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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Learning a generalizable re-identification model from unlabelled data with domain-agnostic expert

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Liu, Fangyi Ye, Mang Du, Bo National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan 430072 China

In response to real-world scenarios, the domain generalization (DG) problem has spurred considerable research in person re-identification (ReID). This challenge arises when the target domain, which is significantly different from the source domains, remains unknown. However, the performance of current DG ReID relies heavily on labor-intensive source domain annotations. Considering the potential of unlabeled data, we investigate unsupervised domain generalization (UDG) in ReID. Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain. To address this, we propose a new approach that trains a domain-agnostic expert (DaE) for unsupervised domain-generalizable person ReID. This involves independently training multiple experts to account for label space inconsistencies between source domains. At the same time, the DaE captures domain-generalizable information for testing. Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting. The results demonstrate the superiority of our method over state-of-the-art techniques. We will make our code and models available for public use. © The Author(s) 2024.

关键词： Domain generalization (DG) Domain-agnostic expert (DaE) Label space inconsistencies Unlabeled source domains

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Periodic Attention Networks for Air Quality Index Prediction 4

Periodic Attention Networks for Air Quality Index Prediction

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Wu, Jiaqing Wu, Yinzhi Chen, Cheng Guangxi Normal University School of Computer Science and Engineering and School of Software Guilin China Yangtze Normal University School of Civil and Architectural Engineering Chongqing China

ISBN: (纸本)9798400709449

Air quality remains a pressing concern in urban regions globally, influencing public health, environmental sustainability, and residents' overall well-being. However, predicting the subsequent day's air quality index (AQI) using its history data is very challenging due to environmental complexities. To address this issue, a novel model named Periodic Attention Networks (PAN) is proposed in this paper, which exploits the hierarchical attention mechanism to capture the periodicity of AQI changes. PAN does a good job of stratifying the weekly periodicity of Beijing's AQI with its monthly periodicity, which enables the model to be constructed with a distinction between important and unimportant contents. The experimental results on the regression prediction task of Beijing's AQI from 2019-2021 show that the applied model surpasses the previous methods to a large extent. © 2023 ACM.

关键词： Air quality

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FED-3DA: A Dynamic and Personalized Federated Learning Framework 48

FED-3DA: A Dynamic and Personalized Federated Learning Frame...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Hui Sun, Jie Wo, Tianyu Liu, Xudong Beihang University Sklsde School of Computer Science and Engineering Beijing China Beihang University Sklsde School of Software Beijing China

ISBN: (纸本)9781728163277

In federated learning, the non-IID data generated from heterogeneous clients may reduce the global model efficiency. Previous studies use personalization as a common approach to adapt the global model to these clients (called the local model). However, client's data distribution may change dynamically with its location or environment, which can degrade the performance of the local model, leading to a new Dynamic Personalized Federated Learning (DPFL) problem. This paper proposes a novel approach to reduce the impact of the dynamic distribution on the local model based on metalearning and distribution distance measurement named Fed-3DA. It calculates the distribution distance periodically to perceive the distribution change on the client and adjust the local model preferences from a global meta-model through the distribution representation. Our experiments on public datasets show that Fed-3DA can effectively reduce the performance fluctuation of the local model in DPFL scenarios. © 2023 IEEE.

关键词： Learning systems

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Graph-Based Feature Learning for Cross-Project software Defect Prediction

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computers, Materials & Continua 2023年第10期77卷 161-180页

作者： Ahmed Abdu Zhengjun Zhai Hakim A.Abdo Redhwan Algabri Sungon Lee School of Software Northwestern Polytechnical UniversityXi’anChina School of Computer Science Northwestern Polytechnical UniversityXi’anChina Department of Computer Science Hodeidah UniversityPO Box 3114Al-HudaydahYemen Research Institute of Engineering and Technology Hanyang UniversityAnsanKorea Department of Robotics Hanyang UniversityAnsanKorea

Cross-project software defect prediction(CPDP)aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source *** existing CPDP approaches rely on static metrics or dynamic syntactic features,which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties,such as complex design patterns,relationships between multiple functions,and dependencies in different software projects,that are important for *** paper introduces a novel approach,a graph-based feature learning model for CPDP(GB-CPDP),that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs(CFGs)and data dependency graphs(DDGs).These graphs capture the structural and data dependencies within the source *** proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory(LSTM)networks to learn predictive *** process involves graph construction,feature learning through graph embedding and LSTM,and defect *** evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve(AUC).The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction.

