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Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

ISBN: (纸本)9798350329964

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

关键词： android

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Enhancing Autonomous Drone Semantic Segmentation for Safe Landing

Enhancing Autonomous Drone Semantic Segmentation for Safe La...

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Data science and Information Technology (DSIT), International Conference on

作者： Yuanjing Zhu Yuhan Liao Yunan Liu Social Science Research Institute Duke University Durham United States Technology Development Department Nanjing Glaway Software Co. LTD Nanjing China Electrical & Computer Engineering Duke University Durham United States

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

Ensuring safe and accurate drone landings is a critical challenge in the development of autonomous drone systems. This study presents a two-fold approach combining granular semantic segmentation with high-level binary safety classification of aerial imagery. Our method first performs detailed segmentation of environmental elements, then categorizes these segments into "safe to land" and "unsafe to land" zones, providing a more comprehensive and practical solution for autonomous landing decisions. We employed three state-of-the-art deep learning algorithms to perform semantic segmentation on a newly-released dataset of aerial footage from residential neighborhoods. Our models achieved a multiclass Mean Intersection over Union (mIoU) score of 0.82 for detailed segmentation and a binary mIoU score of 0.90 for the critical safe/unsafe landing classification. Among the models tested, U-Net with MobileNetV3 encoder stood out by its better generalization performance, faster training times, and a more compact model size. This study highlights the potential for real-world drone applications, emphasizing its suitability for ensuring safe landings through enhanced environmental mapping and classification.

关键词： Training Deep learning Sensitivity Semantic segmentation Wires Vegetation mapping Data augmentation Data models Tuning Drones

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FLOW: Fusing and Shuffling Global and Local Views for Cross-User Human Activity Recognition with IMUs

arXiv

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arXiv 2024年

作者： Qiu, Qi Zhu, Tao Duan, Furong Wang, Kevin I-Kai Chen, Liming Nie, Mingxing Wan, Yaping School of Computer Science University of South China Hunan Hengyang430001 China School of Computing University of Ulster NewtownabbeyBT37 0QB United Kingdom Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116000 China

Inertial Measurement Unit (IMU) sensors are widely employed for Human Activity Recognition (HAR) due to their portability, energy efficiency, and growing research interest. However, a significant challenge for IMU-HAR models is achieving robust generalization performance across diverse users. This limitation stems from substantial variations in data distribution among individual users. One primary reason for this distribution disparity lies in the representation of IMU sensor data in the local coordinate system, which is susceptible to subtle user variations during IMU wearing. To address this issue, we propose a novel approach that extracts a global view representation based on the characteristics of IMU data, effectively alleviating the data distribution discrepancies induced by wearing styles. To validate the efficacy of the global view representation, we fed both global and local view data into model for experiments. The results demonstrate that global view data significantly outperforms local view data in cross-user experiments. Furthermore, we propose a Multi-view Supervised Network (MVFNet) based on Shuffling to effectively fuse local view and global view data. It supervises the feature extraction of each view through view division and view shuffling, so as to avoid the model ignoring important features as much as possible. Extensive experiments conducted on OPPORTUNITY and PAMAP2 datasets demonstrate that the proposed algorithm outperforms the current state-of-the-art methods in cross-user HAR. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Developing an Effective Graph Based Block Chain Framework 1

Developing an Effective Graph Based Block Chain Framework

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1st International Conference on Advances in Computational science and engineering, ICACSE 2020

作者： Ravi Prasad, B. Saurav, Swapnil Sangeerani Devi, A. Loganathan, Venkateshwaran Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Telangana Dundigal India Department of Problem Management Service Now Software India Pvt. Ltd Telangana Hyderabad India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Department of Information Technology KGiSL Institute of Technology Tamil Nadu Coimbatore India

Blockchain's decentralized characteristic is recognized as a potential technology to deliver secure and safe resources. However, the existing blockchain networks cannot fulfil the transaction given the limited bandwidth for practical application demand. In this article, the cubic design of the conventional public blockchain is strengthened with the Directed Graph Probabilistic model. In the current structure, blocks of the lightweight structure are ordered in grades and width. We develop protocols to position newly created blocks in order to make them more effective and secure. It increases confidentiality and time for authentication of transactions in contrast with standard blockchain protocols and enjoys the accuracy and liveliness of blockchain. This paper introduces two potential methods for targeting opposition parties and we also show that the procedure against them is safe and stable. Experiments will generate three hundred inputs every second that is 64 times the output of Bitcoin's transaction. This time Bitcoin's production will hit 27 times its Ethereum's. © Published under licence by IOP Publishing Ltd.

关键词： Directed graphs

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm

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Procedia computer science 2024年 235卷 3397-3407页

作者： B. Valarmathi N. Srinivasa Gupta V. Karthick T. Chellatamilan K. Santhi Dhanush Chalicheemala Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Assistant Systems Engineer Tata Consultancy Services Chennai Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today’s digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved.

