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Collusion-Proof Result Inference in Crowdsourcing

Journal of Computer science & Technology 2018年第2期33卷 351-365页

作者： Peng-Peng Chen Hai-Long Sun Yi-Li Fang Jin-Peng Huai State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing 100191 China

In traditional crowdsourcing, workers are expected to provide independent answers to tasks so as to ensure the diversity of answers. However, recent studies show that the crowd is not a collection of independent workers, but instead that workers communicate and collaborate with each other. To pursue more rewards with little effort, some workers may collude to provide repeated answers, which will damage the quality of the aggregated results. Nonetheless, there are few efforts considering the negative impact of collusion on result inference in crowdsourcing. In this paper, we are specially concerned with the Collusion-Proof result inference problem for general crowdsourcing tasks in public platforms. To that end, we design a metric, the worker performance change rate, to identify the colluded answers by computing the difference of the mean worker performance before and after removing the repeated answers. Then we incorporate the collusion detection result into existing result inference methods to guarantee the quality of the aggregated results even with the occurrence of collusion behaviors. With real-world and synthetic datasets, we conducted an extensive set of evaluations of our approach. The experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.

关键词： crowdsourcing quality control collusion collaborative crowdsourcing result inference

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Illuminating Recommendation by Understanding the Explicit Item Relations

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Journal of Computer science & Technology 2018年第4期33卷 739-755页

作者： Qi Liu Hong-Ke Zhao Le Wu Zhi Li En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027 China School of Computer and Information Hefei University of Technology Hefei 230009 China School of Software Engineering University of Science and Technology of China Hefei 230051 China

Recent years have witnessed the prevalence of recommender systems in various fields, which provide a personalized recommendation list for each user based on various kinds of information. For quite a long time, most researchers have been pursing recommendation performances with predefined metrics, e.g., accuracy. However, in real-world applications, users select items from a huge item list by considering their internal personalized demand and external constraints. Thus, we argue that explicitly modeling the complex relations among items under domain-specific applications is an indispensable part for enhancing the recommendations. Actually, in this area, researchers have done some work to understand the item relations gradually from ＂implicit＂ to ＂explicit＂ views when recommending. To this end, in this paper, we conduct a survey of these recent advances on recommender systems from the perspective of the explicit item relation understanding. We organize these relevant studies from three types of item relations, i.e., combination-effect relations, sequence-dependence relations, and external-constraint relations. Specifically, the combination-effect relation and the sequence-dependence relation based work models the intra-group intrinsic relations of items from the user demand perspective, and the external-constraint relation emphasizes the external requirements for items. After that, we also propose our opinions on the open issues along the line of understanding item relations and suggest some future research directions in recommendation area.

关键词： recommender system item relation recommendation interpretability

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Local differential privacy for deep learning

arXiv

引用

arXiv 2019年

作者： Chamikara, Mahawaga Arachchige Pathum Bertok, Peter Khalil, Ibrahim Liu, Dongxi Camtepe, Seyit Atiquzzaman, Mohammed department of Computer Science and Software Engineering School of Science RMIT University Australia CSIRO Data61 Melbourne Australia School of Computer Science University of Oklahoma

—The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data, such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. Existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners’ devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: (1) convolutional module, (2) randomization module, and (3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g. 91%-96%) with high model quality even under low privacy budgets (e.g. Ε = 0.5). Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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The Characteristics and Geographical Features of Plant Populations in the Forest Region of Wanda Mountains

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IOP Conference Series: Earth and Environmental science 2021年第1期687卷

作者： Jialin Li Jiao Yang XueWei Sun Jincheng Luo Hongbin Qiu Lan Li School of Public Health Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

