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IEEE International Conference on Industrial informatics (INDIN)

作者： Hanson Prihantoro Putro Umi Laili Yuhana Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Engineering Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Various approaches have been proposed to detect code smells, including machine learning models. However, there are still challenges to improving detection accuracy and selecting appropriate quality metrics. This research introduces the use of Relevance Vector Machine (RVM) for code smell detection, especially Magic Value smells, which often affect source code readability and maintainability in a software development industry. This approach utilizes source code analysis using two source code representations (raw source code and token stream) and feature extraction using two text mining techniques (Bag of Words/BoW and Term Frequency-Inverse Document Frequency/TF-IDF). The experiment involves analyzing 983 records of labeled source code datasets. The results show that RVM can detect code smells with a very high level of accuracy, reaching 99.9% on some kernel configurations, also nearly perfect precision, and F1 scores. This paper contributes to introducing the use of RVM for detecting code smells in source code, explores source code representation and feature extraction techniques, and shows that RVM can achieve high detection accuracy.

关键词： Text mining Codes Accuracy Source coding Relevance vector machines Feature extraction Vectors Software reliability Kernel Software development management

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Pending Interest Table (PIT) Performance Analysis in Named Data Networking on Palapa Ring Topology 16

Pending Interest Table (PIT) Performance Analysis in Named D...

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16th International Conference on Telecommunication Systems Services and Applications, TSSA 2022

作者： Sucipto, Adi Jupriyadi Ahdan, Syaiful Arifin, Hasan Nur Zaki Hamidi, Eki Ahmad Syambas, Nana Rachmana School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung35132 Indonesia Uin Sunan Gunung Djati Department of Electrical Engineering Bandung40132 Indonesia

ISBN: (纸本)9781665455947

The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20%. At the amount of interest in 1000 about more than 30%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests. © 2022 IEEE.

关键词： Topology

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A Multi-Criteria Recommender System For NFT Based IAP In RPG Game 23

A Multi-Criteria Recommender System For NFT Based IAP In RPG...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： Pradana, Reza Putra Hariadi, Mochamad Rachmadi, Reza Fuad Arif, Yunifa Miftahul Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia Universitas Islam Negeri Maulana Malik Ibrahim Department of Informatics Engineering Malang Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

The market value of the gaming industry in 2021 is said to be more than 198.40 billion USD. Market value is also supported by the number of gamers, with 4.75 billion in January 2022. In today's In-App Purchase (IAP) income method has become a big trend in today's modern era models of free-to-play games, where gamers can choose or buy many items during the game to speed up the progress of the game or enjoy the full content of the game. However, sometimes players are overwhelmed with the number of items on offer, making it difficult for players to choose because the game content is too diverse. In addition, players are also worried about the security of their digital assets because, in 2021, there will be 7.5 million digital assets lost due to hacking. We propose a recommendation system to make it easier for players to choose items that suit their playstyle to solve this problem. We use the multi-criteria recommender system (MCRS) method because this method can improve the accuracy of recommendations compared to conventional recommendations that only use one criterion. In this study, we used eight criteria to calculate the recommendations. The results of our recommendation test show the accuracy value = 0.71, precision = 0.76, recall = 0.71 and F1 score = 0.66. To address security issues, we propose the implementation of a Non-Fungible Token (NFT) for each item. NFT can increase security because it uses a decentralized blockchain architecture in which every transaction is encrypted. The system guarantees that the assets will remain online so that users do not risk losing ownership of their assets when the developer changes game data, or the game server closes. © 2022 IEEE.

关键词： Recommender systems

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Estimation of Weibull Distribution Parameters by Analytical Methods for the Wind Speed of Jhimpir, Pakistan - A Comparative Assessment

Estimation of Weibull Distribution Parameters by Analytical ...

