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Past, Present, and Future of CASS Educational Programs and Initiatives [Feature]

IEEE Circuits and Systems Magazine 2024年第2期24卷 7-15页

作者： Rokhani, Fakhrul Zaman Zhang, Xinmiao Joshi, Rajiv V. Reis, Ricardo Grimblatt, Victor Tang, Kea-Tiong Li, Yongfu Amara, Amara Delgado-Restituto, Manuel Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Engineering Serdang43400 Malaysia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Institute of Informatics Federal University of Rio Grande do Sul Porto Alegre90010-150 Brazil Synopsys California CA92037 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Shanghai Jiao Tong University Department of Micro and Nano Electronics Engineering Shanghai200240 China Paris75006 France Institute of Microelectronics of Sevilla Sevilla41092 Spain

The Circuits and Systems Society (CASS) places a strong emphasis on advancing education within its domain. Various educational programs and resources have contributed to the professional development of CASS members for more than 20 years now. A major transformation within the Society was started in 2021 with a clear vision to become a premier educational resource within circuits and systems areas of interest. This article provides a comprehensive review of flagship educational programs and past initiatives. Insights from the lessons learned and future trends in the education ecosystem guided society's strategies and frameworks for improving and developing new educational products and services. The benefits of learning with the CASS program are presented, highlighting the Society's deep commitment to serving the community at large in educational pursuit for years to come. © 2001-2012 IEEE.

关键词： engineering education

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Machine Learning Prediction Based UI for Aircraft Cockpit

Machine Learning Prediction Based UI for Aircraft Cockpit

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International Conference on Electrical and Electronics engineering, ELECO

作者： Bilge Topal Levent Çarkacıoğlu Behçet Uğur Töreyin Dept. of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey Aselsan Inc. Ankara Turkey Informatics Institute Istanbul Technical University Istanbul Turkey

Pilot vehicle interfaces on aircraft, similar to the automobile digital displays found in automobiles, are used to present the current status of various systems (e.g., navigation, engine information). Aircraft interfaces exhibit deterministic behavior due to safety concerns, ensuring that user input yields predictable outputs. Consequently, integrating text-based pattern recognition or artificial neural network based solutions to aircraft systems are challenging because of their deterministic nature. In this paper, an approach complementary to the existing user interface models is proposed. This approach is based on learning recurring user interactions during flight. It utilizes text-based pattern recognition and prediction models based on user interaction logs and sensor readings. Test results on sample data suggest that the proposed approach yields faster and context-aware user interaction model that decreases the time needed to perform tasks in the aircraft cockpit, where timing is crucial.

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Chaotic American zebra search optimization algorithm for benchmark challenges 15

Chaotic American zebra search optimization algorithm for ben...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Kamboj, Vikram Kumar Krishna, Ardhala Bala Verma, Chaman Raboaca, Maria Simona Filote, Constantin Stepanova, Maria Schulich School of Engineering University of Calgary AB Canada Malla Reddy Engineering College for Women Dept. of Electrcical and Electronics Engineering Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Edu. Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea240050 Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava720229 Romania Bauman Moscow State Technical University Engineering Business and Management Faculty Russia

ISBN: (纸本)9798350321388

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges. © 2023 IEEE.

关键词： Mapping

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Machine Learning as the Shield: Mitigating ARP Poisoning Attacks in Software Defined Networks 25

Machine Learning as the Shield: Mitigating ARP Poisoning Att...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alquran, Rabee Al Jamal, Mahmoud Aljaidi, Mohammad Al-Jamal, Mohammad Khassawneh, Bashar Alsarhan, Ayoub Alidmat, Omar Samara, Ghassan Almatarneh, Sattam The Hashemite University Faculty of Prince Al-Hussien bin Abdullah for It Department of Information Technology Zarqa Jordan Irbid National University Department of Cybersecurity Irbid Jordan Zarqa University Department of Computer Science Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Yarmouk University Department of Statistics Irbid Jordan Amman Arab University Faculty of Computer Science and Informatics Department of Computer Science Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Artificial Intelligence Zarqa Jordan

ISBN: (纸本)9798331540012

Software-defined networking (SDN) revolutionizes network management by centralizing control, but this centralization also introduces notable vulnerabilities, especially Address Resolution Protocol (ARP) poisoning attacks. These attacks exploit the ARP protocol, redirecting network traffic to malicious destinations. This research provides a deep dive into the nuances of these threats within the SDN context, outlining their mechanisms and potential countermeasures. A significant highlight of this paper is exploring Machine Learning (ML) as a pivotal mitigation approach. By analyzing and distinguishing between benign and malicious traffic patterns in real-time, ML models present a robust solution to enhance SDN security. The findings of our paper underscore the criticality of integrating these advanced predictive techniques in SDNs, paving the way for a safer and more resilient networking future. © 2024 IEEE.

关键词： ARP Poisoning Machine Learning (ML) Software-defined network (SDN)

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Mochammad Yusa Funny Farady Coastera Deni Rahmadani Julia Purnama Sari Kusrini Kusrini Anggit Dwi Hartanto Department of Informatics Faculty of Engineering University of Bengkulu Bengkulu Indonesia Department of Information System Faculty of Engineering University of Bengkulu Bengkulu Indonesia Magister of Informatics Engineering Faculty of Computer Science Universitas AMIKOM Yogyakarta Yogyakarta Indonesia Department of Information System Faculty of Computer Science Universitas AMIKOM Yogyakarta Yogyakarta Indonesia

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals; 2) Filtering and selecting criteria for student academic activities; 3) Transforming and normalizing the data; 4) Applying automatic weighting using CRITIC; 5) Calculating criteria rankings using the PROMETHEE II technique; and 6) Validating the ranking outcomes.

