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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulfattah E. Ba Alawi Hussein M. A. Mohammed Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Energy and Cooling Systems Department Turk Telekom Ankara Turkey Mechatronics and Robotics Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The accurate and fast identification of military equipment on the battlefield is a crucial problem of various military missions and tasks. First, it affects the operation success of the modern armed forces. Second, decision-making is considerably affected by such a capability. In this paper, we address this issue using the potential of the powerful YOLOv8 technique. This allows us to offer an accurate model for identifying various components related to the military and armed forces. The model is tested on the publicly available dataset which contains 11,800 images by evaluating the detection task with YOLOv8-based models. This dataset consists of eleven classes on military-related equipment, which covers different types of military assets. Finally, the YOLOv8x model shows encouraging results in detecting military equipment as demonstrated by a value of 99.1% in terms of mAP50. Through the findings of this study, there has been significant improvement in using AI in military missions, decision-making, and mission readiness which are better than those obtained in the previous studies. According to the obtained findings, this study demonstrates substantial progress in the application of artificial intelligence to military missions and decision-making and operational development.

关键词： Accuracy Decision making Task analysis Artificial intelligence Military equipment

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Enhancing Energy Sector Resilience: Integrating Security by Design Principles

arXiv

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arXiv 2024年

作者： Shirtz, Dov Koberman, Inna Elyashar, Aviad Puzis, Rami Elovici, Yuval Cyber@BGU Cyber Labs Ben-Gurion University Israel Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

Security by design (SbD) is a concept for developing and maintaining systems that are, to the greatest extent possible, free from security vulnerabilities and impervious to security attacks. In addition to technical aspects, such as how to develop a robust Industrial control system (ICS), hardware (HW), software (SW), firmware, communication product, etc., SbD also includes soft aspects, such as organizational management's attitude and behavior, and employee awareness. Under the SbD concept, systems, i.e., in our context ICS, will be considered more trustworthy by users;users' trust in the systems will be derived from meticulous adherence to SbD processes and policies. In accordance with the SbD concept, security is considered. Security measures are implemented, at every stage of the product and system development lifecycle, rather than afterward, which is beneficial, enabling the identification of vulnerabilities early in the development process;when vulnerabilities are identified at an early stage, the ability to address them is improved, and the cost of resolving them is reduced, saving time, money, and effort. As mentioned above, the SbD approach focuses on making systems as secure and immune to attack as possible through the use of measures such as continuous testing, authentication, safeguards, and adherence to best programming practices. As such, SbD enables an organization to formalize the design of its infrastructure and applications and build and integrate security into its IT and OT processes. This document presents the security requirements for the implementation of SbD in industrial control systems. The information presented does not negate any existing security and cyber security standards, quality assurance standards, best practices, or management methods such as COBIT. Instead, we strongly recommend that organizations should implement and comply with those standards and best practices. Security by design is not a one-time process. It starts at the ve

关键词： Cybersecurity

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Emotion and Mentality Monitoring Assistant (EMMA)

Emotion and Mentality Monitoring Assistant (EMMA)

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International Conference on Communication and Electronics systems (ICCES)

作者： Ishini Kiridena Dayan Marasinghe Ruvini Karunarathne Kavindu Wijethunga Harinda Fernando Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Human emotions, psychology, and social well-being are all parts of mental health. It has an impact on how people feel, think, and act. It aids in figuring out how individuals act under pressure, interact with each other, and arrive at decisions. Every period of life, from childhood and adolescence to maturity, is vital for mental health. There are several elements that affect mental health issues, such as experiences with trauma or abuse in life, work-life stress, relationship difficulties, etc. Help is available for many of the mental health issues that are identified within a person. The proposed solution is implementing a friendly chatbot that is capable of capturing human emotional states (happy, angry, sad, etc.) and mental health states (depression, anxiety, stress, etc.) using human computer interaction and health record management. The proposed solution has been developed by considering both regular users and mental health practitioners. The user can communicate with the proposed assistant using text or voice to identify their emotions and mental health based on keywords, particular characteristics of voice and facial recognition used during the conversation. Once the mental health and emotion statuses are analyzed the electronic health records will be readily available on the blockchain as blockchain tokens, this could be shared with the practitioners securely.

