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arXiv 2022年

作者： Zhou, Kun Sisman, Berrak Rana, Rajib Schuller, Björn W. Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electrical and Computer Engineering the University of Texas Dallas United States University of Southern Queensland Australia GLAM – the Group on Language Audio & Music Imperial College London United Kingdom Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Shenzhen Research Institute of Big Data the School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore University of Bremen Bremen Germany

Emotional speech synthesis aims to synthesize human voices with various emotional effects. The current studies are mostly focused on imitating an averaged style belonging to a specific emotion type. In this paper, we seek to generate speech with a mixture of emotions at run-time. We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. During the training, the framework does not only explicitly characterize emotion styles but also explores the ordinal nature of emotions by quantifying the differences with other emotions. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector. The objective and subjective evaluations have validated the effectiveness of the proposed framework. To our best knowledge, this research is the first study on modelling, synthesizing, and evaluating mixed emotions in speech. © 2022, CC BY.

关键词： Speech synthesis

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Atomistic insights into enhancing wear resistance of Cu-Zr metallic glasses by topologically close-packed clusters

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Applied Surface science 2025年 709卷

作者： Zhou, Yu Liang, Yongchao Pu, Yuanwei Zhou, Shaocong Zhou, Lili Tian, Zean Chen, Qian Institute of Advanced Optoelectronic Materials and Technology College of Big Data and Information Engineering Guizhou University Guiyang 550025 China Department of Information Engineering Gannan Medical University Ganzhou 341000 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China

Metallic glasses (MGs) demonstrate exceptional wear resistance attributed to their unique atomic structure. However, the micro-mechanism of the wear resistance enhancement in the friction process is still unclear. Therefore, the scratching of CuxZr100-x (x = 20, 30, 40, 50, 60, 64.5, 80 at%) MGs were simulated by molecular dynamics in this paper. As the Cu element increases, the force applied by the cutting tool increases, while the average friction coefficient decreases. The topologically close-packed (TCP) clusters increase with rising Cu element, undergoing a localized dynamic process of destruction-reorganization cycle. Under the action of cutting tools, TCP clusters were damaged, with typical icosahedral structures exhibiting the highest number and suffering the most severe damage. The high content of icosahedron results in lower volume and higher density of the workpiece. This led to tighter local atomic packing and consequently enhanced tool-workpiece interactions, manifesting as increased friction and normal forces. These intensified mechanical responses facilitate hardness elevation through densification effects, thereby improving wear resistance. This paper systematically investigates microstructural evolution and wear resistance enhancement mechanism of MGs during scratching.

关键词： Cu-Zr alloy Molecular dynamics simulation Nano-scratch Topologically close-packed clusters

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Sentence Pair Similarity Modeling Based on Weighted Interaction of Multi-semantic Embedding Matrix

Sentence Pair Similarity Modeling Based on Weighted Interact...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Junyu Chen Xiaohong Zhu Jun Sang Lu Gong College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

In this paper, we focus on measuring the similarity of sentence pair. Noting that a single sentence vector may lose fine-grained semantic information which is important for sentence matching, we propose an embedding matrix to calculate a multi-granularity similarity matrix and find the true semantic alignment of two sentences. We also propose a semantic importance calculation and semantic decomposition that are simple but effective. The proposed model does not require any sparse features or external resources such as WordNet. Compared with other state-of-the-art models, we successfully train in a short time and achieve competitive results on similarity measurement and paraphrase identification tasks. Experiments and visual analysis show the good performance and interpretability of the model.

关键词： Weight measurement Training Visualization Analytical models Semantics Matrix decomposition Sparse matrices

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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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Research on Mining of Government data Based on Enhanced-Object Exchange Model

Research on Mining of Government Data Based on Enhanced-Obje...

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

作者： Keiin Sa Yu Bai Chenggang Wang Big Data Application on Improving Government Governance Capabilities National Engineering Laboratory Guiyang China College of Computer Science and Technology Harbin Engineering University Harbin China Southwest China Institute of Electronic Technology Chengdu China

ISBN: (纸本)9781665458146

In order to acquire the required knowledge from the vast amounts of government affairs data, accurate data mining has important practical significance. Therefore, a precision mining method of government affairs big data based on E-OEM model is studied. The method firstly uses web crawlers to capture the vast amounts of government data. Government data then performs data cleaning, pattern normalization, format standardization, index information collection and pre-processing, and finally, according to the purpose of data mining, the Kirkpatrick model is used to complete the mining effect evaluation. Experimental comparison specifically includes correlation analysis, cluster analysis, classification analysis, exception analysis and evolution analysis as well as five others. The results show that: compared with the three methods in previous studies, the F1-score obtained by the application of the research method is higher (0.852), which proves that the quality of government affairs big data mining is better.

