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IBEET-AOK:ID-based encryption with equality test against off-line KGAs for cloud medical services

Frontiers of computer science 2021年第6期15卷 187-189页

作者： Yan XU Ming WANG Hong ZHONG Sheng ZHONG School of Computer Science and Technology Anhui UniversityHefei 230601China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction With the rapid development of cloud computing,more and more data are stored in cloud servers as cyphertexts for storage[1,2].However,it faces enormous challenges regarding ciphertext *** example,in the cloud medical services,authorized medical institutions(AMIs)need analyze medical records to figure out gender and age information on patients with the same disease.

关键词： services cloud analysis.

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Melton： a practical and precise memory leak detection tool for C programs

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Frontiers of computer science 2015年第1期9卷 34-54页

作者： Zhenbo XU Jian ZHANG Zhongxing XU Department of Computer Science and Technology University of Science and Technology of China Anhui 230027 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

Memory leaks are a common type of defect that is hard to detect manually. Existing memory leak detection tools suffer from lack of precise interprocedural analysis and path-sensitivity. To address this problem, we present a static interprocedural analysis algorithm, that performs fully pathsensitive analysis and captures precise function behaviors, to detect memory leak in C programs. The proposed algorithm uses path-sensitive symbolic execution to track memory actions in different program paths guarded by path conditions. A novel analysis model called memory state transition graph （MSTG） is proposed to describe the tracking process and its results. In order to do interprocedural analysis, the proposed algorithm generates a summary for each procedure from MSTG and applies the summary at the procedure＇s call sites. A prototype tool called Melton is implemented for this procedure. Melton was applied to five open source C programs and 41 leaks were found. More than 90% of these leaks were subsequently confirmed and fixed by their maintainers. For comparison with other tools, Melton was also applied to some programs in standard performance evaluation corporation （SPEC） CPU 2000 benchmark suite and detected more leaks than the state of the art approaches.

关键词： memory leak bug finding static analysis symbolic execution

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Robust dense reconstruction by range merging based on confidence estimation

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science China(Information sciences) 2016年第9期59卷 22-32页

作者： Yadang CHEN Chuanyan HAO Wen WU Enhua WU School of Computer and Software Nanjing University of Information Science and Technology Department of Computer and Information Science Faculty of Science and Technology University of Macau School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences

Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

关键词： stereo matching 3D reconstruction textureless regions outliers details loss range map

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CADEC: A Combinatorial Auction for Dynamic Distributed DNN Inference Scheduling in Edge-Cloud Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Xu, Xiaolong Hu, Yuhao Cui, Guangming Qi, Lianyong Dou, Wanchun Nanjing University of Information Science and Technology School of Software Nanjing 210044 China Nanjing University of Information Science and Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China China University of Petroleum (East China) College of Computer science and technology Qingdao China Georgia State University Department of Computer Science United States

Deep Neural Network (DNN) Inference, as a key enabler of intelligent applications, is often computation-intensive and latency-sensitive. Combining the advantages of cloud computing (abundant computing resources) and edge computing (fast transmission), edge-cloud collaborative DNN inference is a powerful solution to these problems. However, in edge-cloud networks with heterogeneous resources, how to obtain reasonable decisions on server selection, model partition and resource allocation for efficient distributed DNN inference is a hard challenge. Furthermore, it is non-trivial to design suitable resource prices to maximize the social welfare. These challenges even escalate in dynamic edge-cloud networks where decisions should be generated as soon as each user arrives without future information. Therefore, we design a combinatorial auction for dynamic distributed DNN inference scheduling, named CADEC. CADEC first constructs a bid set for each user based on convex optimization theory for optimal solution searching. Next, prices of resources in the edge-cloud network are adjusted according to changes in supply-demand relationship, and whether to admit the request of each user is decided. Finally, the dynamic distributed inference scheduling decisions are generated through the primal-dual algorithm to maximize the social welfare. Theoretical analysis shows the good competitive ratio and polynomial time complexity of CADEC. Results of simulation experiments present that CADEC improves social welfare by up to 46% compared with state-of-the-art distributed DNN inference schemes. © 2002-2012 IEEE.

关键词： Combinatorial auction distributed DNN inference dynamic scheduling edge-cloud networks resource pricing

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A Layered Co-evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D-MRI

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Chinese Journal of Electronics 2017年第6期26卷 1168-1176页

作者： DING Weiping GUAN Zhijin WANG Jiehua TIAN Di School of Computer Science and Technology Nantong University State Key Laboratory for Novel Software Technology Nanjing University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology

As the conventional feature selection algorithms are prone to the poor running efficiency in largescale datasets with interacting features, this paper aims at proposing a novel rough feature selection algorithm whose innovation centers on the layered co-evolutionary strategy with neighborhood radius hierarchy. This hierarchy can adapt the rough feature scales among different layers as well as produce the reasonable decompositions through exploiting any correlation and interdependency among feature subsets. Both neighborhood interaction within layer and neighborhood cascade between layers are adopted to implement the interactive optimization of neighborhood radius matrix, so that both the optimal rough feature selection subsets and their global optimal set are obtained efficiently. Our experimental results substantiate the proposed algorithm can achieve better effectiveness, accuracy and applicability than some traditional feature selection algorithms.