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RAT: A Refactoring-Aware Tool for Tracking Code History

RAT: A Refactoring-Aware Tool for Tracking Code History

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International Conference on software engineering - Companion, ICSE, Companion

作者： Feifei Niu Junqian Shao Chaofan Xu Christoph Mayr-Dorn Wesley K. G. Assunção Liguo Huang Chuanyi Li Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina USA Department of Computer Science and Engineering Southern Methodist University Dallas Texash USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. Empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT_Demo Demo Video: https://***/VI_xwUaIPp4

关键词： Location awareness software maintenance Codes Source coding computer bugs Maintenance engineering History

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AI-Powered Conceptual Model for Scrum Framework

AI-Powered Conceptual Model for Scrum Framework

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Rana Mdallal Dania Alsawalmeh Dania Jarad Ammar Odeh Department of Software Engineering/Enterprise Systems Engineering (ESE) Princess Sumaya University for Technology (PSUT) Department of Computer Science/Cybersecurity Princess Sumaya University for Technology (PSUT)

Scrum is an incremental, interactive agile framework widely used to develop practical problems; the Scrum team focuses on achieving a common goal each cycle, "Sprint Goal" instead of the traditional sequential technique for delivering products. Although many organizations are adopting Scrum, it is still following the traditional manual approaches. Even though many tools are available in the market to facilitate project management and Scrum master tasks, the support is still limited and lacks the utilization of Artificial Intelligence science. This study focuses on integrating the Scrum framework with Artificial Intelligence to build a predicting model that can assist project management "in particular the Scrum master" to predict the achievement of the Scrum Team per Sprint using Story Points; this prediction is built based on several factors that can affect the burndown charts per Sprint.

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Multi-Objective Decision Making System Based on Spatial -Temporal Logics

Multi-Objective Decision Making System Based on Spatial -Tem...

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International Conference on Control systems and computer Science (CSCS)

作者： Victor Ababii Viorica Sudacevschi Ana Turcan Radu Melnic Viorel Carbune Irina Cojuhari Department of Computer Sciences and Systems Engineering Technical University of Moldova Chisinau Republic of Moldova Department of Software Engineering and Automatics Technical University of Moldova Chisinau Republic of Moldova

Multi-objective decision making systems based on Spatial-Temporal logics presents a class of computational systems based on Artificial Intelligence in which spatial location and time evolution of processes (events) are taken into account. Multi-objective decision making systems showcase a distributed computing architecture based on Agents (Computational Nodes) that form a network for data processing. The functionality of the decision making system is based on events that are spatially and temporally localized. The set of Agents, based on the events, calculates its influence coefficient on the decision taken by the Agent. An Agent can generate both decisions, which lead to personal qualitative evolution, and events that will be processed by other Agents. This work elaborates the following: the multi-objective decision system diagram, the communication model between Agents, the set of operators for spatial-temporal logic, and the model for optimal solution search in event space.

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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Synthesis of Agent-Based Decision-Making systems with Multiple Coalitions

Synthesis of Agent-Based Decision-Making Systems with Multip...

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International Conference on Electromechanical and Power systems (SIELMEN)

作者： Victor Ababii Viorica Sudacevschi Silvia Munteanu Viorel Carbune Radu Melnic Victor Lasco Department of Computer Sciences and Systems Engineering Technical University of Moldova Chisinau Republic of Moldova Department of Software Engineering and Automatics Technical University of Moldova Chisinau Republic of Moldova

This paper presents a synthesis method of decision-making systems based on Agents forming multiple coalitions. The condition for coalition formation is defined by a mathematical model that solves the problem of global optimization of the objective function. The problem of synthesizing the agent-based system was formulated, and the mathematical model for negotiating the coalition formation process was developed. The process of initiation, dialogue and coalition formation is presented based on the sequence diagram. For structural modeling, Membrane Computation (P-systems) models are used that highlight the parallelism and topology of the Multi-Agent system. The Agent structure is defined as a collection of knowledge (data and processing methods) and input/output ports. The functional validation of the Agent and the Multi-Agent system was performed based on the Timed Petri Nets.

