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Convergence Track Based Adaptive Differential Evolution Algorithm(CTbADE)

computers, Materials & Continua 2022年第7期72卷 1229-1250页

作者： Qamar Abbas Khalid Mahmood Malik Abdul Khader Jilani Saudagar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Engineering Oakland UniversityRochesterMIUSA Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research *** crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima *** the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying *** adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this *** proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE *** results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE *** algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

关键词： Differential evolution function optimization convergence track parameter sequence adaptive control parameters

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Emotion and Mentality Monitoring Assistant (EMMA) 8

Emotion and Mentality Monitoring Assistant (EMMA)

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8th International Conference on Communication and Electronics systems, ICCES 2023

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

ISBN: (纸本)9798350396638

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. © 2023 IEEE.

关键词： Blockchain

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Enhancing Soft Skills in Autistic Children: The Next Generation of Mind Champ's Technological Approach 6

Enhancing Soft Skills in Autistic Children: The Next Generat...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Peiris, Kalna Nelaka, Ruchira Manathunga, Kalpani Department of Computer Systems Engineering Sri Lanka Institute of Information and Technology Sri Lanka Department of Information Technology Sri Lanka Institute of Information and Technology Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information and Technology Sri Lanka

ISBN: (纸本)9798331517878

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively. © 2024 IEEE.

关键词： Economic and social effects

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based software systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Analysis and Propagation of Feature Revisions in Preprocessor-based software Product Lines

Analysis and Propagation of Feature Revisions in Preprocesso...

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2024 software engineering, SE 2024

作者： Michelon, Gabriela K. Assunção, Wesley K.G. Grünbacher, Paul Egyed, Alexander Institute of Software Systems Engineering and LIT Lab Johannes Kepler University Linz Linz4040 Austria Department of Computer Science North Carolina State University RaleighNC27695 United States Institute of Software Systems Engineering Johannes Kepler University Linz Linz4040 Austria

This is a summary of a paper [Mi23] published in the IEEE International Conference on software Analysis, Evolution and Reengineering (SANER) 2023. It describes a tool-supported approach for analyzing and propagating f... 详细信息

ISBN: (纸本)9783885797371

关键词： computer software reusability

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FT-AED: benchmark dataset for early freeway traffic anomalous event detection 24

FT-AED: benchmark dataset for early freeway traffic anomalou...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Austin Coursey Junyi Ji Marcos Quinones-Grueiro William Barbour Yuhang Zhang Tyler Derr Gautam Biswas Daniel B. Work Institute for Software Integrated Systems and Department of Computer Science Vanderbilt University Institute for Software Integrated Systems and Department of Civil and Environmental Engineering Vanderbilt University Institute for Software Integrated Systems Department of Computer Science Vanderbilt University Institute for Software Integrated Systems and Department of Computer Science and Department of Civil and Environmental Engineering Vanderbilt University

ISBN: (纸本)9798331314385

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and mistakes from manual crash reporting records make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Tennessee department of Transportation Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://***/ft-aed/ to facilitate future research.

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A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

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computers, Materials & Continua 2023年第4期75卷 165-182页

作者： Muhammad Azhar Sehat Ullah Khalil Ullah Habib Shah Abdallah Namoun Khaliq Ur Rahman Department of Computer Science&IT University of MalakandChakdara18800Pakistan Department of Software Engineering University of MalakandChakdara18800Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time *** (50) volunteers of different ages participated in the experimental *** the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

关键词： Age estimation gait biometrics classical linear regression model

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Global Stock Market Prediction Using Transformer-Based Deep Learning Techniques 15

Global Stock Market Prediction Using Transformer-Based Deep ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gothai, E. Thamilselvan, R. Natesan, P. Vignesh, K. Kongu Engineering College Department of Computer Technology Tamilnadu Perundurai India Kongu Engineering College Department of Cse Tamilnadu Perundurai India Kongu Engineering College Department of It Tamilnadu Perundurai India Kongu Engineering College Software Systems Department of Ct Tamilnadu Perundurai India

ISBN: (纸本)9798350370249

Deep learning approaches' use in financial market forecasting has recently drawn a lot of attention from both investors and scholars. The Transformer framework, initially created for natural language processing, is used in this study to propose a revolutionary approach for forecasting the stock market indices of seven different economies: China, Canada, India, Japan, Russia, the United Kingdom, and the United States. The well-known Transformer architecture, which excels at capturing complex non-linear patterns, is modified to study the unique dynamics of each nation's stock market. The model successfully determines the basic principles driving market behavior by using an encoder-decoder structure and a multi-head attention mechanism. The aforementioned indices are used in back-testing trials, which cover a variety of economic environments. The outcomes demonstrate that the Transformer performs better than conventional techniques, with Mean Squared Error (MSE) acting as a crucial evaluation criterion. Notably, the model shows promise for investors in these widely separated markets to receive excess profits. This study offers a thorough analysis of stock market forecasting, expanding its relevance to a wider global context. comprehension of how this strategy might adjust to various financial ecosystems is improved by the addition of seven unique economies. Future research may go more deeply into applications that are industry-specific and investigate potential extensions to additional international markets. © 2024 IEEE.

关键词： Deep Learning Encoder-Decoder Stock Prediction Transformer

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WIP: An Engaging Undergraduate Intro to Model Checking in software engineering Using TLA+

arXiv

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arXiv 2024年

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Software and Systems Laboratory Department of Computer Science Loyola University Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one’s undergraduate studies, (3) demonstrate how to address successive "failures" through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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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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2023 International Conference on computer Communication and Informatics, ICCCI 2023

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

ISBN: (纸本)9798350348217

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%. © 2023 IEEE.

关键词： Object detection

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