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19th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2022

作者： Alaei, Amir Mohammad Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Virtual reality has become a new option to inform the customers about product before purchasing. However, providing virtual reality may create new challenges. For instance, consumers may obtain essential information about products by using virtual reality option, but eventually they buy their products from the offline channel. This phenomenon is called webrooming. Another challenge is cyber-attacks. E-tailers and their consumers face risks from cyber-attacks. Thus, e-tailers are investing to improve cybersecurity. We consider a dual-channel supply chain consisting of an offline retailer and an e-tailer who purchase the same product from a manufacturer at the same wholesale price. The e-tailer offers a partial refund policy in order to attract the customers. Also, to reduce consumer valuation uncertainty, the e-tailer faces the decision on whether to introduce virtual reality. We analyze three scenarios. Firstly, the e-tailer does not offer virtual reality. Secondly, the e-tailer offers virtual reality, but he does not invest on cybersecurity. Thirdly, the e-tailer offers virtual reality and invests on cybersecurity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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Decomposition and Reorganization of Phonetic Information for Speaker Embedding Learning

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 1745-1757页

作者： Hong, Qian-Bei Wu, Chung-Hsien Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701 Taiwan Academia Sinica Taipei115 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan

Speech content is closely related to the stability of speaker embeddings in speaker verification tasks. In this paper, we propose a novel architecture based on self-constraint learning (SCL) and reconstruction task (RT) to remove the influence of phonetic information on speaker embedding generation. First, SCL is used to reduce the divergence of frame-level features, which can avoid ambiguity between the resulting embeddings of the two utterances being compared. Second, RT is used to further remove phonetic information in frame-level layers, focusing on speaker-discriminative feature transformation. In our experiments, the speaker embedding models were trained on the VoxCeleb2 dataset and evaluated on the VoxCeleb1, Librispeech, SITW and VoxMovies datasets. Experimental results on VoxCeleb1 show that the proposed DROP-TDNN system reduced the EER by 7.5%, compared to the state-of-the-art ECAPA-TDNN system. Furthermore, the proposed DROP-TDNN system also outperformed the ECAPA-TDNN system in the experiments on SITW, Librispeech and VoxMovies under cross-dataset conditions. In the experiments on SITW, the proposed system reduced the EER by 3.4% compared to the ECAPA-TDNN system. In the experiments on Librispeech, the proposed system demonstrated the advantage of removing phonetic information under the clean speech condition, with a significant reduction of 25.5% in EER compared to the ECAPA-TDNN system. In the experiments on VoxMovies, the proposed system reduced the EER by up to 7.9% compared to the ECAPA-TDNN system under different pronunciation and background conditions. © 2014 IEEE.

关键词： Speech recognition

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Developing an Expert System for Mobile-based Gastroenteritis Detection Using ESDLC and Best First Search Methods

Developing an Expert System for Mobile-based Gastroenteritis...

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International Conference on Informatics and Computing (ICIC)

作者： R. Rizal Isnanto Catur Edi Widodo Ike Pertiwi Windasari Misik Puspajati Saputri Information Systems Doctoral Program Diponegoro University Semarang Indonesia Computer Engineering Department Diponegoro University Semarang Indonesia

Gastroenteritis is a common gastrointestinal disorder with varying degrees of severity, including cases without dehydration, mild dehydration, moderate dehydration, and severe dehydration. This research focuses on the development of a mobile-based expert system for detecting gastroenteritis, capable of distinguishing between these different dehydration levels. The methodology employed integrates the Extreme Structure Development Life Cycle (ESDLC) and the Best First Search algorithm. The expert system is designed to provide swift and accurate responses to users inputting symptoms, categorizing gastroenteritis cases based on the severity of dehydration. The application of ESDLC ensures a structured and organized development process, while the Best First Search method enhances the efficiency of the diagnostic process, facilitating timely and precise recommendations. Accessible through mobile platforms, the system allows users to input symptoms conveniently and receive rapid recommendations. Rigorous testing and evaluation affirm the system's high accuracy in discerning dehydration levels in gastroenteritis patients. In conclusion, the developed mobile-based expert system exhibits promising potential for advancing diagnostic services, particularly in the categorization of dehydration levels in gastroenteritis cases. The successful implementation of ESDLC and Best First Search paves the way for similar solutions in other healthcare domains, contributing positively to early and accurate disease management efforts. This innovation marks a significant stride towards improving healthcare technology for enhanced decision support in gastroenteritis diagnosis.

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National Laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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Gastritis Diagnosis Expert System Using Android-Based Certainty Factor Method

Gastritis Diagnosis Expert System Using Android-Based Certai...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： R. Rizal Isnanto Purwanto Yudi Eko Windarto Annas Setiawan Information Systems Doctoral Program Diponegoro University Semarang Indonesia Computer Engineering Department Diponegoro University Semarang Indonesia

In the modern era, an increasing number of diseases are emerging because of human lifestyle choices and bacterial transmission. Gastritis, characterized by inflammation in the stomach lining leading to frequent abdominal pain, is one such ailment. Recognizing the significance of addressing these health issues, there is a need for an expert system application specifically designed for diagnosing gastritis. The primary objective is to disseminate information to the public regarding the early symptoms of gastritis, promoting awareness from a young age. The research method used is the web-based Expert System Development Life Cycle (ESDLC). The diagnostic process employs the Certainty Factor (CF) method to ensure accurate results. By utilizing symptom CF values chosen by the user, the Certainty Factor approach facilitates a diagnosis based on calculations established by medical specialists. The research culminates in the development of an expert system application capable of accurately diagnosing the disease Android based. The accuracy rates for the first, second, and third test cases are reported as 98.848%, 99.8464%, and 99.99115264%, respectively. System testing, conducted through black-box testing, validates the application's functionality, demonstrating a 100% success rate for each component.

