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

作者： Purnomo, Agung Susanti, Triana Ramadania, Siti Mutiah Firdaus, Mega Maulana, Fairuz Iqbal Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia Sekolah Tinggi Ilmu Ekonomi Mahardhika Accounting Department Surabaya60234 Indonesia Universitas Nahdlatul Ulama Sidoarjo English Language Education Department 61218 Indonesia Universitas Nahdlatul Ulama Sidoarjo Institute for Research and Community Services 61218 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Research on e-procurement with entrepreneurial spirit continues to develop but is limited to one country and/or year. From the perspective of the bibliometric approach, this study purposes to visually research mapping and research trends in the field of e-procurement on an international scale. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyze search results function on Scopus. This study analyzed 1, 389 scientific documents published from 1975 to 2019. According to the research, Monash University, and Vaidya, K. had the most active affiliated institutions and individual scientists in e-procurement research. computer science;and ACM International Conference Proceeding Series were the most studied and disseminated outlets of e-procurement research. There were three category maps of collaborative researchers from around the world. Based on the identification of a collection of knowledge accumulated from over forty-four years of publication, this research proposes a grouping of e-procurement research themes: Public procurement, E-commerce, Procurement management, Electronic data, Electronic business process, Commerce, and E-business, abbreviated as PEPEECE research themes. © 2021 IEEE.

关键词： Mapping

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Model Decision Support System for Diagnosis COVID-19 Using Forward Chaining: A Case in Indonesia 8

Model Decision Support System for Diagnosis COVID-19 Using F...

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8th International Conference on Cyber and IT Service Management, CITSM 2020

作者： Henderi Maulana, Miftah Warnars, Harco Leslie Hendrie S. Setiyadi, Didik Qurrohman, Taufik University of Raharja Department of Informatics Engineering Tangerang Indonesia Postgraduate of Information Engineering University of Raharja Tangerang Indonesia BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Insani University Department of Informatics Engineering Bekasi Indonesia

ISBN: (纸本)9781728177014

The government in Indonesia and its staff work together to make tactical steps to prevent the spread of COVID-19 in the community. From the ministerial level to the heads of the provinces, regencies, and even the government. Therefore, this study aims to make a model decision support system to diagnose patients exposed to Covid-19, such as people in control, patients in oversight, and those who are positive for the Covid-19 Virus. Model decision support system development aims to provide information about the development of COVID- 19 and help the community in diagnosing themselves related to COVID-19 infection. In this study, the authors use the forward chaining method in application to get conclusions from the symptoms of the Covid-19. This research resulted in an application that patients exposed to the Covid-19, and it's also provided a solution for healing from patients. And this could be a reference for patients before consulting further with the doctor. © 2020 IEEE.

关键词： Decision support systems

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Improvement of Spatial Ambiguity in Multi-Channel Speech Separation Using Channel Attention

Improvement of Spatial Ambiguity in Multi-Channel Speech Sep...

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2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021

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

ISBN: (纸本)9789881476890

Multi-channel speech separation has been successfully applied in a complex real-world environment such as the far-field condition. The common solution to deal with the far-field condition is using a multi-channel signal captured by a structured microphone array and leveraging the inner difference between channels to enhance the speech separation performance. The spatial feature has been widely used in recent speech separation research. This feature appears to be insufficient when the location information becomes ambiguous. This is known as the spatial ambiguity problem. In order to deal with the spatial ambiguity problem, this study proposes an attention mechanism for the Temporal-Spatial Neural Filter (TSNF), in which the channel attention on merged features and the feature map of 1D convolution block in the temporal convolution network is proposed. The proposed method is evaluated on the multi-channel reverberant dataset which is built based on the WSJ0-2mix dataset. The dataset is simulated in the real-environment room by using the Room Impulse Response generator. In the experimental results, the proposed methods produced the SI -SNR improvement of about 1.2dB in close speakers' case, while a small decrease of 0.1dB in other cases. © 2021 APSIPA.

