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22nd International Conference on Enterprise Information systems, ICEIS 2020

作者： Santos, Rafael Meneses Santos, Israel Meneses Rodrigues, Methanias Colaço de Mendonça Neto, Manoel Gomes Graduate Program in Computer Science Federal University of Bahia Salvador Brazil Department of Information Systems Federal University of Sergipe Itabaiana Brazil

ISBN: (纸本)9789897584237

Context: In software development, new functionalities and bug fixes are required to ensure a better user experience and to preserve software value for a longer period. Sometimes developers need to implement quick changes to meet deadlines rather than a better solution that would take longer. These easy choices, known as Technical Debt, can cause long-term negative impacts because they can bring extra effort to the team in the future. Technical debts must be managed and detected so that the team can evaluate the best way to deal with them and avoid more serious problems. One way to detect technical debts is through source code comments. Developers often insert comments in which they admit that there is a need to improve that part of the code later. This is known as Self-Admitted Technical Debt (SATD). Objective: Evaluate a Long short-term memory (LSTM) neural network model combined with Word2vec for word embedding to identify design and requirement SATDs from comments in source code. Method: We performed a controlled experiment to evaluate the quality of the model compared with two language models from literature and LSTM without word embedding in a labelled dataset. Results: The results showed that the LSTM model with Word2vec have improved in recall and f-measure. The LSTM model without word embedding achieves greater recall, but perform worse in precision and f-measure. Conclusion: Overall, we found that the LSTM model and word2vec can outperform other models. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Long short-term memory

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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computer System, Information Technology, and Electrical Engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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RISC-V SoC Physical Implementation in 180 nm CMOS with a Quark Core Based on FemtoRV32

RISC-V SoC Physical Implementation in 180 nm CMOS with a Qua...

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IEEE Ecuador Technical Chapters Meeting (ETCM)

作者： Felipe F. Nascimento Rodrigo N. Wuerdig André F. Ponchet Bruno Sanches Denis S. Loubach Roberto D’Amore Marcus H. Victor Junior Walter S. Oliveira Vitor O. Kuribara Luiz C. Moreira Electronic Engineer Division IEEA Aeronautics Institute of Technology São José dos Campos Brazil Graduate Program on Microelectronics UFRGS Porto Alegre Brazil Department of Electronic Systems University of São Paulo São Paulo Brazil Department of Computer Systems Aeronautics Institute of Technology São José dos Campos Brazil

This article presents the first known physical implementation of a RISC-V core based on the FemtoRV32 project, using the Quark core to implement the RV32I instruction set. Our primary goal is to validate the micro-architecture for ASIC development. Implemented using 180 nm CMOS technology, the core demonstrates functionality at frequencies as high as 120 MHz, which represents a 150% improvement over FPGA implementations. Power dissipation tests estimate a range of 68 to 108 mW, under different PVT corners, and slack margins of 0.5 and 2 ps, respectively. The core also passed standard regression tests for RISC-V ISA compliance, executed within an automated verification environment. Additionally, our paper outlines the ASIC implementation tools and methodologies employed, adding to its viability as a foundation for developing low-cost microprocessors or custom ASICs for specific applications.

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Ultrasound Stimulation Potentiates Management of Diabetic Hyperglycemia

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Ultrasound in Medicine and Biology 2023年第5期49卷 1259-1267页

作者： Chang, Chia-Hsuan Fan, Kang-Chih Cheng, Yuan-Pin Chen, Jung-Chih Chen, Gin-Shin Graduate Degree Program of the College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes Miaoli County Taiwan Division of Endocrinology and Metabolism Department of Internal Medicine National Taiwan University Hospital Hsinchu Branch Hsinchu City Taiwan Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei City Taiwan Electronic Systems Research Division National Chung-Shan Institute of Technology Taoyuan City Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan Catholic Mercy Hospital Catholic Mercy Medical Foundation Hsinchu County Taiwan Medical Device Innovation & Translation Center National Yang Ming Chiao Tung University Hsinchu City Taiwan

Objective: Glucose homeostasis is the only way to manage diabetic progression as all medications used do not cure diabetes. This study was aimed at verifying the feasibility of lowering glucose with non-invasive ultrasonic stimulation. Methods: The ultrasonic device was homemade and controlled via a mobile application on the smartphone. Diabetes was induced in Sprague–Dawley rats through high-fat diets followed by streptozotocin injection. The treated acupoint CV12 was at the middle of the xiphoid and umbilicus of the diabetic rats. Parameters of ultrasonic stimulation were an operating frequency of 1 MHz, pulse repetition frequency of 15 Hz, duty cycle of 10% and sonication time of 30 min for a single treatment. Discussion: The diabetic rats exhibited a significant decrease of 11.5% ± 3.6% in blood glucose in 5 min of ultrasonic stimulation (p © 2023 World Federation for Ultrasound in Medicine & Biology