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Automatic Feasibility Restoration for 5G Cloud Gaming

Automatic Feasibility Restoration for 5G Cloud Gaming

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IEEE International Conference on Communications (ICC)

作者： Ramy Mohamed Ioannis Lambadaris Aris Leivadeas John Chinneck Todd Morris Petar Djukic Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Software and IT Engineering Ecole de technologie supérieure Montreal Canada Ciena Ottawa Canada

Cloud gaming offers excellent potential; however, it presents significant challenges for 5G networks because of its strict requirements for high reliability and low latency. Cloud gaming service deployment can be modeled as a Virtual Network Functions Chain Placement Problem (VNF-CPP), where a service instance is represented by a chain of interconnected Virtual Network Functions (VNFs). Solving the VNF-CPP may result in infeasible solutions when the underlying network infrastructure can not meet the service requirements. In this paper, we propose an automatic feasibility restoration technique and explain how network operators can use it to meet cloud gaming's demands. Our proposed algorithms reveal the origins of the infeasibilities so that network operators can understand the reasons behind them. Moreover, the algorithms suggest the best way to alter the network to regain feasibility by providing realtime elastic resource management. Specifically, two approaches are proposed and evaluated. The first approach is the Irreducible Infeasible Set (IIS) Repair, and the second is the Minimum Cost Redesign. We evaluate the proposed algorithms using two practical use cases: an offline use case, where we need to fix the infeasibility for a bulk of service instances, and an online use case, where we need to fix the infeasibility for a single service instance in realtime. Furthermore, theoretical analysis and results show that the Minimum Cost Redesign method outperforms the IIS Repair method. Results also verify that our algorithms can provide practical solutions for both use cases in realtime.

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Yolo Based License Plate Detection Of Triple Riders And Violators

Yolo Based License Plate Detection Of Triple Riders And Viol...

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International Conference on computer Communication and Informatics (ICCCI)

作者： R G Nandhakumar K. Nirmala Devi N. Krishnamoorthy S. Shanthi V R Pranesh S Nikhalyaa Department of Computer Science and Engineering Kongu Enginering College Perundurai Erode Tamilnadu India Department of Software Systems and Engineering School of Information and Engineering Vellore Institutte of Technology Vellore India

Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, triple riding etc. Even though there are many smart systems to monitor these violations, it is complicated to keep a track of the data and view it efficiently anywhere. Also monitoring the permit details of other state vehicles is hard. A method was proposed to address this issue. based on Deep learning and Optical Character Recognition (OCR). Here we detect the riders not wearing helmet and triple riders using object detection by comparing YOLOv2 and YOLOv3. This simplifies the task of traffic police who can’t continuously and efficiently monitor all the violators. We gathered our data from three different sources. Kaggle was used to collect the data set. A number of items can be recognized by the real-time object recognition system YOLO in a single frame. It recognizes object more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. It divides the image into m*m matrix and each grid detects objects within itself. The trained model is tested using a test dataset set after training in order to measure the performance. For Number plate detection we use OCR based character recognition. The recognized license plate images of violators are captured and stored in a database which is then sent to the concerned department. The stored data can be easily accessed using the developed mobile app. Metrics considered for evaluation is Accuracy. The average is utilized to assess object detection models like R-CNN and YOLO. The average calculates a score by comparing the detected box to the ground-truth bounding box. The better the score, the more precise the model’s detections are. When compared to YOLOv2, YOLOv3 exhibits strong performance in terms of accuracy with 95.5%. Keywords—Deep learning; Object detecti

关键词： Head Road accidents Databases Computational modeling Object detection Road safety Safety

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