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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Rajagopal, Sushma Bharathi Mohan, G. Doss, Srinath Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350372816

Given the global rise in antibiotic resistance, antimicrobial peptides (AMPs) provide a viable substitute for traditional antibiotics. Through a comparison of transformer-based, deep learning (DL), and machine learning (ML) techniques, this work seeks to classify AMPs. AMP sequences from curated databases were compared with non-AMP sequences obtained from UniProtKB/Swiss-Prot. The study compares performance across many assessment variables to assess how well various algorithms separate AMPs from non-AMPs. Significantly, RoBERTa outperformed DistilBERT (84.43%) in accuracy (86.53%). To provide a thorough knowledge of the algorithms' performance, the research also included a variety of other assessment criteria, including area under the receiver operating characteristic curve (AUC-ROC), recall, F1 score, and accuracy. The performance of the LSTM and GRU models was notable, despite their significantly lower accuracies. Furthermore, conventional machine learning methods such as Naive Bayes and Logistic Regression demonstrated competitive accuracy. This thorough analysis clarifies the benefits and drawbacks of various AMP classification techniques, providing insightful information for next studies on antimicrobial peptides and their potential to fight antibiotic resistance. © 2024 IEEE.

关键词： Contrastive Learning

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Comparative Analysis of News Articles Summarization using LLMs

Comparative Analysis of News Articles Summarization using LL...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Archanaa, N. Shivanesh, B. Suwin Kumar, J.D.T. Bharathi Mohan, G. Doss, Srinath Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Botho University Faculty of Engineering and Technology Botswana

ISBN: (纸本)9798350361537

In the rapidly evolving domain of Natural Language Processing (NLP), the efficiency of Large Language Models (LLMs) in generating abstractive text summaries plays a pivotal role in information synthesis. This study advances the understanding of LLM performance by conducting a comprehensive evaluation of seven cutting-edge models on four distinct datasets. The models selected for comparison include Distilbart-cnn-12-6, Led-base-16384, Google's Bigbird, Microsoft's ProphetNet, Facebook's BART, T5 fine-tuned, and Google's PEGASUS. Each model's summarization prowess is rigorously assessed using a battery of metrics: ROUGE, METEOR, BERTScore, Cosine Similarity, and BLEU. The goal is to discern the intricate relationship between dataset characteristics and model efficacy, delivering insights into the inherent advantages and limitations of each model in handling specific data contexts. The results contribute to a refined understanding of LLM applicability, offering empirical evidence to aid in the selection of the most suitable model for varied summarization needs. © 2024 IEEE.

关键词： Natural language processing systems

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A Yet Powerful Routing Protocol for Vehicular Ad-hoc Networks (VANETs) Utilizing Software Defined Network (SDN) and Fog computing 8

A Yet Powerful Routing Protocol for Vehicular Ad-hoc Network...

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8th International Conference on Information Technology, Information Systems and electrical engineering, ICITISEE 2024

作者： Darabkh, Khalid A. Al-Mistarihi, Mamoun F. Al-Maaitah, Mera Ismail School of Engineering The University of Jordan Computer Engineering Department Amman11942 Jordan Jordan University of Science and Technology Faculty of Engineering Electrical Engineering Department Irbid22110 Jordan

ISBN: (纸本)9798350368970

In this article, we propose a novel protocol that employs different technologies together, such as Software Defined Network (SDN) for the purpose of offering scalability, programmability, and global network information, and Fog computing for the reason of providing location services that meet V ANET standards. Additionally, we propose a novel cluster head selection algorithm for the purpose of enhancing the efficiency of the protocol by considering three different dimensions, namely, lifetime, average distance, and signal-to-interference noise ratio. The novelty of this proposed algorithm comes from reducing the number of control overheads, reducing long-distance communications, and reducing packet loss due to collisions and contentions inside each cluster, respectively. The proposed protocol has been assessed using N s3 simulator whereas its results are so promising and worth publishing. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions,and Future Directions

