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A Unified Conceptual Model Based on Business Artifacts for Information systems

A Unified Conceptual Model Based on Business Artifacts for I...

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Information Technology (ACIT)

作者： Maroun Abi Assaf Charbel Fares Ali Rachini Youakim Badr Kablan Barbar Department of Computer Science and Information Technology Holy Spirit University of Kaslik Jounieh Lebanon School of Graduate Professional Studies The Pennsylvania State University USA Department of Computer Science Faculty of Sciences Lebanese University Jdeidet Lebanon

Conceptual modeling captures Information systems requirements and specifications in a graphical representation at a high-level without including low-level implementation details. Entity Relationship Model (ER Model) and Business Process Model and Notation (BPMN) are two major conceptual models for modeling respectively data and process aspects of an Information System. On the other hand, Conceptual Artifact Model (CAM) is an alternative conceptual model for modeling both data and process aspects in a single model based on the notion of Artifacts. In this article, we formalize the CAM and define algorithms for generating both Database and Workflow specifications from this model. these specifications are then used to create the Relational Database and execute the Workflow of the Information System. Benefits of such alternative modeling approach are not limited to reducing the time and effort of designing Information systems but also of implementing them.

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Predictive Analytics intelligent decision-making framework and testing it through sentiment analysis on Twitter data 23

Predictive Analytics intelligent decision-making framework a...

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24th international conference on computer systems and Technologies, CompSysTech 2023

作者： Azeroual, Otmane Nacheva, Radka Nikiforova, Anastasija Störl, Uta Fraisse, Amel Berlin10117 Germany Department of Informatics University of Economics Varna Varna9002 Bulgaria Institute of Computer Science University of Tartu Tartu51009 Estonia Chair of Databases and Information Systems University of Hagen Hagen58097 Germany University of Lille GERIICO-Groupe d'Études et de Recherche Interdisciplinaire en Information et COmmunication France

ISBN: (纸本)9798400700477

the volume of data in companies and in the private sector is already gigantic today. Mobile devices such as smartphones constantly collect data on all possible environmental conditions. When surfing the Internet, everyone leaves an endless digital trail. the Internet of things (IoT) promises comprehensive networking of all everyday devices and production tools that surround people. Nevertheless, the modern knowledge society has to face the question of whether we are really actively using all the data or whether useful knowledge has increased as a result. Answering this question is not trivial. It is true that today's opportunities for exploring data, for transforming data into information and thus for gaining knowledge from it, are greater than ever. But it is also true that this new knowledge, which consists of the hidden connections in data, does not appear in our mind's eye on its own. We must explore it to bring it to the surface which is related to recognizing patterns in the world of data. In the last step, these patterns must be correctly interpreted. Predictive analytics (PA) is going exactly in this direction. they are currently one of the most important application areas of big data and are seen as the most actionable form of business intelligence (BI). Predictive analytics can be used for a variety of purposes, from predicting customer behaviour in sales and marketing to determining risk profiles for financing. Another widely known application is credit reporting, used by financial institutions to determine the likelihood that customers will repay future loans on time. It can also be used when working with big data in predicting user behaviour and opinion. In this regard, the purpose of this paper is to develop a predictive analytics-driven decision framework based on machine learning and data mining methods and techniques. To test it, we conducted an experiment for predicting sentiments and emotions in social media posts, as well as discussed topics and

关键词： Predictive analytics

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A KNOWLEDGE-BASED IDEATION APPROACH FOR BIO-INSPIRED DESIGN 24

A KNOWLEDGE-BASED IDEATION APPROACH FOR BIO-INSPIRED DESIGN

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24th international conference on Engineering Design, ICED 2023

作者： Chen, Liuqing Cai, Zebin Jiang, Zhaojun Long, Qi Sun, Lingyun Childs, Peter Zuo, Haoyu Department of Computer Science and Technology Zhejiang University Hangzhou310030 China Singapore Innovation and Ai Joint Research Lab Zhejiang University Hangzhou310030 China School of Mechanical Engineering Tianjin University Tianjin300350 China Zhejiang University-University of Illinois at Urbana-Champaign Institute Haining314400 China Dyson School of Design Engineering Imperial College London LondonSW7 2AZ United Kingdom

Bio-inspired design (BID) involves generating innovative ideas for engineering design by drawing inspiration from natural biological phenomena and systems, using a form of design-by-analogy. Despite its many successes, BID approaches encounter research challenges including unstructured data and existing models that hinder comprehension and processing, limited focus on finding biological knowledge compared to defined problems, and insufficient guidance of the ideation process with algorithms. this paper proposes a knowledge-based approach to address the challenges. the approach involves transforming unstructured data into structured knowledge, including information about natural sources, their benefits, and applications. the structured knowledge is then used to construct a semantic network, enabling designers to retrieve information for BID in two ways. Furthermore, a three-step ideation method is developed to encourage divergent thinking and explore additional potential solutions by drawing inspiration and utilizing knowledge. the knowledge-based BID approach is implemented as a tool and design cases are conducted to illustrate the process of applying this tool for BID. © the Author(s), 2023. Published by Cambridge University Press.

