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31st International Conference on software Engineering and Knowledge Engineering, SEKE 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China

ISBN: (纸本)1891706489

Just-in-time software defect prediction (JIT-SDP) is an active topic in the filed of software engineering, and many methods have been proposed to solve this problem. State-of-the-art method MULTI applies multi-objective optimization algorithm to the effort-aware JIT-SDP problem, and obtains good average performance. Although the average performance of the MULTI method is high, there are many optimal solutions with poor performance. If an optimal solution is randomly selected, a poor prediction model may be obtained. In order to further improve the performance of the MULTI method, we propose three optimal solutions selection strategies: benefit priority (BP), cost priority (CP), and a compromise between cost and benefit (CCB). In order to compare and validate the effectiveness of the strategies, we conduct a large-scale empirical study on data sets of six open source projects. The experimental results show that, compared with the average performance of MULTI, the optimal solutions selection strategy based on BP has a significant improvement in ACC and Popt indicators. Therefore, we recommend using the BP-based optimal solutions selection strategy to improve the performance of MULTI when using the MULTI method to solve the effort-aware JIT-SDP problem. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Multiobjective optimization

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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地理大数据揭示中国主要城市建成环境物质存量的空间格局

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Engineering 2024年第3期34卷 143-153页

作者： Zhou Huang Yi Bao Ruichang Mao Han Wang Ganmin Yin Lin Wan Houji Qi Qiaoxuan Li Hongzhao Tang Qiance Liu Linna Li Bailang Yu Qinghua Guo Yu Liu Huadong Guo Gang Liu Institute of Remote Sensing and Geographical Information Systems School of Earth and Space SciencesPeking UniversityBeijing 100871China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China SDU Life Cycle Engineering Department of Green TechnologyUniversity of Southern DenmarkOdense 5230Denmark Department of Construction Management School of Civil EngineeringTsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China School of Geographical Sciences East China Normal UniversityShanghai 200062China Land Satellite Remote Sensing Application Center Ministry of Natural ResourcesBeijing 100048China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Geography California State UniversityLong BeachCA 90840USA Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China College of Urban and Environmental Sciences Peking UniversityBeijing 100871China

The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important,yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources,waste,and climate ***,our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited,largely owing to the lack of sufficient high spatial resolution *** study leveraged multi-source big geodata,machine learning,and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained *** per capita built environment stock of many cities(261 tonnes per capita on average)is close to that in western cities,despite considerable disparities across cities owing to their varying socioeconomic,geomorphology,and urban form *** is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past ***’s urban expansion tends to be more“vertical”(with high-rise buildings)than“horizontal”(with expanded road networks).It trades skylines for space,and reflects a concentration-dispersion-concentration pathway for spatialized built environment stocks development within cities in *** results shed light on future urbanization in developing cities,inform spatial planning,and support circular and low-carbon transitions in cities.

关键词： Urban system engineering Built environment stock Spatial pattern Urban sustainability Big geodata

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Optimizing Data Placement for Hybrid SPM with SRAM and Racetrack Memory

Optimizing Data Placement for Hybrid SPM with SRAM and Racet...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Rui Xu Edwin H.-M. Sha Qingfeng Zhuge Shouzhen Gu Liang Shi Software/Hardware Co-design Engineering Research Center Ministry of Education Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

In this paper, a novel hybrid scratchpad memory (SPM) with SRAM and racetrack memory (RM) is proposed. The basic idea is to smartly place data on SPM by taking the advantages of these two memories. First, a metric is proposed to represent the access cost of data; Second, a data placement scheme is proposed based on the metric; Finally, to maximize the size of SPM, a scheme is further proposed to minimize the size of SRAM. Experimental results show that the proposed scheme reduces the shift operations of RM by 80.12% and reduces the cost of SPM by 80.72% with only 17.63% SRAM compared with a baseline SPM with pure RM.

