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international Conference on Signal Processing and integrated Networks (SPiN)

作者： Sudeep Sharan Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Avda. de la Universidad de Cádiz 10 11519 Puerto Real Cádiz Spain

in the modern world of high-speed technologies, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have created a Bioinspired algorithm to solve the problems encountered in realworld activities. The Ant Colony optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. in this work-in-progress article, we propose a new way of using the ACO algorithm which ensures solving the problems encountered in traditional ACO algorithms. This algorithm was tested on two environments to examine output efficiency and computational output time. The results show that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably better results were observed on testing it in the bigger-scale environment. The evaluations prove that the ACO algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test it in any real-world scenarios by increasing the number of ants.

关键词： Ant colony optimization Signal processing algorithms Signal processing Path planning Computational efficiency Robots Optimization

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3D-Based Facial Emotion Recognition using Depthwise Separable Convolution 2022

3D-Based Facial Emotion Recognition using Depthwise Separabl...

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14th international Conference on Machine Learning and Computing, iCMLC 2022

作者： Abubakar, Hassan Sani Hossin, Md Altab Yussif, Sophyani Banaamwini Fargalla, Mandela Ali Margan Rashid, Ramadhan Said Umar, Yusuf Jamilu Chima, Ukwuoma Chiagoziem Kuupole, Ewald Erubaar School of Information and Communication Engineering Medialab University of Electronics Science and Technology of China Sichuan Chengdu China School of Management and Economics University of Electronics Science and Technology of ChinaChengdu Sichuan China School of Computer Science and Engineering University of Electronics Science and Technology of ChinaChengdu Sichuan China Institute of Unconventional Petroleum Science and Technology China University of Petroleum Beijing China School of Life Science University of Electronics Science and Technology of ChinaChengdu Sichuan China School of Mathematics Central South University Changsha China School of Information and Software Engineering University of Electronics Science and Technology of ChinaChengdu Sichuan China Directorate of ICT Services University of Technology and Applied SciencesNavrongo Ghana

ISBN: (纸本)9781450395700

Facial images play a significant role in expression prediction. The 3D features of facial expression provides significant information. in the area of facial emotion recognition, the 3D geometry and 2D texture helps to improve the recognition rate. A lot of research works had achieved state-of-the-art results using handcrafted and deep convolutional neural networks containing many trainable parameters which require high computing power. in this paper, we employ two kinds of convolutions i.e., regular or normal convolution on the 2D texture image and separable convolution on the 3D depth map images. We run experiments with our proposed network on the BU-3DFER database. The proposed model was trained from scratch to adjust the weights and biases of the learnable layers on various image features and achieved state-of-the-art accuracy of 81.81% on the 2D texture image, 79.10% recognition accuracy on the 3D depth map, and 83.01% for combined 2D and 3D features. © 2022 ACM.

关键词： Convolution

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A Framework for Autonomous Vehicle Testing Using Semantic Models

A Framework for Autonomous Vehicle Testing Using Semantic Mo...

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iEEE international Conference on Artificial intelligence Testing (AiTest)

作者： Yejun He Muslim Razi Jerry Gao Chuanqi Tao Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu China Computer Engineering Department and Applied Data Science Department San Jose State University San Jose USA

With the rapid development of big data and artificial intelligence, autonomous vehicles (AV) have achieved great success in diverse application domains. Although the current development of autonomous driving technology has been greatly improved, the accident rate of driverless vehicles still rises. Therefore, how to ensure the quality of AVs using different testing technologies has become an important issue. Traditional testing approaches such as decision tables and state charts struggle with diversified test inputs. Hence, there is a strong demand for new test models for AV testing. in this paper, we present a model-based testing framework that utilizes semantic trees and 3-dimensional (3D) test tables to model driving scenarios into individual test cases. Using the proposed framework, we generate fine-grained examples corresponding to the real world. Also, we evaluate the behavior of the Apollo AV system in 30 scenarios in SVL simulator. The results show that the scenarios generated by our framework can cover most conditions occurring, thus addressing the challenge of testing AVs for safety and reliability.

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software Testing issues improvement in Quality Assurance

Software Testing Issues Improvement in Quality Assurance

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Business Analytics for Technology and Security (iCBATS), international Conference on

作者： Mohammed Salahat Raed A. Said Khalid Hamid Usama Haseeb Elsaid Abdel Maguid Abdel Ghani Abedallah Abualkishik Muhammad Waseem iqbal Mohammad inairat College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Faculty of Management Canadian University Dubai Dubai UAE Department of Computer Science Superior University Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Software Engineering Superior University Lahore Pakistan Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above.

关键词： software testing Quality assurance Philosophical considerations Process control Documentation Production software

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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international Conference on Emerging Technologies, iCET

作者： Muhammad Haseeb ishaq Rafay Mustafa Usama Arshad Zain ul Abideen Raja Hashim Ali Abdullah Habib Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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international Conference on Emerging Technologies, iCET

作者： Tafreed Ahmed Ahmad Maaz Danyaal Mahmood Zain ul Abideen Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.

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EEG and HRV-Based Assessment of Neurosurgeons Training for Anxiety Regulation and Stress Monitoring 2

EEG and HRV-Based Assessment of Neurosurgeons Training for A...

