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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

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Differential Privacy Protection with Group Onion Routing based on AI-based URL Classification 20

Differential Privacy Protection with Group Onion Routing bas...

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Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： I-Hsien Liu Yung-Lin Chang Jung-Shian Li Chuan-Gang Liu Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan Department of applied informatics and multimedia Chia-Nan University of Pharmacy and Science Tainan Taiwan

ISBN: (纸本)9781450383042

Due to the rapid spread of tablet computers, smartphones, and other mobile information devices, wireless communication technology is fully developed and deployed widely. Mobile Internet access has been a main and important way to communicate the world in daily and it privacy protection also catches much attentions. The Onion Router, better known as Tor, is a technique for anonymous communication over internet without regional restrictions for privacy protection. Apart from this, Tor can reach sites that normal search engine cannot search. As the opinion of Tor, the transmitted data has been encrypted layer by layer, just like onion, before it reaching server. Our research proposed a system predicting URL's category with the use of machine learning technique before visiting. According to the category prediction, we represent different privacy level with three kinds of RSA key lengths on onion routing. Depending on various situations, our system can obtain the balance between security and time cost. Hence, our proposed scheme can make onion routing more flexible and efficiently.

关键词： URL classification Machine learning Privacy Anonymous transmission Tor

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Modern Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Wu, Huaming Patros, Panos Ottaviani, Carlo Arora, Priyansh Pujol, Victor Casamayor Haunschild, David Parlikad, Ajith Kumar Cetinkaya, Oktay Lutfiyya, Hanan Stankovski, Vlado Li, Ruidong Ding, Yuemin Qadir, Junaid Abraham, Ajith Ghosh, Soumya K. Song, Houbing Herbert Sakellariou, Rizos Rana, Omer Rodrigues, Joel J.P.C. Kanhere, Salil S. Dustdar, Schahram Uhlig, Steve Ramamohanarao, Kotagiri Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Center for Applied Mathematics Tianjin University Tianjin China Raygun Performance Monitoring Wellington New Zealand Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Microsoft Schiphol Netherlands Distributed Systems Group Vienna University of Technology Vienna Austria Detecon International GmbH Munich Germany Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Department of Computer Science University of Western Ontario London Canada Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Institute of Science and Engineering Kanazawa University Japan Tecnun School of Engineering University of Navarra Spain Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Bennett University Greater Noida India Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Baltimore United States Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia COPELABS Lusófona University Lisbon Portugal Sydney Australia The University of Melbourne Victoria Australia Laboratory School of Computing and Information Systems The University of Melbourne Australia

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress. © 2024, CC BY.

关键词： Obsolescence

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Security Protection with Dynamic Assigned Address Mechanism in Industrial Control Systems 20

Security Protection with Dynamic Assigned Address Mechanism ...

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Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： Chuan-Gang Liu Chin-Jui Wu I-Hsien Liu Chi-Che Wu Jung-Shian Li Department of Applied Informatics and Multimedia Chia-Nan University of Pharmacy and Science Tainan Taiwan Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781450383042

In recent years, industrial control systems (ICS) are widely used in many occasions, which makes information security of ICS an important issue. This study discusses the communication and management on ICS to make ICS intelligent. We proposed a method to avoid attack of communication, and elaborated the means of our proposed communication in which all communication parties communicate with each other without transmitting IP information. Moreover, as an intelligent ICS, the transmission module we design can detect anomaly in the system and change the transmission rule based on our algorithm, also known as a type of moving target defense. Therefore, we proposed the scheme to improve the transmission security in ICS. The experimental results also show that our scheme can be feasible in ICS.

关键词： Intelligent system Industrial control systems Moving target defense Network security IPv6

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Prediction of Heat Energy Release Rate for Ammonia Combustion in a Constant Volume Combustion Chamber: A Machine Learning Approach

Prediction of Heat Energy Release Rate for Ammonia Combustio...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Lijia Fang Hardeep Singh Mitsuhisa Ichiyanagi Takashi Suzuki Maria Simona Raboaca Constantin Filote Chaman Verma Department of Engineering and Applied Sciences Sophia University Tokyo Japan Turbulence and Energy Laboratory University of Windsor Windsor Canada ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Faculty of Informatics Eötvös Loránd University Budapest Hungary

Addressing global warming requires immediate and concerted action. Transitioning to non-conventional energy sources, such as solar and Aeolian power, reducing emissions from transportation and industry, and implementing sustainable land-use practices are crucial steps. Therefore, Ammonia attracts researchers when used as a fuel, and offers several potential benefits such as higher energy density. However, due to poor combustion speed of Ammonia offers slower energy release. To enhance the average flame velocity of Ammonia, the current investigation employed the exhaust gas expelled from the secondary chamber to compress the mixture within the primary chamber and facilitate its HCCI combustion. The main goal of research is the optimization on air-fuel ER to contribute to the development of more efficient and sustainable constant volume combustion. However, the time required for constructing physical prototypes and conducting experiments can be considerable, thus, to mitigate these challenges, the authors turned to machine learning tools as an alternative approach. In the proposed research, results were evaluated through Support Vector Regression. However, due to poor prediction accuracy, authors tried KNeighborsRegressor model for predicting and validating the results. Finally, it is revealed from the KNN model that data sets are highly accurate.

