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International Conference on information Technology and Electrical engineering (ICITEE)

作者： Zeba Shamsi Anish Kumar Saha Ripon Patgiri Khoirom Motilal Singh Laiphrakpam Dolendro Singh Dept. of Computer Science and Engineering National Institute of Technology Silchar Assam India Dept. of Computer Science and Engineering Indian Institute of Information Technology Senapati Manipur India

Recently, Bansal et al. proposed a dual-layer message security technique using steganography and cryptography. An index table maps each text data to an index point and an elliptic curve point. The text data are encrypted using elliptic curve cryptography, and the corresponding index values are embedded in a cover image using the Least Significant Bit Inversion technique. The proposed paper breaks the security of the key exchange technique in the Bansal et al. method and the secret text is extracted.

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Machine Learning-Based Wine Quality Prediction

Machine Learning-Based Wine Quality Prediction

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International Conference on Advances in information Technology (ICAIT)

作者： Shiv Ashish Dhondiyal Rohit Kumar Himanshu Verma Ashish Dhyani Sumeshwar Singh Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India GNIOT Institute of Professional Studies Greater Noida Dept. of Hospitality Management Graphic Era Deemed to be University Dept. of Computer Science and Engineering Jaypee Institute of information technology

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The main goal of this attempt is to assess the quality of wine and determine if it is excellent or terrible. Humans have been engaging in the act of tasting for generations and have consistently made predictions using their sensory organs. A number of industries have recently embraced new technology and used it to a variety of uses. However, human knowledge is required in a number of businesses, such as product quality assurance. With the increasing popularity of the product, modern innovations have increased the cost of the treatment. To ensure the quality of the output, this study looks at a number of machines learning algorithms, including Random Forest Classification, Decision Tree classifiers, and Support Vector Machines (SVM). These methods make use of the product's current attributes to simplify the quality control procedure.

关键词： Support vector machines Technological innovation Sensitivity Simulation Quality control Predictive models Vectors Quality assessment Decision trees Random forests

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Occluded Retail Products Detection Through Attention-Reweighted

Occluded Retail Products Detection Through Attention-Reweigh...

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2022 IET International Conference on engineering Technologies and Applications, IET-ICETA 2022

作者： Chan, Hung-Tse Chen, Vincent Hsia, Chih-Hsien National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Interlake High School WA United States National Ilan University Dept.of Computer Science and Information Engineering Yilan Taiwan Chaoyang University of Technology Dept. of Business Administration Taichung Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

In recent years, developments in artificial intelligence (AI) and computer vision (CV) have led to an increase in research regarding retail product detection. However, effective CV use for identifying objects continues to face major obstacles, such as customers% random placement of products at checkout partially obscuring items from view. This paper explores the use of faster region-based convolutional neural networks (Faster RCNN) and attentional reweighting to detect obscured retail products. This attention-based method allows CV to identify and process various occlusion patterns. While the training set used to teach the network excluded heavily occluded data, the test set included a range of obscuration levels to accurately reflect the random positioning of products in real life. Experimental results suggest that the proposed method returns better detection accuracy with occlusion problems than current approaches employing convolutional neural networks (CNNs). © 2022 IEEE.

关键词： Convolutional neural networks

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Semi-Automated Behavior-Driven Testing for the Web Front-Ends

Semi-Automated Behavior-Driven Testing for the Web Front-End...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Shang-Pin Ma Yu-An Chen Yi-Jie Guo Yu-Sheng Su Dept. of Computer Science & Engineering National Taiwan Ocean University Keelung Taiwan Dept. of Computer Science & Information Engineering National Chung Cheng University Chiayi Taiwan

The required effort and time increase as web applications grow in size and complexity. Regression testing becomes time-consuming and repetitive, and there is a higher chance of human errors and omissions. Therefore, automating tests by recording test scripts and writing test case code (i.e., code for conducting specific test cases) can reduce effort, time, and human errors. However, technical personnel may not always understand and describe user requirements correctly, leading to issues of misaligned requirements. Therefore, integrating Behavior-Driven Development (BDD) into front-end automated testing can help facilitate communication and discussion with users. By collaboratively establishing user stories and scripts for testing, it should be possible to reduce issues of unclear or misaligned requirements. Currently, some project teams combine automated testing with BDD methods, which has been recognized for its effectiveness. However, the manual creation of test cases and the definition of BDD steps increase the workload. Moreover, there still needs to be a systematic workflow and supporting tools for effectively incorporating script recording tools into BDD testing. To address these issues, we propose a systematic process called “Recorder-based BDD Testing for Front-end“ (RBTF), which combines the script recording capabilities of automated testing tools with BDD practices. We also developed a tool called “JUnit to Cucumber Converter“ (JUCC) that facilitates the conversion between BDD specifications and test case codes.

