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1st International Workshop on Cyber-Physical Security for Critical Infrastructures Protection, CPS4CIP 2020 in conjunction with the European Symposium on Research in computer Security, ESORICS 2020

作者： Bakalos, Nikolaos Voulodimos, Athanasios Doulamis, Nikolaos Doulamis, Anastasios Papasotiriou, Kassiani Bimpas, Matthaios School of Rural and Surveying Engineering National Technical University of Athens Athens15773 Greece Department of Informatics and Computer Engineering University of West Attica Athens12243 Greece Columbia University New YorkNY10027 United States

ISBN: (纸本)9783030697808

In this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models. © 2021, The Author(s).

关键词： Long short-term memory

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Fall Detection System for Elderly (FDS-E) using Low-cost Camera Based on LSTM and OpenPose

Fall Detection System for Elderly (FDS-E) using Low-cost Cam...

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IEEE Instrumentation and Measurement Technology Conference

作者： Herti Miawarni Tri Arief Sardjono Eko Setijadi Wijayanti Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Biomedical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Architecture Faculty of Engineering Universitas Diponegoro Semarang Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

Recently, many countries have been dealing with an increasing amount of elderly who are living alone. Unfortunately, fall incidents in the elderly tend to happen, and without proper dependable care would lead to fatal injuries. The elderly must be closely monitored under constant observation. Accidental falls are a leading cause of injury, mortality, and mobility limitations in the elderly. The expenses of national healthcare systems are also significantly impacted by accidental falls. These findings highlight the importance of investing heavily in the study and advancement of fall detection and intervention technologies. It is essential to recognize them early and provide fast assistance by developing technologies that can simultaneously improve the standard and the safety of the elderly's living environment. In this paper, we present a computer vision algorithm for falling incident detection using a low-cost camera and deep learning model. Ultimately, this algorithm can be used for the cases of elderly, in which falling risk is high and fatal. Furthermore, one self-developed dataset was used to validate the suggested approach experimentally. We used an OpenPose-based feature using a low-cost camera input and then classified each event or activity as either fall or non-fall. Classification tasks are carried out by Long Short-Term Memory (LSTM) with multiple validation metrics. The LSTM in this work is also optimized using Bayesian method. Finally, our approach to recognize falls has accuracy of 99.5%, which is distinct from other past works.

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Extreme Learning Machine for Parking Slot Recognition in Smart Parking System

Extreme Learning Machine for Parking Slot Recognition in Sma...

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International Conference on Electrical, Telecommunication and computer engineering (ELTICOM)

作者： Romi Fadillah Rahmat Sharfina Faza Ilham Syahputra Pauzi Ibrahim Nainggolan Riza Sulaiman Mohd Amiruddin Abd Rahman Department of Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Science Universiti Putra Malaysia Selangor Malaysia

ISBN: (数字)9798331531249

ISBN: (纸本)9798331531256

In the modern era, technology has become an integral part of human life, particularly in image processing. This advanced technology is now applied to parking areas to identify vacant parking spaces. The application is designed to detect empty parking slots in real time using image data captured by a camera as input. The system then automatically scans for available parking spaces, displaying the results on the interface with green indicators for empty slots and red for occupied ones. The process involves several key stages, such as image pre-processing, image segmentation, and object detection. The system employs the Extreme Learning Machine (ELM) method to enhance its object detection capabilities. This system achieves a high accuracy rate of 94%under well-lit conditions. This novel technology optimizes parking management, providing an effective and dependable method to monitor parking availability in real time.

关键词： Accuracy Thresholding (Imaging) Translation Extreme learning machines Target recognition System performance Lighting Object detection Real-time systems Automobiles

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The Complexity of Temporal Vertex Cover in Small-Degree Graphs

arXiv

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arXiv 2022年

作者： Hamm, Thekla Klobas, Nina Mertzios, George B. Spirakis, Paul G. Algorithms and Complexity Group TU Wien Vienna Austria Department of Computer Science Durham University United Kingdom Department of Computer Science University of Liverpool United Kingdom Computer Engineering & Informatics Department University of Patras Greece

Temporal graphs naturally model graphs whose underlying topology changes over time. Recently, the problems Temporal Vertex Cover (or TVC) and Sliding-Window Temporal Vertex Cover (or ∆-TVC for time-windows of a fixed-length ∆) have been established as natural extensions of the classic problem Vertex Cover on static graphs with connections to areas such as surveillance in sensor networks. In this paper, we initiate a systematic study of the complexity of TVC and ∆-TVC on sparse graphs. Our main result shows that for every ∆ ≥ 2, ∆-TVC is NP-hard even when the underlying topology is described by a path or a cycle. This resolves an open problem from literature and shows a surprising contrast between ∆-TVC and TVC for which we provide a polynomial-time algorithm in the same setting. To circumvent this hardness, we present some fixed-parameter and approximation algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Approximation algorithms

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Analysis of the Best Employee Selection Decision Support System Using Analytical Hierarchy Process (AHP) 1

Analysis of the Best Employee Selection Decision Support Sys...

