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A New HARQ Scheme for 5G Systems via Interleaved Superposition Retransmission

China Communications 2023年第4期20卷 1-11页

作者： Qianfan Wang Li Chen Xiao Ma School of Computer Science and Engineering Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhou 510006China College of Computer Science Jiaying UniversityMeizhou 514015China School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou 510006China

Within the framework of the 5G new radio(NR),we propose a new hybrid automatic repeat request(HARQ)scheme to improve the throughput *** difference between the proposed scheme and the conventional one lies in the first retransmission,where the erroneous coded block group is interleaved and superimposed(XORed)onto a fresh coded block *** the receiver,an iterative message-passing decoding algorithm can be employed to recover the target erroneous code block group(CBG).Only when the superposed retransmission fails,the conventional incremental redundancy(IR)or repetition redundancy(RR)retransmission is *** any case,since the first retransmission is along with but has negligible effect on the fresh CBG,it costs neither transmitted power nor ***-Carlo simulation results reveal that the presented HARQ schemes can achieve throughput improvements up to 10%over block fading channels and up to 50%over fast fading channels in comparison with the original 5G CBG-level HARQ scheme but without excessively increasing the implementation complexity.

关键词： 5G NR HARQ LDPC codes superposition retransmission

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Run-time Control Flow Model Extraction of Java Applications 7th

Run-time Control Flow Model Extraction of Java Applications

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7th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022

作者： Saravanan, Gokul Subramani, Goutham Akshay, P.N.S.S. Kanigolla, Nithesh Jevitha, K.P. Department of Computer Science and Engineering Amrita School of Engineering AmritaVishwaVidyapeetham Coimbatore India

ISBN: (纸本)9789811954818

In this paper, we discuss a technique for run-time control flow model extraction of Java application with the help of domain-specific language for instrumentation (DiSL) and Java interactive visualization environment (JIVE) frameworks. DiSL framework is used for bytecode-level instrumentation of Java programs. It provides higher code coverage than other aspect-oriented languages. The application to be analysed is instrumented with DiSL to extract its execution trace. The extracted trace is then fed into the JIVE system to extract its state model based on the key attributes of the execution trace. JIVE framework provides options to generate the sequence, object and state model of the application from its execution trace. The extracted model from the execution trace based on the method calls in the application represents the run-time control flow model. The system was tested with three case studies (two small and one large application). The run-time execution trace and the model were successfully extracted in all the cases with very minimal overhead. The extracted run-time model aids in the comprehension of the run-time behaviour of complex applications and also for verification of properties of interest-based on the execution flow of the application. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extraction

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A novel optimised GWOAN algorithm for scheduling task and power consumption in MEC network

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International Journal of Intelligent Systems Technologies and Applications 2023年第4期21卷 386-401页

作者： Sivakumar, P. Murugan, Murali Somasundaram, S.K. Prabhu, S. Nagendra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Engineering Retail Business Professional Georgia United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Chennai632014 India

In recent decades, the 5G and internet of things (IoT) are occupied with several applications like face recognition, traffic control, video surveillance and telecommunication, etc. Mobile-edge computing (MEC) is a promising paradigm in wireless communication which is carried by the computational scheduling of mobile devices. To enhance the computation design, the AlexNet of the DL model is applied which is based on the convolutional neural network (CNN) used to train a large number of attributes. To provide an optimal solution, the metaheuristics algorithm of grey wolf optimisation (GWO) is combined with an AlexNet which is named a novel grey wolf optimisation-based AlexNet (GWOAN) algorithm. In the proposed GWOAN algorithm, the AlexNet hyperparameters (weights, biases and other parameters) are fine-tuned by GWO and then performed a classification. As a result, the GWOAN has achieved a higher scheduling task and low latency than the standard AlexNet, ResNet-18 and VGGNet-16 respectively. © 2023 Inderscience Enterprises Ltd.