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Technical Debt Management: The Road Ahead for Successful Software Delivery

Technical Debt Management: The Road Ahead for Successful Sof...

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Software engineering: Future of Software engineering (ICSE-FoSE), IEEE/ACM International Conference on

作者： Paris Avgeriou Ipek Ozkaya Alexander Chatzigeorgiou Marcus Ciolkowski Neil A. Ernst Ronald J. Koontz Eltjo Poort Forrest Shull Dept. of Computing Science University of Groningen Groningen The Netherlands Software Engineering institute Carnegie Mellon University Pittsburgh PA USA Dept. of Applied informatics University of Macedonia Thessaloniki Greece QAware GmbH München Germany Department of Computer Science University of Victoria Victoria Canada The Boeing Company Mesa AZ USA CGI Rotterdam The Netherlands

Technical Debt, considered by many to be the ‘silent killer’ of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a system, either deliberately or inadvertently. We understand Technical Debt is not all derogatory, often serving the purpose of expediency. But, it is associated with a clear risk, especially for large and complex systems with extended service life: if we do not properly manage Technical Debt, it threatens to “bankrupt” those systems. Software engineers and organizations that develop software-intensive systems are facing an increasingly more dire future state of those systems if they do not start incorporating Technical Debt management into their day to day practice. But how? What have the wins and losses of the past decade of research and practice in managing Technical Debt taught us and where should we focus next? In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt; we subsequently distill the gaps in industrial practice and the research shortcomings, and synthesize them to define and articulate a vision for what Technical Debt management looks like five years hence.

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Accelerating SDN control plane with GPGPU-based packet classification 17

Accelerating SDN control plane with GPGPU-based packet class...

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17th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Balaz, Martin Helebrandt, Pavol Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (纸本)9781728149677

The increasing number of network traffic and packet processing in Software Defined Networks can be a challenge for the slower handling performance by control plane in software. Parallel processing of network traffic offers a solution to increase the performance of software-based control planes. Integrating large amounts of additional general-purpose CPU cores is relatively costly for achieving a substantial boost in packet classification by parallel processing. In contrast, simplified compute cores in the GPUs of today deliver better performance in execution of massively parallel tasks and offer better scalability. The motivation of this work is to utilize the performance of the GPU computational power for acceleration of control plane. The focus of this paper is packet classification in parallel processing on GPU by applying the principles of parallel programming into existing algorithms. © 2019 IEEE.

关键词： Graphics processing unit

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Enhanced approach on solving Online Knapsack Problem to improve Load Balancing

Enhanced approach on solving Online Knapsack Problem to impr...

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International Conference on Telecommunications and Signal Processing (TSP)

作者： Marek Galinski Juraj Volko Ivan Kotuliak Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (数字)9781728163765

ISBN: (纸本)9781728163772

Distributing data flows of given bandwidth between various disjoint network paths of given bandwidth capacity, is a non-trivial task when the flows are considered unsplittable, thus we cannot send its packet via different paths, but all packets of a flow must be transmitted within the same path. This problem can be described as a modification of the well-known Knapsack problem. In this paper, we solve the Knapsack problem applied to resource allocation. We try to match items to individual knapsack and to use the maximum capacity of all knapsacks. Packing of items is managed by the QFLA algorithm, which tries to pack the coming item to knapsack with the biggest remaining capacity and if it is impossible then try to move items between knapsacks to make a bigger capacity of some knapsack and to place the item there. It is necessary to work in the real-time so we decided to use the structure of Binary Search Trees. It is used to keep knapsack ordered with its remaining capacity.

关键词： Time complexity Bandwidth Real-time systems Binary search trees Monitoring Approximation algorithms

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National institute of Standard and Technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model 2

Latent fingerprint enhancement and segmentation technique ba...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Miaikil, Othman A.M. Mahmoud Altrad, Abdullah Baker El-Ebiary, Yousef A. Hilles, Mohanad M Contreras, Jennifer Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia University College of Applied Science Information Technology Department Gaza Palestine FEU Institute of Technology Department of Information Technology Manila Philippines

ISBN: (纸本)9781665432221

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE.

关键词： Image enhancement

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