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1st International Conference on Innovation and Application of Science and Technology, ICIASTECH 2019

作者： Lia Hananto, April Priyatna, Bayu Fauzi, Ahmad Yuniar Rahman, Aviv Pangestika, Yulia Tukino Faculty of Computing Universiti Teknologi Malaysia Skudai Johor Malaysia Faculty of Technology and Computer Science Universitas Buana Perjuangan Karawang Indonesia Department of Informatics Engineering Universitas Widyagama Malang Indonesia Faculty of Postgraduate Management Computer Indonesia of University Indonesia

The award is given by the company to the best employees always to provide the best performance for companies that carry out duties and obligations in the company. How to give rewards to employees not only for how long they have been working at the company but about the results achieved. The gift can be in the form of salary or inventory from the company. So employees have great loyalty to the company. Decision making to determine the company can do the best employee by assessing the performance that has been done by employees at a specific time. Performance evaluation of Hon Chuan Company is influenced by several criteria, namely discipline, innovation, and responsibility. The support system for the best employee selection method uses the Analytical Hierarchy Process (AHP) method. The decision-making system is carried out to assess choices based on predetermined criteria. AHP method is applied to choose the best prospective employees. The calculation of the AHP method gives the results of the order of importance of criteria, namely discipline level (0.5584), responsibility (0.3196), and innovation (0.1220) and the best employee selection results obtained by Hendra (0.189), Vienna (0.189), Yin (0.145), Paul (0.109), Kandi (0.108), Kusworo (0.101), Bayu (0.088), and Ade Muhidin (0.072). © Published under licence by IOP Publishing Ltd.

关键词： Decision making

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Designing and Simulating a Smart Water Harvesting System Using PLC Techniques

Designing and Simulating a Smart Water Harvesting System Usi...

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Innovation and Intelligence for informatics, Computing, and Technologies (3ICT), International Conference o

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Haitham B. Qasim Thabit H. Thabit Firas M. Mahmood Dept. of Medical Instruments Techniques Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Computer Science Al-Esraa University College Baghdad Iraq Dept. of Computer Engineering and Information Ninevah University Mosul Iraq Dept. of Informatics and Software Engineering Cihan University-Erbil Iraq

The presence of water in human life is crucial, and God has blessed humans much with its availability. This paper tries to reduce the amount of water that people use for washing in all aspects of daily life and instead utilize it to irrigate gardens and maintain hygienic facilities. The importance of water as a fundamental ingredient and the range of social and age groups that regularly take advantage of the washing process made it an ideal location for water waste during the washing process. This is why the authors choose this region to conduct their research. The monitoring and management of water usage during the washing process are simulated using PLC techniques.

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Goal-Aware RSS for Complex Scenarios via Program Logic

Goal-Aware RSS for Complex Scenarios via Program Logic

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IEEE Symposium on Intelligent Vehicle

作者： Ichiro Hasuo Clovis Eberhart James Haydon Jérémy Dubut Rose Bohrer Tsutomu Kobayashi Sasinee Pruekprasert Xiao-Yi Zhang Erik André Pallas Akihisa Yamada Kohei Suenaga Fuyuki Ishikawa Kenji Kamijo Yoshiyuki Shinya Takamasa Suetomi National Institute of Informatics (NII) Tokyo Japan SOKENDAI (The Graduate University for Advanced Studies) Japan Japanese-French Laboratory for Informatics (IRL 3527) Tokyo Japan Dept. Computer Science Worcester Polytechnic Institute Worcester MA USA Inst. Software & Systems Engineering University of Augsburg Augsburg Germany Cyber Physical Security Research Center AIST Tokyo Japan Graduate School of Informatics Kyoto University Kyoto Japan Mazda Motor Corporation Fuchu Japan

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules; we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement.