关键词： Optimization

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An Ensemble of Bacterial Foraging, Genetic, Ant Colony and Particle Swarm Approach EB-GAP: A Load Balancing Approach in Cloud Computing

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Recent Advances in computer science and Communications 2022年第5期15卷 693-699页

作者： Dewangan, Bhupesh Kumar Jain, Anurag Shukla, Ram Narayan Choudhury, Tanupriya Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Virtualization School of Computer Science University of Petroleum and Energy Studies Dehradun248007 India Department of Computer Science & Engineering School of Engineering OP Jindal University Raigarh India

Background: In the cloud environment, the satisfaction of service level agreement (SLA) is the prime objective. It can be achieved by providing services in a minimum time in an efficient manner at the lowest cost by efficiently utilizing the resources. This will create a win-win situation for both consumers and service providers. Through literature analysis, it has been found that the procedure of resource optimization is quite costly and time-consuming. Objectives: The research aims to design and develop an efficient load-balancing technique for the satisfaction of service level agreement and the utilization of resources in an efficient manner. Methods: To achieve this, the authors have proposed a new load-balancing algorithm named EBGAP by picking the best features from Bacterial Foraging, Genetic, Particle-Swarm, and AntColony algorithm. A fitness value is assigned to all virtual machines based on the availability of resources and load on a virtual machine. Results: A newly arrived task is mapped with the fittest virtual machine. Whenever a new task is mapped or left the system, the fitness value of the virtual machine is updated. In this manner, the system achieves the satisfaction of service level agreement, the balance of the load, and efficient utilization of resources. To test the proposed approach, the authors have used the real-time cloud environment of the amazon web service. In this, waiting time, completion time, execution time, throughput, and cost have been computed in a real-time environment. Conclusion: Through experimental results, it can be concluded that the proposed load balancing approach EB-GAP has outperformed other load balancing approaches based on relevant parameters. © 2022 Bentham science Publishers.

关键词： Virtual machine

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A Dedicated Consensus Algorithm for Improving Performance of Futuristic Energy Blockchain

A Dedicated Consensus Algorithm for Improving Performance of...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yapa, Charithri De Alwis, Chamitha Wijewardhana, Uditha Liyanage, Madhusanka University of Sri Jayewardenepura Department of Electrical and Electronic Engineering Gangodawila Sri Lanka University of Bedfordshire School of Computer Science United Kingdom University College Dublin School of Computer Science Ireland

ISBN: (纸本)9798350324136

Integrating renewable energy generation in the consumer end has transformed users acting as both buyers and sellers. Uncoordinated interconnections lead to degraded power quality, which demands for continuous network monitoring. This study evaluates the applicability of the network monitoring process to develop a novel consensus protocol, customized for blockchain platforms integrated with smart grids. The motivation for integrating grid monitoring with the blockchain consensus mechanism lies in the significance of decentralized architectures such as distributed ledger technologies for future energy grids. Existing blockchain consensus mechanisms have exhibited excessive energy usage and low scalability due to the additional workload associated. Hence, the study proposes a customised solution, which has the advantage of combining grid monitoring with the blockchain consensus protocol. This eliminates the additional computation burden, reduces the cost of execution, and improves the transaction throughput of the blockchain consensus mechanism. © 2024 IEEE.

关键词： Blockchain Consensus Mechanism Power Line Monitoring Power Quality Smart Grid 2.0

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Detection of Bullying Text: A Multi-faceted Approach using Machine Learning and Natural Language Processing 5

Detection of Bullying Text: A Multi-faceted Approach using M...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumar, Kotyada Mohan Kiran Ullas, Karthik Reddy, Vivek Srikar Snehitha, Munaga Sai Malkhed, Mahadevi E. Kumar, K. Dinesh Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Computer Engineering Bengaluru India

ISBN: (纸本)9798331504403

With the dawn of the new age of communication, i.e., social media, the world has come into the palms of every individual at every corner of the world. However, easier access to messaging implies easier forms of abuse. As the usage of social media increases, the other side of communication, bullying, also increases. Cyberbullying refers to the concept of harassing individuals or groups through means of the internet with the help of messages or multimedia. Cyberbullying has a huge impact on mental health and research is ongoing in this area to accurately detect bullying text. Existing models are embedded into social media platforms to either ban or apply sanctions on the users involved in the passage of such materials. The proposed study aims to develop a model with a similar approach to detect cyberbullying in comment text. Also, sentiment analysis is performed on the comment text to get an idea of the message's context, thus improving the chances of bullying detection. The work is divided into two stages. In the first stage, the text and the sentiment score are directly passed to the machine learning models. Later, during the second stage word embedding using Word2Vec, a natural language processing technique, is performed on the comment text to obtain vectorized features which are then given to the machine learning models along with sentiment features. It was observed that the latter model shows high levels of success and that it can be deployed in real-world applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Investigating Natural Language Techniques for Accurate Noun and Verb Extraction 2

