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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Suleiman, Ahmad D. Dewaters, Jan Hou, Daqing Liu, Yu Shepherd, David Rochester Institute of Technology RochesterNY United States Institute for Stem Education Clarkson University PotsdamNY United States Rochester Institute of Technology Software Engineering Department RochesterNY United States Clarkson University PotsdamNY United States Louisiana State University Department of Computer Science Baton RougeLA United States

ISBN: (纸本)9798350351507

In this research paper, we conducted a comparative study to measure the effectiveness of the provided technical support in computing project-based learning (PjBL) courses. Students learn much better by solving authentic real-world problems through PjBL. PjBL in computing education has proven to boost student motivation and engagement while enhancing academic performance. Crucial to PjBL in computing is the technical support that the instructors can provide to students, which is required for sustained, successful learning during project tasks. Without adequate support, PjBL will fall short of accomplishing its goals, leading to a rise in student frustration, a loss of motivation and engagement, and compromised learning outcomes. Measuring the impacts and effectiveness of the provided support is imperative for fostering continuous improvement, informed decision-making, and student success. It enables instructors to assess the impacts of their strategies, improve their approaches, and utilize their resources more effectively. To measure the impacts of technical support on students during PjBL, we performed a comparative study on two undergraduate computing courses in Spring 2024, Fundamentals of software engineering and Database Systems. In both courses, students work on two assigned projects, one with little and inadequate support and the other with adequate support. We administered a post-survey after each project was completed. We analyzed students' selfreflection responses across four sub-scales, support satisfaction, motivation, self-efficacy, and project satisfaction. The results show a statistically significant increase in the supported project in the Fundamentals of software engineering course and no difference in the Database Systems course. This finding is likely due to other differences between the two projects for the Database Systems course beyond support, such as project scale. Qualitative analysis of students' responses also indicates the need for suppor

关键词： Students

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Exploring Data Management Challenges and Solutions in Agile software Development: A Literature Review and Practitioner Survey

arXiv

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arXiv 2024年

作者： Fawzy, Ahmed Tahir, Amjed Galster, Matthias Liang, Peng School of Mathematical and Computational Sciences Massey University New Zealand Department of Computer Science and Software Engineering University of Canterbury New Zealand School of Computer Science Wuhan University China

Context: Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst continuous change and adaptation. Objective: The paper systematically explores data management challenges and potential solutions in agile projects, aiming to provide insights into data management challenges and solutions for both researchers and practitioners. Method: We employed a mixed-methods approach, including a systematic literature review (SLR) to understand the state-of-research followed by a survey with practitioners to reflect on the state-of-practice. The SLR reviewed 45 studies, identifying and categorizing data management aspects along with their associated challenges and solutions. The practitioner survey captured practical experiences and solutions from 32 industry practitioners who were significantly involved in data management to complement the findings from the SLR. Results: Our findings identified major data management challenges in practice, such as managing data integration processes, capturing diverse data, automating data collection, and meeting real-time analysis requirements. To address these challenges, solutions such as automation tools, decentralized data management practices, and ontology-based approaches have been identified. These solutions enhance data integration, improve data quality, and enable real-time decision-making by providing flexible frameworks tailored to agile project needs. Conclusion: The study pinpointed significant challenges and actionable solutions in data management for agile software development. Our findings provide practical implications for practitioners and researchers, emphasizing the development of effective data management practices and tools to address those challenges and improve project success. © 2024, CC BY.

关键词： Data quality

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White Box Testing Module for Evaluation of Transformers-Based DNNs

White Box Testing Module for Evaluation of Transformers-Base...

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Innovations in Intelligent Systems and Applications Conference

作者： Gokhan Cetiner Cem Baglum Ugur Yayan Ahmet Yazici Computer Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye Software Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Artificial intelligence is actively becoming widespread in every field. The complex structure of deep neural network models, especially those used in real-world applications, may contain faults that lead to unexpected model behavior, even well-trained AI models. To detect these types of faults and model behaviors, it is necessary to perform a white box test of the model's reliability and adequacy by mutating its layers, activation functions, and similar model structures and parameters. White box testing is a method that tests the logical flow of software to detect possible faults and deficiencies in the code. The proposed module performs model-based testing using this testing technique. The module was developed as an extension of the traditional testing tool IM-FIT. It was inspired by IM-FIT's advanced fault detection capacity in traditional software testing, autonomous or customized testing options, and detailed report generation capabilities. The module carries out an advanced scanning and mutation process using its own unique fault library for deep neural networks. The module successfully performed the test by autonomously generating mutants and classifying these mutants. Tests were performed on a Transformer model using the TensorFlow library during the testing process. As a result of the tests performed with 5, 10, and 20 epoch values, a total of 606 mutants were produced. While 414 of these mutants were classified as killed, 192 were classified as survived. This study presents it to the literature as an effective mutation testing tool focusing on deep neural networks.

