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Welcome from the General Chairs

Proceedings of the 2021 IEEE International Conference on Pro...

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Proceedings of the 2021 ieee international conference on progress in informatics and computing, pic 2021 2021年 8-9页

作者： Fujita, Hamido Wang, Yinglin Zhou, Mengqi Iwate Prefectural University Iwate Japan Shanghai University of Finance and Economics China IEEE Beijing Section China

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A Secure Federated Learning Framework using Blockchain and Differential Privacy 9

A Secure Federated Learning Framework using Blockchain and D...

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9th ieee international conference on Cyber Security and Cloud computing and 8th ieee international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Firdaus, Muhammad Larasati, Harashta Tatimma Rhee, Kyung-Hyune Pukyong National University Department of Artificial Intelligence Convergence Korea Republic of Pusan National University Department of Computer Engineering Korea Republic of Institut Teknologi Bandung School of Electrical Engineering and Informatics Indonesia

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Federated learning (FL) has considerably emerged as a promising solution to enhance user privacy and data security by enabling collaboratively multi-party model learning without exchanging confidential data. Nevertheless, most existing FL approaches still rely on a central server to obtain a global model by collecting all uploaded models from participants, which may lead to several threats from malicious participants and even expose participant privacy. therefore, to tackle these problems, we proposed a secure FL framework by empowering blockchain to replace the centralized aggregator sever and utilize Differential Privacy (DP) to address various attacks, e.g., membership inference attacks, during the collaborative FL model training process. the proposed framework has been implemented through two scenarios, i.e., blockchain-based FL to form a decentralized system and DP-based FL to construct the randomized privacy protection using the IBM DP Library. © 2022 ieee.

关键词： Blockchain

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Water Quality Classification Using Machine Learning 8

Water Quality Classification Using Machine Learning

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8th ieee international conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Arifin, Fikri Firas Tajul Idrus, Zanariah Halim, Shamimi A. Ahmarofi, Ahmad Afif Ahmad, Khairul Adilah Shah Alam College of Computing Informatics and Mathematics Selangor Shah Alam Malaysia Kedah Branch College of Computing Informatics and Mathematics Merbok Kedah Malaysia

ISBN: (纸本)9798350315516

Water quality is crucial as it directly affects the ecosystem and human health. However, current water quality classification methods are inefficient because they do not compare prediction accuracy between machine learning methods. In this regard, the objective of this study is to classify water quality based on the proposed machine learning tools. To fulfill that, a preliminary study was conducted by collecting related information in the research domain through articles, electronic books, and online databases. the data collection for the prototype's dataset was obtained from an electronic book published by the Pakistan Council of Research in Water Resources 2021. Subsequently, the data pre-processing phase was conducted by using WEKA software which includes the crucial steps to transform the data into a cleaner format and make the model more accurate. the model for each technique was developed using Python in Jupyter Notebook. the results of the accuracy score for each model were also conducted in this phase. the findings of this research show that the Decision Tree model performs excellently with an accuracy of 97.37% compared to the Support Vector Machine and K-Nearest Neighbour models, with an accuracy of 95.69% and 74.72%, respectively. Consequently, implementing a multi-class classification system can help future researchers classify more accurately and reduce the misclassification of water quality. © 2023 ieee.

关键词： Decision trees

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the AI enabled chatbot framework for intelligent citizen-government interaction for delivery of services 8

The AI enabled chatbot framework for intelligent citizen-gov...

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Hasan, Iqbal Rizvi, Sam Jain, Sandeep Huria, Sakshi Jamia Millia Islamia National Informatics Centre New Delhi India National Informatics Centre New Delhi India

ISBN: (纸本)9789380544434

Semantic modeling of domain knowledge for natural language question-answering based on conversational assistants nowadays has gained popularity with the development of chatbots and voice assistants. Chatbots based on artificial intelligence and interactive technologies are helping organizations and governments to interact and accomplish tasks such as question answering, instant messaging, and promoting any ideas or services. As various government services are provided to fellow citizens through manual interaction or through electronic means. this causes a huge burden of works on various government departments and many queries of the citizens remain unresolved. Nowadays, intelligent chatbots can mimic humans and help users who are not acquainted with technologies to avail answers to their domain-specific queries. However, providing answers with domain-specific capabilities still remain a challenge. In this paper, we present the design of a conversational assistant for answering user queries and administrative support. It is developed using Google Dialogflow and trained on a domain-specific semantic model with intelligent abilities to answer user queries and process service requests. On analysis, we observed that the developed chatbot performs with approx. 95% accuracy in responding to the queries. the chatbot has been deployed in NIC for assistance to user queries regarding e-District services. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to ieee by Bharati Vidyapeeth.

关键词： Semantics

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Intelligent Traffic Management System using Internet of things: A Systematic Literature Review 8

Intelligent Traffic Management System using Internet of Thin...

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8th ieee international conference on computing, Engineering and Design, ICCED 2022

作者： Marcos, Hendra Gernowo, Rahmat Rosyida, Isnaini Nurhayati, Oky Dwi Diponegoro University Doctoral Program of Information System School of Postgraduate Studies Semarang Indonesia Universitas Amikom Purwokerto Department of Informatics Indonesia Universitas Negeri Semarang Faculty of Mathematics and Natural Sciences Department of Mathematics Semarang Indonesia

ISBN: (纸本)9781665453899

the number of vehicles on the road will continue to expand as cities worldwide grow in population. this increases transportation congestion, especially in major cities and especially during rush hour. this phenomenon continuously pushes academics, city authorities, and urban planners to enhance traffic control systems for safety and efficiency. the application of Internet of things (IoT) in the field of intelligent transportation systems is getting the attention of many parties. A well-controlled transportation management system can reduce congestion, especially at intersections and roads. this study was conducted to observe and study the development of research in transportation management, especially the use of IoT technology. there are 743 documents according to the keywords given related to IMS and ITS, the period 2012 until September 2021. Mapping of these articles, both in terms of publishers, the relationship between authors, and the methods used from keywords, abstracts, and titles, is expected to help the author determine research gaps and future research directions. © 2022 ieee.