关键词： Measurement Conferences Random access memory computer architecture

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Quantum Memristors with Quantum computers

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Physical Review Applied 2022年第2期18卷 024082-024082页

作者： Y.-M. Guo F. Albarrán-Arriagada H. Alaeian E. Solano G. Alvarado Barrios International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Physics Department Shanghai University Shanghai 200444 China Departamento de Física Universidad de Santiago de Chile (USACH) Avenida Víctor Jara 3493 Santiago 9170124 Chile Center for the Development of Nanoscience and Nanotechnology Estación Central 9170124 Chile Elmore Family School of Electrical and Computer Engineering Department of Physics and Astronomy Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA IKERBASQUE Basque Foundation for Science Plaza Euskadi 5 Bilbao 48009 Spain Kipu Quantum Kurwenalstrasse 1 Munich 80804 Germany

We propose the encoding of memristive quantum dynamics on a digital quantum computer. Using a set of auxiliary qubits, we simulate an effective non-Markovian environment inspired by a collisional model, reproducing memristive features between expectation values of different operators in a single qubit. We numerically test our proposal in an IBM quantum simulator with 32 qubits, obtaining a pinched hysteresis curve that characterizes our quantum memristor model. Furthermore, we extend our method to the case of two coupled quantum memristors, opening the door to the study of neuromorphic quantum computing in the current noisy devices.

关键词： Neuromorphic computing Quantum algorithms Quantum correlations in quantum information Memristors Non-Markovian processes

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Research of Micro-expression Recognition Model based on Feature Unit

Research of Micro-expression Recognition Model based on Feat...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Fei Yin Jinyi Xu Yixiang Chen School of Computer Science and Software Engineering East China Normal University Shanghai China MOE Enginering Research Center for Software/Hardware Co-design Technology and Application East China Normal University Shanghai China

ISBN: (数字)9781728189154

ISBN: (纸本)9781728189161

Micro-expression which is the transient expression will be disclosed when people try to hide some kind of real inner emotions. Micro-expression changes so fast that few people detect its existence. As an effective behavioral clue, it is of great significance to understand the change of peoples true feelings. Based on this, image feature extraction in machine learning has made remarkable progress in the past two years. In order to improve the accuracy and practicability of micro-expression recognition, this paper deeply analyzed the practical micro-expression recognition method. Based on landmark detection. We construct a quantitative model and established a machine learning model of micro-expression judgment based on the comprehensive changes of eyes, mouth and eyebrows. This paper combine the feature unit and model application scene for judgement of micro-expression. This method reflect the design idea that the changes of face locality contributed to the overall micro-expression judgment, and helped to complete the high autonomous construction of the micro expression judgment model.

关键词： Face recognition Mouth Machine learning software quality software reliability Eyebrows Transient analysis

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Spatial and Temporal Heterogeneity of Air Pollution in East Africa

SSRN

引用

SSRN 2022年

作者： Kalisa, Wilson Zhang, Jiahua Igbawua, Tertsea Henchiri, Malak Mulinga, Narcisse Nibagwire, Deborah Umuhoza, Mycline Remote Sensing and Digital Earth Center School of Computer Science and Technology Qingdao University Qingdao266071 China Global Change and Disaster Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Department of Physics Federal University of Agriculture Makurdi Nigeria Department of Agricultural Economics and Rural Development University of Rwanda Rwanda Nigeria UNEP-Tongji Institute of environment for Sustainable Development Shanghai200092 China

East Africa's air pollution levels are deteriorating due to biomass burning and unfavorable weather conditions. This study investigates spatiotemporal changes in air pollution and quantitatively explores relative contributions of potential influencing factors and their interactions with air pollution dynamics in East Africa from 2001 to 2021. The result revealed that air pollution effect experienced an increasing (decreasing) trend in PHS (PCS) with values of -5.415E-4 (3.157E-4) from 2001-2021. The major pollution seasons (HPL1 and HPL2) were correlated with wildfire seasons (DJF and JJA). At an annual scale, spatial OLS analysis between air pollution and 14 response factors found that percentage increase in BA, BCS, OCS, SOS, and DSC would worsen air pollution. In contrast, negative spatial OLS coefficients of SS, P, RH, WS, SLP, NDVI, PD, and ELV suggest that air pollution would decrease with increase of these variables. The variance presented in ascending order based on their influence is given by DSC(R2=68.93) > SLP(R2=36.59) > RH(R2=24.12) > T(R2=23.78) > SOS(R2=23.16) > BA(R2=20.30) > ELV(R2=7.80) > OCS(R2=4.48) > NDVI(R2=3.21) > BCS(R2=3.04) > WS(R2=2.89) > PD(R2=0.66) > SS(R2=0.55) > P(R2=0.50). The factor interaction detector analysis revealed that DSC (q = 0.681), SOS (q = 0.389), TMP (q = 0.324), and SLP (q = 0.472) showed significant high response power at an annual scale, among all factors that have significant influences on air pollution in the study area. NDVI (q = 0.096), PD (q = 0.012), BCS (q = 0.054), SS (q = 0.078), P (0.093), and OCS (q=0.086) had the least influence on air pollution, which is consistent with the results of the correlation analysis. In conclusion, the study covered the dynamics of air pollution and their influencing factors, spatiotemporal heterogeneity, and interaction between influencing factors. All this put together can help in providing timely management strategies to combat air pollution. © 2022, The Authors. All rights r

