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A Comparative Analysis of Goal-Oriented Requirements engineering and Model-Based Systems engineering Frameworks for Managing Requirements of Systems of Systems 25

A Comparative Analysis of Goal-Oriented Requirements Enginee...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alhajhassan, Suhair Murrar, Samer Samhan, Ahmad Odeh, Mohammed Elrashidi, Ali Applied Science Private University Software Engineering Department Amman Jordan Applied Science Private University Computer Science Department Amman Jordan Zarqa University Software Engineering Department Zarqa Jordan Global Academy for Digital Health Bristol United Kingdom University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs);GORE-based approach to Energy Management Systems (EnMS);Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services;GORE and reference architecture approach for microgrid systems;and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems. © 2024 IEEE.

关键词： Conceptual design

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Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

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computer Systems science & engineering 2022年第12期43卷 1357-1374页

作者： Zubair Nabi Ramzan Talib Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Software Engineering Government College UniversityFaisalabad38000Pakistan

Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the *** experimental results and evaluations show significant improvements incomparison with the available techniques.

关键词： Natural language processing judicial corpora contextual text mining ontologies information extraction information retrieval

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A Survey on Evolutionary Computation Based Drug Discovery

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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DrDiff: Drug Response Prediction Through Controllable Diffusion-GE and Graph Attention Network 27

DrDiff: Drug Response Prediction Through Controllable Diffus...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Choi, Seungyeon Seo, Samgmin Choi, Jonghwan Park, Chihyun Park, Sanghyun Department of Computer Science Yonsei University Seoul Korea Republic of Division of Software Hallym University Chuncheon Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9781643685489

The accurate prediction of drug responses based on the genomic profile of a patient is essential to progress in the field of precision *** advent of various deep-learning algorithms based on publicly available large-scale omics datasets is the driving force behind research in this *** characteristics of biological datasets, characterized by high dimensions and low sample sizes, pose challenges of overfitting and limited generalization in prediction ***, constructing prediction models using biological data such as gene expression is further complicated by the need to account for the complex relationships among genes, which exacerbates the aforementioned *** address these challenges, we propose a drug response prediction framework (DrDiff) that integrates a denoising diffusion probabilistic model (DDPM) based data augmentation module with a graph attention network based drug response prediction *** proposed model showed a 10% higher AUC than the state-of-the-art models for drug response prediction for the six drugs considered in the study, suggesting the superior generalization performance of DrDiff over other baseline ***, we demonstrated the feasibility of generative models, which form one of the modules of the proposed framework, in overcoming the fundamental limitations of omics *** experiments bear out the feasibility of generative models, which form one of the modules of the proposed framework, in augmenting gene expression data. © 2024 The Authors.

关键词： Prediction models

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Enhanced Machine Learning for Real-Time Plant Replication in Embedded Systems 50

Enhanced Machine Learning for Real-Time Plant Replication in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Chowdhury, Abhisek Jung, Jane Kuo, Matthew M.Y. Sinha, Roopak Computer Science and Software Engineering Department Auckland University of Technology Auckland New Zealand School of IT Deakin University Melbourne Australia

ISBN: (纸本)9781665464543

This paper examines the use of supervised machine learning to construct a digital twin model replicating a physical plant. An inverted pendulum simulation has been used as a case study. A comparative study was conducted on single-step and multi-step Dense models, Convolutional Neural Network (CNN), Recurrent Neural Net (RNN), and a Residual Neural Net (RNN2) models to investigate the most appropriate model for replicating the plant model. The study found that single-step models were consistently more accurate than multi-step models due to single-step model’s iterative nature. Whereas, multi-step models were better at revealing prediction patterns and identifying causes for large deviations. The best-performing model was the RNN2 model, however, signs of overfitting were observed. In general, all models were able to take into account minor random actuation, however, large changes such as the pendulum falling over caused the models to behave sporadically. © 2024 IEEE.

关键词： Inverted pendulum

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The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

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Neural Computing and Applications 2024年第11期36卷 5757-5797页

作者： Amiri, Zahra Heidari, Arash Navimipour, Nima Jafari Esmaeilpour, Mansour Yazdani, Yalda Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Computer Engineering Department Hamedan Branch Islamic Azad University Hamedan Iran Immunology Research Center Tabriz University of Medical Sciences Tabriz Iran

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability. © The Author(s

关键词： Internet of things

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Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： computer vision

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Optimal Bounds for Distinct Quartics 51

Optimal Bounds for Distinct Quartics

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Charalampopoulos, Panagiotis Gawrychowski, Pawel Ghazawi, Samah School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Institute of Computer Science University of Wroclaw Poland Department of Computer Science University of Haifa Israel Department of Software Engineering Braude College of Engineering Karmiel Israel

ISBN: (纸本)9783959773225

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form UU, a string of length n can contain as fragments. It turns out that this is always O(n), and the bound cannot be improved to sublinear in n [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms – it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an n × n 2D string is O(n2 log2 n) and that they can be computed in O(n2 log2 n) time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of n × n 2D strings with Ω(n2 log n) distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an n × n 2D string is O(n2 log n) and they can be computed in the worst-case optimal O(n2 log n) time. As expected, our solution heavily exploits the periodic structure implied by occurrences of quartics. However, the two-dimensional nature of the problem introduces some technical challenges. Somewhat surprisingly, we overcome the final challenge for the combinatorial bound using a result of Marcus and Tardos [JCTA 2004] for permuta

关键词： Optimization

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MaGIC: Multi-modality Guided Image Completion 12

MaGIC: Multi-modality Guided Image Completion

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Hao Yu, Yongsheng Luo, Tiejian Fan, Heng Zhang, Libo School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Software Chinese Academy of Sciences China Department of Computer Science University of Rochester United States Department of Computer Science and Engineering University of North Texas United States

Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signals into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, thereby avoiding the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image segmentation

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SSA: a novel method for Single-cell and Spatial transcriptomics Alignment 16th

SSA: a novel method for Single-cell and Spatial transcriptom...

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16th International Conference on Bioinformatics and Computational Biology, BICOB 2024

作者： Tran, Bang Tran, Dao Nguyen, Tin Department of Engineering & Computer Science California State University 6000 J St SacramentoCA95819 United States Department of Computer Science and Software Engineering 345 W Magnolia Ave AuburnAL36849 United States

Single-cell RNA sequencing (scRNA-seq) provides expression profiles of individual cells but fails to preserve crucial spatial information. On the other hand, Spatial Transcriptomics technologies are able to analyze specific regions within tissue sections, but lack of the capability to examine in single-cell resolution. To overcome these issues, we present Single-cell and Spatial transcriptomics Alignment (SSA), a novel technique that employs an optimal transport algorithm to assign individual cells from a scRNA-seq atlas to their spatial locations in actual tissue based on their expression profiles. SSA has demonstrated superior performance compared to existing methods SpaOTsc, Tangram, Seurat and DistMap using 10 semi-simulated datasets generated from a high-resolution spatial transcriptomics human breast cancer dataset with 100,064 cells. This advancement provides a refined tool for researchers to delve deeper in understanding of the relationship between cellular spatial organization and gene expression. © 2024, EasyChair. All rights reserved.

关键词： Tissue

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