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15th IFIP Wireless and Mobile Networking Conference, WMNC 2024

作者： Memarian, Mohsen Kassler, Andreas Grinnemo, Karl-Johan Laki, Sandor Pongracz, Gergely Forsman, Johan Karlstad University Computer Science Department Karlstad Sweden Institute of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany ELTE Eötvös Loránd University Faculty of Informatics Budapest Hungary Ericsson Research Budapest Hungary TietoEvry TBD Sweden

ISBN: (纸本)9783903176683

The traditional method of data plane programming involves deploying a single P4 program to a single target. However, different targets have varying capabilities, functionalities, and support for various programming languages beyond P4. Therefore, disaggregating a single data plane program into multiple subprograms that run on different targets can allow us to leverage the strengths of each target, which becomes particularly important in the context of 5G, where some data plane processing functions, such as buffering and retransmission for RLC processing, cannot be effectively expressed in P4. This paper delves into the decomposition of a 5G gNB across a P4-programmable SmartNIC and an x86 server using DPDK-based processing, thus harnessing the strengths of each target. Our evaluation revealed that offloading certain processing to an x86 server can improve throughput by up to 50%, thanks to the scalability of DPDK applications' performance with the number of CPU cores. However, offloading does introduce a slight increase in latency, so the approach should be adjusted based on the specific needs and available resources. © 2024 IFIP.

关键词： Data handling

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Annealing-Based Optimization for Selecting Training Space in Ensemble Learning 10

Annealing-Based Optimization for Selecting Training Space in...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Yulianti, Lenny Putri Trisetyarso, Agung Santoso, Judhi Surendro, Kridanto School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia School of Computer Science Bina Nusantara University Department of Mathematics Jakarta Indonesia

ISBN: (纸本)9798350329919

Ensemble learning integrates many learners to achieve better performance, however, it can increase the complexity dramatically. Redundant learners should be pruned to reduce ensemble complexity while allowing for increased accuracy. There are two common ways to achieve this, which include selecting the optimal training space and the optimal trained learners. This study contributes to reducing redundant learners by selecting the optimal training space using annealing-based optimization. The optimal training space was defined by 1) measuring the similarity between training spaces (clusters) and 2) searching for the best set of training spaces by minimizing the similarity between the selected training spaces. The proposed method was tested on 10 datasets from the UCI repository and compared to five state-of-The-Art random subspace methods. The results showed that the proposed method could reduce the size of the ensemble while significantly increasing its accuracy. © 2023 IEEE.

关键词： Annealing

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Link Prediction in Educational Graph Data to Predict Elective Course using Graph Convolutional Network Model

Link Prediction in Educational Graph Data to Predict Electiv...

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2023 International Conference on computer Science, Information technology and Engineering, ICCoSITE 2023

作者： Indah Susanti, Meilia Nur Heryadi, Yaya Rosmansyah, Yusep Budiharto, Widodo Computer Science Bina Nusantara University Computer Science Departement Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta Indonesia

ISBN: (纸本)9798350320954

Personalized learning has achieved the attention of many researchers in the Education field. Personalized learning is a teaching model in which students (learners) have a central role in the learning process. By using this approach, educational methods, and techniques are customized and adapted to be better suited for each learner, with their unique learning style, background, needs, and previous experiences. Based on what the learners have already learned, subjects have already known, and skills have already developed each student in a personalized learning process will get a "learning plan". This approach is different from a conventional approach or known as the "one size fits all"approach. The challenge of personalized learning is how to connect a learner's previous knowledge, skills and with learning materials that will link that understanding with new knowledge. This paper presents a novelty technique to implement personalized learning by automating a predicted linkage between a student in higher education and elective courses based on previous learning achievement. In this study, Graph Convolutional Networks (GCNs) are used to address link prediction tasks between student and elective courses. The empirical results showed that the GCN model can be used to predict elective courses for a student with 62.5 % average accuracy. © 2023 IEEE.