关键词： Internet of things

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation 24

Matching Using Sufficient Dimension Reduction for Heterogene...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and Systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China University of South Australia Stem Australia Kyushu Institute of Technology Department of Computer Science and Systems Engineering Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics Wuhan430070 China Huazhong Agricultural University Hubei Hongshan Laboratory Wuhan430070 China Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement Wuhan430070 China Wuhan University State Key Laboratory of Hybrid Rice 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350319934

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. the existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. the experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. © 2022 ieee.

关键词： Causal Inference Individual Causal Effect Matching Sufficient Dimension Reduction

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Automatic Semantic Modeling by Cross-Modal Retrieval 24

Automatic Semantic Modeling by Cross-Modal Retrieval

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作者： Xu, Ruiqing Mayer, Wolfgang Wang, Yulong Zhang, Hong-Yu Ning, Da Duan, Yucong He, Keqing Feng, Zaiwen College of Informatics Huazhong Agricultural University Wuhan430070 China Industrial Ai Research Centre University of South Australia Mawson LakesSA5095 Australia Hubei Key Laboratory of Agricultural Bioinformatics Huazhong Agricultural University Wuhan430070 China Wuhan Maritime Communication Research Institute China State Ship Building Corporation Limited Wuhan430223 China School of Computer Science and Technology Hainan University Haikou China School of Computer Science Wuhan University 299 Bayi Rd Wuhan430070 China Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan430070 China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural University Wuhan430070 China State Key Laboratory of Hybrid Rice Wuhan University 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350319934

Semantic models of data sources describe the concepts and relations within the data. Building semantic models with the help of a common ontology is a key step in automatically publishing the semantics of structured data into knowledge graphs. However, modeling semantics of data manually requires considerable human cost, and expertise and can be error-prone. Most of the research contributions are focused on semantic annotations of source attributes, and yet it is significantly critical to explicitly infer the relations between attributes. In this paper, we present a novel approach that applies the cross-modal retrieval model as a scoring function to identify the most plausible semantic model for a target data source. As far as we know, this is the first technique that uses cross-modal retrieval models to explore the relations within data sources and semantic models in an end-to-end manner. Preliminary experiments demonstrate that our approach outperforms the state-of-the-art method. © 2022 ieee.

关键词： Knowledge graph

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An Optimized Single Sign-On Schema for Reliable Multi-Level Security Management in Clouds 8

An Optimized Single Sign-On Schema for Reliable Multi-Level ...

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

作者： Badirova, Aytaj Dabbaghi, Shirin Moghaddam, Faraz Fatemi Wieder, Philipp Yahyapour, Ramin Göttingen Germany Institute of Informatics Georg-August-Universitat Gottingen Germany

ISBN: (纸本)9781665425742

Because of its irreplaceable facilities, Cloud computing is a widely-accepted technology in both industry and academy. On-demand services, flexible provisioning, a broad range of functionalities are some of the advantages of this trend. Being able to use diverse services from distinct providers encourages organizations and individuals to follow this tendency. On the other hand, this flexibility brings security threats as well, especially in the area of authorization and authentication. In this paper, a flexible and secure access control method is proposed that supports security-level based authorization technique. While attribute mapping approach makes access control easy by categorizing a diverse range of attributes and authentication methods (e.g. PKI, FIDO U2F, LoA, etc.), utilized multi-factor authentication design helps to keep security consistent. Moreover, reliability and distinction of the proposed model are evaluated by security and competitive analysis. © 2021 ieee.

关键词： Authentication

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th international conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. the Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. this vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. the strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 ieee.

关键词： Strategic planning

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the Effect of Adding the Heuristic Evaluation Method to the Usability Evaluation Method of Design thinking in the UNSIL Vclass Application 8

The Effect of Adding the Heuristic Evaluation Method to the ...

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8th international conference on informatics and computing, ICIC 2023

作者： Isfahani, Faisal Al Rahmatulloh, Alam Shofa, Rahmi Nur Darmawan, Irfan Siliwangi University Department of Informatics Tasikmalaya Indonesia Telkom University Department of Information System Bandung Indonesia

ISBN: (纸本)9798350342604

Siliwangi University's VClass Application (UNSIL), launched in 2021, grappled with substantial interface issues affecting the user experience. the developer encountered challenges that significantly impacted user interaction and satisfaction. this study aims to identify usability problems in the UNSIL VClass application using the heuristic evaluation method, inserted at the empathize stage of the design thinking method, to identify specific and in-depth problems based on heuristic principles that users sometimes overlook. Data was collected from 70 students who actively used this application through the System Usability Scale (SUS) questionnaire. the initial evaluation results obtained a value of 54 with a score of F with an acceptance range of 'not acceptable. However, with the application of the heuristic evaluation method, a prototype was successfully developed, which showed a significant increase in three main aspects of usability: effectiveness, efficiency, and satisfaction. Time completion was reduced by 86%, the error rate decreased by more than 90%, and the completion rate reached 100%. Testing the satisfaction aspect on the prototype using SUS showed a value of 61 with a score of D, with 'acceptable' acceptance. the insertion has helped enrich the usability evaluation process and identified problems that users might have missed. this revision also provides alternative solutions to increase the usability of the UNSIL VClass application. © 2023 ieee.

关键词： Heuristic methods

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