关键词： Big data

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RIS-assisted Channel Prediction Model for Mine V2X systems 5

RIS-assisted Channel Prediction Model for Mine V2X Systems

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5th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Cheng, Yuanxun Hu, Qingsong Luo, Yujia Zhang, Yuansheng China University of Mining and Technology School of Information and Control Engineering Xuzhou China Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China

ISBN: (纸本)9798331533991

the complex and dynamic environment of mines leads to time-variability and uncertainty in channel propagation, which affects the stability and reliability of Vehicle-to-Everything (V2X)communication in mine networks. Channel prediction can help mitigate these issues. Introducing reconfigurable intelligence surface (RIS) into the mine V2X system can enhance signal coverage and quality in the complex mine environment. To address the issue of long-term error accumulation in existing channel prediction models, this paper analyzes the prediction error of channel state information (CSI) in mine V2X systems. It was observed that the error distribution exhibits characteristics of an approximate normal distribution, revealing its predictability. Utilizing this characteristic, this paper proposes an error correction strategy to suppress long-term error accumulation during the prediction process. Furthermore, an adaptive method for optimizing RIS phases is designed based on the Snow Ablation Optimizer. Specifically, this paper utilizes a metaheuristic optimization algorithm and deep learning techniques to propose a mine V2X channel prediction model that incorporates the error correction strategy. Experimental results show that optimizing the RIS phase can effectively enhance channel gain, and the mine V2X channel prediction model incorporating the error correction strategy can effectively suppress cumulative errors during the prediction process, thereby improving prediction accuracy. © 2024 IEEE.

关键词： Normal distribution

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Machine Learning for Time Series Forecasting Using State Space Models 24th

Machine Learning for Time Series Forecasting Using State Sp...

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Sanchez-Bornot, Jose M. Sotero, Roberto C. School of Computing Engineering and Intelligent Systems Ulster University Derry/Londonderry United Kingdom Department of Radiology and Hotchkiss Brain Institute University of Calgary CalgaryAB Canada

ISBN: (纸本)9783031482311

State-space models (SSMs) are becoming mainstream for time series analysis because their flexibility and increased explainability, as they model observations separately from unobserved dynamics. Critically, using SSMs based on multivariate autoregressive equations enhances understanding of system evolution and its dynamical interactions. However, some challenges remain unsolved, such as estimation in large-scale scenarios, with very noisy data, and model selection. Here, we explore a state-space alternating least squares (SSALS) algorithm for time series forecasting, demonstrating its application with simulated and real data, and how to solve model selection in noisy scenarios with a novel cross-validation technique. Altogether, testing this methodology with time series forecasting is ideal to demonstrate its strengths and weaknesses, and appreciate its advantages compared to current methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Machine learning

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Hebbian Learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks 24th

Hebbian Learning-Guided Random Walks for Enhanced Community ...

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Sotero, Roberto C. Sanchez-Bornot, Jose M. Department of Radiology and Hotchkiss Brain Institute University of Calgary CalgaryAB Canada Intelligent Systems Research Centre Ulster University Derry Londonderry United Kingdom

ISBN: (纸本)9783031482311

Community detection in complex signed networks is a significant challenge, traditionally addressed using the Louvain method directly applied to the correlation matrix. this study introduces a two-tier approach that integrates a Hebbian learning rule within an adaptive signed random walk (ASRW) framework, then applies the Louvain method to the final weight matrix. this approach refines the network analysis process, providing a new tool for exploring community structure. Tested extensively on synthetic signed networks with defined community structures, our methodology consistently outperformed the traditional Louvain approach, particularly when communities were less clearly demarcated. Further application to resting-state functional MRI data from the ABIDE Preprocessed Initiative highlighted functional connectivity differences between neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). Our approach found key areas of significant difference, including several cerebellum regions, consistent with existing ASD literature. Our findings underscore the potential of the proposed technique to advance community detection in correlation-based networks. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Population dynamics

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A NOVEL MODEL FOR thE MATERIAL SELECTION OF SMART OBJECTS FOR HAND-REHABILITATION: A CASE STUDY 24

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FO...

