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21st International Learning and Technology Conference, L and T 2024

作者： Elgazzar, Mohamed Hossny, Eman Omara, Fatma A. Faculty of Computers and Informaiton Software Engineering Dept Cairo Egypt Faculty of Computers and Informaiton Computer Science Dept Cairo Egypt

ISBN: (纸本)9798350393569

Recently, Flaky Tests have gotten the attention of the research community. These tests not only waste time and resources, but also undermine the reliability of the automated tests and hence the software systems are affected. So, the detection of Flaky Tests is very important and has a wide audience from both industry and academia. In addition, the research work is very promising having a great potential for the contribution that maps to a true value added to the up-to-date industry projects. Although lots of effort has been done to detect Flakiness in the software systems, the majority of the existed works focuses on different techniques in isolation. The aim of the work in this paper is to overlook the existed flakiness detection techniques, try to analyze them, and hold a comparison, indicating the proper context for each one. Most of the existed flakiness detection techniques are done on unit tests and library/technical projects. According to the comparative study among these techniques, it is found gaps and wide potential for contribution. On the other hand, flakiness detection techniques can be clustered under three main headers;First, study the phenomena under different testing levels by trying to focus on projects with integration, system, and end-toend tests. Second, study the phenomena under different Project types and data sets by studying business projects with user interfaces and mobile applications and so on. Third, study the phenomena toward a faster delivery cycle by studying flake tests in the context of parallel execution and Continuous Integration and Continuous Deployment (CI/CD). We can conclude that not all detection techniques have the same potential where some techniques are very competent while others still struggle to achieve considerable efficiency. © 2024 IEEE.

关键词： User interfaces

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Smart Glove with Mobile Application to Detect Static Arabic Hijaiyah Hand Code for Quran Recitation 21

Smart Glove with Mobile Application to Detect Static Arabic ...

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21st International Learning and Technology Conference, L and T 2024

作者： Elshareif, Mohamed Elgilani Eltahir Ahmad Alias, Nurul Aswani Jomhari, Nazean Sofian, Hazrina University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350393569

The use of assistive technology in the field of education is now a common practice in today's tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise. © 2024 IEEE.

关键词： Assistive technology

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Location Detection of an Object Non Line of Sight with Audio Signal 8

Location Detection of an Object Non Line of Sight with Audio...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Olgun, Nevzat Calisan, Mucahit Afyon Kocatepe University Department of Software Engineering Afyonkarahisar Turkey Engineering and Architecture Faculty Department of Computer Engineering Bingöl Turkey

ISBN: (纸本)9798331531492

Locating objects Non-Line-of-Sight is an important challenge in many fields such as defense applications, autonomous vehicles, natural disasters, etc. With the advancement of signal processing techniques, there has been an increased interest in the detection of objects in hidden areas. In this respect, audio signals have a strong potential as a low-cost technology for object localization. This study presents an experimental application to detect the location of hidden objects Non-Line-of-Sight based on audio signals. The signals from the sound source strike the hidden object through a reflecting surface, such as a wall, and are received back from the reflecting surface as secondary signals. In this study, object detection can be performed with acoustic sound signals without physically intervening in an area beyond the line of sight and without locating any sensors in this area. The experimental results obtained from the study show that the position detection of an object located Non-Line-of-Sight can be done satisfactorily. © 2024 IEEE.

关键词： Audio signal processing

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Waste Classification Using Random Forest Classifier with DenseNet201 Deep Features

Waste Classification Using Random Forest Classifier with Den...

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作者： Akyol, Kemal Karacı, Abdulkadir Faculty of Engineering and Architecture Computer Engineering Kastamonu University Kastamonu Turkey Faculty of Engineering Software Engineering Samsun University Samsun Turkey

The successful management of solid waste, which has become a major issue in urban life, reduces environmental pollution. To address this issue, numerous studies using various deep learning models have been conducted. This study focuses on traditional convolutional neural networks and transformer-based deep learning architectures and provides a detailed examination summary. In this context, many experimental studies dealing with the PoolFormer and ResNet transformers and the DenseNet201 and ResNet50 pre-trained CNN models were run on the TrashNet dataset containing waste images. The Random Forest (RF) classifier was trained on different deep features extracted from 80% of this dataset, and the remaining 20% was reserved as the test dataset. Test dataset was used to validate the performance of the RF. According to the results, RF with DenseNet201 deep features presents 96.4% classification accuracy. Furthermore, the RF-based hybrid model fed with the trained DenseNet201 outperformed other studies using the same dataset in the literature in terms of classification accuracy. The hybrid method employed used in this study may serve as a model for future research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Detection Algorithm for Safety Helmet Wearing of Chemical Plant Personnel Based on Improved YOLOv5m

