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arXiv 2023年

作者： Groner, Raffaela Witte, Thomas Raschke, Alexander Hirn, Sophie Pekaric, Irdin Frick, Markus Tichy, Matthias Felderer, Michael Institute of Software Engineering and Programming Languages Ulm University Germany Department of Computer Science University of Innsbruck Austria Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Germany Department of Mathematics and Computer Science University of Cologne Germany

Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and Attack Trees and can be used to model and model-check a holistic view on both safety and security. Manually creating a complete AFT for the whole system is, however, a daunting task. It needs to span multiple abstraction layers, e.g., abstract application architecture and data flow as well as system and library dependencies that are affected by various vulnerabilities. We present an AFT generation tool-chain that facilitates this task using partial Fault and Attack Trees that are either manually created or mined from vulnerability databases. We semi-automatically create two system models that provide the necessary information to automatically combine these partial Fault and Attack Trees into complete AFTs using graph transformation rules. © 2023, CC BY-NC-ND.

关键词： Trees (mathematics)

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Ensemble of Learning Project Productivity in software Effort Based on Use Case Points

arXiv

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arXiv 2018年

作者： Azzeh, Mohammad Nassif, Ali Bou Banitaan, Shadi López-Martín, Cuauhtémoc Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy United States Department of Information Systems Universidad de Guadalajara Mexico

It is well recognized that the project productivity is a key driver in estimating software project effort from Use Case Point size metric at early software development stages. Although, there are few proposed models for predicting productivity, there is no consistent conclusion regarding which model is the superior. Therefore, instead of building a new productivity prediction model, this paper presents a new ensemble construction mechanism applied for software project productivity prediction. Ensemble is an effective technique when performance of base models is poor. We proposed a weighted mean method to aggregate predicted productivities based on average of errors produced by training model. The obtained results show that the using ensemble is a good alternative approach when accuracies of base models are not consistently accurate over different datasets, and when models behave diversely. Copyright © 2018, The Authors. All rights reserved.

关键词： Productivity

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Mining Top-K Closed Itemsets Using Best-First Search

Mining Top-K Closed Itemsets Using Best-First Search

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International Conference on computer and Information Technology (CIT)

作者： Panida Songram Veera Boonjing Software Systems Engineering Laboratory Department of Mathematics and Computer Science Faculty of Science King Mongkut''s Institute of Technology Ladkrabang Bangkok Thailand Department of Computer Science Faculty of Informatics Mahasarakham University Maha sarakham Thailand

In this paper, we proposed an efficient algorithm, TOPK_CLOSED, for mining top-k closed itemsets. This algorithm mines top-k closed itemsets using best-first search. The closed itemsets with the highest supports are firstly found from this algorithm. Consequently, the method quickly finds top-k closed itemsets, which leads to an efficient pruning unnecessary itemsets and stop mining rapidly. In addition, this algorithm can generate closed itemsets without keeping candidates in main memory.

关键词： Itemsets Generators Data mining Pediatrics Complexity theory Algorithm design and analysis Databases

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Concrete examples of abstraction as manifested in students' transformative experiences 08

Concrete examples of abstraction as manifested in students' ...

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4th International Computing Education Research Workshop, ICER'08

作者： Moström, Jan Erik Boustedt, Jonas Eckerdal, Anna McCartney, Robert Sanders, Kate Thomas, Lynda Zander, Carol Department of Computing Science Umea University 901 87 Umea Sweden Dept. of Mathematics Natural and Computer Science Högskolan i Gävle S80176 Gävle Sweden Department of Information Technology Uppsala University Uppsala Sweden Dept. of Computer Science and Engineering University of Connecticut Storrs CT United States Department of Math and Computer Science Rhode Island College Providence RI United States Department of Computer Science Aberystwyth University Aberystwyth United Kingdom Computing and Software Systems University of Washington Bothell Bothell WA United States

ISBN: (纸本)9781605582160

This paper examines transformational learning experiences of computing students as a way to better understand threshold concepts in computing. From empirical evidence we found that students often describe transformative experiences as learning situations in which they were led to use various kinds of abstraction, for example modularity, data abstraction, inheritance, polymorphism, reuse, design patterns, and complexity. Some students describe an abstract concept as coming first, and then needing to be made concrete though application;others describe transformations in which they learn the advantages of these abstract concepts from their experience of not using them. Abstraction is certainly of central importance in computer science. It appears, however, from our students' descriptions of transformative experiences, that abstraction per se is not a threshold, but that particular concepts in which abstraction is paramount exhibit the characteristics of threshold concepts. Copyright 2008 ACM.

关键词： Students

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Prediction of global spread of covid-19 pandemic: A review and research challenges

TechRxiv

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TechRxiv 2020年

作者： Shah, Saloni Mulahuwaish, Aos Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Department of Computer Science and Information Systems Saginaw Valley State University MI United States Department of Software Engineering Salahaddin University Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq

Since the initial reports of the Coronavirus surfacing in Wuhan, China;the novel virus currently without a cure has spread like a wildfire across the globe. The virus spread exponentially across all inhabited continent;catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyse and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modelling tools to latest AI technology;the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response – whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance including lockdowns. On one hand, this paper shows what accuracy of research brings to enable fighting this disease;while on the other hand it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt in compiling different research methods and comparing their accuracy in predicting the spread of COVID-19. © 2020, CC BY.

关键词： Viruses

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Development of a deep learning system for preventive detection of heart failure due to pulmonary disease

Development of a deep learning system for preventive detecti...

