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9th International Conference on Information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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Unveiling Disparities: NLP Analysis of Software Industry and Vocational Education Gaps

Unveiling Disparities: NLP Analysis of Software Industry and...

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Natural Language-Based Software engineering (NLBSE), IEEE/ACM International Workshop on

作者： Emil Bäckstrand Rasmus Djupedal Lena-Maria Öberg Francisco Gomes De Oliveira Neto Dept. of Quality Management Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers|University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798400705762

ISBN: (纸本)9798350352559

The rapid growth of software industry highlights the importance of the education system in producing competent professionals to meet industry demands. Previous research has identified a gap between industry needs and the content of educational programs. This study presents Vocational Education and Labour Market Analyser (VELMA), a tool designed to extract information from job ads and educational curricula (both from Sweden), utilising topic modelling to identify the diverse technologies and skills in demand within the industry and those covered by professional education. Particularly, we use Latent Dirichlet Allocation (LDA) to categorise keywords into cohesive themes for document frequency analysis. Our findings highlight industry demand for skills in cloud and embedded technologies, security engineering, and software architecture. In contrast, the Higher Vocational Education (HVE) curricula emphasise the education of web developers and general object-oriented programming languages. CCS CONCEPTS • Applied computing $\rightarrow$ Document management and text processing; Education; $\cdot$ Software and its engineering $\rightarrow$ Designing software.

关键词： Industries Analytical models Text analysis Object oriented modeling Education Software Natural language processing

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Multiband Microstrip Patch Antenna Operating at Five Distinct 5G mm-Wave Bands

Multiband Microstrip Patch Antenna Operating at Five Distinc...

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2021 International Conference of Technology, Science and Administration, ICTSA 2021

作者： Saeed, Osama Y. A. Saeed, Ahmed A. A. Gaid, Abdulguddoos S. A. Aoun, Amjad M. H. Sallam, Amer A. Taiz University Faculty of Engineering and IT Dept. of Communication and Computer Engineering Taiz Yemen Taiz University Faculty of Engineering and IT Dept. O F Computer Networks and Distributed Systems Taiz Yemen

ISBN: (纸本)9781665428620

A novel mm-Wave multiband microstrip patch antenna for 5G wireless communication is designed and presented in this paper. The proposed design is an H-shaped with inset feed and a rectangular slit in the upper side of the radiating patch. The 5G mm-Wave antenna resonates at frequencies of 23.2, 40.3, 59.3, 86.9 and 104.3 GHz with a maximum bandwidth of 1.1, 2.8, 18.1, 7.7 and 10.7 GHz respectively. The antenna has sufficient realized gains of 6.4882, 7.1198, 7.9062, 8.1804 and 9.8156 dBi at the resonant frequencies respectively. Furthermore, the antenna covers the most interest 5G mm-Wave spectrum with a total bandwidth of 40.4 GHz. The proposed multiband antenna is designed using microstrip technology with features such as light weight, low cost, low profile, high gain and efficiency. The 5G antenna is designed using a high frequency structure simulator software (HFSS). It has a small form factor of 7.5 × 8.5 × 0.55 mm3. The antenna is compact enough to incorporate into smart devices and used for various 5G applications. © 2021 IEEE.

关键词： Bandwidth

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Auto Disturbance Rejection Control Based on A Neural Network Predictive State Observer

Auto Disturbance Rejection Control Based on A Neural Network...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Shengzhi Du BJ van Wyk NFS Nahri Chunling Tu Dept. Electrical Engineering Tshwane University of Technology Pretoria South Africa Dept. Computer Systems Engineering Tshwane University of Technology Pretoria South Africa

This paper addresses the auto disturbance rejection control (ADRC) for systems with time delay. ADRC is an effective control for most linear or nonlinear systems with noises and disturbances, because of its advantage on disturbance estimation and compensation. The extended state observer (ESO) is a critical component of ADRC. However, this observer is usually designed for systems without time delay. The time delay in a system could lead the ADRC unstable when the phase delay of the response cannot be ignored. This paper proposes a neural network based predictive state observer (NNPSO) to predict the response of systems with time delay. The simulation results validate the proposed method when systems with time delay are controlled.

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Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-Terrorist Attacks

Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-T...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Samaraweera S. A. R. P Siriwardana S. M. D. N W. D. V. Fernando D. N. Rajakarunaratne Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Maritime cyber-terrorist attacks have become a major concern to the entire world in recent decades. Everyone should be more aware of marine strategies to prevent cyberterrorist attacks, both locally and globally. This study investigates how Sri Lanka and the Sri Lanka Navy (SLN) may successfully address maritime security concerns by utilizing current resources and developing a marine strategy to counter such maritime cyber-terrorism assaults. The literature review in this thesis primarily evaluates the level of understanding in Sri Lanka concerning when marine cyber terrorism strikes may occur. Furthermore, this thesis investigates if Sri Lanka has any maritime rules and regulations. The use of such laws and regulations will concentrate on how they can increase their expertise.