关键词： Cross-project defect prediction graphs features deep learning graph embedding

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Efficient adaptive rendering of planetary-scale terrains

Efficient adaptive rendering of planetary-scale terrains

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作者： Masood, Zafar Jiangbin, Zheng Irfan, Muhammad Ahmad, Idress School of Software Northwestern Polytechnical University Shaanxi Xi'an China Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan

Planetary-scale terrain requires adaptive simplification and tessellation for highperformance rendering. Real-world visualisation requires adaptive rendering of the world-scale geometric model, which is a challenging task especially on consumer-level notebooks. In this work, we propose a method for the adaptive rendering of the Earth's surface model using modern GPU features. The multi-resolution model is partitioned into patches and is submitted efficiently using the geometry-instancing feature for high-performance rendering. Patch-based algorithm culls worldscale model in graphics pipeline to reduce simplification and rendering load. The proposed method performs the adaptive simplification of patches with geometric-error control in image space for optimum triangulation. Simplified patches are tessellated using power-of-two tessellation factors to avoid boundary cracks. After tessellation, a three-dimensional surface is constructed by displacement mapping using a regular height data grid. Comparative evaluation of the proposed method with clipmaps and hardware-based tessellation methods is performed and results are presented. © 2023 Inderscience Enterprises Ltd.

关键词： Geographic information systems

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Explainable Story and Visualization for Learning Data Structure Concepts 5

Explainable Story and Visualization for Learning Data Struct...

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5th International Conference on computer science and Technologies in Education, CSTE 2023

作者： Wang, Bingshu Zhu, Xinyi Shi, Cheng Cheng, Yan School of Software Northwestern Polytechnical University Xi'an China School of Computer Science and Engineering Xi'An University of Technology Xi'an China

ISBN: (纸本)9798350325720

The 'Data Structure' course is a fundamental subject within the entire computer course system, and it plays a vital role in the field of computer and information disciplines. However, the abstraction and logic of data structures have made this course challenging for many underestimated students. It involves many concepts, which can be difficult to understand. To enhance teaching efficiency and facilitate students' comprehension of data structure concepts, we propose a method that combines storytelling and visualization. Our goal is to present these concepts clearly and promote a deeper understanding among students. We have developed new expressions that serve as a bridge between data structure concepts and their logical structures. Our teaching practice has shown that this approach is highly effective. It not only increases the teacher's confidence in their ability to teach, but also enhances students' subjective initiative when it comes to learning. © 2023 IEEE.

关键词： Visualization

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High-quality Trajectory Generation for Autonomous Driving: A Lightweight Federated Learning-based Diffusion Model

High-quality Trajectory Generation for Autonomous Driving: A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Gao, Runquan Kang, Jiawen Lai, Bingkun Xu, Minrui Sun, Geng Zhang, Tao Zhang, Weiting Yang, Dong Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China

ISBN: (纸本)9798350351255

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization. © 2024 IEEE.

关键词： Differential privacy

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A method of monitoring and locating eggs laid by breeding geese based on photoelectric sensing technology

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Information Processing in Agriculture 2022年第3期9卷 406-416页

作者： Yidan Xu Qiuju Xie Liwei Wang College of Mechanical and Automotive Engineering Zhaoqing UniversityZhaoqing526061China School of Computer Science and Software Zhaoqing UniversityZhaoqing 526061China

On the current breeding goose farm,the detection of individual egg laying mainly depends on some judgement experiences of farm *** present,there have been some egg laying detection systems developed with images and weighing sensors,which only signal the eggs being laid,but no egg position being ***,the detection rate of the system is not high due to environment limitations like dim light of the goose ***,to solve these problems mentioned above,an intelligent detection and positioning system is pro-posed by integrating technologies of the Radio Frequency(RF)and photoelectric sensors,together with the geometric calculation *** this research,individual egg laying information of breeding geese in a non-cage state was examined to improve the level of auto-matic detection and positioning in the field of breeder egg *** results showed that an accurate detection and positioning of an egg in a nest filled with the artificial turf could be achieved under some conditions:the height of sensor is 3.5 cm from the bottom plate of the egg laying nest,the spacing of the photoresistor module is 5 cm,and the external light intensity is less than 110 *** also shown that the error of the goose egg position recognition is 0.443 cm with a suitable level of straw in the ***,the monitoring system and positioningmethod that was developed in this research could provide a reference for the analysis of individual egg laying behavior,and could result in an improvement in the automatic egg collection for the breeding geese production.

关键词： Breeding geese Egg laying monitoring Goose egg location Photoelectric sensing technology RFID technology

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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