关键词： Forecasting

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Palm Oil Maturity Classification Using K-Nearest Neighbors Based on RGB and L*a*b Color Extraction 27

Palm Oil Maturity Classification Using K-Nearest Neighbors B...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Saifullah, Shoffan Prasetyo, Dessyanto Boedi Indahyani Drezewski, Rafal Dwiyanto, Felix Andika Institute of Computer Science AGH University of Science and Technology Kraków Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta Indonesia

This study aims to classify the maturity level of oil palm using the K-Nearest Neighbors (KNN) method based on the extraction of RGB and L*a*b color features. This classification determines the optimal oil production based on the color of the palm. However, this process is often carried out by humans, so sorting is not optimal because it is done manually, and it takes a long time. Thus, we propose automatic detection using machine learning based approach. In this study, the image processing methods are used to classify the level of oil palm maturity. The process starts with taking 150 photos with a smartphone. Each of them is then grouped into 3 classes, namely ripe (50), intermediate (50), and unripe (50). The oil palm image is pre-processed using the segmentation method (removing background), resizing, and cropping. Then, color features using RGB and L*a*b are extracted. Each of these extracted color features becomes input in the training and testing process of the KNN algorithm and is validated using k-fold cross-validation, with the results of accuracy being 95.4% and 97.4%. The combination of KNN and L*a*b color extraction approaches has better accuracy than KNN and RGB in terms of model accuracy (by 2-4.8 percentage points). © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Color Extraction Image Processing K-Nearest Neighbors L*a*b Palm Oil Maturity Classification RGB

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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 2nd

New Cloud Computing Authentication Based on Secure Hash Algo...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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Age-of-Information and Energy Optimization in Digital Twin Edge Networks

Age-of-Information and Energy Optimization in Digital Twin E...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Yongna Fu, Yaru Zhang, Yan Quek, Tony Q. S. Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Hong Kong Metropolitan University School of Science and Technology Hong Kong University of Oslo Department of Informatics Oslo Norway Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350351255

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency. © 2024 IEEE.

关键词： Costs

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Deep Learning Approaches for Stress Detection: A Survey

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IEEE Transactions on Affective Computing 2024年第2期16卷 499-517页

作者： Kyrou, Maria Kompatsiaris, IoannisSenior Petrantonakis, Panagiotis C. Aristotle University of Thessaloniki Artificial Intelligence and Information Analysis Laboratory Dept. of Informatics GR 54124 Greece Information Technologies Institute Centre for Research and Technology Hellas GR 57001 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering GR 54124 Greece

Stress has a severe impact on individuals irrespective of age, sex, work, or background. The reliable development of stress detection techniques enhances the social, educational, physical, economic, and professional quality of life, preventing chronic stress and proposing alleviation strategies. Research studies examine psychological, cognitive, behavioral, and physiological reactions to identify stress adequately. Deep Learning (DL) has received significant attention in recent years as it deals with high-dimensional, heterogeneous data and automatically learns representative features. This paper presents a survey on stress detection with recent DL approaches, leveraging data from all possible sources (physiological, speech, facial expressions, gestures, and social media content). The methodological outlines, the best results, and the main contributions of each study are discussed. We also describe publicly available datasets used by several of the presented works. Finally, we emphasize various open issues within the field of research and highlight key directions for future work © 2024 IEEE.

关键词： Social psychology

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QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction 22nd

QG-SKI: Question Classification and MCQ Question Generation ...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Dhanvardini, R. Deepak, Gerard Santhanavijayan, A. Health Care Informatics Domain Optum-United Health Groups Hyderabad India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

E-Learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. The utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. This paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. The proposed QG-SKI model uses LOD Cloud and Wiki Data to generate Ontologies dynamically and Knowledge reservoir is performed. The dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. These algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. The suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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Integrating Data Protection at Interface of RISC-V Processor Core

Integrating Data Protection at Interface of RISC-V Processor...

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Panhellenic Conference on Electronics and Telecommunications (PACET)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Slovakia

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead.

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The Goofus & Gallant Story Corpus for Practical Value Alignment 23

The Goofus & Gallant Story Corpus for Practical Value Alignm...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Al Nahian, Md Sultan Tasrin, Tasmia Frazier, Spencer Riedl, Mark Harrison, Brent Institute for Biomedical Informatics University of Kentucky LexingtonKY United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States University of Kentucky Department of Computer Science LexingtonKY United States

ISBN: (纸本)9798350374889

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact. © 2024 IEEE.

关键词： Adversarial machine learning

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