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IEEE International Conference on Automated software engineering (ASE)

作者： Siang Zhao Zhongyang Li Zhenbang Chen Ji Wang College of Computer National University of Defense Technology China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China State Key Laboratory of High Performance Computing National University of Defense Technology China

Fuzzy logic is widely applied in various applications. However, verifying the correctness of fuzzy logic models can be difficult. This extended abstract presents our ongoing work on verifying fuzzy logic models. We treat a fuzzy logic model as a program and propose a verification method based on symbolic execution for fuzzy logic models. We have developed and implemented the environment models for the common functions and the inference rules in fuzzy logic models. Our preliminary evaluation shows the potential of our verification method.

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Algorithm of Coordination of Swarm of Autonomous Robots

Algorithm of Coordination of Swarm of Autonomous Robots

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Abzal Kyzyrkanov Sabyrzhan Atanov Shadi Aljawarneh Nazira Tursynova Samat Kassymkhanov Department of Computer Engineering Astana IT University L. N. Gumilyov Eurasian National University Astana Kazakhstan Computer And Software Engineering L. N. Gumilyov Eurasian National University Astana Kazakhstan Software Engineering Department Jordan University of Science and Technology Irbid Jordan Department Of Artificial Intelligence Technologies L. N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Engineering Astana IT University Astana Kazakhstan

The task of keeping a specific geometric configuration while following a designated path is common in various fields that involve autonomous robots. When done correctly, this approach can result in several advantages, including a reduction in system costs and an increase in system reliability and efficiency, while maintaining the adaptability and flexibility of the system. The concept of maintaining a particular geometric pattern during motion is frequently utilized in different scenarios. For example, unmanned military vehicles must maintain a particular formation to explore and cover terrain during missions. Another example is in “Smart Highways,” where the traffic system's capacity can be enhanced significantly by having vehicles move in groups at the same speed while maintaining a certain distance between them. This research presents an algorithm that allows a group of self-governing mobile robots to move while maintaining a specific geometric structure. A behavior-based control algorithm is advanced to regulate the movement of these systems. The coordination of the robots within the system is accomplished through the implementation of a leader-follower technique, with virtual leader.

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A Novel Gooseneck Barnacle Optimizer Based Machine Learning Approach for Rainfall Prediction

A Novel Gooseneck Barnacle Optimizer Based Machine Learning ...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Marzia Ahmed Mohd Herwan Sulaiman Mostafijur Rahman Ahmad Johari Mohamad Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pahang Malaysia Department of Software Engineering Daffodil International university Dhaka Bangladesh Department of Computer Science and Engineering Green University of Bangladesh

In dynamic meteorological prediction, accurate rainfall forecasting is a mystery. In a complex and dynamic natural environment with unpredictable sky movements, we propose an innovative methodology that forecasts weekly average rainfall patterns using the Gooseneck Barnacle Optimizer and the Least Squares Support Vector Machine (LSSVM). The significance of rainfall prediction is shown by its widespread implications, including public health. However, conventional single-model techniques and machine-learning methods must represent rainfall pattern changes, limiting our preparation. With a 2014–2018 dataset from reliable meteorological libraries, our research explores the possibility of this novel combination. The Gooseneck Barnacle Optimizer (GBO) serves as the bedrock of our methodology, introducing a novel evolutionary algorithm inspired by the intricate mating behaviors of gooseneck barnacles. GBO aptly captures the dynamic interplay of factors such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, culminating in two crucial optimization stages: exploration and exploitation. In contrast to previous algorithms like the Barnacle Mating Optimizer (BMO), GBO stands out by more accurately emulating the unique mating behaviors of gooseneck barnacles. The prediction job is performed by integrating the Least Squares Support Vector Machines (LSSVM) algorithm with GBO as an objective function, using the optimized hyper-parameter values. The results suggest that the GBO algorithm exhibits superior performance compared to existing methodologies. This is accomplished by efficiently augmenting the original random population for a particular problem, resulting in a convergence towards the global optimum and producing significantly enhanced optimization outcomes.