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Collaborative User Mobility Prediction in Distributed Edge Computing Framework

Collaborative User Mobility Prediction in Distributed Edge C...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Sardar Jaffar Ali Syed M. Raza Hyunseung Choo Dept. of AI Systems Engineering Sungkyunkwan University Suwon South Korea James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

User attachment forecasting with Deep Learning (DL) is an effective tool for proactive mobility management in dense Beyond 5G (B5G)/6G deployments with reduced cell sizes. However, collecting user data in a central cloud to facilitate DL model learning causes extensive overhead and privacy concerns. Distributed edge cloud-based federated learning solves these issues, but it faces challenges in handling out-of-distribution data from decentralized edges at the network periphery, and the model biases due to data heterogeneity. This paper addresses these limitations by proposing a fully distributed Collaborative User Mobility Prediction (CUMP) framework that mitigates the out-of-distribution data issue through collaboration among initial layers of DL models in edges that are selected using inter-edge mobility rates. The remaining part of each model only trains on local data, preserving biases towards their respective edges. This enhances the generalization, robustness, and predictive performance of the DL models. Results show that CUMP outperforms conventional global learning and state-of-the-art distributed personalized federated learning and cyclic incremental institutional learning by 63%, 12%, and 10% in predicting the next Point of Attachment (PoA) of a user and by 70%, 22%, and 28% in predicting user dwell time in current PoA, respectively. Thus, CUMP improves prediction performance while reducing network and storage overheads while preserving privacy.

关键词： Privacy Federated learning Microprocessors Distributed databases Collaboration Predictive models Smart systems Data models Robustness Forecasting

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TangiGuru: Tangible E-Learning Solution for Early Childhood Development 4

TangiGuru: Tangible E-Learning Solution for Early Childhood ...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Hettiarachchi, Thiwanka Cholitha Semasinghe, Lakisuru Sathyajith Lokuliyana, Shashika Gamage, Narmada de Silva, Rajitha Dept. Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka Lincoln Institute for Agri-Food Technology University of Lincoln United Kingdom

ISBN: (纸本)9798350398090

Exploration and manipulation of physical objects are essential for early childhood learning. Previous investigations found several TUI uses in other fields. Less research has been done on tangible learning for youngsters;thus, it is unclear if they are more collaborative, playful, or functional. TangiGuru consists of 12 tangible, manipulative objects known as TangiCubes, which are used as a tangible user interface between children and the e-Learning application. It can carry out cognitive learning activities related to colors, languages, shapes and basic math by dynamically varying assigned values by changing the external appearance of TangiCubes. This dynamic nature of the Tang- iCubes makes it possible to use the same tangibles with endless possibilities compared to traditional tangible learning solutions with static value for each tangible. After the prototyping phase, children were evaluated with the traditional tangible learning solutions compared to TangiGuru. They concluded that the more interactive tangible interfaces could make the children perform activities more engagingly. © 2022 IEEE.

关键词： E-learning

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Maximizing Reachability in Factored MDPs via Near-Optimal Clustering with Applications to Control of Multi-Agent systems

Maximizing Reachability in Factored MDPs via Near-Optimal Cl...

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IEEE Conference on Decision and Control

作者： Carmel Fiscko Soummya Kar Bruno Sinopoli Dept. of Electrical and Systems Engineering Washington University St. Louis MO Dept. of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA

We consider cluster-based control of agents mod-eled as a transition-independent Markov decision process (MDP), and the objective of assigning agents to clusters to maximize the size of the reachable state space. This goal is relevant to applications for which the same MDP model may be used to compute policies for different reward functions. The system controller wishes to define clusters to maximize flexibility within the attainable outcomes. Under the transition-independent MDP formulation, we first show that the size of the reachable state space is a submodular function. While maximizing the reachable state space subject to a desired number of clusters is a hard problem, properties of submodular optimization can be leveraged to propose approximate clustering techniques. We next demonstrate that a greedy clustering approach is a viable approximate solution and has a bounded optimality gap. We compare the performance in terms of value and computation complexity in using the flexibility-optimized clustering assignment versus a clustering assignment optimized for a specific reward function; there will be a loss in value at a savings in complexity. Finally, we demonstrate the utility of the flexibility-optimized clustering assignment in simulation on the same MDP model with various reward functions.

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Common Object Request Broker-based Publisher-Subscriber Middleware for Internet of Things - Edge Computing

Common Object Request Broker-based Publisher-Subscriber Midd...

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2022 International Research Conference on Smart Computing and systems engineering, SCSE 2022

作者： Perera, Hansani Jayakody, Anuradha Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

The edge computing layer in IoT reduces the flow of a massive amount of data directly to the cloud by processing some data in the local network. The middleware in the layer enables this processing of data and the communication between heterogeneous devices and services in the nearby layers. CORBA, which uses as a powerful middleware technology in developing middleware solutions in enterprise-level distributed applications, has been abandoned in the current generation. The paper presents the design, and the performance evaluation of a publisher-subscriber middleware implemented using CORBA that was studied when exploring the applicability of CORBA as an IoT edge computing middleware. The evaluation was continued in two steps to analyse several parallel connections (Load test) and handle requests in a unit time (burst test) via simulating an IoT environment in a cloud environment. © 2022 IEEE.

关键词： Middleware

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Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?

arXiv

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arXiv 2023年

作者： Zhou, Jingyan Hu, Minda Li, Junan Zhang, Xiaoying Wu, Xixin King, Irwin Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for overgeneralizing the moral stances of a limited group of annotators and lacking explainability. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research. The theory-guided top-down framework can incorporate various moral theories. Our experiments demonstrate the effectiveness of the proposed framework on datasets derived from moral theories. Furthermore, we show the alignment between different moral theories and existing morality datasets. Our analysis exhibits the potential and flaws in existing resources (models and datasets) in developing explainable moral judgment-making systems. © 2023, CC BY-NC-ND.

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