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computers, Materials & Continua 2024年第8期80卷 2511-2533页

作者： Ahmad Rahdari Ahmad Jalili Mehdi Esnaashari Mehdi Gheisari Alisa A.Vorobeva Zhaoxi Fang Panjun Sun Viktoriia M.Korzhuk Ilya Popov Zongda Wu Hamid Tahaei Institute of Artificial Intelligence Shaoxing UniversityShaoxing312000China Department of Computer Engineering Gonbad Kavous UniversityGonbad-e Kavus49717-99151Iran Faculty of Computer Engineering K.N.Toosi University of TechnologyTehran196976-4499Iran Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Secure Information Technologies Department National Research University ITMOSt.Petersburg197101Russia School of Electrical and Computer Engineering Shiraz UniversityShiraz71946–84334Iran Department of Computer Science and Engineering Islamic Azad UniversityDamavand***Iran Department of R&D Shenzhen BKD Co.Ltd.Shenzhen518000China

Software-Defined Networking(SDN)represents a significant paradigm shift in network architecture,separating network logic from the underlying forwarding devices to enhance flexibility and centralize ***-rently,the Internet of Things(IoT)connects numerous devices to the Internet,enabling autonomous interactions with minimal human ***,implementing and managing an SDN-IoT system is inherently complex,particularly for those with limited resources,as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN *** findings of this study underscore the primary security and privacy challenges across application,control,and data planes.A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy *** investigations have explored cutting-edge methods,such as leveraging blockchain for transaction recording to enhance security and privacy,along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service(DoS)and Distributed DoS(DDoS)***,the analysis indicates that encryption and hashing techniques are prevalent in the data plane,whereas access control and certificate authorization are prominently considered in the control plane,and authentication is commonly employed within the application ***,this paper outlines future directions,offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.

关键词： Security privacy-preserving software-defined network internet of things

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TCOA: Triple-Check Offloading Algorithm for Roadside Units and Vehicular Microclouds in 5G Networks and Beyond

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IEEE Access 2023年 11卷 84985-85001页

作者： Qiu, Bo-Jun Hsieh, Cheng-Ying Chen, Jyh-Cheng Dressler, Falko National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Tu Berlin School of Electrical Engineering and Computer Science Berlin10623 Germany

Next-generation intelligent transportation systems aim to achieve many cooperative perception and cooperative driving functions that require considerable computational resources. Offloading such tasks via mobile edge computing is considered part of the solution;this approach is currently being studied within the scope of 5G networks and beyond. In the automotive context, such edge systems could be roadside units (RSUs), which can easily be overloaded at peak times. Vehicular microcloud approaches have been proposed to overcome such problems by sharing the computational resources of nearby cars. In this study, we propose an offloading system architecture to enable such offloading in a vehicular microcloud interconnected by a 5G core network. We model the system as a queueing model to derive closed-form solutions for selected performance metrics. Based on our insights, we propose the triple-check offloading algorithm (TCOA) to obtain both the best offloading ratio of the vehicular microcloud to the entire offloading system and the optimal maximum number of the remaining vehicle instances in the vehicular microcloud. Our simulation results show that the proposed TCOA achieves better system performance than four other offloading schemes in terms of cost, response time, service rate, and cost response-time production service rate division (CRPSD). © 2013 IEEE.