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EHG Signal Analysis for Prediction of Term and Preterm using Variational Mode Decomposition and Artificial Neural Networks

EHG Signal Analysis for Prediction of Term and Preterm using...

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Frontiers of Information technology (FIT)

作者： Muhammad Umar Khan Sumair Aziz Khushbakht Iqtidar Raul Fernandez Rojas Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan Faculty of Science & Technology University of Canberra Canberra Australia

ISBN: (纸本)9798350345940

Preterm deliveries are an important cause of mortality and morbidity in newborns. Accurate and early prediction of a premature delivery can prove helpful in providing proper medication and treatment. Recording of electrical activity known as Electrohysterogram (EHG) from the abdominal surface of pregnant women corresponds to the uterus contractions. A new direction is open using EHG signals for the diagnosis of preterm births. In this research, we present a new method for the accurate classification of preterm and term EHG signals. The proposed method first filters a three-channel EHG signal using bandpass filters. Next, we combined the filtered three-channel EHG into one signal using an accumulation operation. The accumulated EHG signal was post-processed through variational mode decomposition (VMD). VMD algorithm splits the input signal into finite modes using center frequencies known as intrinsic mode functions (IMFs). An energy-based intelligent signal reconstruction approach is designed to combine IMFs having an energy level above the computed threshold. Next, the reconstructed EHG signals were split into continuous windows, and time, frequency, and Hjorth features were extracted. These features were fused to construct a distinct feature representation and were reduced using the ReliefF algorithm. We trained an artificial neural network (ANN) to obtain 98.8 % average accuracy using 10-fold cross-validation.

关键词： Band-pass filters Time-frequency analysis Artificial neural networks Filtering algorithms Feature extraction Signal reconstruction Classification algorithms

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Development of an Educational Game Visual Novel Introduction to Traditional Dance Based on Android: Case Study of Typical Dance of Lombok Island

Development of an Educational Game Visual Novel Introduction...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Dyah Susilowati Dian Syafitri Chani Saputri Abdul Rahim Tomi Tri Sujaka Helna Wardhana Sausan Husnita Melati Rosanensi Kartarina Augustin Department of Information Technology Education Bumigora University Mataram Indonesia Department of Software Engineering Bumigora University Mataram Indonesia Department of Computer Science Bumigora University Mataram Indonesia

This research aims to produce a practical Visual Novel Game learning media. This research is development research with the Multimedia Development Life Cycle (MDLC) method, including 4 stages, namely concept, design, material collection, manufacture, testing, and distribution. The subjects in this study were elementary school students, dance teachers, and songwriters with a purposive sampling technique. The data collection instrument was a questionnaire sheet. The analysis technique uses quantitative descriptive statistics and percentages. The results of testing the functionality of the application from black box testing show the results of the Visual Novel Game learning media are appropriate. The last test on the black box using the User Acceptance Test (UAT) shows the user satisfaction with Visual Novel Game learning media with very good results. The application feasibility results show very good with a percentage of 87.76% and the usability of the application shows very good with a percentage of 90.03%. The visual novel educational game adds insight into the typical dances of Lombok Island with a score of 92%, helps students understand the meaning of the Beriuk Tinjal dance with a score of 92%, and makes learning easy and fun with a score of 92%.