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2022 International Conference on Emerging Trends in electrical, Control, and Telecommunication engineering, ETECTE 2022

作者： Abbas, Ghulam Gu, Jason Asad, Muhammad Usman Balas, Valentina E. Farooq, Umar Khan, Irfan A. The University of Lahore Department of Electrical Engineering Lahore Pakistan Dalhousie University Department of Electrical and Computer Engineering Halifax Canada Aurel Vlacieu University of Arad Department of Automation and Applied Informatics Arad Romania Lab GalvestonTX77553-1675 United States

ISBN: (纸本)9781665492423

Assessing the potential of a wind farm requires looking into how the wind behaves throughout a certain time frame. One of the most popular ways to statistically model wind data is with the Weibull distribution. Estimating two parameters of the Weibull PDF is crucial for a better fit between the PDF and wind speed data. In this study, Weibull distribution parameters for 2019 wind speed data in the Jhimpir region of Pakistan are determined using four analytical techniques: the empirical method (EM), the maximum likelihood method (MLM), the method of moments (MoM), and the energy pattern factor (EPF) approach. Each technique is evaluated using several different metrics, including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), and the coefficient of correlation (R). Statistical analyses show that the shape (k) and scale (c) parameters of the Weibull distribution estimated by the EM, MLM, and MoM are quite close to one another compared to the ones obtained by EPF for the available data. The MATLAB environment-based numerical results expressed that the EPF method performed the best in terms of R and RMSE and worst in terms of MAE and MARE. © 2022 IEEE.

关键词： Weibull distribution

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A Low-Cost Real-Time Cyber Physical System for Overcoming Excess Braking Issues In Race Cars

A Low-Cost Real-Time Cyber Physical System for Overcoming Ex...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： V. Samaras K. Tatas A. Lontos Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Department of Mechanical Engineering Frederick University Nicosia Cyprus

This paper presents the development of proof of concept for a low-cost, real-time Cyber Physical System (CPS)/IoT that deals with observed excess braking issues in Formula Student (FS) race cars. The goal is to utilize telemetry aspects through a smart monitoring system under the FS low budget competition restrictions, with low-cost materials, open-source software, Long Range wireless communication (LoRa) and cloud services. The paper also introduces an algorithm for implementing an adaptive sampling rate based on the proximity of monitored parameters to warning levels to minimize unnecessary data transmission. The system prototype was tested under realistic driving conditions, obtaining useful preliminary data that can be used to guide both driving behavior and future vehicle design. The results demonstrate that the proposed approach can be useful for the race team in FS conditions and may be possible to expand it to other similar driving scenarios outside FS.

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Adversarial-aware Machine Learning for Enhancing Smart Grid Security 25

Adversarial-aware Machine Learning for Enhancing Smart Grid ...

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25th International Middle East Power System Conference, MEPCON 2024

作者： Sodiq, Yusuf Ademola Gaber, Tarek Amin, Reham Jimenez-Aranda, Angel Galeela, Mohamed The University of Salford School of Science Engineering & Environment Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Computer Sciences Department Ismailia Egypt The University of Salford Centre for Sustainable Innovation Salford Business School Manchester United Kingdom TNEI Service LTD Manchester United Kingdom Cairo University Faculty of Engineering Electrical Power Engineering Department Egypt

ISBN: (纸本)9798350379648

Smart grid systems are essential components of modern power infrastructures, where machine learning has found wide-ranging applications, particularly in the development of intrusion detection systems. However, such systems remain vulnerable to adversarial attacks, including data poisoning techniques like label flipping attack. This paper proposes a novel, adversarial-aware machine learning-based intrusion detection system that is robust against label flipping attacks, thereby enhancing the integrity and reliability of the energy distribution network. The proposed solution was evaluated using the ICS cyber-attack dataset from the University of Queensland, with multiple models undergoing progressive testing. Initially, the base model employed three key algorithms: Random Forest, K-Nearest Neighbors, and XGBoost. These models were subjected to simulated label flipping attacks with varying intensities (10%-50%), which caused a significant decline in performance. To counter this, a countermeasure technique was integrated into the system, successfully restoring model accuracy and achieving a consistent accuracy of 75.96% across all attack intensities. Among the algorithms, Random Forest demonstrated the greatest resilience, showing a notable recovery after mitigation. This study underscores the critical role of preventive defense strategies and robust machine learning algorithms in safeguarding smart grids against cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive Strategies for Enhancing Quality of Experience in 360° Video Streaming

Adaptive Strategies for Enhancing Quality of Experience in 3...