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Deep Learning Training Strategies for Severely Imbalanced Data in Organ Segmentation Tasks 6

Deep Learning Training Strategies for Severely Imbalanced Da...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Wang, Hsin-Hui Liu, Chin-Yun Hung, Shih-Kai Chen, Liang-Cheng Hsieh, Hui-Ling Liu, Wei-Min National Chung Cheng University Dept.of Computer Science and Information Engineering Chiayi Taiwan Tzu Chi University School of Medicine Hualien Taiwan Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation R.O.D.T.C.H Dept. of Radiation Oncology Chiayi Taiwan National Cheng Kung University Dept.of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350301953

Radiotherapy is one of the common methods for cancer treatment. Developing a radiotherapy plan requires professional medical physicists or physicians to manually contour the organ boundaries in CT series, which is time-and labor-consuming. If artificial intelligence (AI) could assist with the task, it could alleviate the workload of medical staff, especially when medical resources are tight. We propose an AI-based automatic organ segmentation system trained by clinical datasets. However, this task is prone to be non-robust models in CT image series where the background occupies the majority of the scene. To remedy such data imbalance situation, we propose adopting three strategies during the model training steps: region classification, knowledge discovery in database, and sampler. The major segmentation task is based on U-Net and ResNet34 model where all convolution layers and batch normalization are replaced with group normalization and weight standardization to ensure effectiveness in small-batch data training. In this study, 33 organs throughout the body were segmented. The ablation experiments were conducted to prove all the training models have better performance than the original method. In the future, if a hospital needs to train model with their own private datasets, the three above strategies can be adopted to prevent unsuccessful training. © 2023 IEEE.

关键词： Radiotherapy

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Towards A Reliable Ground-Truth For Biased Language Detection 21

Towards A Reliable Ground-Truth For Biased Language Detectio...

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Proceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries

作者： Timo Spinde David Krieger Manuel Plank Bela Gipp Dept. of Computer and Information Science University of Konstanz Constance Germany School of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany

ISBN: (纸本)9781665417709

Reference texts such as encyclopedias and news articles can manifest biased language when objective reporting is substituted by subjective writing. Existing methods to detect bias mostly rely on annotated data to train machine learning models. However, low annotator agreement and comparability is a substantial drawback in available media bias corpora. To evaluate data collection options, we collect and compare labels obtained from two popular crowdsourcing platforms. Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset. By creating such a framework and gathering a first dataset, we are able to improve Krippendorff's α = 0.144 (crowdsourcing labels) to α = 0.419 (expert labels). We conclude that detailed annotator training increases data quality, improving the performance of existing bias detection systems. We will continue to extend our dataset in the future.

关键词： media bias

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Application to Enhance the Programming Knowledge of Children

Application to Enhance the Programming Knowledge of Children

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Advancements in Computing (ICAC), International Conference on

作者： Anuga Dissanayake Vishwa Lakpriya Mahendran Sanjeevan Ahamed Fahmi Samanthi E.R. Siriwardana Mihiri Samaraweera Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information Technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information Technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information Technology education, equipping Sri Lankan students with essential programming skills to succeed globally.

关键词： Adaptation models Translation Machine learning algorithms Computational modeling Retrieval augmented generation Learning (artificial intelligence) Real-time systems Proposals information technology Programming profession

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Intelligent IoT Platform for Real-Time Monitoring of Indoor Air Quality in Healthcare Facilities

Intelligent IoT Platform for Real-Time Monitoring of Indoor ...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Yin-Ting Cheng Dept. Computer Science and Information Engineering SHU-TE University Kaohsiung Taiwan R.O.C.