引用

International Conference on software Process Improvement (CIMPS)

作者： Axel Frederick Félix Jiménez Isaul Ibarra Belmonte Vania Stephany Sánchez Lee Uziel Jaramillo Avila Ezra Federico Parra González Computer Systems Engineering National Polytechnic Institute Zacatecas México Software Engineer Center for Research in Mathematics Zacatecas Zacatecas México Computer Science Department Center for Research in Mathematics Zacatecas Zacatecas México

ISBN: (纸本)9798350398977

In the context of addressing the problem of people who do not undergo a diagnosis of heart failure due to pulmonary conditions on time, a solution to this problem would allow early preventive detection to avoid the development of severe disease efficiently. Our approach employs the use of medical data retrieved from the user to determine and predict whether there is a likelihood of a potential condition. To solve this problem, according to a users medical measurement history, a deep learning model can be implemented to determine a preventive diagnosis or otherwise to follow up on an already detected condition. By posing the problem as a classification task, it can be taken advantage of a deep learning model focused on heart failure or pulmonary conditions to make a preliminary diagnosis and determine if there are signs of any symptomatology.

关键词： Cardiovascular diseases Diseases Deep learning Medical diagnostic imaging Lung Prediction algorithms Medical services

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A SLA-Oriented Management of Containers for Hosting Stateful Web Services

A SLA-Oriented Management of Containers for Hosting Stateful...

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IEEE International Conference on e-science and Grid Computing

作者： Christoph Reich Kris Bubendorfer Matthias Banholzer Rajkumar Buyya Department of Computer Science Hochschule Furtwangen University Germany School of Mathematics Statistics and Computer Science Victoria University of Wellington New Zealand Grid Computing and Distributed Systems (GRIDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

Service-oriented architectures provide integration of interoperability for independent and loosely coupled services. Web services and the associated new standards such as WSRF are frequently used to realise such service-oriented architectures. In such systems, autonomic principles of self-configuration, self-optimisation, self-healing and self- adapting are desirable to ease management and improve robustness. In this paper we focus on the extension of the self management and autonomic behaviour of a WSRF container connected by a structured P2P overlay network to monitor and rectify its QoS to satisfy its SIAs. The SLA plays an important role during two distinct phases in the life-cycle of a WSRF container. Firstly during service deployment when services are assigned to containers in such a way as to minimise the threat of SLA violations, and secondly during maintenance when violations are detected and services are migrated to other containers to preserve QoS. In addition, as the architecture has been designed and built using standardised modern technologies and with high levels of transparency, conventional Web services can be deployed with the addition of a SLA specification.

关键词： Containers Web services Service oriented architecture Quality of service computer science Monitoring Grid computing Conference management engineering management mathematics

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A Smart Assistive Device for the Blind People 7th

A Smart Assistive Device for the Blind People

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7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Chellatamilan, T. Santhi, K. Reddy, R. Hemadri Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789811996375

Mobility is the biggest issue for blind individuals. In order to move around, they must rely on others. Therefore, we are creating a navigational aid for blind people, to help the blind people in the world from various difficulties they face in day-to-day life. The World Health Organization (WHO) reported that a survey was conducted in 2014 and that there are an estimated 285 million visually impaired persons worldwide, of which 37 million are blind, with over 15 million of them being Indian. The majority of blind persons depend on other people for their activities since INDIA accounts for 21% of the global population of blind people (Khanam et al. in Int J Adv Res Sci Eng 7:195–199, 2018). Smart shoes play an important role. Obstacle detection is performed using ultrasonic sensor which is also done by many people in the previous projects or researches. The new concept that is included in this paper is that it can also detect any flame and potholes around the person using flame sensor and ultrasonic sensor, respectively. And we use shock sensor to detect any shock that occurs. If the smart assistive device or smart shoes senses an object, a pothole, a fire (from about three feet away), or a shock in front of the user, a buzzer will sound. These sensors are integrated using Arduino Uno. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstacle detectors

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication 7

Towards Cluster-Based Split Federated Learning Approach for ...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Wazzeh, Mohamad Arafeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Trois-Rivières Canada Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon New York University Division of Science Abu Dhabi United Arab Emirates Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security. © 2023 IEEE.

关键词： Internet of things

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An energy-aware distributed open market model for UAV-assisted communications

An energy-aware distributed open market model for UAV-assist...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Rafay Iqbal Ansari Nouman Ashraf Christos Politis School of Computer Science and Mathematics Faculty of Science Engineering and Computing Kingston University London United Kingdom Telecommunications Software and Systems Group (TSSG) Waterford Institute of Technology Waterford Ireland.

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Unmanned aerial vehicles (UAVs) have opened up numerous opportunities in terms of connectivity, especially in the context of realizing the vision of ubiquitous connectivity for 5G and beyond (B5G). The ease of mobility makes the UAV base stations (UAV-BSs) a viable candidate for providing `on demand' services to the users. Moreover, viewing the spectrum crunch experienced by traditional cellular networks, the UAV-BSs can share the burden of providing connectivity. UAV-BSs can open up several business opportunities for mobile network operators (MNOs). In this paper, we propose an open market model, where a UAV-BS has the opportunity to establish a link with a terrestrial BS (TBS) of an MNO that provides the best connectivity and offers a lower price. A distributed model is considered where the decision making power lies with the UAV-BS. The TBS-selection problem is modeled as an integer linear programming problem, where we compare the performance of the Greedy heuristic algorithm (GHA) and the backtracking algorithm (BA) to solve our selection problem. We also incorporate an energy prediction model which impacts the selection criteria. We analyze the performance GHA and BA algorithm by presenting a tradeoff between the two algorithms in terms of accuracy of TBS selection and convergence time.

关键词： Unmanned aerial vehicles Prediction algorithms Optimization Base stations Heuristic algorithms Predictive models Standardization

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