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Which One Is Better, Simple or Complex Metrics?

Journal of Computer and Communications 2015年第11期3卷 52-57页

作者： Fangjun Wu School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China High Level Engineering Research Center of Electronic-Commerce Jiangxi Provincial Colleges and Universities Nanchang China

At the early stage of software lifecycle, the complexity measurement of UML class diagrams plays an important role in software development, testing and maintenance, and provides guidance for developing high quality software. In order to study which one is better, simple or complex metrics, this paper analyzes and compares four typical metrics of UML class diagrams from experimental software engineering view points. Understandability, analyzability and maintainability were classified and predicted for 27 class diagrams related to a banking system by means of algorithm C5.0 within the famous toolkit Weka. Results suggest that the performance of simple metrics is not inferior to that of complex metrics, in some cases even better than that of complex metrics.

关键词： Software Measurement UML Class Diagram Empirical Validation Classification

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Synchronous replication framework of databases servers supporting reliable electronic commerce applications

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Journal of Computational Information Systems 2009年第2期5卷 849-858页

作者： Liao, Guoqiong Chen, Shijun School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China Siemens Ltd. China Corporate Technology Beijing 100102 China

To support electronic commerce applications with high reliability requirements, a synchronous replication framework of database servers is designed in this paper. At first, a three-tier architecture consisting of Web Servers (WS), Electronic Commerce Servers (ECS) and database Servers (DBS) for a reliable electronic commerce system is presented. Then, a primary copy synchronous replication model under the structure is given. Finally, a data synchronization method using one-phase commit (1PC) protocol for the model is discussed in details. The analysis result shows that 1PC can reduce response time than the two-phase commit protocol. © 2009 Binary Information Press March, 2009.

关键词： Electronic commerce

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Global and Adaptive Local Label Correlation for Multi-label Learning with Missing Labels

Global and Adaptive Local Label Correlation for Multi-label ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qingxia Jiang Peipei Li Yuhong Zhang Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (the ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui Province China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei Anhui Province China

Label missing is a major challenge in multi-label learning. Many existing methods try to use label correlation to recover ground-truth labels, but they only focus on the label correlation within the original label space, however, the label correlation learned in this way is incomplete. Thus, inspired $b$ y the matrix adaptive column correlation, we propose a method to continuously adjust the label correlation matrix while the labels are filled in by adaptive column correlation learning method. Specifically, to reduce the impact of the missing label s on label correlation, the label space is firstly completed through manifold regularization while learning the local label information by adaptive column correlation learning in the complemented label space. Secondly, the global label correlation is utilized by adding a low-rank constraint to the entire label space. Finally, by jointly taking advantage of the global and adaptive local label correlation, our proposed approach achieves superior performance on both synthetic and real-world data sets from diverse domains compared to state-of-the art baselines.

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Butler matrix enabled multi-beam optical phased array for two-dimensional beam-steering and ranging

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Photonics Research 2024年第5期12卷 912-920页

作者： ZUOYU ZHOU WEIHAN XU CHUXIN LIU RUIYANG XU CHEN ZHU XINHANG LI LIANGJUN LU JIANPING CHEN LINJIE ZHOU State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Laboratory of Navigation and Location ServicesShanghai Institute for Advanced Communication and Data ScienceDepartment of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu 314200China State Grid Sichuan Electric Power Company Chengdu Xinjin Power Supply Branch Xinjin 611430China

Based on the wavelength transparency of the Butler matrix(BM)beamforming network,we demonstrate a multibeam optical phased array(MOPA)with an emitting aperture composed of grating couplers at a 1.55μm pitch for wavelength-assisted two-dimensional *** device is capable of simultaneous multi-beam operation in a field of view(FOV)of 60°×8°in the phased-array scanning axis and the wavelength-tuning scanning axis,*** typical beam divergence is about 4°on both *** multiple linearly chirped lasers,multibeam frequency-modulated continuous wave(FMCW)ranging is realized with an average ranging error of 4 cm.A C-shaped target is imaged for proof-of-concept 2D scanning and ranging.

关键词： optical tuning beam

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Efficient Processing of Global Top-k Aggregate Queries in Uncertain database

Efficient Processing of Global Top-k Aggregate Queries in Un...

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International Conference on Information engineering and Computer Science (ICIECS)

作者： Xianli Xing Dexi Liu Principal's Office Jiangxi University of Finance and Economics Nanchang China School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9781424449934

Top-k queries in uncertain databases are quite popular and useful due to its wide application usage. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. A Top-k aggregate query ranks groups of tuples by their aggregate values, sum or average for example, and returns k groups with the highest aggregate values. As a powerful semantic of top-k, global top-k, returns A highest-ranked tuples according to their probabilities of being in the top-k anTopswers in possible worlds. We propose a dynamic programming based method to process global top-k aggregate queries in uncertain database, where the number of retrieved tuples and group states generated on these tuples are minimized. Experiment results show that our algorithm is effective.