关键词： 5G mobile communication systems

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CRAYM: Neural Field Optimization via Camera RAY Matching 38

CRAYM: Neural Field Optimization via Camera RAY Matching

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhanced Cross Z-Complementary Set and Its Application in Generalized Spatial Modulation

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 4674-4690页

作者： Huang, Zhen-Ming Pai, Cheng-Yu Liu, Zilong Chen, Chao-Yu Institute of Computer and Communication Engineering National Cheng Kung University Tainan701 Taiwan National Cheng Kung University Department of Engineering Science Tainan701 Taiwan National Cheng Kung University Department of Electrical Engineering Tainan701 Taiwan University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

Generalized spatial modulation (GSM) is a novel multiple-antenna technique offering flexibility among spectral efficiency, energy efficiency, and the cost of RF chains. In this paper, a novel class of sequence sets, called enhanced cross Z-complementary set (E-CZCS), is proposed for efficient and feasible training sequence design in broadband GSM systems. Specifically, an E-CZCS consists of multiple CZCSs possessing front-end and tail-end zero-correlation zones (ZCZs), whereby any two distinct CZCSs have a tail-end ZCZ when a novel type of cross-channel aperiodic correlation sums is considered. The theoretical upper bound on the ZCZ width is first derived, upon which E-CZCSs with maximum ZCZ width and flexible parameters are constructed. For optimal channel estimation over frequency-selective channels, we introduce and evaluate a novel GSM training framework employing the proposed E-CZCSs. Numerical results demonstrate that the proposed E-CZCS-based training can achieve the minimum channel estimation mean square error (MSE) and outperform other classes of sequences. © 2020 IEEE.

关键词： Mean square error

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Broadband spin and angle co‑multiplexed waveguide‑based metasurface for six‑channel crosstalk‑free holographic projection

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eLight 2024年第1期4卷 223-237页

作者： Zeyang Liu Hao Gao Taigao Ma Vishva Ray Niu Liu Xinliang Zhang L.Jay Guo Cheng Zhang School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Physics University of MichiganAnn ArborMI 48109USA Lurie Nanofabrication Facility Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborMI 48109USA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA

Metasurface-based holograms,or metaholograms,offer unique advantages including enhanced imaging quality,expanded field of view,compact system size,and broad operational ***-channel metaholograms,capable of switching between multiple projected images based on the properties of illuminating light such as state of polarization and angle of incidence,have emerged as a promising solution for realizing switchable and dynamic holographic ***,existing designs typically grapple with challenges such as limited multiplexing channels and unwanted crosstalk,which severely constrain their practical ***,we present a new type of waveguidebased multi-channel metaholograms,which support six independent and fully crosstalk-free holographic display channels,simultaneously multiplexed by the spin and angle of guided incident light within the glass *** employ a k-space translation strategy that allows each of the six distinct target images to be selectively translated from evanescent-wave region to the center of propagation-wave region and projected into free space without crosstalk,when the metahologram is under illumination of a guided light with specific spin and azimuthal *** addition,by tailoring the encoded target images,we implement a three-channel polarization-independent metahologram and a two-channel full-color(RGB)***,the number of multiplexing channels can be further increased by expanding the k-space’s central-period region or combing the k-space translation strategy with other multiplexing techniques such as orbital angular momentum *** work provides a novel approach towards realization of high-performance and compact holographic optical elements with substantial information capacity,opening avenues for applications in AR/VR displays,image encryption,and information storage.

关键词： Metasurface Multi-channel metahologram Color metahologram Waveguide display

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Shower vs. Bath: A Comparative Study of Water Usage and Environmental Impact

Shower vs. Bath: A Comparative Study of Water Usage and Envi...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： George, Angelina Ashik, F. Jose, Alphonsa Kumar, T.M. Mohan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Chemistry Bengaluru India

ISBN: (纸本)9798350359688

This study investigates the usage of water in urban areas, with particular attention to location, age, water quality, and bathing habits. We examined the data using machine learning, more especially a RandomForestClassifier. After accounting for several variables, the study's accuracy rate in predicting the types of baths was 83.63% without SMOTE and reached 90.51% after SMOTE analysis. Our knowledge of urban water consumption is improved by this research, which also shows how flexible machine learning is in identifying intricate links within datasets. The results of this study can help with resource management, urban planning, and environmental initiatives. © 2024 IEEE.

关键词： Bath Type Preferences Dataset Analysis Machine Learning Urban Water Consumption

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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