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

In recent years, people have been confronted with unprecedented emerging infectious diseases. Medical institutions typically manage high-risk infectious diseases, which are transmitted via droplets or airborne routes, by using negative pressure isolation rooms to house patients and provide medical care. These isolation areas and rooms are specially designed to effectively prevent the spread of pathogens, with the goal of protecting healthcare personnel and preventing large-scale hospital-acquired *** most medical institutions have already completed the installation of hardware, wired network construction is not ideal for hospital environments, as it would require modifications to existing infrastructure, indirectly affecting the quality of the hospital environment, such as noise from construction and the layout of piping systems. Therefore, this study proposes an "Intelligent IoT Platform for Real-Time Monitoring of Indoor Air Quality in Healthcare Institutions." The system leverages smart sensors integrated with the Internet of Things (IoT), utilizing the small size and ease of deployment of the sensors as part of the overall technical framework. The platform also incorporates health and safety indicators relevant to hospital hygiene, enabling the monitoring of various environmental factors to ensure the safety of healthcare personnel.

关键词： Chemical sensors Infectious diseases Hospitals Real-time systems Sensor systems Internet of Things Personnel Monitoring Intelligent sensors Indoor air quality

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IMPROVING REAL-TIME MUSIC ACCOMPANIMENT SEPARATION WITH MMDENSENET

arXiv

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arXiv 2024年

作者： Wang, Chun-Hsiang Wang, Chung-Che Wang, Jun-You Jang, Jyh-Shing Roger Chu, Yen-Hsun Dept. of Computer Science and Information Engineering National Taiwan Univ. Taiwan Realtek Semiconductor Corp Taiwan

Music source separation aims to separate polyphonic music into different types of sources. Most existing methods focus on enhancing the quality of separated results by using a larger model structure, rendering them unsuitable for deployment on edge devices. Moreover, these methods may produce low-quality output when the input duration is short, making them impractical for real-time applications. Therefore, the goal of this paper is to enhance a lightweight model, MMDenstNet, to strike a balance between separation quality and latency for real-time applications. Different directions of improvement are explored or proposed in this paper, including complex ideal ratio mask, self-attention, band-merge-split method, and feature look back. Source-to-distortion ratio, real-time factor, and optimal latency are employed to evaluate the performance. To align with our application requirements, the evaluation process in this paper focuses on the separation performance of the accompaniment part. Experimental results demonstrate that our improvement achieves low real-time factor and optimal latency while maintaining acceptable separation quality. © 2024, CC BY.

关键词： Source separation

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Edge Computing with Early Exiting for Adaptive Inference in Mobile Autonomous Systems

Edge Computing with Early Exiting for Adaptive Inference in ...

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IEEE International Conference on Communications (ICC)

作者： Simone Angelucci Roberto Valentini Marco Levorato Fortunato Santucci Carla Fabiana Chiasserini Dept. of Information Engineering Computer Science and Mathematics Centre Ex-EMERGE University of L'Aquila IT The Donald Bren School of Information and Computer Science UC Irvine CA US Dept. of Electronics and Telecommunications Politecnico di Torino Torino IT

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Early Exiting (EE) is an emerging computing paradigm where Deep Neural Networks (DNNs) are equipped with earlier classifiers, enabling trading-off accuracy with inference latency. EE can be effectively combined with edge computing, a paradigm that allows mobile nodes to offload complex tasks, such as the execution of DNNs, to servers at the edge of the network, thus reducing computing times and energy consumption at the mobile devices. The integration of such technologies is particularly attractive for the support of applications for connected and automated driving. In this paper, we consider a system that jointly leverages the benefits of EE and edge computing, and we model their complex interactions by means of a Markov Decision Process (MDP). We then formulate an optimization problem to select the inference strategy that maximizes the average task accuracy. Importantly, such an optimization problem has low complexity, as the optimal policy can be derived by mapping the MDP into a linear program. Our numerical results focus on a use case centered on automated vehicles connected with an edge server under varying channel and network conditions, and show that our solution achieves up to 11% higher accuracy compared to the optimal policy with no EE.

关键词： Accuracy Computational modeling Markov decision processes Artificial neural networks Mobile handsets Servers Task analysis

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