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检索条件"任意字段=2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022"

共 960 条记录，以下是31-40 订阅

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data vault modelling as alternative to dimensional modelling to embrace complexity in data driven decision systems: a critical systems perspective

Data vault modelling as alternative to dimensional modelling...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Goede, Roelien North-West University Unit for Data Science and Computing Potchefstroom South Africa

ISBN: (纸本)9781665453059

We live in a complex world where uncertainty is the only certainty. Today's compelling business problem is replaced tomorrow with a problem which was not even imagined yesterday. data driven decision systems are used by managers in support of their strategic decisions and should be agile and flexibility to survive in our ever change world of complex decisions. The argument presented in this conceptual paper is that data vault modeling is inherently better equipped than dimensional modelling to handle the turbulations of our complex world. The paper investigates the underlying assumptions of dimensional modelling and data vault modelling in terms of requirements collection and the resulting data modelling techniques. It uses critical systems thinking as guiding philosophy to reflect on the benefits of understanding and modelling a variety of perspectives in a problem situation. Critical systems thinking also promotes equal opportunity and accountability. It is argued that both these aspirations can better be achieved by using dimensional modeling as alternative to dimensional modelling. We hope to promote the development of sustainable data driven decision systems which can stand the test of our turbulent times. © 2022 ieee.

关键词： Systems thinking

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Orchestrating the Resilience of Cloud Microservices Using Task-Based Reliability and Dynamic Costing

Orchestrating the Resilience of Cloud Microservices Using Ta...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： O'Neill, Vyas Soh, Ben La Trobe University Department of Computer Science and Information Technology Melbourne Australia

ISBN: (纸本)9781665453059

In recent years, cloud computing has grown in popularity as enterprises realize the technical and economic benefits of managing resources using an on-demand model. With this growth come user expectations of quality of service, availability and reliability. As the trend in cloud systems architecture moves from monolithic systems to microservices, opportunities exist to develop granular solutions for improving the reliability of the overall system. In this paper we propose an approach to the resilience of microservices-based cloud systems using task-based reliability, quantifying of the reliability requirement with respect to the marginal cost of additional redundancy and the cost of failure. Our approach enables the cloud orchestrator to manage replication of microservice containers to reach an optimal, cost-effective level of redundancy. Empirical testing showed our approach achieved a lower overall cost in comparison with a fixed-reliability approach under an exponential failure function. © 2022 ieee.

关键词： Cloud computing

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Interpretable machine learning-based terrorist attack success rate prediction

Interpretable machine learning-based terrorist attack succes...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Luo, Lanjun Li, Boxiao Wang, Xueyan Cui, Lei Liu, Gang School of Management North Sichuan Medical College Nanehong China School of Management Huazhong University of Science and Technology Wuhan China Information Centre Affiliated Hospital of North Sichuan Medical College Nanchong China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665453059

The success of terrorist attacks reflects the capability of terrorists and the vulnerability of the security defense, explainable prediction of the average attack success rate at the country-annual level is crucial for governments. In this study, terrorist attack data from 146 countries between 2002 to 2020 was obtained from the global terrorism database (GTD), and a two-stage prediction task was conducted. First, multiple machine learning models, including XGBoost and Random Forest (RF), are used to predict the average success rate of terrorist attacks in the next year, considering terrorism root factors and statistical results from the previous year. The results show that the RF model performs the best. Second, the prediction outputs of the RF model are explained using interpretable methods, including Accumulate Local Effect (ALE) and SHapley Additive exPlanation (SHAP), to provide counterterrorism insights applicable to countries around the world. © 2022 ieee.

关键词： Terrorism

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Supporting End User Comprehension in Intelligent Decision Support Systems Using data Visualisation Techniques

Supporting End User Comprehension in Intelligent Decision Su...

