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Personalized trajectory data perturbation algorithm based on quadtree indexing

The Journal of China Universities of Posts and Telecommunications 2024年第4期31卷 17-27页

作者： Liu Kun Jin Junhui Wang Hui Shen Zihao Liu Peiqian School of Software Henan Polytechnic UniversityJiaozuo 454000China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo 454000China

To solve the privacy leakage problem of truck trajectories in intelligent logistics,this paper proposes a quadtreebased personalized joint location perturbation(QPJLP)algorithm using location generalization and local differential privacy(LDP)***,a flexible position encoding mechanism based on the spatial quadtree indexing is designed,and the length of the encoding can be adjusted freely according to data ***,to meet the privacy needs of different locations of users,location categories are introduced to classify locations as sensitive and ordinary ***,the truck invokes the corresponding mechanism in the QPJLP algorithm to locally perturb the code according to the location category,allowing the protection of non-sensitive locations to be reduced without weakening the protection of sensitive locations,thereby improving data *** experiments demonstrate that the proposed algorithm effectively meets the personalized trajectory privacy requirements while also exhibiting good performance in trajectory proportion estimation and top-k classification.

关键词： intelligent logistics quadtree indexing local differential privacy(LDP) trajectory privacy protection location categories

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An Automated Approach to Discovering software Refactorings by Comparing Successive Versions

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IEEE Transactions on software Engineering 2025年第5期51卷 1358-1380页

作者： Liu, Bo Liu, Hui Niu, Nan Zhang, Yuxia Li, Guangjie Jiang, He Jiang, Yanjie University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States National Innovation Institute of Defense Technology Beijing China School of Software Dalian University of Technology Dalian China Beijing Institute of Technology School of Computer Science and Technology Beijing China Peking University School of Computer Science Beijing China

software developers and maintainers frequently conduct software refactorings to improve software quality. Identifying the conducted software refactorings may significantly facilitate the comprehension of software evolution, and thus facilitate software maintenance and evolution. Besides that, the identified refactorings are also valuable for data-driven approaches in software refactoring. To this end, researchers have proposed a few approaches to identifying software refactorings automatically. However, the performance (especially precision) of such approaches deserves substantial improvement. To this end, in this paper, we propose a novel refactoring detection approach, called REEXTRACTOR+. At the heart of REEXTRACTOR+ is a reference-based entity matching algorithm that matches coarse-grained code entities (e.g., classes and methods) between two successive versions, and a context-aware statement matching algorithm that matches statements within a pair of matched methods. We evaluated REEXTRACTOR+ on a benchmark consisting of 400 commits from 20 real-world projects. The evaluation results suggested that REEXTRACTOR+ significantly outperformed the state of the art in refactoring detection, reducing the number of false positives by 59.6% and improving recall by 19.2%. We also evaluated the performance of the proposed matching algorithms that serve as the cornerstone of refactoring detection. The evaluation results suggested that the proposed algorithms excel in matching code entities, substantially reducing the number of mistakes (false positives plus false negatives) by 67% compared to the state-of-the-art approaches. © 2025 IEEE.

关键词： software quality

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StepIdeator: Utilizing Mixed Representations to Support Step-By-Step Design With Generative Artificial Intelligence

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Journal of Mechanical Design 2025年第7期147卷 071703页

作者： Yao, Jiayi Chen, Pei Li, Zhuoshu Cai, Yichen Wu, Yexinrui You, Weitao Sun, Lingyun School of Software Technology Zhejiang University Ningbo315048 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

In the product conceptual design, designers utilize multiple design representations to ideate, externalize, and refine concepts iteratively. Mixed representations, defined as the simultaneous presentation of multiple representations, foster deeper insights and facilitate broader exploration compared to focusing on a single representation. However, designers often struggle with the cumbersome process of creating, transforming, and refining these representations. Advanced artificial intelligence (AI) capabilities now significantly lower the barriers to creating text, images, and 3D models, presenting substantial potential for application in design practice. However, the current design tools based on generative AI tend to produce polished product images directly, hindering designers from continuously deliberating on mixed representations and thus limiting their creative potential. Therefore, on the basis of a formative study, we propose StepIdeator, a step-by-step design tool driven by generative AI. This tool facilitates seamless transition and refinement of mixed-design representations. Through a comparative study (N = 16), we validated StepIdeator's effectiveness in improving idea externalization and enhancing creativity. Furthermore, the results revealed that designers perceived greater confidence, ownership, and sense of contribution when collaborating with StepIdeator. © 2025 by ASME.