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24th international conference on Engineering Design, ICED 2023

作者： Bos, Mikaël Ivar Karahanoǧlu, Armaǧan Haarman, Juliet Nizamis, Kostas Chair of Systems Engineering and Multidisciplinary Design Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Interaction Design Research Group Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Human Media Interaction Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. the materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection. © the Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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LMI-Based Approach for Non-Rigid System control

LMI-Based Approach for Non-Rigid System Control

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Sergey V. Romadov Andrey V. Kozyr Andrey G. Efromeev Department of Automatic Control Systems Tula State University Tula Russia

the paper solves the problem of controlling a cargo UAV with a flexible elongated cargo. Construction of inhomogeneous flexible link mathematical model based on Euler-Bernoulli equation issues are considered. A universal algorithm for determining the parameters of an elastic system and compiling its mathematical model is presented. A convenient method for the analytical determination of eigenmodes of vibrations of an inhomogeneous structure is used. Vibrations model was built using the method of initial parameters, which discretizes the diagrams of mass distribution and bending stiffness. the equations of system dynamics are obtained using the Euler-Lagrange equations. the control system, which provides fast response with minimal link fluctuations, is designed using LQR and LMI methods. It is shown that the LMI method allows you to directly limit the control action and set the required characteristics of the system in a more intuitive way. the results are confirmed by computer simulation. It is proposed to use an observer to obtain strain coordinates without measurement.

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Noise-Augmented Contrastive Learning for Sequential Recommendation 24th

Noise-Augmented Contrastive Learning for Sequential Recomme...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： He, Kun Meng, Shunmei Li, Qianmu Liu, Xiao Beheshti, Amin Chi, Xiaoxiao Zhang, Xuyun School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819972531

Recently, contrastive learning has been widely used in the field of sequential recommendation to solve the data sparsity problem. CL4Rec augments data through simple random crop, mask, and reorder, while DuoRec proposes a model-level data augmentation method. However, these methods do not take into account the issue of noisy data in sequential recommendation, such as false clicks during browsing. the noise may lead to poor representations of learned sequences and negatively affect the augmented data. Current sequential recommendation methods tend to learn the user’s intention from their original sequences, but these methods have certain limitations as the user’s intention for the next interaction may change. Based on the above observations, we propose Noise-augmented Contrastive Learning for Sequential Recommendation (NCL4Rec). Our NCL4Rec proposes sequential noise probability-guided data augmentation. We introduce supervised noise recognition during training instead of obtaining it from original sequences. Moreover, we design positive and negative augmentations of the sequence and design unique noise loss function to train them. through experiments, it is verified that our NCL4Rec consistently outperforms the current state-of-the-art models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Contrastive Learning Sequential Recommendation

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ATDG: An Automatic Cyber threat Intelligence Extraction Model of DPCNN and BIGRU Combined with Attention Mechanism 24th

ATDG: An Automatic Cyber Threat Intelligence Extraction Mode...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Cui, Bo Li, Jinling Hou, Wenhan Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9789819972531

With the situation of cyber security becoming more and more complex, the mining and analysis of Cyber threat Intelligence (CTI) have become a prominent focus in the field of cyber security. Social media platforms like Twitter, due to their powerful timeliness and extensive coverage, have become valuable data sources for cyber security. However, these data often comprise a substantial amount of invalid and interfering data, posing challenges for existing deep learning models in identifying critical CTI. To address this issue, we propose a novel CTI automatic extraction model, called ATDG, designed for detecting cyber security text and extracting cyber threat entities. Specifically, our model utilizes a Deep Pyramid Convolutional Neural Network (DPCNN) and BIGRU to extract character-level and word-level features from the text, to better extract of semantic information at different levels, which effectively improved out of vocabulary (OOV) problem in threat intelligence. Additionally, we introduce a self-attention mechanism at the encoding layer to enable the model to focus on key features and enhance its performance, which dynamically adjusts the attention given to different features. Furthermore, to address the issue of imbalanced sample distribution, we have incorporated Focal Loss into ATDG, enhancing our model capability to effectively handle data imbalances. Experimental results demonstrate that ATDG (92.49% F1-score and 93.07% F1-score) outperforms the state-of-the-art methods in both tasks, and effectiveness of introducing self-attention mechanism and Focal Loss is also demonstrated. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cybersecurity

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