关键词： Glucose

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Economics of Semantic Communication in Metaverse: An Auction Approach

Economics of Semantic Communication in Metaverse: An Auction...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Zi Qin Liew Hongyang Du Wei Yang Bryan Lim Zehui Xiong Dusit Niyato Han Yu Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore School of Computer Science and Engineering Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

Metaverse provides embodied artificial-reality experience to the users in the virtual spaces. Many innovative and creative services such as virtual conference and tourism have been realized in the digital twins maintained by the virtual service providers (VSP) in Metaverse. Digital twins are digital copies of the physical world constructed virtually by the VSPs using real-world data. For a realistic experience, VSPs need to collect data that is up-to-date and relevant to their services. In this paper, we propose an incentive design framework to support the data trading between VSPs and edge devices. In the auction model, we model the valuation of data by considering data relatedness and data freshness. In our model, the semantic communication model is used to filter the relevant data, and the age of information (AoI) metric is used to assess the data freshness. Results show that by considering the data freshness, our mechanism helps to increase the average update frequency so that the VSPs obtain fresh data for construction of digital twins. Our model ensures the desired properties of individual rationality, incentive compatibility, and budget balance.

关键词： Measurement Metaverse Data integrity Semantics Streaming media Information filters Data models

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Database management system design for automatic weather information with twitter data collection 6

Database management system design for automatic weather info...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Purwandari, Kartika Perbangsa, Anzaludin Samsinga Sigalingging, Join W. C. Krisna, Adhi Asta Anggrayani, Suci Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Meteorological Climatological and Geophysical Agency Database Center Division of Bmkg Jakarta10720 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Improvement of data management about weather in Indonesia is very supportive of the need for weather information. Currently, the weather information that is disseminated to the public is only based on sensor location points that are spread across Indonesia. However, supporting data generated from these sensors is only limited to certain areas. We hence develop a database system that can help collect weather information automatically via Twitter. This facilitates the Indonesian people in knowing weather information based on the completeness of data from sensor data and Twitter. The steps from the database that are built will be very helpful in collecting data about the weather throughout Indonesia. The basic step that we use in our database are conceptual, logical and physical design. The information obtained in this study is very important for efficient and effective data collection to achieve real-time automation of weather information. © 2021 IEEE.

关键词： Database systems

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Blockchain Technology Open Problems and Impact to Supply Chain Management in Automotive Component Industry 6

Blockchain Technology Open Problems and Impact to Supply Cha...

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6th International Conference on Computing, Engineering, and Design, ICCED 2020

作者： Surjandy Meyliana Spits Warnars, Hareco Leslie Hendric Abdurachman, Edi School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728191898

The study of the adoption of Blockchain Technology (BT) of the Supply Chain Management System (SCMS) automotive components industry still in infancy. BT's previous research topic mostly in virtual currency, security, and IoT. However, the automotive market's significant development causes several issues in the SCMS automotive components industry, such as fake components. Blockchain Technology (BT), as identified, might be possible as a way out for the development of SCMS automotive components. There are several research questions in this research, such as how BT can facilitate SCMS open issues? What is the BT impact in the SCM area? The study method used the systematic literature review (SLR) method to gather all information from 12 publishers and an international index journal. Seven open issues identified in this study and SCM's core operational aspect, such as supplier, logistics, manufacturer, distribution, and customer, have been identified impact with BT. © 2020 IEEE.

关键词： Automotive industry

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A Blockchain Technology-Based for University Student Enrollment Process 6

A Blockchain Technology-Based for University Student Enrollm...

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6th International Conference on Computing, Engineering, and Design, ICCED 2020

作者： Meyliana Surjandy Fernando, Erick Cassandra, Cadelina Eka Widjaja, Henry Antonius Chandra, Yakob Utama Prabowo, Harjanto School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728191898

University in Indonesia contemporary is looking for a solution to improve the university documentation such as certificate and transcript. But fake diploma certificate and transcript is an issue reported in several articles recently. The new technology called Blockchain has been elaborate and predicted can be used to solve the issue because of blockchain characteristics which are immutable, secure, tamperproof and unchangeable. The enrollment process is one of the vital process for university before the document can be made. Therefore, the research tries to elaborate deeply to create a model of the university student enrollment process. The research question of this research is how Blockchain Technology can be merge in the enrollment process? The model has been validated by several universities in Indonesia. Forum Group Discussion had been performed to discuss for enrollment process. In the end, many benefits come up from blockchain technology utilization to produce a reliable document such as diploma certificate and transcripts. © 2020 IEEE.

关键词： Blockchain

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis

Learning Adaptation and Generalization from Human-Inspired M...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Yi-Hsin You Chi-Wei Feng Yen-Min Chen Bo-Yi Kuo Academia Sinica Institute of Information Science Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Taiwan University Taipei Taiwan

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a human-inspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future.

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