关键词： software testing Technological innovation Fault detection Focusing Artificial neural networks Transformers Libraries software reliability Intelligent systems Glass box

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Optimization design of highway tunnel lighting entrance section for safety and energy saving

Optimization design of highway tunnel lighting entrance sect...

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第35届中国控制与决策会议

作者： Lunhui Xu Runnan Xu Mengyao Zhu Guangdong University of Science and Technology School of computer Jiangxi University of Science and Technology School of Electrical Engineering and Automation Software Engineering Institute of Guangzhou Department of electronics

In order to solve the contradiction between over-luminance and under-luminance in the highway tunnel lighting system,an entrance section luminance curve optimization design method is proposed to the purpose of traffic safety and energy consumption ***,under the constraints of the Rules and Guidelines,the objective function of luminance curves optimization considering over-luminance and under-luminance is constructed,and the four-step optimization model and design parameters of the entrance section luminance curve are *** the luminance curve optimization model is evaluated and analyzed in terms of comfort,safety and energy saving ***,combined with the actual tunnel,it is verified that compared with the Rule method,the proposed design method reduces the maximum luminance ratio of adjacent sections by 26.5%,and the lighting step curve in the second half of the entrance section is reduced by 95.7% compared with the adaptive *** power consumption was reduced by 6.1% and 6.3% for HPS and LED lights,respectively,compared to the design method considering only traffic safety.

关键词： Tunnel lighting Optimized design Tunnel security Energy saving and Consumption reduction

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"It’s Sink or Swim": Exploring Patients’ Challenges and Tool Needs for Self-Management of Postoperative Acute Pain

arXiv

引用

arXiv 2024年

作者： Zghab, Souleima Pagé, Gabrielle Lussier, Mélanie Bédard, Sylvain Cheng, Jinghui Department of Computer and Software Engineering Polytechnique Montreal MontrealQC Canada Department of Anesthesiology and Pain Medicine University of Montreal MontrealQC Canada Montreal QC Canada

Poorly managed postoperative acute pain can have long-lasting negative impacts and pose a major healthcare issue. There is limited investigation to understand and address the unique needs of patients experiencing acute pain. In this paper, we tackle this gap through an interview study with 14 patients who recently underwent postoperative acute pain to understand their challenges in pain self-management and their need for supportive tools. Our analysis identified various factors associated with the major aspects of acute pain self-management. Together, our findings indicated that tools for supporting these patients need to carefully consider information and support delivery to adapt to rapid changes in pain experiences, offer personalized and dynamic assistance that adapts to individual situations in context, and monitor emotion when promoting motivation. Overall, our work provided valuable knowledge to address the less-investigated but highly-needed problem of designing technology for the self-management of acute pain and similar health conditions. Copyright © 2024, The Authors. All rights reserved.

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Managing Information Updating with Edge Computing: A Distributed and Learning Approach

Managing Information Updating with Edge Computing: A Distrib...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Junyi He Di Zhang Shumeng Liu Yuezhi Zhou Yaoxue Zhang School of Software Engineering Beijing Jiaotong University School of Science and Engineering The Chinese University of Hong Kong Shenzhen Department of Computer Science and Technology Tsinghua University

The rapid proliferation of some real-time applications (e.g., video surveillance) has driven enormous interest in maximizing information freshness, quantified by the age of information (AoI). For some computation-intensive updates such as images or videos, the real-time update processing requires intensive resources, which edge servers can provide in mobile edge computing (MEC). In this paper, we investigate information updating scheduling with multiple users in MEC. Due to the centralized algorithms’ limitations in distributed systems where users are self-interested, we investigate an efficient distributed scheduling algorithm. We model the information updating scheduling as an uncooperative game and propose a distributed algorithm to compute the unique Nash equilibrium. Considering the unavailability of some global network information, we propose a learning algorithm where each user learns how to make decisions based on observable information in a distributed manner. Extensive evaluation results show the efficiency of the proposed algorithms.