关键词： COVID-19 Natural Language Processing Text Mining Long Short-Term Memory WordCloud Opinion mining Sentiment analysis Opinion extraction

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Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization

arXiv

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arXiv 2023年

作者： Hu, Quanqi Qiu, Zi-Hao Guo, Zhishuai Zhang, Lijun Yang, Tianbao Department of Computer Science and Engineering Texas A&M University College StationTX United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In this paper, we consider non-convex multi-block bilevel optimization (MBBO) problems, which involve m 1 lower level problems and have important applications in machine learning. Designing a stochastic gradient and controlling its variance is more intricate due to the hierarchical sampling of blocks and data and the unique challenge of estimating hyper-gradient. We aim to achieve three nice properties for our algorithm: (a) matching the state-of-the-art complexity of standard BO problems with a single block;(b) achieving parallel speedup by sampling I blocks and sampling B samples for each sampled block per-iteration;(c) avoiding the computation of the inverse of a high-dimensional Hessian matrix estimator. However, it is non-trivial to achieve all of these by observing that existing works only achieve one or two of these properties. To address the involved challenges for achieving (a, b, c), we propose two stochastic algorithms by using advanced blockwise variance-reduction techniques for tracking the Hessian matrices (for low-dimensional problems) or the Hessian-vector products (for high-dimensional problems), and prove an iteration −3 complexity of (formula presented) for finding an ϵ-stationary point under appropriate conditions. We also conduct experiments to verify the effectiveness of the proposed algorithms comparing with existing MBBO algorithms. © 2023, CC BY.

关键词： Iterative methods

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Dual-View Interaction-Aware Lane Change Prediction for Autonomous Driving 39

Dual-View Interaction-Aware Lane Change Prediction for Auton...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Yuhuan Zhang, Zhen Bai, Rufan Liu, Han Wang, Wei Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China Department of Computer and Information Science University of Macau China Department of Computer Science and Engineering Southeast University China School of Software Dalian University of Technology China School of Medical Technology Beijing Institute of Technology China

ISBN: (纸本)157735897X

As artificial intelligence techniques evolve, we are approaching a critical moment for the widespread deployment of autonomous vehicles. Subsequently, the emergence of mixed-autonomy traffic environments presents formidable challenges to autonomous vehicles, especially for the accurate prediction of lane change intentions of their surrounding human-driven vehicles, which is crucial for ensuring the safety of autonomous vehicles. Existing lane change prediction models mainly focus on capturing the temporal variations in the movement dynamics of individual vehicles. However, the neglect to consider inter-vehicle interactions hinders their capability in complex lane change scenarios, resulting in suboptimal prediction performance. Moreover, current interaction-aware approaches for autonomous driving fail to explicitly model future interactions between vehicles, leading to unreasonable prediction results that can cause collisions between vehicles. To address the above issues, we propose to incorporate the concept of perceived safety into future interaction modeling and design a dual-view interaction-aware lane change prediction model. We evaluate the proposed model on two real-world datasets and experimental results show that the proposed model achieves average improvements of 11.7-12.4% in classification ability and 75.6-95.7% in forecast ability over the best-performing baselines across the two datasets. The ablation study and investigation into future interaction modeling demonstrate that our model has advantages in interpreting lane change scenarios from a driving safety perspective. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A Weighted Federated Averaging Framework to Reduce the Negative Influence from the Dishonest Users 1

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13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020

作者： Zhao, Fengpan Huang, Yan Zhu, Saide Malladi, Venkata Wu, Yubao Department of Computer Science Georgia State University AtlantaGA30303 United States Department of Software Engineering and Game Development Kennesaw State University KennesawGA30144 United States

ISBN: (数字)9783030688516

ISBN: (纸本)9783030688509

Federated learning becomes popular for it can train an excellent performance global model without exposing clients’ privacy. However, most FL applications failed to consider there exists fake local trained models returned from attackers or dishonest users. Not only would the fake parameters be harmful to the convergence of the global model but also be wasting of other users’ computational resources. In this paper, we propose a framework to grade the users’ credit score based on the performances of the returned local models on the testing dataset. We also consider historical data using the exponential moving average to give a relatively higher weight for the most recent testing results. The experiments show that our system can efficiently and effectively find out the fake local models and then speed up the convergence of the global model. © 2021, Springer Nature Switzerland AG.

关键词： Statistical tests

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Gender Detection from Handwritten Documents Using Concept of Transfer-Learning 1

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： AL-Qawasmeh, Najla Suen, Ching Y. Department of Computer Science and Software Engineering Concordia University CENPARMI Montreal Canada

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Offline gender detection from Arabic handwritten documents is a very challenging task because of the high similarity between an individual’s writings and the complexity of the Arabic language as well. In this paper, we propose a new way to detect the writer gender from scanned handwritten documents that mainly based on the concept of transfer-learning. We used a pre-trained knowledge from two convolution neural networks (CNN): GoogleNet, and ResNet, then we applied it on our data-set. We use this two CNN architectures as fixed feature extractors. For the analysis and the classification stage, we used a support vector machine (SVM). The performance of the two CNN architectures concerning accuracy is 80.05% for GoogleNet, 83.32% for ResNet. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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An Empirical Evaluation of Collapsible Panel Interfaces

An Empirical Evaluation of Collapsible Panel Interfaces

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2020 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2020

作者： Leung, Joshua Cockburn, Andy University of Canterbury Department of Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9781665419741

Although collapsible panel widgets have been widely used in industry for many years in various contexts, there have been no formal evaluations conducted to compare user performance and satisfaction with these interfaces. There is also a lack of formal guidance for user interface designers about how to choose among the many variations of collapsible panel schemes in existence. In this paper, we develop a classification framework and propose an empirical method for evaluating user performance with these interfaces. An experiment was conducted comparing accordions, collapsible panels, and flat panel layouts. This found a clear preference for accordions, with the need to scroll to find targets in the other interfaces cited as a key disadvantage of those approaches. Implications of these findings are discussed. © 2020 IEEE.

关键词： Graphical user interfaces

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