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

作者： Ali Daneshmandnia Computer System Technology Department City University of New York Brooklyn College of Technology New York City NY US

Information security effectiveness has become the focus of organizations whether they manage their own information, or rely on providers. The research literature suggests that organizational characteristics have a significant, positive effect on information security [1]–[4]. This paper examines the impact of organizational characteristics, specifically, the size of the organization, culture, IT budget, frequency of reported IT audits, and executive support, on the effectiveness of information security processes at higher educational institutions (HEI.) Nine information governance processes used in this study include communication of information security roles, the protection of sensitive data, and business agreement audits. Findings suggest that organizational culture, frequency of reported audits, and executive-level support positively influence information effectiveness, but the size of institutions and level of IT budget did not show any significant effect.

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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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MLFBERT: Advancing News Recommendation with Multi-Layer Fusion over DistilBERT

MLFBERT: Advancing News Recommendation with Multi-Layer Fusi...

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

作者： Shiyu Lin Xinfeng Ye Sathiamoorthy Manoharan School of Computer Science University of Auckland Auckland New Zealand

Traditional news recommendation methods usually encode news text and user interests directly into vectors and then calculate the matching degree of the two. Such approaches may lose low-level matching signals and different levels of textual information. We propose a BERT-based news recommendation model, MLFBERT. This model consists of a BERT-based news encoder and a user encoder. The news encoder uses a PLM Fusion Module and additive attention to generate embeddings of available news items. The user encoder builds a user profile by aggregating the representations of their news browsing history. Recommendations are made by computing matching scores between the user profile and candidate news. Experiments on the MIND news dataset demonstrate that our model outperforms existing methods on multiple evaluation metrics.

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Automated Chemical Mixing Plant

Automated Chemical Mixing Plant

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

作者： Srikanthan Nadarajah Rasendram Muralitharan Upul Jayasinghe Rohan G Ragel Muthusamyiyer Sasitharagurukkal Nilani Inuvaisubramaniam Rockwell Collins United States of America Department of Computer Engineering Faculty of Engineering University of Peradeniya Sri Lanka Office of the Deputy Chief Secretary Engineering Service Northern Province Sri Lanka

The chemical mixing plant could be automated using supervisory control and data acquisition (SCADA) technique. The SCADA system contains a central control unit, a remote terminal unit, and a communication medium. The chemical mixing plant operator may use the central control unit to automate the operations of the chemical mixing plant. The field devices of the chemical mixing plant are connected with the remote terminal unit. Ethernet, Profinet, or WAN network could be used to establish the communication medium between the central control unit and the remote terminal unit. The central control unit acts as the server and the remote terminal unit act as the client. The simple server-client communication system is used to exchange the messages between the central control unit and remote terminal unit. The communication medium uses Genesys protocol to exchange the messages between the central control unit and the remote terminal unit.

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Supply Chain Optimization in Cement Industry Using Blockchain Technology

Supply Chain Optimization in Cement Industry Using Blockchai...

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

作者： Rabeya Hamood Imran Makhdoom Waseem Iqbal Tariq Jamil Department of Information Security National University of Sciences and Technology Islamabad Pakistan Department of Electrical and Computer Engineering Sultan Qaboos University AlKhod Muscat Oman

The ubiquitous use of Internet of Things devices in supply chain management has facilitated manufacturers and other stakeholders to monitor various aspects of the product or shipment. However, conventional solutions have numerous challenges ranging from data integrity, product provenance, and trust issues to scalability problems. This research demonstrates how scalability and provenance of cement bags can be ensured through a combination of blockchain technology and the Internet of Things devices. The distributed ledger securely records each step, from acquiring raw materials to delivering the finished product. The inherent transparency of blockchain technology promotes confidence among all the stakeholders in the supply chain and makes it easier to comply with legal requirements. The security and performance analysis determines that the proposed solution is secure and efficient with minimal performance overheads.

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Exploring the Synergy: Raspberry Pi, ARM Assembly, Interfacing, and AI in computer Architecture Course

Exploring the Synergy: Raspberry Pi, ARM Assembly, Interfaci...

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

作者： Costa Gerousis Joshua McCloud Department of Physics Computer Science and Engineering Christopher Newport University Newport News Virginia USA

In recent years, computer architecture instruction has undergone significant transformation, moving away from a primarily theoretical approach, and placing greater emphasis on practical, hands-on learning. Leading this shift is the Raspberry Pi (RaPi), a credit card-sized computer powered by the versatile Broadcom BCM2835 system-on-a-chip (SoC). RaPi offers an open-source operating system, an extensive software library, and abundant online documentation, creating an ideal environment for dynamic and experiential learning. This paper features the successful integration of RaPi into the computer architecture curriculum at CNU, with a primary focus on hands-on labs. These labs cover diverse topics, including RaPi setup and connectivity, low-level programming, Python-based sensor interfacing, and AI-driven image processing for object detection and recognition. The paper provides valuable insights for educators looking to incorporate this single-board computer into their teaching methods, highlighting its multifaceted applications in AI and computer architecture to enhance students' learning experience.

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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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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...

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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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Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced data Classification

IEEE Transactions on Artificial Intelligence

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ieee Transactions on Artificial Intelligence 2024年第2期5卷 776-785页

作者： Roy, Debasmit Pramanik, Rishav Sarkar, Ram Jadavpur University Department of Computer Science and Engineering WB Kolkata700032 India

In supervised learning algorithms, the class imbalance problem often leads to generating results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal aspect of separating the overlapping classes. This is the reason why most of these methods are prone to overfit on the training data. To this end, we propose a novel loss function, namely margin-aware adaptive-weighted loss. Here, we first use the large margin softmax to leverage intraclass compactness and interclass separability. Further to learn an unbiased representation of the classes, we put forward a dynamically weighted loss for imbalanced data classification. This weight dynamically adapts on every minibatch based on the inverse class frequencies. In addition, it takes care of the hard-to-train samples by using the confidence scores to learn discriminative hidden representations of the data. The overall framework is found to be effective when evaluated on the following two widely used datasets: 1) Canadian Institute for Advanced Research (CIFAR)-10 and 2) Fashion-MNIST. Additional experiments on human against machine and asia pacific tele-ophthalmology society 2019 blindness detection datasets prove the robustness of our methodology. © 2023 ieee.

关键词： Learning algorithms

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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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