Detection Algorithm for Safety Helmet Wearing of Chemical Pl...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Li, Qiangqiang Yu, Kun Wang, Haoran Guan, Qianjun Gao, Shihong Jiang, Jiamin Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an Jiangsu China

ISBN: (纸本)9798350306293

Wearing a safety helmet at the work site of a chemical plant can effectively prevent safety accidents caused by head injuries, so it is very important to detect whether employees wear safety helmets. In order to solve problems such as serious information loss, large number of parameters, and weak detection ability of small targets in complex scenes in the sampling process of helmet detection algorithm. In this paper, an improved lightweight safety helmet detection algorithm LP- YOLOv5m (Lightweight And High Precision YOLOv5m) based on YOLOv5m is designed. LP-YOLOv5m incorporates the Wise-IoU (Wiou) loss function to enhance model generalization. Additionally, we add a small object detection layer to improve the feature extraction ability for small objects. Furthermore, upsampling was performed using CARAFE. Finally, a lightweight module named C3-F was proposed, which replaced the Bottleneck in the C3 module with FasterNet Block, which could effectively reduce the network model size. Comparative laboratory findings demonstrate that our improved model achieves a mAP50 of 92.1%, surpassing YOLOv5m by 3 percentage points while reducing the model size by 23.3%. The LP-YOLOv5m model can be better applied to practical scene requirements, and can effectively solve practical problems. © 2023 IEEE.

关键词： Chemical plants

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Survey and Tutorial on Hybrid Human-Artificial Intelligence

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Tsinghua Science and Technology 2023年第3期28卷 486-499页

作者： Feifei Shi Fang Zhou Hong Liu Liming Chen Huansheng Ning School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Computer Science and Software Engineering East China Normal UniversityShanghai 200062China Faculty of Computing Ulster UniversityNewtownabbey BT370QBUK

The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.

关键词： hybrid human-artificial intelligence(H-AI) Internet of Things(IoT) artificial intelligence(AI)

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Building a Full-text Search Index Using "Transformer" Neural Network 17

Building a Full-text Search Index Using "Transformer" Neural...

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17th IEEE International Conference on Application of Information and Communication Technologies, AICT 2023

作者： Dobrynin, Vyacheslav Abramovich, Roman Platonov, Alexey Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia

ISBN: (纸本)9798350303568

The use of deep neural networks in information retrieval significantly improves its effectiveness, but negatively affects the performance of the process. To deal with this, we propose a new ranking model that uses the deep neural network of the "Transformer"architecture (in particular, BERT) for efficient information retrieval. In accordance with the proposed approach, contextualized vector representations are extracted from documents during indexing, after which these representations are clustered for each independent token. The resulting clusters reflect different meanings of the words and are indirectly used as inverted index keys. The values represent the documents in which these contextualized word meanings occur, along with the distances from each document to the contextualized embedding. Thus, after the indexing process, we obtain an index containing pre-calculated distances between the contextualized meanings of dictionary elements and documents. This approach helps us avoid the performance overhead of calculating distances online. At the search stage, the query is transformed into a set of contextualized vectors representing each query token, which allows us to use these vectors to retrieve most semantically close neighbor-tokens and use them to extract relevant documents from the index. This way of searching for contextualized embeddings consumes less memory and is more performant due to the use of an inverted index. © 2023 IEEE.

关键词： Search engines

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Lightweight Protective Clothing Detection Algorithm Based on Ghost Convolution and GSConv Convolution

Lightweight Protective Clothing Detection Algorithm Based on...

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Guan, Qianjun Yu, Kun Wang, Haoran Li, Qiangqiang Gao, Shihong Huaiyin Institute of Technology Faculty of Computer and Software Engineering Huai'an Jiangsu China

ISBN: (纸本)9798350306293

GhostNet is proposed by Huawei Noah's Ark Laboratory in CVPR2020, which can be used with the same accuracy, with less speed and less computation than SOTA method. This convolution is a low-cost operation used to decrease the amount of computation and related parameters. Ghost convolution can be used as an alternative to traditional convolution operations because they have the same accuracy. However, utilize significantly less computation and a much smaller parameter count. GSConv(Group-Shared Convolution) is a convolution operation based on group shared weights. This operation achieves efficient parallel convolution operations by grouping convolution kernels. In this way, the parameters used and calculation complexity of the model can be reduced to some extent. This makes the network more computationally efficient and smaller in model size while maintaining accuracy. The algorithm can detect the location of protective clothing quickly and efficiently, and its accuracy and efficiency have been verified. Because the algorithm adopts lightweight network structure, real-time detection can be realized on lowpower devices efficiently. The model architecture enhances the performance based on the YOLOv5 target detection algorithm by incorporating additional features. So that the model can maintain a high precision, which is crucial for the protective clothing detection algorithm. © 2023 IEEE.

关键词： Convolution

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Enhancing software Cost Estimation Using Feature Selection and Machine Learning Techniques

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computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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