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8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Rumsamrong, Maneerat Chiou, Andrew Mahidol Univ Sch Interdisciplinary Studies Kanchanaburi Campus Kanchanaburi Thailand Cent Queensland Univ Sch Engn & Technol Sydney Campus Sydney NSW Australia

ISBN: (纸本)9781665495523

This project explores the use of data visualisation techniques to render the outcome of an inference process using graphics, symbolic and schemas in stylised pictograms that supports understanding. The example demonstrated in this paper is a continuation of CRISIS-Expert, an intelligent decision support system that accepts multiple input of various data types and then generate an outcome which is further rendered into symbols that within reason is, understandable by the end user.

关键词： data visualization intelligent support systems gamification self explanatory

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Enhancing Customer Experience, and Brand Awareness through Familiar Gameplay in Advergames

Enhancing Customer Experience, and Brand Awareness through F...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Khezrian, Navid Farzinnejad, Forouzan Baghaei, Nilufar Coburg University of Applied Sciences Dept. Electrical Engineering and Computer Science Coburg Germany University of Queensland Dept. Information Technology and Electrical Engineering Brisbane Australia

ISBN: (纸本)9781665453059

The marketing field has been looking for more creative and effective ways to attract customers. Advergames have recently attracted a lot of attention in increasing brand awareness and attracting wider groups of audiences. Research studies have indicated that customers have a more positive attitude toward a brand and stronger purchase intentions when playing advergames with brand interactivity rather than without brand interactivity. Despite the increased use of advergames as a brand promotion strategy by a range of well-known brands, there is still a limited understanding of several issues related to the effective use of such games. In this paper, we conducted one study (n= 242) held in one exhibition to examine familiar gameplay in advergames and its effectiveness in attracting and engaging more people. Our results showed that the presence of familiar gameplay in advergames is an essential feature in attracting more people, enhancing their user experience, and increasing brand awareness. © 2022 ieee.

关键词： Sales

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beta Link: A Pattern Recognition Approach for Identifying Superior Event-to-Sink Links in a Spatiotemporal Correlated Sensor Network

beta Link: A Pattern Recognition Approach for Identifying Su...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Muriira, Lawrence Mwenda School of Science and Technology Kenya Methodist University Nairobi Kenya

ISBN: (纸本)9781665453059

In large-scale wireless sensor networks (WSNs) deployment, sensed data for dissemination are highly spatial-temporal correlated within close geographical proximity. The sink is not interested in individual's sensor data but accurate information of the physical phenomenon. Pattern recognition has been proven to be a successful technique in identifying WSN links with superior performance for data transmission among spatial-temporal correlated links. This paper proposes βLink a data-driven design framework, which exploits a pattern recognition technique to identify this superior performance route for data transmission (Beta link). The technique provides an insight into;a metric that measures the degree of temporal link correlation using a Two State Markov Chain approach, and pattern classification using Support Vector Machine that also models spatiotemporal link correlation. The scheme could reduce the number of links to be utilized for data dissemination to 21 %, a significant reduction to the overall network's transmission cost. Therefore, the results to be presented are encouraging as it clearly indicates the potential of Pattern Recognition approach in identifying Beta links. © 2022 ieee.

关键词： Wireless sensor networks

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Blockchain-based Reviewer Selection

Blockchain-based Reviewer Selection

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Wen, Jianmin Manoharan, Sathiamoorthy Ye, Xinfeng School of Computer Science The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665453059

Universities require their students to undertake projects as partial fulfilment of the students' degree. These projects typically go through an independent external review process so that academic standards are upheld. Finding appropriate external reviewers with the domain knowledge is a time-consuming process. In this paper, we propose a system that helps advisors find examiners for their students' projects. The system uses machine learning techniques to identify the subject areas of the students' projects from the text of students' dissertations. It then matches scholars who have research interests in the identified subject areas. The system is implemented using a blockchain and aims to provide a transparent environment that allows scholars to freely participate in the system. In this project, we collected a set of papers from several top journals and conferences. Using this dataset, we have evaluated the performance of several classification algorithms, e.g., multinomial Naive Bayes, K-Means Clustering with multinomial logistic regression and Labeled-LDA. The experimental study shows that the weighted multinomial Naive Bayes model performs best among the three models in terms of accuracy and computational time. © 2022 ieee.