关键词： Product design

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Edge Intelligence with Distributed Processing of DNNs:A Survey

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computer Modeling in Engineering & sciences 2023年第7期136卷 5-42页

作者： Sizhe Tang Mengmeng Cui Lianyong Qi Xiaolong Xu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210000China School of Computer Science Qufu Normal UniversityQufu273100China

Withthe rapiddevelopment of deep learning,the size of data sets anddeepneuralnetworks(DNNs)models are also *** a result,the intolerable long time for models’training or inference with conventional strategies can not meet the satisfaction of modern tasks ***,devices stay idle in the scenario of edge computing(EC),which presents a waste of resources since they can share the pressure of the busy devices but they do *** address the problem,the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices,which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge *** with existing papers,this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge *** the practicalities,commonly used lightweight models in a distributed system are introduced as *** the key technique,the parallel strategy will be described in *** some typical applications of distributed processing will be ***,the challenges of distributed processing with edge computing will be described.

关键词： Distributed processing edge computing parallel strategies acceleration of DNN processing

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Multi-Task Visual Semantic Embedding Network for Image-Text Retrieval

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Journal of computer science & technology 2024年第4期39卷 811-826页

作者： Xue-Yang Qin Li-Shuang Li Jing-Yao Tang Fei Hao Mei-Ling Ge Guang-Yao Pang School of Computer Science and Technology Dalian University of TechnologyDalian 116024China School of Computer Science Shaanxi Normal UniversityXi’an 710119China School of Computer Engineering Weifang UniversityWeifang 261061China Guangxi Colleges and Universities Key Laboratory of Intelligent Industry Software Wuzhou UniversityWuzhou 543002 China

Image-text retrieval aims to capture the semantic correspondence between images and texts,which serves as a foundation and crucial component in multi-modal recommendations,search systems,and online *** mainstream methods primarily focus on modeling the association of image-text pairs while neglecting the advantageous impact of multi-task learning on image-text *** this end,a multi-task visual semantic embedding network(MVSEN)is proposed for image-text ***,we design two auxiliary tasks,including text-text matching and multi-label classification,for semantic constraints to improve the generalization and robustness of visual semantic embedding from a training ***,we present an intra-and inter-modality interaction scheme to learn discriminative visual and textual feature representations by facilitating information flow within and between ***,we utilize multi-layer graph convolutional networks in a cascading manner to infer the correlation of image-text *** results show that MVSEN outperforms state-of-the-art methods on two publicly available datasets,Flickr30K and MSCOCO,with rSum improvements of 8.2%and 3.0%,respectively.

关键词： image-text retrieval cross-modal retrieval multi-task learning graph convolutional network

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Multimodal Neural Machine Translation Based on Knowledge Distillation and Anti-Noise Interaction

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computers, Materials & Continua 2025年第5期83卷 2305-2322页

作者： Erlin Tian Zengchao Zhu Fangmei Liu Zuhe Li School of Software Zhengzhou University of Light IndustryZhengzhou450001China School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450001China

Within the realm of multimodal neural machine translation(MNMT),addressing the challenge of seamlessly integrating textual data with corresponding image data to enhance translation accuracy has become a pressing *** saw that discrepancies between textual content and associated images can lead to visual noise,potentially diverting the model’s focus away from the textual data and so affecting the translation’s comprehensive *** solve this visual noise problem,we propose an innovative KDNR-MNMT *** combines the knowledge distillation technique with an anti-noise interaction mechanism,which makes full use of the synthesized graphic knowledge and local image interaction masks,aiming to extract more effective visual ***,the KDNR-MNMT model adopts a multimodal adaptive gating fusion strategy to enhance the constructive interaction of different modal *** integrating a perceptual attention mechanism,which uses cross-modal interaction cues within the Transformer framework,our approach notably enhances the quality of machine translation *** confirmthemodel’s performance,we carried out extensive testing and assessment on the extensively utilized Multi30K *** outcomes of our experiments prove substantial enhancements in our model’s BLEU and METEOR scores,with respective increases of 0.78 and 0.99 points over prevailing *** accomplishment affirms the potency of our strategy for mitigating visual interference and heralds groundbreaking advancements within themultimodal NMT domain,further propelling the evolution of this scholarly pursuit.

关键词： Knowledge distillation anti-noise interaction mask occlusion door control fusion

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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computer Modeling in Engineering & sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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Machine learning automation

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National science Review 2024年第8期11卷 7-8页

作者： Zongben Xu Zhi-Hua Zhou Wenwu Zhu School of Mathematics and Statistics Xi'an Jiaotong University National Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Tsinghua University

With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significant innovations across various aspects of machine learning, including data exploitation, network architecture development, loss function settings and algorithmic innovation.

关键词： automation learning machine

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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science China(Information sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and Engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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