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Proposal of a Support System for Visualization of Acquired Skills Using iCD in Project-Based Learning for Software Development and How to Acquire its data

Proposal of a Support System for Visualization of Acquired S...

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Conference on Software engineering Education & Training (CSEE&T)

作者： Hane Kondo Hiroaki Hashiura Atsuo Hazeyama Graduate School of Education Tokyo Gakugei University Tokyo Japan Faculty of Advanced Engineering Nippon Institute of Technology Saitama Japan Dept. Information Science Tokyo Gakugei University Tokyo Japan

There have been many practices using software development as a subject for Project-Based Learning (PBL). While PBL for software development has high educational effects, it is difficult for learners to realize many of its educational effects because they overly focus on the activities themselves. Therefore, this paper proposes a support system for visualization of acquired skills in PBL for software development using i-Competency Dictionary. We also describe the data acquisition method for the realization of the system.

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Delay Tolerant and Energy Reduced Task Allocation in Internet of Things with Cloud Systems 1

Delay Tolerant and Energy Reduced Task Allocation in Interne...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Raghunath Patil, Dipak Borkar, Bharat Markad, Ashok Kadlag, Sunil Kumbhkar, Makhan Jamal, Ahmad Amrutvahini College of Engineering Dept. of Computer Engineering Pune Sangamner India Amrutvahini College of Engineering Dept. of Information Technology Pune Sangamner India Amrutvahini College of Engineering Department of Electrical Engineering Pune Sangamner India Christian Eminent College Indore & Visiting Faculty School of Data Science and Forecasting Devi Ahilya University Department of Computer Science & Electronics Indore India Tulas Institute Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665456876

Through the utilization of the Internet, cloud computing supplies storage and computation resources to deliver services for many sectors. The speed of such systems suffers, though, since delay-sensitive systems, such as smart city and health applications, increasingly need for processing over massive volumes of data transmitted to centralised datacenters. When compared to cloud services, the paradigms of fog as well as edge computing provide innovative solutions by affecting the financial closer to the customer and by having low energy consumption and latency effectiveness. To optimise expense and resource efficiency, enhance QoS, and increase security and privacy, it is crucial to discover the best locations for services and assets inside the 3 IoT. The bipartite graph task scheduling method we provide in this research enhances cost effectiveness in real world applications with strict deadlines. The proposed method's performance in terms of delay, network congestion, as well as cost is deployed and tested to use the iFogSim simulator, an expanded version of CloudSim. The evaluation results demonstrate that the suggested algorithm outperforms Round-Robin and Minimum Response Time methods in terms of both cost as well as throughput. © 2022 IEEE.

关键词： Internet of things

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Crack Catcher AI - Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI - Enabling Smart Fracture Mechanics Approac...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Preston, Gabriel Gonzolez, Greer Karnyoto, Andrea S. Mozar, Fitya S. Henry, Matthew M. Isnan, Mahmud Nugroho, Kuncahyo S. Candra, Henry Doris, Muhamad Putri, Dianing N. N. Sari, Tyas K Pardamean, Bens Djuana, Endang Speaks, Derrric Budiman, Arief S. Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls 97601 OR United States Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls 97601 OR United States Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta 11480 Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas Trisakti Jakarta 11440 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia Industrial Eng. Dept. Bina Nusantara University Jakarta 11480 Indonesia Computer Sci. Dept. Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

ISBN: (纸本)9798331504779

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability. © 2025 IEEE.

关键词： 3D Integrated Devices/Packages Fracture Mechanics Machine Learning Approaches Silicon Crack

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

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IIAI International Conference on advanced Applied Informatics (IIAIAAI)

作者： Sujan Shrestha Yoji Taniguchi Tetsuo Tanaka Course of Information and computer sciences Graduate school of Engineering Kanagawa Institute of Tehnology Atsugi Japan Department of Information Artificial Intelligence and Data Science Daiichi Institute of Technology Tokyo Japan Dept. of Information and Computer Sciences Faculty of Information Technology Kanagawa Institute of Technology Atsugi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence; however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset.

关键词： Deep learning Recurrent neural networks Accuracy Surveillance Benchmark testing Feature extraction Convolutional neural networks Security Informatics Long short term memory

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Towards Effective Time Series Classification of Multi-Class Imbalanced Learning

Towards Effective Time Series Classification of Multi-Class ...