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2nd Edition iEEE international Conference on Metrology for eXtended Reality, Artificial intelligence and Neural engineering, MetroXRAiNE 2023

作者： Arpaia, Pasquale Carone, Giovanni Castelli, Nicolò D'Errico, Giovanni Gargiulo, Ludovica Maffei, Luigi Mastrati, Giovanna Moccaldi, Nicola Nalin, Marco Perin, Alessandro Picciafuoco, Mauro Puttilli, Cosimo Ramos, Pedro M. Robbio, Rachele University of Naples Federico Ii Department of Electrical Engineering and Information Technology Naples Italy University of Naples Federico Ii Naples Italy I.R.C.C.S. Carlo Besta Neurological Institute Milan Italy Polytechnic University of Turin Department of Applied Science and Technology Turin Italy Instituto Superior Técnico University of Lisbon Department of Electrical and Computer Engineering Lisbon Portugal University of Salento Department of Engineering for Innovation Lecce Italy Ab Medica Spa Milan Italy

ISBN: (纸本)9798350300802

A neurofeedback (NF) -supported training is proposed to enable neurosurgeons to learn how to regulate their emotions. Electroencephalographic (EEG) signal and heart rate (HR) of 5 neurosurgeons were acquired in 4 sessions while performing NF-based emotion regulation (ER). Subjects tried to counteract anxious and stressful states through NF, that is by decreasing the power in the beta band measured in the midline areas of the scalp. The assessment of the neurofeedback effectiveness was based on the use of Heart Rate Variability (HRV) in combination with EEG. Therefore, EEG signal was exploited in real-time to guide the feedback and also in post processing, together with the HRV, to assess the effectiveness of the whole training. As the EEG signal is concerned, the results showed that the power in the beta band decreased within each session when the ER ability was strengthened, as reported in the literature. On the contrary, the HRV did not exhibit the expected increasing trend within each trial. When considering each the session on the whole, thus computing the HRV on a greater time interval, the HRV during the NF training is typically higher than the HRV computed during the rest and negative baseline phases. © 2023 iEEE.

关键词： EEG emotion regulation HRV Neurofeedkack surgery

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Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime

arXiv

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arXiv 2022年

作者： Alghamdi, Wael Asoodeh, Shahab Calmon, Flavio P. Kosut, Oliver Sankar, Lalitha Wei, Fei The School of Engineering and Applied Science Harvard University United States The Department of Computing and Software McMaster University Canada The School of Electrical Computer and Energy Engineering Arizona State University United States

Most differential privacy mechanisms are applied (i.e., composed) numerous times on sensitive data. We study the design of optimal differential privacy mechanisms in the limit of a large number of compositions. As a consequence of the law of large numbers, in this regime the best privacy mechanism is the one that minimizes the Kullback-Leibler divergence between the conditional output distributions of the mechanism given two different inputs. We formulate an optimization problem to minimize this divergence subject to a cost constraint on the noise. We first prove that additive mechanisms are optimal. Since the optimization problem is infinite dimensional, it cannot be solved directly;nevertheless, we quantize the problem to derive near-optimal additive mechanisms that we call "cactus mechanisms" due to their shape. We show that our quantization approach can be arbitrarily close to an optimal mechanism. Surprisingly, for quadratic cost, the Gaussian mechanism is strictly sub-optimal compared to this cactus mechanism. Finally, we provide numerical results which indicate that cactus mechanism outperforms the Gaussian mechanism for a finite number of compositions. © 2022, CC BY.

关键词： Additives

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Research on Supply Planning and Transshipment Scheme Based on Genetic Algorithm

Research on Supply Planning and Transshipment Scheme Based o...

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information Systems and Computing Technology (iSCTech), international Conference on

作者： Zhenkun Zhong Junyu Ren Heng Li Hui Peng School of Software Engineering Xinjiang University Xinjiang China School of Mathematics and applied mathematics Xinjiang University Xinjiang China School of Computer Science and Engineering Central South University Changsha China

Aiming at the optimal cost planning problem in the process of raw material ordering and transshipment, this pa-per studies and develops a supply chain planning manage-ment model based on genetic algorithm by constructing ob-jective function and constraints. On the basis of analyzing the raw material demand of production enterprises, combined with the supplier's supply capacity and the transshipment loss rate of the transshipment carrier, the mathematical model of the optimal ordering scheme and the transshipment scheme is constructed, and the optimal supply planning model that can meet the actual supply chain planning needs is proposed, and the planning model is improved by using genetic algorithms, which greatly improves the operation speed and has good applicability.

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A Review of Algorithms’s Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms’s Complexities on Different Valued S...

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iT and industrial Technologies (iCiT), international Conference on

作者： Attia Shabbir Asad Majeed Mahnoor iftikhar Raja Hashim Ali Usama Arshad Muhammad Zeeshan Shabbir Ali Zeeshan ijaz Nisar Ali Ali Aftab Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan Faculty of Electronic Systems Engineering University of Regina Regina Canada

Sorting and searching are critical processes for effecttive data analysis. in this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. it was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant.

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