关键词： Convolution

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Innovations in Social Informatic: A Patent Landscape Review

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Procedia computer science 2024年 234卷 1228-1235页

作者： Nur Afia Agung Purnomo Fairuz Iqbal Maulana Yogi Tri Prasetyo Ma. Janice J. Gumasing Elsa Rosyidah Satria Fadil Persada Institute for Research and Community Services Universitas Nahdlatul Ulama Sidoarjo Sidoarjo 61234 Indonesia English Language Education Universitas Nahdlatul Ulama Sidoarjo Sidoarjo 61234 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department Bina Nusantara University Jakarta 11480 Indonesia School of Industrial Engineering and Engineering Management Mapúa University Manila 1002 Philippines International Bachelor Program in Engineering Yuan Ze University Taoyuan 32003 Taiwan

Patent intellectual property in social informatics with entrepreneurship support creates greater opportunities to improve human life. It has the potential to open enormous innovation opportunities in various fields, such as technology, health, services, and society. This research was conducted to review the patent landscape and the main trends of patents on social informatics in all countries. Researchers applied a patent landscape analysis using data on 487 patent documents over 16 years from the *** database. The study results show yearly patented development patterns related to social informatics. There is a royalty-free opportunity by using 15 patent discontinued. Patent related to social informatics was dominated by company ownership and inventors from the United States. The patent physics (G) section overwhelmed patents related to social informatics. There were 334 simple family patents on social informatics in nine jurisdictions; the United States was the highest.

关键词： entrepreneurship, patent, social informatics innovation

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Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression

arXiv

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arXiv 2024年

作者： Lou, Siyu Liu, Chengchun Chen, Yuntian Mo, Fanyang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315200 China School of Materials Science and Engineering Peking University Beijing100871 China -Preferred Program Peking University Shenzhen Graduate School Shenzhen518500 China

Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those driven by artificial intelligence, remains a challenge. Current approaches, utilizing either high-dimensional molecular fingerprints or domain-knowledge-driven feature engineering, often face a dilemma between expressiveness and interpretability. To bridge this gap, we introduce Unsupervised Hierarchical Symbolic Regression (UHiSR), combining hierarchical neural networks and symbolic regression. UHiSR automatically distills chemical-intuitive polarity indices, and discovers interpretable equations that link molecular structure to chromatographic behavior. Copyright © 2024, The Authors. All rights reserved.

关键词： Thin layer chromatography

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How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception

arXiv

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arXiv 2024年

作者： Shahzad, Sahibzada Adil Hashmi, Ammarah Peng, Yan-Tsung Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan The Department of Computer Science National Chengchi University Taipei Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan The Institute of Information Science Academia Sinica Taiwan

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learning-based forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks. © 2024, CC BY.

关键词： Audiovisual

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Terahertz Time-Domain Imaging for Radiotherapy Treatment Monitoring in Pancreatic Ductal Adenocarcinoma

Terahertz Time-Domain Imaging for Radiotherapy Treatment Mon...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York Laboratory for Laser Energetics University of Rochester Rochester New York Department of Surgery University of Rochester Medical Center Rochester New York Department of Electrical and Computer Engineering University of Rochester Rochester New York

ISBN: (纸本)9798350369311

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment.

关键词： Maximum a posteriori estimation Terahertz wave imaging Laser theory Sensitivity Refractive index Radiation therapy Time-domain analysis Spatial resolution Probes Monitoring

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AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in Smart Crane Grabber 3

AI-Based Ripeness Grading for Oil Palm Fresh Fruit Bunch in ...

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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Harsawardana Rahutomo, Reza Mahesworo, Bharuno Cenggoro, Tjeng Wawan Budiarto, Arif Suparyanto, Teddy Surya Atmaja, Don Bosco Samoedro, Bayu Pardamean, Bens Institut Pertanian STIPER Yogyakarta55281 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mechatronics and AI Research and Development Center Biotechnic Innovation Yogyakarta55281 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is one of the biggest palm oil exporters in the world. For Indonesia to stay competitive in thepalm oil industry, the harvesting and evacuating process in its oil palm plantation need to be optimized. This research introduces machinerycalled as Smart Crane Grabber. This machinery can be used for automatic harvesting and evacuation process of oil palm fresh fruit bunch. To enable automation, Smart Crane Grabber is equipped with an Artificial Intelligencesystem for automatic ripeness sorting. TheArtificial Intelligence system developed for Smart Crane Grabber achieves 71.34% accuracy by using only 400 images as preliminary training data. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Palm oil

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Dynamic Healthcare Embeddings for Improving Patient Care

arXiv

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arXiv 2023年

作者： Jang, Hankyu Lee, Sulyun Hasan, D.M. Hasibul Polgreen, Philip M. Pemmaraju, Sriram V. Adhikari, Bijaya Department of Computer Science University of Iowa Iowa CityIA United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa CityIA United States CDC MInD Healthcare Network United States

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (***) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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