关键词： Adaptation models Government software algorithms Standardization software quality big data data models

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Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

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Journal of Healthcare engineering 2023年第1期2023卷 9919269页

作者： Xu, Qian Xie, Wenzhao Liao, Bolin Hu, Chao Qin, Lu Yang, Zhengzijin Xiong, Huan Lyu, Yi Zhou, Yue Luo, Aijing Second Xiangya Hospital of Central South University No. 139 Renmin Road Central Hunan Changsha China School of Life Sciences Central South University Hunan Changsha China College of Computer Science and Engineering Jishou University Hunan Jishou China Key Laboratory of Medical Information Research Third Xiangya Hospital Central South University College of Hunan Province Hunan Changsha China Clinical Research Center for Cardiovascular Intelligent Healthcare Hunan Changsha China Big Data Institute Central South University Changsha410083 China

Background. Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective. This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives. Methods. A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of science, PubMed, scienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization. Results. Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS. Conclusions. The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results co

关键词： Sensitivity analysis

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Text Cover Steganography Using Font Color of the Invisible Characters and Optimized Primary Color-intensities 19

Text Cover Steganography Using Font Color of the Invisible C...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Rabevohitra, Feno Heriniaina Li, Yantao Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University College of Computer Science Chongqing China

ISBN: (纸本)9781728105352

Text cover steganography is concerned with using text support for hiding information. Despite its long and old history, the art of cover writing is still attracting the attention of researchers in the domain of information hiding. Unfortunately, only a few related works exist in terms of text-covered writing. We present a visual attack on the previous related work. Then, we use invisible characters to hide secret data within the text support. We further use a combination and variation of the three primary colors intensities to vehiculate the message when painting non-printing characters. The proposed algorithm makes use of the low-value color spaces to decrease the color variations of the embedded secret message. It also further strengthens the hiddenness of the non-printing characters even when replaced by visible characters. © 2019 IEEE.

关键词： Color

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Two-Stage Attention-Based Model for Code Search with Textual and Structural Features

Two-Stage Attention-Based Model for Code Search with Textual...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Ling Xu Huanhuan Yang Chao Liu Jianhang Shuai Meng Yan Yan Lei Zhou Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

Searching and reusing existing code from a large scale codebase can largely improve developers' programming efficiency. To support code reuse, early code search models leverage information retrieval (IR) techniques to index a large-scale code corpus and return relevant code according to developers' search query. However, IR-based models fail to capture the semantics in code and query. To tackle this issue, developers applied deep learning (DL) techniques to code search models. However, these models either are too complex to determine an effective method efficiently or learning for semantic correlation between code and query *** bridge the semantic gap between code and query effectively and efficiently, we propose a code search model TabCS (Two-stage Attention-Based model for Code Search) in this study. TabCS extracts code and query information from the code textual features (i.e., method name, API sequence, and tokens), the code structural feature (i.e., abstract syntax tree), and the query feature (i.e., tokens). TabCS performs a two-stage attention net-work structure. The first stage leverages attention mechanisms to extract semantics from code and query considering their semantic gap. The second stage leverages a co-attention mechanism to capture their semantic correlation and learn better code/query representation. We evaluate the performance of TabCS on two existing large-scale datasets with 485k and 542k code snippets, respectively. Experimental results show that TabCS achieves an MRR of 0.57 on Hu et al.'s dataset, outperforming three state-of-the-art models CARLCS-CNN, DeepCS, and UNIF by 18%, 70%, 12%, respectively. Meanwhile, TabCS gains an MRR of 0.54 on Husain et al.'s, outperforming CARLCS-CNN, DeepCS, and UNIF by 32%, 76%, 29%, respectively.

关键词： Deep learning Correlation Semantics Syntactics Programming Feature extraction Information retrieval

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Semantics Analytics of Origin-Destination Flows from Crowd Sensed big data

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computers, Materials & Continua 2019年第7期61卷 227-241页

作者： Ning Cao Shengfang Li Keyong Shen Sheng Bin Gengxin Sun Dongjie Zhu Xiuli Han Guangsheng Cao Abraham Campbell College of Computer Information and Engineering Nanchang Institute of TechnologyNanchangChina College of Information Engineering Sanming UniversitySanmingChina School of Data Science and Software Engineering Qingdao UniversityQingdaoChina School of Computer Science and Technology Harbin Institute of TechnologyWeihaiChina Public Teaching Department Qingdao Technical CollegeQingdaoChina School of Computer Science University College DublinDublinIreland

Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human ***-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD *** this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in *** spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD *** based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows *** propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately.

关键词： Origin-destination(OD)flows semantics analytics complex network big data analysis

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Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

arXiv

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arXiv 2022年

作者： Guo, Yong Chen, Yaofo Zheng, Yin Chen, Qi Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui The School of Software Engineering South China University of Technology China Max Planck Institute for Informatics Germany The Weixin Group Tencent China The School of Computer Science The University of Adelaide Australia Tencent AI Lab Tencent China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China

Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world applications. To achieve this goal, existing methods often perform an independent architecture search process for each target budget, which is very inefficient yet unnecessary. More critically, these independent search processes cannot share their learned knowledge (i.e., the distribution of good architectures) with each other and thus often result in limited search results. To address these issues, we propose a Pareto-aware Neural Architecture Generator (PNAG) which only needs to be trained once and dynamically produces the Pareto optimal architecture for any given budget via inference. To train our PNAG, we learn the whole Pareto frontier by jointly finding multiple Pareto optimal architectures under diverse budgets. Such a joint search algorithm not only greatly reduces the overall search cost but also improves the search results. Extensive experiments on three hardware platforms (i.e., mobile device, CPU, and GPU) show the superiority of our method over existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Pareto principle

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