关键词： Aggregates databases Finance Power generation economics Dynamic programming Information technology Laboratories knowledge engineering Information retrieval Performance analysis

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Reducing Uncertainty of Low-Sampling-Rate Trajectories

Reducing Uncertainty of Low-Sampling-Rate Trajectories

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International Conference on data engineering

作者： Kai Zheng Yu Zheng Xing Xie Xiaofang Zhou School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia Microsoft Research Asia Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781467300421

The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.

关键词： Trajectory Roads Global Positioning System Inference algorithms Uncertainty Artificial neural networks Educational institutions

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Image manipulation detection by multi-view multi-scale supervision

arXiv

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arXiv 2021年

作者： Chen, Xinru Dong, Chengbo Ji, Jiaqi Cao, Juan Li, Xirong MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

The key challenge of image manipulation detection is how to learn generalizable features that are sensitive to manipulations in novel data, whilst specific to prevent false alarms on authentic images. Current research emphasizes the sensitivity, with the specificity overlooked. In this paper we address both aspects by multi-view feature learning and multi-scale supervision. By exploiting noise distribution and boundary artifact surrounding tampered regions, the former aims to learn semantic-agnostic and thus more generalizable features. The latter allows us to learn from authentic images which are nontrivial to be taken into account by current semantic segmentation network based methods. Our thoughts are realized by a new network which we term MVSS-Net. Extensive experiments on five benchmark sets justify the viability of MVSS-Net for both pixel-level and image-level manipulation detection. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Image Manipulation Detection by Multi-View Multi-Scale Supervision

Image Manipulation Detection by Multi-View Multi-Scale Super...

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International Conference on Computer Vision (ICCV)

作者： Xinru Chen Chengbo Dong Jiaqi Ji Juan Cao Xirong Li MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AIMC Lab School of Information Renmin University of China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Media Convergence Production Technology and Systems

ISBN: (纸本)9781665428132

关键词： Representation learning Image segmentation Computer vision Sensitivity Costs Image edge detection Semantics

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EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding

arXiv

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arXiv 2024年

作者： Huang, Muye Lai, Han Zhang, Xinyu Wu, Wenjun Ma, Jie Zhang, Lingling Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China MOE KLINNS Lab Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China

Chart understanding enables automated data analysis for humans, which requires models to achieve highly accurate visual comprehension. While existing Visual Language Models (VLMs) have shown progress in chart understanding, the lack of high-quality training data and comprehensive evaluation benchmarks hinders VLM chart comprehension. In this paper, we introduce EvoChart, a novel self-training method for generating synthetic chart data to enhance VLMs’ capabilities in real-world chart comprehension. We also propose EvoChart-QA, a noval benchmark for measuring models’ chart comprehension abilities in real-world scenarios. Specifically, EvoChart is a unique self-training data synthesis approach that simultaneously produces high-quality training corpus and a high-performance chart understanding model. EvoChart-QA consists of 650 distinct real-world charts collected from 140 different websites and 1,250 expert-curated questions that focus on chart understanding. Experimental results on various open-source and proprietary VLMs tested on EvoChart-QA demonstrate that even the best proprietary model, GPT-4o, achieves only 49.8% accuracy. Moreover, the EvoChart method significantly boosts the performance of open-source VLMs on real-world chart understanding tasks, achieving 54.2% accuracy on EvoChart-QA. © 2024, CC BY.

关键词： Visual languages

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A knowledge Graph Completion Model with Path-Specific Learning

A Knowledge Graph Completion Model with Path-Specific Learni...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xiulin Zheng Zan Zhang Rui Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) and also with the School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Inspur Academy of Science and Technology Shandong China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

knowledge graphs (KGs) play an increasingly im-portant role in many knowledge-aware tasks. However, existing KGs are struggle with incompleteness, which motivates knowledge graph completion (KGC), that is, predicting the lost links between entities based on observed triples. Reasoning over relation paths in incomplete KGs is popular. Nonetheless, some significant issues are still remained to be addressed, such as path noise and ambiguity of inferred relation. To address these problems, we propose a novel path augmented _Reasoning model with avoidance of Path noise and Disambiguation of inferred relation in this paper, referred to as RPD. In this model, we calculate the sum of resource allocation for each relation path to measure its reliability to avoid the inference of path noise. To address the ambiguity of an inferred relation, we introduce position embedding to denote the relation position along the path when learning path representation. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our proposal RPD model in the handling of KGC tasks compared to SOTAs.

关键词： Heavily-tailed distribution Noise knowledge graphs Benchmark testing Transformers Cognition Reliability Resource management Proposals Noise measurement

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