairness power control reconfigurable intelligent surface spectral efficiency

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