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Asilomar Conference on Signals, Systems & computers

作者： Ioanna Valiandi Marios S. Pattichis Efthyvoulos Kyriacou Andreas S. Panayides VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Department of Electrical Eng. Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

The unprecedented growth of immersive virtual and mixed reality technologies is driving the increasing demand of 360° video streaming applications. Facilitating unique opportunities, but also challenges, 360° videos require substantially higher video resolutions, and hence bandwidth demands, for a better viewing experience. This study examined four tile-based Region of Interest (ROI) encoding techniques to optimize 360° video encoding and streaming. These techniques encode the user's viewport regions with higher quality compared to the background, non-ROI areas. A dataset of ten 4K omnidirectional videos at 30 frames per second was used to evaluate the performance of four distinct tile-based ROI methods using four video encoding standards, namely hevc _ nvenc, x265, h264 _ nvenc, and x264. Overall, the proposed tile-based ROI encoding methods achieve substantial bitrate reductions compared to the uniform full-frame encoding while maintaining adequate subjective video quality levels, demonstrating their potential for efficient and scalable 360° video streaming. The ROI-3 method, which uses a sliding window approach to encode the user's field of view based on eye gaze movement (EM) data coordinates translated to tile regions, was found to achieve the best trade-off between bitrate demands savings and perceptual quality for all investigated video compression standards.

关键词： Video coding Bit rate Mixed reality Streaming media Video compression Encoding Quality assessment Quality of experience Standards Video recording

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A Novel Full Swing Re-Configurable Spiking Neuron (FSR-SN)

A Novel Full Swing Re-Configurable Spiking Neuron (FSR-SN)

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Prasanna Kawatkar Shelby Williams Prosen Kirtonia Kasem Khalil Magdy Bayoumi School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA USA Department of Electrical and Computer Engineering University of Louisiana at Lafayette Lafayette LA USA Department of Electrical and Computer Engineering Univ. of Mississippi Oxford MS USA Department of Electrical Engineering Assiut Univ. Assiut Egypt

ISBN: (数字)9798350377200

ISBN: (纸本)9798350377217

Spiking neuron technology holds immense potential for Spiking Neuron Network (SNN) applications, by emulating remarkable computational capabilities of the human brain in the realm of neuromorphic computing. The proposed novel Full Swing Re-configurable Spiking Neuron (FSR-SN) design results in a railto-rail spike which leads to more accurate encoding of information and the re-configurable spike increases computational efficiency of SNNs by reducing quantization errors. This FSR-SN neuron design is employed using the Integrate & Fire (I&F) spiking model, utilizing Metal Oxide Semiconductor (MOS) transistors. This innovative symmetrical design delivers unparalleled energy efficiency, with an ultra-low energy consumption of only 0.482 femtojoules (fJ) per spike, while achieving an impressive spiking frequency of 1.54 GHz. Comprehensive benchmarking against numerous state-of-the-art designs in terms of spiking frequency, energy usage, voltage, and other key metrics has demonstrated the FSR-SN's superior performance, often surpassing the capabilities of existing solutions. This makes the FSR-SN attractive for developing energy-efficient neuromorphic systems and artificial intelligence applications requiring detection of high frequency oscillations.

关键词： Energy consumption Neuromorphic engineering Neurons Voltage Spiking neural networks Energy efficiency Computational efficiency Ultrafast electronics Artificial intelligence Oscillators

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WEST GCN-LSTM: Weighted Stacked Spatio-Temporal Graph Neural Networks for Regional Traffic Forecasting

arXiv

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arXiv 2024年

作者： Theodoropoulos, Theodoros Maroudis, Angelos-Christos Makris, Antonios Tserpes, Konstantinos Department of Informatics and Telematics Harokopio University of Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Greece

Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims at expanding upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions, as well as the populations that traverse them, in order to establish a more efficient prediction model. The end-product of this scientific endeavour is a novel spatio-temporal graph neural network architecture that is referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the inclusion of the aforementioned information is conducted via the use of two novel dedicated algorithms that are referred to as the Shared Borders Policy and the Adjustable Hops Policy. Through information fusion and distillation, the proposed solution manages to significantly outperform its competitors in the frame of an experimental evaluation that consists of 19 forecasting models, across several datasets. Finally, an additional ablation study determined that each of the components of the proposed solution contributes towards enhancing its overall performance. © 2024, CC BY.

关键词： Forecasting

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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