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Emotion Recognition Using Bayesian Learning from a Multi-Label Data Corpus

Emotion Recognition Using Bayesian Learning from a Multi-Lab...

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2022 International Conference on Computing, Networking, Telecommunications and engineering Sciences Applications, CoNTESA 2022

作者： Swathi, Bandi Kumar, Madapuri Rudra Riyaz Belgaum, Mohammad Alansari, Zainab G. Pulla Reddy Engineering College Computer Science and Engineering Kurnool India G Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia University of Malaya Computer System and Information Technology Kuala Lumpur Malaysia Information Technology Muscat Oman

ISBN: (纸本)9798350398014

In the digital era of information systems, emotion detection from audio signals is crucial for forensic services and operator or driver emotion monitoring in large-scale companies' safety and security. Speech is a uniquely human trait used to express and communicate one's point of view to others. Emotion recognition from speech audio signals is obtaining a presenter's emotions from the presenter's audio signal. Machine learning is used to develop emotion recognition systems, a critical research goal in current engineering research. The three main stages in speech emotion recognition are extraction of features, feature engineering, and classification. Even though powerful machine learning-based emotion identification algorithms for speech audio signals exist, the detection rate with maximum specificity and sensitivity is not scalable using most modern methods. A significant research objective is feature optimization for emotion recognition from speech audio signals. This article introduced a unique technique called Supervised Bayes Learning on Digital Features (SBL-DF). To scale its performance, the trials compared the suggested approach's performance to an earlier model depicted against a similar goal. Experiment results indicate that the proposed method for optimizing features could be used on a large scale. The reference classifiers' performance in making accurate detections with few false positives. © 2022 IEEE.

关键词： Genetic algorithms

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Sharding:A Scalability Solutions for Blockchain Networks 3

Sharding:A Scalability Solutions for Blockchain Networks

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kale, Dattatray Raghunath Jadhav, Amolkumar N. Kruishna Prasad, K. Salunkhe, Somanath Janardan Hirve, Sumit Goswami, Chandrashekhar Mit Art Design and Technology University Mit School of Computing Department of Computer Science & Engg Pune India Annasaheb Dange College of Engineering and Technology Department of Computer Science & Engg Ashta India Shrinivas University Department of Computer Science & Engg Mangalore India D Y Patil Agriculture and Technical University Department of Computer Science & Engg Talsande India Sir Padampat Singhania University Faculty of Computing and Informatics Rajasthan Udaipur India

ISBN: (纸本)9798350354348

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks. © 2024 IEEE.

关键词： Distributed ledger

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Indonesian Sign Language (SIBI) Detection: A Landmark-Based Approach with Scikit-Learn Utilizing Random Forest Classifier

Indonesian Sign Language (SIBI) Detection: A Landmark-Based ...

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International Conference on informatics and Computing (ICIC)

作者： Agung Rachmat Raharja Vito Hafizh Cahaya Putra Ghanim Kanugrahan Cuk Tho Faculty of Health and Engineering Informatics Bandung University Bandung Campus Bandung Indonesia Informatics Department Faculty of Creative Technology Satu University Bandung Campus Bandung Indonesia Computer Science Department School of Computer Science BinaNusantara University Jakarta Campus Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This paper discusses computer vision-based sign language by utilizing landmark detection and Scikit-Learn. The objective of this research is to develop a model that proficient in the accurate recognition of hand gestures specific to sign language. Pictures of hand gestures are taken using a camera and hand landmark are detected by utilizing MediaPipe. Following to landmark detection, a machine learning model is developed using Scikit-Learn employing features extracted from these landmarks. Evaluation of the model using the Indonesian sing language (SIBI) yielded competitive result, proving the efficacy of the proposed model without specific hardware. This work significantly improves the communication technology for the deaf, leveraging computer vision to broaden the research opportunity in sign language recognition utilizing available equipment. The accuracy obtained from this model is $100 \%$ which means the classification is correct, and reinforces the effectiveness of the approach to the recognition and classification of hand movements in sign language.

关键词： Hands Sign language computer vision Visualization Accuracy Computational modeling Feature extraction Cameras Data models Random forests

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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm

Improving bot detection of fake followers on Twitter via a h...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Haider Alzeyadi Fecir Duran Department of Computer Science Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Engineering Faculty of Technology Gazi University Ankara Turkey

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset.

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Comparing Holt-Winter and Multi Layer Perceptron in Forecasting The Amount of Rice Supply 4

Comparing Holt-Winter and Multi Layer Perceptron in Forecast...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Sugiarto, Dedy Hidayat, Wahyu Ariatmanto, Dhani Yaqin, Ainul PJJ Master of Informatics Engineering Universitas Amikom Yogyakarta Indonesia Informatics Faculty of Computer Science Universitas Amikom Jogyakarta Indonesia

ISBN: (纸本)9781665433945

Rice supply through the Cipinang Rice Main Market is volatile and seasonal with a total supply from all production centers in 2020 of 795179 tons of which 33% or most of it is supplied from the Cirebon area. The existence of climate change associated with shifting planting seasons and shrinking paddy fields makes it necessary to make accurate supply forecasts so that the price control mechanism through rice market operations can run effectively. The supply forecast uses monthly supply data from the Cirebon area from 2011 to 2020 and is forecasted using the Holt-Winter and Multi Layer Perceptron (MLP) methods. The results show that forecasting using the MLP method with two hidden layer produces the most accurate values where the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values are 544.17 and 2.08, respectively. © 2021 IEEE

关键词： Forecasting

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