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2021 International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021

作者： Priyadarsini, K. Hemakesavulu, O. Karthik, S. Karanam, Santoshachandra Rao Vels Institute of Science Technology and Advanced Studies Dept of Cse Tamilnadu India Annamacharya Institute of Technology and Sciences Dept of Eee Rajampet Andhra Pradesh India Srm Institute of Science and Technology Vadapalani Faculty of Engineering and Technology Dept of Ece Chennai Vadapalani India Anurag University Dept. of It Hyderabad India

ISBN: (纸本)9781728195377

The problem of classification with multiple classes and imbalanced samples poses a new challenge over the problem of binary classification. Methods for handling imbalanced learning are proposed, but most of them are specifically designed for problems of binary classification. Multi-class inequality poses extra problems for time series, e.g. weather scoring, when used. In this work we introduce the new multi-class imbalanced problem handling algorithm with time series data. Our proposed algorithm is designed to deal with both multiclass imbalances and time series classification issues and inspires the Nearest Neighbour Classification Algorithm of the Imbalanced Fuzzy-Rough Ordered Weighted Average. The feasibilities of our suggested algorithm are tested by an empirical evaluation in a telecom application in Ericsson, Sweden, in which weather classifications are based on data from commercial microwave connections. Our proposed algorithm is compared to the Ericsson model, which is a one-dimensional neural convolution network, and three other model deeper learning. The empirical evaluation shows that our proposed weather classification algorithm efficiency is comparable to that of the current solution. The two most powerful research models are our proposed algorithm and the existing solution. © 2021 IEEE.

关键词： Time series

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Efficient Type and Polarity Classification of Chromosome Images using CNNs: a Primary Evaluation on Multiple datasets 9

Efficient Type and Polarity Classification of Chromosome Ima...

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9th IEEE International Conference on Communications and Electronics, ICCE 2022

作者： Anh, Le Quoc Thanh, Vu Duy Son, Nguyen Huu Hoang Phuong, Doan Thi Kim Anh, Luong Thi Lan Ram, Do Thi Minh, Nguyen Thanh Binh Tung, Tran Hoang Thinh, Nguyen Hong Ha, Le Vu Ha, Luu Manh Advanced Institute of Engineering and Technology VNU-UET Viet Nam Dept. of Biology and Medical Genetics Hanoi Medical University Viet Nam Center of Clinical Genetics and Genomic Hanoi Medical University Hospital Viet Nam Hematology and Blood Transfusion Center Bach Mai Hospital Viet Nam Ict Lab University of Science and Technology of Hanoi Viet Nam Faculty of Electronics and Telecommunications VNU-UET Viet Nam

ISBN: (数字)9781665497459

ISBN: (纸本)9781665497442

Karyotyping is critical for screening genetic diseases in an early stage. However, the manual karyotyping process is a labor-intensive task. This paper focuses on automatic chromosome image classification, which is a step in karyotyping. We propose a convolutional neural network architecture for efficient type and polarity classification of the chromosome image, namely ETPC, which is leveraged from the EfficientNet family's development. The ETPC's classifier with a weighted classification loss function are designed for efficiency in the training process. We perform our experiment with two training scenarios on four clinical datasets. The experiment on a dataset of 28,225 original chromosome images demonstrates that the proposed network achieved comparable results, with an accuracy of 95.3% for the type classification and 99% for the polarity classification, while having a significantly smaller number of parameters than state-of-the-art methods. Furthermore, the experiment on multiple datasets shows that the proposed network can dramatically improve the classification accuracy on new datasets with a small amount of fine-tuning data. © 2022 IEEE.

关键词： Chromosomes

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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Malicious Traffic Classification in WSN using Deep Learning Approaches

Malicious Traffic Classification in WSN using Deep Learning ...

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Chhote Lal Prasad Gupta Dinesh Rajassekharan Dilip Kumar Sharma Mohanraj Elangovan Varatharaj Myilsamy Kamal Upreti Dept. of Computer Science and Engineering Bansal Institute of Engineering and Technology Lucknow India Faculty of Computer Science Peninsula College Peninsula Malaysia Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science K. S. Rangasamy College of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST(Deemed to be University) Delhi-NCR Ghaziabad India

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data.

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Delay Tolerant and Energy Reduced Task Allocation in Internet of Things with Cloud Systems

Delay Tolerant and Energy Reduced Task Allocation in Interne...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Dipak Raghunath Patil Bharat Borkar Ashok Markad Sunil Kadlag Makhan Kumbhkar Ahmad Jamal Dept. of Computer Engineering Amrutvahini College of Engineering Sangamner Pune India Dept. of Information Technology Amrutvahini College of Engineering Sangamner Pune India Department of Electrical engineering Amrutvahini College of Engineering Sangamner Pune India Department of Computer Science & Electronics Christian Eminent CollegeIndore & Visiting faculty at school of data science and forecasting Devi Ahilya University Indore Department of Computer Science and Engineering Tulas Institute Dehradun Uttarakhand India

关键词： Cloud computing Costs Smart cities Quality of service Throughput Delays Time factors

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Public Kaggle Competition "IceCube – Neutrinos in Deep Ice" 38

Public Kaggle Competition "IceCube – Neutrinos in Deep Ice"

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38th International Cosmic Ray Conference, ICRC 2023