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Expert System for Diagnosis of Diphtheria Disease Using Fuzzy Logic

Expert System for Diagnosis of Diphtheria Disease Using Fuzz...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： R.Rizal Isnanto Mustafid Agung Budi Prasetijo Safira Isna Fitria Ali Information Systems Doctoral Program Diponegoro University Semarang Indonesia Computer Engineering Department Diponegoro University Semarang Indonesia

Diphtheria is an infectious disease that affects the upper respiratory system and throat, arising suddenly and caused by Corynebacterium diphtheriae. The symptoms of diphtheria such as fever, swollen neck and slimy nose are often interpreted as influenza or laryngitis. Diphtheria is a contagious and deadly disease that requires specialized and rapid treatment and care. In today's technological era, it can be overcome with a system that can diagnose diphtheria like a doctor's diagnosis ability commonly called an expert system. This study describes the application of an expert system to diagnose diphtheria disease. The research method used is the web-based Expert System Development Life Cycle (ESDLC). The method used to process input values is Forward Chaining. Fuzzy Tsukamoto to calculate the input value from the user. The research has been tested with Blackbox testing showing the results that the expert system works well and accuracy testing with 92% results. The results show effectiveness with a satisfactory level of accuracy in detecting diphtheria disease. This research contributes to the development of expert systems in the health sector with a focus on diagnosing diphtheria disease using a fuzzy logic approach.

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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A Comprehensive Key Features Analysis and Recommendations based Cyber Intrusion Detection for Satellite-Terrestrial Networks

A Comprehensive Key Features Analysis and Recommendations ba...

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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Esraa Shehab Shaimaa Ahmed Elsaid Ahmed M. Mattar Dept. of Electrical Engineering Computer Engineering and Systems program Badr University in Cairo (BUC) Cairo Egypt Dept. of Electronics and Communications Engineering Zagazig University Zagazig Egypt Dept. of Computer engineering and artificial intelligence military technical college Cairo Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

The integration of Satellite-Terrestrial Networks (ISTN) necessitates advanced security measures, particularly Intrusion Detection systems (IDSs). This study introduces hybrid sequential intrusion detection models for ISTNs, combining Deep Learning (DL) and Machine Learning (ML) techniques. The models employ both anomaly-based and signature-based detection to enhance accuracy, utilizing methods such as Extra Trees (ET), Decision Trees (DT), Random Forest (RF), XGBoost (XGB), and Gated Recurrent Units (GRU). These models are chosen for their superior performance and are used sequentially to improve IDSs effectiveness. RF-based Sequential Feature Selection (RF-SFS) is also utilized to reduce dataset dimensionality, which in turn decreases the computational costs for each model. Evaluations using UNSW-NB15 and STIN datasets-representing terrestrial and satellite traffic, respectively-demonstrate the models' superiority over traditional IDSs. The XGB-ET model achieved 99.99% accuracy in anomaly detection, while the XGB-GRU model attained 89% accuracy in signature-based detection on the UNSW-NB15 dataset. On the STIN dataset, the ET-DT-GRU model reached 96.47% accuracy in signature-based detection. Additionally, RF-SFS reduced execution times, with training and testing speedups up to 2.8x.

关键词： Training Accuracy Satellites Computational modeling Intrusion detection Space-air-ground integrated networks Feature extraction Security Random forests Testing

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System for Automating Medication Orders to the Hospital Pharmacy by Nurses in Peru Through Voice Recognition

System for Automating Medication Orders to the Hospital Phar...

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Software engineering and computer Science (CSECS), International Conference on

作者： Conrado Moreno-Fernández Fabian Colque-Fernández José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The management of nurses' orders to a Peruvian hospital pharmacy faces efficiency challenges, which affects time spent on patient care. This context highlights the need for a system that optimizes this process. In this sense, a web-based system is proposed for the automation of orders from the nurse's cell phone, which uses a customized model for automatic speech recognition. The development of the system was divided into three stages: first, data collection with 21 orders placed by three nurses and 978 audio recordings to validate the automatic speech recognition model; second, implementation of the system allowing agile order processing; and finally, evaluation of results in terms of time and accuracy. The findings indicate that the processing time ranged from 37.70 to 60.15 seconds, representing a 97%-time reduction in operational tasks. In addition, the Word Error Rate was 10.51%, significantly lower than the 25.05% of Whisper Large V2. These results demonstrate the system's potential for adoption in various hospital contexts.

关键词： Automation Accuracy Hospitals Error analysis Terminology Transforms Time measurement Artificial intelligence Automatic speech recognition Software engineering

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