关键词： Layered co-evolution Rough feature se lection Adaptive neighborhood radius hierarchy Longitudinal cortical surface labeling

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Predicted Robustness as QoS for Deep Neural Network Models

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Journal of computer science & technology 2020年第5期35卷 999-1015页

作者： Yue-Huan Wang Ze-Nan Li Jing-Wei Xu Ping Yu Taolue Chen Xiao-Xing Ma State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science University of SurreyGuilfordGU27XHU.K.

The adoption of deep neural network(DNN)model as the integral part of real-world software systems necessitates explicit consideration of their quality-of-service(QoS).It is well-known that DNN models are prone to adversarial attacks,and thus it is vitally important to be aware of how robust a modeFs prediction is for a given input instance.A fragile prediction,even with high confidence,is not trustworthy in light of the possibility of adversarial *** propose that DNN models should produce a robustness value as an additional QoS indicator,along with the confidence value,for each prediction they *** approaches for robustness computation are based on adversarial searching,which are usually too expensive to be excised in real *** this paper,we propose to predict,rather than to compute,the robustness measure for each input ***,our approach inspects the output of the neurons of the target model and trains another DNN model to predict the *** focus on convolutional neural network(CNN)models in the current *** show that our approach is accurate,with only 10%-34%additional errors compared with the offline heavy-weight robustness *** also significantly outperforms some alternative *** further validate the effectiveness of the approach when it is applied to detect adversarial attacks and out-of-distribution *** approach demonstrates a better performance than,or at least is comparable to,the state-of-the-art techniques.

关键词： deep neural network quality of service robustness prediction

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What is Discussed about COVID-19:A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

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computers, Materials & Continua 2020年第9期64卷 1453-1471页

作者： Hengyang Lu Yutong Lou Bin Jin Ming Xu School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214122China National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing210023China

Starting from late 2019,the new coronavirus disease(COVID-19)has become a global *** the development of online social media,people prefer to express their opinions and discuss the latest news *** have witnessed the positive influence of online social media,which helped citizens and governments track the development of this pandemic in *** is necessary to apply artificial intelligence(AI)techniques to online social media and automatically discover and track public opinions posted *** this paper,we take Sina Weibo,the most widely used online social media in China,for analysis and *** collect multi-modal microblogs about COVID-19 from 2020/1/1 to 2020/3/31 with a web crawler,including texts and images posted by *** order to effectively discover what is being discussed about COVID-19 without human labeling,we propose a unified multi-modal framework,including an unsupervised short-text topic model to discover and track bursty topics,and a self-supervised model to learn image features so that we can retrieve related images about *** results have shown the effectiveness and superiority of the proposed models,and also have shown the considerable application prospects for analyzing and tracking public opinions about COVID-19.

关键词： COVID-19 public opinion microblog topic model self-supervised learning

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SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

arXiv

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arXiv 2025年

作者： Li, Nan Wang, Xiaolu Du, Xiao Cai, Puyu Wang, Ting Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education the Shangh ai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Software Engineering Institute East China Normal University Shanghai200050 China Computer Science Department New York University New YorkNY10012 United States

Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they often struggle with high computational and communication overhead. To address these issues, model pruning is introduced as a strategy to streamline computations. However, existing pruning methods, when applied solely based on local data, often produce sub-models that inadequately reflect clients’ specific tasks due to data insufficiency. To overcome these challenges, this paper introduces SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalized federated learning through client-specific clustering and Similar Client Structure Information (SCSI)-guided model pruning. SAFL employs a two-stage process: initially, it groups clients based on data similarities and uses aggregated pruning criteria to guide the pruning process, facilitating the identification of optimal sub-models. Subsequently, clients train these pruned models and engage in server-based aggregation, ensuring tailored and efficient models for each client. This method significantly reduces computational overhead while improving inference accuracy. Extensive experiments demonstrate that SAFL markedly diminishes model size and improves performance, making it highly effective in federated environments characterized by heterogeneous data. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Federated Digital Twin Construction via Distributed Sensing: A Game-Theoretic Online Optimization with Overlapping Coalitions

arXiv

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arXiv 2025年

作者： Chen, Ruoyang Yi, Changyan Zhou, Fuhui Kang, Jiawen Wu, Yuan Niyato, Dusit College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Electronic and Information Engineering Nanjing University of Astronautics and Aeronautics Nanjing210016 China School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT’s model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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AocML： A Domain-Specific Language for Model-Driven Development of Activity-Oriented Context-Aware Applications

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Journal of computer science & technology 2018年第5期33卷 900-917页

作者： Xuan-Song Li Xian-Ping Tao Wei Song Kai Dong School of Computer Science and Engineering Nanjing University of Science and Technology Nanfing 210093 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China School of Computer Science and Engineering Southeast University Nanjing 211189 China

Activity-oriented context-aware （AOCA） applications are representative in pervasive computing. These appli- cations recognize daily-life human activities, perceive the environment status related to the activities, and react to ensure the smooth performance of the activities. Existing research proposed a specific light-weight, incremental method to support the development of such applications; however it is not easy to learn and use. This paper aims to facilitate the development of such applications and improve the productivity of developers. We propose AocML, a textual domain-specific language which provides a high-level abstraction of AOCA applications. Specifically, we first show the software model of AOCA applications and the abstract syntax of AocML. Then, we introduce the concrete syntax of AocML. We also implement the tools for AocML, including the development environment as well as the generation of Java code and ontology specification. Moreover, we use a case study and evaluation to demonstrate the advantages of AocML.

关键词： pervasive computing context-awareness model-driven development domain-specific language

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