作者： Eller, Philipp Abbasi, R. Ackermann, M. Adams, J. Agarwalla, S.K. Aguilar, J.A. Ahlers, M. Alameddine, J.M. Amin, N.M. Andeen, K. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S.N. Bai, X. Balagopal, A.V. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker Tjus, J. Beise, J. Bellenghi, C. Benning, C. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Butterfield, D. Campana, M.A. Carloni, K. Carnie-Bronca, E.G. Chattopadhyay, S. Chau, N. Chen, C. Chen, Z. Chirkin, D. Choi, S. Clark, B.A. Classen, L. Coleman, A. Collin, G.H. Connolly, A. Conrad, J.M. Coppin, P. Correa, P. Cowen, D.F. Dave, P. De Clercq, C. DeLaunay, J.J. Delgado, D. Deng, S. Deoskar, K. Desai, A. Desiati, P. de Vries, K.D. de Wasseige, G. DeYoung, T. Diaz, A. Díaz-Vélez, J.C. Dittmer, M. Domi, A. Dujmovic, H. DuVernois, M.A. Ehrhardt, T. Eller, P. Ellinger, E. El Mentawi, S. Elsässer, D. Engel, R. Erpenbeck, H. Evans, J. Evenson, P.A. Fan, K.L. Fang, K. Farrag, K. Fazely, A.R. Fedynitch, A. Feigl, N. Fiedlschuster, S. Finley, C. Fischer, L. Fox, D. Franckowiak, A. Fritz, A. Fürst, P. Gallagher, J. Ganster, E. Garcia, A. Gerhardt, L. Ghadimi, A. Glaser, C. Glauch, T. Glüsenkamp, T. Goehlke, N. Gonzalez, J.G. Goswami, S. Grant, D. Gray, S.J. Gries, O. Griffin, S. Griswold, S. Groth, K.M. Günther, C. Gutjahr, P. Haack, C. Hallgren, A. Halliday, R. Halve, L. Halzen, F. Hamdaoui, H. Ha Minh, M. Hanson, K. Hardin, J. Harnisch, A.A. Hatch, P. Haungs, A. Helbing, K. Hellrung, J. Henningsen, F. Heuermann, L. Heyer, N. Hickford, S. Hidvegi, A. Hill, C. Hill, G.C. Hoffman, K.D. Hori, S. Hoshina, K. Hou, W. Huber, T. Hultqvist, K. Hünnefeld, M. Hussain, R. Hymon, K. In, S. Ishihara, A. Jacquart, M. Janik, O. Jansson, M. Japaridze, G.S. Technical University of Munich Munich Data Science Institute Walther-von-Dyck-Strasse 10 Garching85748 Germany III. Physikalisches Institut RWTH Aachen University AachenD-52056 Germany Department of Physics University of Adelaide Adelaide5005 Australia Dept. of Physics and Astronomy University of Alaska Anchorage 3211 Providence Dr. AnchorageAK99508 United States Dept. of Physics University of Texas at Arlington 502 Yates St. Science Hall Rm 108 Box 19059 ArlingtonTX76019 United States CTSPS Clark-Atlanta University AtlantaGA30314 United States School of Physics Center for Relativistic Astrophysics Georgia Institute of Technology AtlantaGA30332 United States Dept. of Physics Southern University Baton RougeLA70813 United States Dept. of Physics University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Institut für Physik Humboldt-Universität zu Berlin BerlinD-12489 Germany Fakultät für Physik & Astronomie Ruhr-Universität Bochum BochumD-44780 Germany Université Libre de Bruxelles Science Faculty CP230 BrusselsB-1050 Belgium Dienst ELEM BrusselsB-1050 Belgium Department of Physics Laboratory for Particle Physics and Cosmology Harvard University CambridgeMA02138 United States Dept. of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Physics The International Center for Hadron Astrophysics Chiba University Chiba263-8522 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Dept. of Physics and Astronomy University of Canterbury Private Bag 4800 Christchurch New Zealand Dept. of Physics University of Maryland College ParkMD20742 United States Dept. of Astronomy Ohio State University ColumbusOH43210 United States Dept. of Physics Center for Cosmology and Astro-Particle Physics Ohio State University ColumbusOH43210 United States Niels Bohr Institute University of Copenhagen Copenh

The reconstruction of neutrino events in the IceCube experiment is crucial for many scientific analyses, including searches for cosmic neutrino sources. The Kaggle competition "IceCube – Neutrinos in Deep ice" was a public machine learning challenge designed to encourage the development of innovative solutions to improve the accuracy and efficiency of neutrino event reconstruction. Participants worked with a dataset of simulated neutrino events and were tasked with creating a suitable model to predict the direction vector of incoming neutrinos. From January to April 2023, hundreds of teams competed for a total of $50k prize money, which was awarded to the best performing few out of the many thousand submissions. In this contribution I will present some insights into the organization of this large outreach project, and summarize some of the main findings, results and takeaways. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Neutrons

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