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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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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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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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Systematic Literature Review Langchain Proposed

Systematic Literature Review Langchain Proposed

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International Electronics Symposium (IES)

作者： Rakha Asyrofi Mutia Rahmi Dewi Muhammad Irfan Lutfhi Prasetyo Wibowo Faculty of Marine Science and Engineering Universitas Hangtuah Surabaya Indonesia Department of Computer Science and Information Engineering National Central University Taiwan Faculty of Engineering Universitas Negeri Yogyakarta Yogyakarta Indonesia Graduate Institute of Network Learning Technology National Central University Taiwan Department of Information Technology Politeknik Negeri Padang Padang Indonesia Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

While systematic literature reviews are frequently carried out within software engineering research, performing them in a rigorous and reproducible manner can be difficult. This paper proposes some new methods for evaluating and validating systematic literature reviews. Our approach consists of several steps, such as: Selecting a set of relevant scientific papers to analyze, Developing a list of questions and criteria to evaluate each literature review, and Determining what types of functionality and performance should be evaluated. We tested our method by having multiple experts evaluate the literature reviews based on our questions and criteria. We measured the similarity in scores between each expert to determine the reliability of the evaluations. The average similarity index between experts was 0.58 to 0.83, indicating a reasonable level of agreement in their assessments. This shows our evaluation method can produce fairly consistent results, even when different experts are involved. The relatively high level of agreement is notable considering each expert brings their perspectives and opinions in analyzing literature reviews. By providing concrete questions, criteria, and evaluation methods, we aimed to guide the experts toward more uniform evaluations. In summary, we developed and tested a new approach for evaluating and validating systematic literature reviews in software engineering. By assessing reliability via interrater agreement, we showed that consistent and reproducible results are possible using our evaluation framework and methodology. Our methods could help researchers gain more insight into what makes for an effective and high-quality literature review.

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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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Single MCMC Chain Parallelisation on Decision Trees

arXiv

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arXiv 2022年

作者： Drousiotis, Efthyvoulos Spirakis, Paul G. Department of Electrical Engineering and Electronics United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Department of Computer Engineering and Informatics University of Patras Patras26504 Greece

Decision trees are highly famous in machine learning and usually acquire state-of-the-art performance. Despite that, well-known variants like CART, ID3, random forest, and boosted trees miss a probabilistic version that encodes prior assumptions about tree structures and shares statistical strength between node parameters. Existing work on Bayesian decision trees depend on Markov Chain Monte Carlo (MCMC), which can be computationally slow, especially on high dimensional data and expensive proposals. In this study, we propose a method to parallelise a single MCMC decision tree chain on an average laptop or personal computer that enables us to reduce its run-time through multi-core processing while the results are statistically identical to conventional sequential implementation. We also calculate the theoretical and practical reduction in run time, which can be obtained utilising our method on multi-processor architectures. Experiments showed that we could achieve 18 times faster running time provided that the serial and the parallel implementation are statistically identical. © 2022, CC BY.

关键词： Decision trees

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An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

arXiv

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arXiv 2024年

作者： Aziz, Abdul Pham, Nhat Vora, Neel Reynolds, Cody Lehnen, Jaime Venkatesh, Pooja Yao, Zhuoran Harvey, Jay Vu, Tam Ding, Kan Nguyen, Phuc College Information and Computer Sciences University of Massachusetts Amherst AmherstMA United States School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science & Engineering University of Texas at Arlington ArlingtonTX United States Department of Neurology The University of Texas Southwestern Medical Center DallasTX United States Earable Inc. BoulderCO United States

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD, a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing-computing-communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD’s ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality. © 2024, CC BY-NC-ND.

关键词： Hearing aids

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), International Conference on

作者： Hassaan Th. H. Thabet Aqeel A. Al-Hilali Mohammed G. S. Al-safi Thabit H. Thabit Firas M. Mahmood Dept. of Computer Technology Engineering Al-Hadba University College Mosul Iraq Dept. of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq Dept. of Accounting 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 Kurdistan Region Iraq

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper.

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Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

Reconfigurable Intelligent Computational Surfaces for MEC-As...

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IEEE Conference on Wireless Communications and Networking

作者： Bo Yang Xueyao Zhang Zhiwen Yu Xuelin Cao Chongwen Huang George C. Alexandropoulos Yan Zhang Mérouane Debbah Chau Yuen School of Computer Science Northwestern Polytechnical University Xi'an Shaanxi China School of Cyber Engineering Xidian University Xi'an Shaanxi China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Informatics University of Oslo Oslo Norway Center for 6G Technology the Khalifa University of Science and Technology Abu Dhabi UAE School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

关键词： Simulation Vehicle-to-infrastructure Vehicular ad hoc networks Transforms Safety Servers Task analysis

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Automatically Designing U-Nets Using A Genetic Algorithm for Tree Image Segmentation

Automatically Designing U-Nets Using A Genetic Algorithm for...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Binke Xu Ying Bi Bing Xue Jan Schindler Brent Martin Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Manaaki Whenua - Landcare Research Informatics Wellington New Zealand

ISBN: (纸本)9781665487696

Tree image segmentation is an important task of analysing the forest cover in ecosystems. Convolutional neural networks (CNNs) particularly U-Nets are popular methods for image segmentation, but they often require rich expertise from both the problem domain and the neural network domain to design effective model architectures. Recently, automatically designing U-Nets has shown promise, but none of them has been proposed for tree image segmentation. When designing U-Nets, the encoding and decoding schemes are the keys to the success of the search, which have not been fully investigated yet. Therefore, this paper investigates a new approach based on genetic algorithms (GAs) to automatically design/learn U-N ets for tree segmentation from remote-sensing images. The new encoding scheme enables chromosomes of a GA to represent potential U-Net models of variable length based on blocks. The decoding scheme decodes chromosomes to potential U-Net models with the characteristics of U-Nets. More importantly, the new designs allow the new approach to effectively and efficiently search for U-Net models of variable lengths by only optimising several key parameters. The results show that the proposed approach achieves better performance than a well-known manually designed U-Net by automatically learning the U-Net model with a significantly smaller number of parameters on one tree image segmentation dataset.

关键词： Image segmentation Search methods Neural networks Forestry Encoding Decoding Task analysis

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