关键词： Intelligent vehicles computer architecture Software Cognition Safety Planning Complexity theory

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Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection

arXiv

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arXiv 2024年

作者： Guo, Yuejun Patsakis, Constantinos Hu, Qiang Tang, Qiang Casino, Fran Luxembourg Institute of Science and Technology Luxembourg Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece Information Management Systems Institute of Athena Research Centre Greece Department of Computer Science The University of Tokyo Japan Department of Computer Engineering and Mathematics Rovira i Virgili University Tarragona Spain

The significant increase in software production driven by automation and faster development lifecycles has resulted in a corresponding surge in software vulnerabilities. In parallel, the evolving landscape of software vulnerability detection, highlighting the shift from traditional methods to machine learning and large language models (LLMs), provides massive opportunities at the cost of resource-demanding computations. This paper thoroughly analyses LLMs’ capabilities in detecting vulnerabilities within source code by testing models beyond their usual applications to study their potential in cybersecurity tasks. We evaluate the performance of six open-source models that are specifically trained for vulnerability detection against six general-purpose LLMs, three of which were further fine-tuned on a dataset that we compiled. Our dataset, alongside five state-of-the-art benchmark datasets, were used to create a pipeline to leverage a binary classification task, namely classifying code into vulnerable and non-vulnerable. The findings highlight significant variations in classification accuracy across benchmarks, revealing the critical influence of fine-tuning in enhancing the detection capabilities of small LLMs over their larger counterparts, yet only in the specific scenarios in which they were trained. Further experiments and analysis also underscore the issues with current benchmark datasets, particularly around mislabeling and their impact on model training and performance, which raises concerns about the current state of practice. We also discuss the road ahead in the field suggesting strategies for improved model training and dataset curation. Copyright © 2024, The Authors. All rights reserved.

关键词： Pipeline codes

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COVID19 Impact on Medical Clinics, Analysing and Devising a Safety Software Model for Clinics 4

COVID19 Impact on Medical Clinics, Analysing and Devising a ...

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4th International Conference on Computing, Electronics and Communications engineering, iCCECE 2021

作者： Fetaji, Majlinda Fetaji, Bekim Aliu, Azir Snopce, Halil Zeqiri, Izet Ebibi, Mirlanda Ali, Maaruf South East European University Faculty of Computer Sciences Tetovo Macedonia Mother Teresa University Faculty of Informatics Skopje Macedonia Faculty of Economy South East European University Tetovo Albania Epoka University Department of Computer Engineering Tirana Albania

ISBN: (纸本)9781665449113

The focus of this research is on investigating and identifying what constitutes a safe software model of a medical clinic that will enable and enhance its protection for healthcare providers and its patients by devising a model of a software system. There is a gap in the published research regarding the understanding of impacting factors of COVID19 in the safe functioning of a medical clinic. This Case Study investigated several impacting factors. The aims of this research project are: increasing the safety effectiveness of both the hospital personnel and patients. Next, to solve the problems with tracking the illnesses of patients, a more rigorous database indexing system was implemented with data provenance. With this technique in the database, it kept track of illnesses (when, why, in whom did it appear or any kind of other developments), login details (when, who is logged in, logged out), which is more important especially for medical systems. Also, which doctors interacted with the patients (what drug have been administered, their performance, percentage of efficacy, successfully or unsuccessfully) and other data mining information. Because this project is envisioned to be continually supported, it should evolve and deal with the problems encountered in modern hospitals. The system has been evaluated on its functionalities and the safety usability, on two groups of users: Those with a computer science background and those from a different field. Afterwards regression analyses were used to determine the impact. The research study contributed with identified impacting factors for such systems, analyses of the improvement in conceptual increase of safety and usability. Insights and recommendations are provided. © 2021 IEEE.

关键词： COVID-19

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Research on knowledge reasoning of TCM based on knowledge graphs

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Digital Chinese Medicine 2022年第4期5卷 386-393页

作者： GUO Zhiheng LIU Qingping ZOU Beiji School of Informatics Hunan University of Chinese MedicineChangshaHunan 410208China School of Computer Science and Engineering Central South UniversityChangshaHunan 410083China

With the widespread use of Internet,the amount of data in the field of traditional Chinese medicine(TCM)is growing ***,there is much attention on the collection of useful knowledge as well as its effective organization and *** graphs have thus emerged,and knowledge reasoning based on this tool has become one of the hot spots of *** paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning,and explores the significance of knowledge ***,the mainstream knowledge reasoning methods,including knowledge reasoning based on traditional rules,knowledge reasoning based on distributed feature representation,and knowledge reasoning based on neural networks are ***,using stroke as an example,the knowledge reasoning methods are expounded,the principles and characteristics of commonly used knowledge reasoning methods are summarized,and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted ***,we summarize the problems faced in the development of knowledge reasoning in TCM,and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.

关键词： Traditional Chinese medicine(TCM) Stroke Knowledge graph Knowledge reasoning Assisted decision-making Transloction Embedding(TransE)model

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