关键词： Air pollution

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Efficient Federated Learning for AIoT Applications Using Knowledge Distillation

arXiv

引用

arXiv 2021年

作者： Liu, Tian Ling, Zhiwei Xia, Jun Fu, Xin Yu, Shui Chen, Mingsong MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States School of Computer Science University of Technology Sydney Australia

As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (AIoT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale AIoT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for AIoT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to AIoT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. Comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead. Copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

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A Hybrid Clock Logic System for CPS 14

A Hybrid Clock Logic System for CPS

引用

14th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019

作者： Wang, Peifen Chen, Yixiang Wu, Hengyang East China Normal University School of Software Engineering Shanghai China East China Normal University Moe Enginering Research Center for Software Hardware Co-design Technology and Application Shanghai China Shanghai Polytechnic University School of Computer and Information Engineering Shanghai China

ISBN: (纸本)9781728123486

Cyber-Physical System (CPS) is a typical intelligent system composed of agents. It has an important feature of spatial temporal constraints. To describe the constraints on agents, Chen proposed the spatial-temporal consistency language (STeC) in 2012 and the hybrid clock system in 2014. In this paper we will set up the hybrid clock logic systems (HCL) to specify the spatial temporal constraint property of agents in CPS. After giving the syntax and semantics of HCL, we consider the satisfaction relationship between a STeC design and HCL specification of a CPS agent. Some case studies show this satisfaction relationship is reasonable. We also introduce a model-checking algorithm for HCL. © 2019 IEEE.

关键词： Cyber Physical System

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An empirical study on progressive sampling for just-in-time software defect prediction 7

An empirical study on progressive sampling for just-in-time ...

引用

7th International Workshop on Quantitative Approaches to software Quality, QuASoQ 2019

作者： Yang, Xingguang Yu, Huiqun Fan, Guisheng Yang, Kang Shi, Kai Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Alibaba Group Hangzhou China

Just-in-time software defect prediction (JIT-SDP) is an active research topic in the field of software engineering, aiming at identifying defect-inducing code changes. Most existing JIT-SDP work focuses on improving the prediction performance of the model by improving the model. However, a frequently ignored problem is that collecting large and high quality defect data sets is costly. Specifically, when labelling the samples, experts in the field are required to carefully analyze the defect report information and log of code modification, which requires a lot of effort. Therefore, how to build a high-performance JIT-SDP model with a small number of training samples is an issue worth studying, which can reduce the size of the defect data sets and reduce the cost of data sets acquisition. This work thus provides a first investigation of the problem by introducing a progressive sampling method. Progressive sampling is a sampling strategy that determines the minimum number of training samples while guaranteeing the performance of the model. However, progressive sampling requires that the learning curve of the prediction model be well behaved. Thus, we validate the availability of progressive sampling in the JIT-SDP issue based on six open-source projects with 227417 changes. Experimental results demonstrate that the learning curve of the prediction model is well behaved. Therefore, the progressive sampling is feasible to tackle the JITSDP problem. Further, we investigate the optimal training sample size derived by progressive sampling for six projects. Empirical results demonstrate that a high-performance prediction model can be built using only a small number of training samples. Thus, we recommend adopting progressive sampling to determine the size of training samples for the JIT-SDP problem. Copyright © 2019 for this paper by its authors.

关键词： Defects

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