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Coupling Global Context and Local Contents for Weakly-Supervised Semantic Segmentation

arXiv

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arXiv 2023年

作者： Wang, Chunyan Zhang, Dong Zhang, Liyan Tang, Jinhui The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China

Thanks to the advantages of the friendly annotations and the satisfactory performance, Weakly-Supervised Semantic Segmentation (WSSS) approaches have been extensively studied. Recently, the single-stage WSSS was awakened to alleviate problems of the expensive computational costs and the complicated training procedures in multi-stage WSSS. However, results of such an immature model suffer from problems of background incompleteness and object incompleteness. We empirically find that they are caused by the insufficiency of the global object context and the lack of the local regional contents, respectively. Under these observations, we propose a single-stage WSSS model with only the image-level class label supervisions, termed as Weakly-Supervised Feature Coupling Network (WS-FCN), which can capture the multi-scale context formed from the adjacent feature grids, and encode the fine-grained spatial information from the low-level features into the high-level ones. Specifically, a flexible context aggregation module is proposed to capture the global object context in different granular spaces. Besides, a semantically consistent feature fusion module is proposed in a bottom-up parameter-learnable fashion to aggregate the fine-grained local contents. Based on these two modules, WS-FCN lies in a self-supervised end-to-end training fashion. Extensive experimental results on the challenging PASCAL VOC 2012 and MS COCO 2014 demonstrate the effectiveness and efficiency of WS-FCN, which can achieve state-of-the-art results by 65.02% and 64.22% mIoU on PASCAL VOC 2012 val set and test set, 34.12% mIoU on MS COCO 2014 val set, respectively. The code and weight have been released at: WS-FCN. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Real-Time Domestic Garbage Detection Method Based on Improved YOLOv5 8th

Real-Time Domestic Garbage Detection Method Based on Improve...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Kan, Shengqi Fang, Wei Wu, Jiayi Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067662

With the substantial improvement of people’s living standards, the amount of domestic garbage is increasing rapidly, and intelligent waste classification has become an urgent need in modern society. In this paper, we propose a real-time garbage detection model based on the improved YOLOv5 (you only look once version 5) algorithm. Firstly, mosaic data enhancement is introduced to enrich the background of the detection object and improve the robustness of the network. Secondly, Distance-IOU Non-Maximum Suppression is used to replace the traditional Non-Maximum Suppression to improve the suppression effect of prediction boxes. Finally, the network is further optimized from the aspect of activation function. The experimental results show that among the four versions of YOLOv5, their mean average precision(mAP) all reach more than 84%, The improved YOLOv5x has the best recognition effect, whose mAP reaches 89.4%, which is 2.1% higher than that of YOLOv5x and 5.3% higher than that of YOLOv5s. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Dynamic Language Refinement for Graceful Degradation in Scene Text Recognition

Dynamic Language Refinement for Graceful Degradation in Scen...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Shaocong Tian Rize Jin Joon-Young Paik Chen Fu School of Software Tiangong University Tianjin China School of Computer Science &Technology Tiangong University Tianjin China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China

Scene text recognition is a mature technology which can get a near-perfect result in regular scenes. However, there are still troubles in recognizing scene texts with image blur, characters missing, and occlusion. Recent end-to-end frameworks have investigated the importance of linguistic knowledge, utilizing either implicit or explicit language models. However, joint training of language and vision models tends to interfere the tunings of each other. In this paper, we present a novel gradient isolation method to train language and vision models separately, yet in an end-to-end architecture. In addition, we propose a dynamic iterative training procedure for graceful refining the language model. Extensive experiments confirm that the proposed method has superiority on regular and irregular scene text images and achieves state-of-the-art results.

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Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

arXiv

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arXiv 2023年

作者： Dong, Yanjie Wang, Luya Chi, Yuanfang Wang, Jia Zhang, Haijun Yu, Fei Richard Leung, Victor C.M. Hu, Xiping The Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China The College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China The Department of Systems and Computer Engineering Carleton University Ottawa Canada The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks. © 2023, CC BY.

关键词： Momentum

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A COMPREHENSIVE STUDY ON OCCLUSION INVARIANT FACE RECOGNITION UNDER FACE MASK OCCLUSIONS

arXiv

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arXiv 2022年

作者： Hemathilaka, Susith Aponso, Achala Department of Computer Science and Software Engineering University of Westminster London United Kingdom Department of Computer Science Informatics Institute of Technology Colombo Sri Lanka

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face, and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features. © 2022, CC BY.

关键词： Face recognition

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