Investigating Natural Language Techniques for Accurate Noun ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nair, Reshma P. Thushara, M.G. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

Natural language processing (NLP) has witnessed significant advancements in recent decades. Automatically classifying parts of speech, like nouns and verbs, from textual input has transformed text analysis and language understanding. Using natural language processing techniques, we explore various methods for identifying noun and verb phrases automatically, with an emphasis on high accuracy. Our study explores rule-based, statistical, and Machine Learning (ML) approaches for determining the nouns and verbs from sentences. The effectiveness of these approaches is clearly evident, especially when NLP libraries such as SpaCy and the Natural Language Toolkit (NLTK) are used. As well as demonstrating their potential applications across diverse language processing tasks and industries, we conduct comparative research to showcase their advantages and disadvantages. The performance of these methods is also examined in terms of retrieving subject and action terms. SpaCy achieves an impressive accuracy of 95% in noun and verb extraction, while Part-Of-Speech (POS) technology tagging delivers an even higher accuracy of 96%. The results obtained with these methods illustrate how nouns, verbs, and names can be classified in text successfully. © 2024 Elsevier B.V.. All rights reserved.

关键词： Extraction

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Discovering Coherent Topics from Urdu Text: A Comparative Study of Statistical Models, Clustering Techniques and Word Embedding 6

Discovering Coherent Topics from Urdu Text: A Comparative St...

引用

6th International Conference on Information and computer Technologies, ICICT 2023

作者： Mustafa, Mubashar Zeng, Feng Manzoor, Usama Meng, Lin Central South University School of Computer Science and Engineering Changsha China Hunan University Department of Computer Science Changsha China Ritsumeikan University Department of Electronic and Computer Engineering Japan

ISBN: (纸本)9798350300956

The volume of data on the internet is continuously expanding due to the abundance of news sources, journals, blogs, contents, and other online publications. The use of Urdu online has grown significantly, much like other languages. Information retrieval (IR) is getting more challenging as data amount rises. The natural language processing (NLP) technique of topic modelling (TM) is crucial for extracting themes or aspects from text. Although there is a long tradition of TM in both English and other western languages, Urdu falls behind in terms of sophisticated NLP tools and resources for TM. The rich morphology of the Urdu language makes TM a challenging task. In this study, we developed a framework of TM and analysed word embedding, statistical models, and clustering techniques for Urdu documents. The aim of this work is to evaluate and compare three distinct approaches based on the coherence measure of extracted topics. The findings of a thorough experiment and evaluation demonstrate that word embedding fails to extract coherent topics in Urdu language, and that the average coherence measure of topics retrieved by clustering approaches outperforms that discovered through statistical models. © 2023 IEEE.

关键词： Embeddings

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Effective and Efficient Community Search with Graph Embeddings 26

Effective and Efficient Community Search with Graph Embeddin...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Gou, Xiaoxuan Xu, Xiaoliang Wu, Xiangying Chen, Runhuai Wang, Yuxiang Wu, Tianxing Ke, Xiangyu Hangzhou Dianzi University School of Computer Science China Southeast University School of Computer Science and Engineering China Zhejiang University School of Software Technology China

ISBN: (纸本)9781643684369

Given a graph G and a query node q, community search (CS) seeks a cohesive subgraph from G that contains q. CS has gained much research interests recently. In the database research community, researchers aim to find the most cohesive subgraph satisfying a specific community model (e.g., k-core or k-truss) via graph traversal. These works obtain good precision, however suffering from the low efficiency issue. In the AI research community, a new thought of using the deep learning model to support CS without relying on graph traversal emerges. Supervised end-to-end models using GCN are presented, which perform efficiently, but leave a large room for precision improvement. None of them can achieve a good balance between the efficiency and effectiveness. This motivates our solution: First, we present an offline community-injected graph embedding method to preserve the community's cohesiveness features into the learned node representations. Second, we resort to a proximity graph (PG) built from node representations, to quickly return the community online. Moreover, we develop a self-augmented method based on KL divergence to further optimize node representations. Extensive experiments on seven real-world graphs show our solution's superiority on effectiveness (at least 39.3% improvement) and efficiency (one to two orders of magnitude faster). © 2023 The Authors.

关键词： Efficiency

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