关键词： Blockchain

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AlgoCert: Adopt Non-transferable NFT for the Issuance and Verification of Educational Certificates using Algorand Blockchain

AlgoCert: Adopt Non-transferable NFT for the Issuance and Ve...

引用

2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Tahlil, Tahlil Gomasta, Sarmistha Sarna Ali, A B M Shawkat Institute of Information Technology University of Dhaka Dhaka Bangladesh Daffodil International University Dept of Software Engineering Dhaka Bangladesh The University of Fiji Dept of Computer Science & Engineering Fiji

ISBN: (纸本)9781665453059

In addition to tanking the economy and society, credential fraud frequently damages the credibility of higher education. As the majority of conventional verification and issuance procedures are arduous, expensive, and obsolete, fraud has proliferated in numerous forms, ranging from paper-based counterfeit certificates to replicas enabled by database manipulation, and has reached exponential quantities. In this paper, the authors propose AlgoCert, a comprehensive credential verification solution that incorporates blockchain, self-sovereign identity (SSI), and decentralized identifier (DID) to introduce a more proficient, attack-resistant system that is user-friendly as well as effective at reducing widespread credential fraud. In addition, AlgoCert leverages stateful smart contracts on the Algorand blockchain and non-transferable non-fungible tokens (NFT), and neither students nor organizations are required to handle credentials in order to use the system. This paper provides a prototype of our solution and detail our attempts to develop a verification service with features, including data security, transcript authentication, and selective data disclosure. © 2022 ieee.

关键词： Blockchain

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OkkhorNet: A Deep Convolutional Neural Network to Classify Bengali Handwritten Characters

OkkhorNet: A Deep Convolutional Neural Network to Classify B...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Sikder, Shihab Uddin Muslebeen, Md. Shafiul Karim, Dewan Ziaul Saha, Ramkrishna Bushra, Tasfia Anika Brac University Computer Science and Engineering Dhaka Bangladesh Daffodil International University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453059

Handwritten letter classification of any given language has the potential to be used in various fields such as literature, educational institutions, digitization of government records etc. Bengali language with its complex sets of mixed characters, poses significant complexities in terms of automatic recognition of characters. In the Bengali character set, there are over 360 distinct characters among which a lot of similarities are present between different characters. Thus, the classification of these characters gets harder as the recognition system incorporates all these distinct characters. In recent years, a lot of research has been done to solve this problem on isolated datasets with significant results. Continuing the advancement in image processing, In this paper, we have proposed a custom CNN model which has been trained on Bangla Lekha Isolated dataset containing 1,66,106 images belong to 84 distinct classes with the capability to detect individual handwritten Bengali letters including digits, vowels, consonants and compound characters. Our proposed model was able to achieve 94.73% accuracy while using less number of parameters compared to existing popular models © 2022 ieee.

关键词： Convolutional neural networks

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BanglaSarc: A dataset for Sarcasm Detection

BanglaSarc: A Dataset for Sarcasm Detection

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Apon, Tasnim Sakib Anan, Ramisa Modhu, Elizabeth Antora Suter, Arjun Sneha, Ifrit Jamal Alam, Md. Golam Rabiul Brac University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665453059

Being one of the most widely spoken language in the world, the use of Bangla has been increasing in the world of social media as well. Sarcasm is a positive statement or remark with an underlying negative motivation that is extensively employed in today's social media platforms. There has been a significant improvement in sarcasm detection in English over the previous many years, however the situation regarding Bangla sarcasm detection remains unchanged. As a result, it is still difficult to identify sarcasm in bangla, and a lack of high-quality data is a major contributing factor. This article proposes BanglaSarc, a dataset constructed specifically for bangla textual data sarcasm detection. This dataset contains of 5112 comments/status and contents collected from various online social platforms such as Facebook, YouTube, along with a few online blogs. Due to the limited amount of data collection of categorized comments in Bengali, this dataset will aid in the of study identifying sarcasm, recognizing people's emotion, detecting various types of Bengali expressions, and other domains. The dataset is publicly available at https://***/datasets/sakibapon/banglasarc. © 2022 ieee.

关键词： Social networking (online)

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