关键词： Signal processing algorithms Games Streaming media Signal processing Nash equilibrium Video surveillance Real-time systems

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SHEDAD: SNN-ENHANCED DISTRICT HEATING ANOMALY DETECTION FOR URBAN SUBSTATIONS

arXiv

引用

arXiv 2024年

作者： van Dreven, Jonne Cheddad, Abbas Alawadi, Sadi Ghazi, Ahmad Nauman Al Koussa, Jad Vanhoudt, Dirk Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Mol Belgium EnergyVille Genk Belgium

District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH network topology and allow for local anomaly detection without disclosing sensitive information, such as substation locations. The approach leverages a multi-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial neighborhood creation. Moreover, it introduces a merging technique that reduces noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD) and modified z-scores to flag anomalous substations. The results reveal that SHEDAD outperforms traditional clustering methods, achieving significantly lower intra-cluster variance and distance. Additionally, SHEDAD effectively isolates and identifies two distinct categories of anomalies: supply temperatures and substation performance. We identified 30 anomalous substations and reached a sensitivity of approximately 65% and specificity of approximately 97%. By focusing on this subset of poorperforming substations in the network, SHEDAD enables more targeted and effective maintenance interventions, which can reduce energy usage while optimizing network performance. © 2024, CC BY.

关键词： Nearest neighbor search

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Seg-CapNet:A Capsule-Based Neural Network for the Segmentation of Left Ventricle from Cardiac Magnetic Resonance Imaging

引用

Journal of computer Science & Technology 2021年第2期36卷 323-333页

作者： Yang-Jie Cao Shuang Wu Chang Liu Nan Lin Yuan Wang Cong Yang Jie Li School of Software Zhengzhou UniversityZhengzhou 450000China Center of Modern Analysis and Gene Sequencing Zhengzhou UniversityZhengzhou 450000China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200000China

Deep neural networks(DNNs)have been extensively studied in medical image ***,existing DNNs often need to train shape models for each object to be segmented,which may yield results that violate cardiac anatomical structure when segmenting cardiac magnetic resonance imaging(MRI).In this paper,we propose a capsulebased neural network,named Seg-CapNet,to model multiple regions simultaneously within a single training *** Seg-CapNet model consists of the encoder and the *** encoder transforms the input image into feature vectors that represent objects to be segmented by convolutional layers,capsule layers,and fully-connected *** the decoder transforms the feature vectors into segmentation masks by *** maps of each down-sampling layer in the encoder are connected to the corresponding up-sampling layers,which are conducive to the backpropagation of the *** output vectors of Seg-CapNet contain low-level image features such as grayscale and texture,as well as semantic features including the position and size of the objects,which is beneficial for improving the segmentation *** proposed model is validated on the open dataset of the Automated Cardiac Diagnosis Challenge 2017(ACDC 2017)and the Sunnybrook Cardiac Magnetic Resonance Imaging(MRI)segmentation *** results show that the mean Dice coefficient of Seg-CapNet is increased by 4.7%and the average Hausdorff distance is reduced by 22%.The proposed model also reduces the model parameters and improves the training speed while obtaining the accurate segmentation of multiple regions.

关键词： capsule neural network image segmentation left ventricle segmentation cardiac magnetic resonance imaging

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A Genetic Algorithm approach for optimization of geothermal power plant production: Case studies of direct steam cycle in Kamojang

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South African Journal of Chemical engineering 2023年 45卷 1-9页

作者： Rudiyanto, Bayu Birri, Mochammad Syahrul Widjonarko Avian, Cries Kamal, Dianta Mustofa Hijriawan, Miftah Energy Engineering Laboratory Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Graduate School of Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Tegalboto Jember 68121 Indonesia Departement of Electronic and Computer Engineering National Taiwan University of Science and Technology No.43 Keelung Rd. Sec.4 Da'an Dist. Taipei City 106335 Taiwan Department of Mechanical Engineering Politeknik Negeri Jakarta Jl. Prof. Dr. G. A. Siwabessy Depok 16425 Indonesia Graduate Program of Mechanical Engineering Universitas Sebelas Maret Jl. Ir. Sutami 36 Surakarta 57126 Indonesia

Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%. © 2023 The Author(s)

关键词： Dry steam cycle Exergy Genetic Algorithm Geothermal power plant Optimization

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An Improved I-V model for Resistive Random Access Memory

An Improved I-V model for Resistive Random Access Memory

引用

2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Chung, Harry Shin, Hyungsoon Sun, Wookyung Park, Jisun Graduate Program in Smart Factory Ewha Womans University Dept. of Electronic and Electrical Engineering Seoul Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, we performed a simulation for a physical resistive random-access device (RRAM) to propose an improved model representing its electrical characteristics. Voltage ThrEshold Adaptive Memristor (VTEAM) model that operates based on the voltage threshold of the device adapted for the RRAM simulation. We applied some physical features to the model to adjust the internal state variable to improve the simulation results. The internal state variable used in the model was designed after the physical feature such as the distance between the end of the filament and the electrode of the device. Therefore, applying some of these physical conditions affects the simulation results. Simulation results with the newly applied model showed optimized I-V characteristics that resemble the physical device's behavior more than the existing model. © 2022 IEEE.

关键词： Memristors

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