The study of species diversity and fauna characteristics in the Wandashan area is of great significance for the protection and utilization of plant resources in the future. The relevant characteristics and characteristics of species diversity in the Wandashan area are analyzed. The various species diversity indices of the Dashan area are calculated, reflecting the species richness, dominance, uniformity and the information volume and community complexity of the Wandashan area. Through Matlab, the location of each plot in the Wandashan area and the species diversity index of Joe, Irrigation, Grass and all species are represented in the form of graphs, reflecting the different species diversity indices in other areas of the mountainous region. Changes combining the results of the field survey with the existing literature, the floristic analysis is carried out to obtain the characteristics of the flora of the mountainous area, and reflec the information about the species diversity of the mountainous area. Through the similarity coefficient comparison and analysis of plant distribution, the relationship between the flora of Wandashan, Changbai Mountain and Xiaoxing'anling was determined. The results of flora analysis show that the flora of the Wandashan area belongs to the North Temperate Zone and has a general connection with other floras in the world. The flora of Northeast China mainly includes Xiaoxing'anling area, Wandashan area and Changbai Mountain. By comparing the similarity coefficients at the three levels of families, genera and species, it can be seen that the similarity coefficient between Xiaoxing'anling and Wandashan is higher than that between Changbai Mountain and Wandashan.

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Temporal and Spatial Succession Law of Abor Diversity in Xiaoxing'an Mountains

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IOP Conference Series: Earth and Environmental science 2021年第1期687卷

The national forest nature reserve in the Xiaoxing'anling area was studied. Based on the diversity of α and β, three forest types of grassland, birch forest, and larch forest were analyzed. Based on the diversity law of latitude gradient, the nature reserve was discussed in forest type. A difference in a diversity of different forest types was succession changes. A method based on quantitative evaluation of species conservation value was constructed. The community structure and species composition of nature reserves were systematically analyzed, and the conservation value of species and the conservation value of rare and endangered species were scientifically evaluated. The results showed that the Shannon Wiener index and Margalef index of grassland, birch forest, and larch forest showed a decreasing trend with the increase of latitude. Compared with the other four latitudes, the grassland, birch forest and larch forest of Dazhanhe Nature Reserve showed a decreasing trend from the north to south Jaccard index and Sorenson index, and the Cody index showed an increasing trend. The Jaccard index and the Sorenson index have peaks in friendly areas, and the species similarity is high, indicating that the friendly areas may be the interlaced areas of Xiaoxing'anling. The Shannon Wiener index and the Margalef index of the nature reserve from the early stage of succession to the middle and late succession increased continuously, reaching the maximum in the middle and late stages, and began to decline in the later stage of succession, which is consistent with the mid-specie.

关键词：

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Crossover-net: leveraging the vertical-horizontal crossover relation for robust segmentation

arXiv

引用

arXiv 2020年

作者： Yu, Qian Shi, Yinghuan Zheng, Yefeng Gao, Yang Zhu, Jianbing Dai, Yakang State Key Laboratory for Novel Software Technology Nanjing University China School of Data and Computer Science Shandong Women’s University China Youtu Lab Tencent China Suzhou Science and Technology Town Hospital China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences China

—Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patients. In this paper, we present an end-to-end trainable deep segmentation model termed Crossover-Net for robust segmentation in medical images. Our proposed model is inspired by an insightful observation: during segmentation, the representation from the horizontal and vertical directions can provide different local appearance and orthogonality context information, which helps enhance the discrimination between different tissues by simultaneously learning from these two directions. Specifically, by converting the segmentation task to a pixel/voxel-wise prediction problem, firstly, we originally propose a cross-shaped patch, namely crossover-patch, which consists of a pair of (orthogonal and overlapped) vertical and horizontal patches, to capture the orthogonal vertical and horizontal relation. Then, we develop the Crossover-Net to learn the vertical-horizontal crossover relation captured by our crossover-patches. To achieve this goal, for learning the representation on a typical crossover-patch, we design a novel loss function to (1) impose the consistency on the overlap region of the vertical and horizontal patches and (2) preserve the diversity on their non-overlap regions. We have extensively evaluated our method on CT kidney tumor, MR cardiac, and X-ray breast mass segmentation tasks. Promising results are achieved according to our extensive evaluation and comparison with the state-of-the-art segmentation *** Codes 14J60 (Primary) 14F05, 14J26 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Tissue

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A prototype for software refactoring recommendation system

引用

International Journal of Performability engineering 2020年第7期16卷 1095-1104页

作者： Gao, Yuan Zhang, Youchun Lu, Wenpeng Luo, Jie Hao, Daqing China National-Local Joint Engineering Lab of Next Generation Internet Data Processing Technology University of Electronic Science and Technology of China Chengdu610054 China China School of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Luoyang Bearing Research Institute Co. Ltd. Luoyang471039 China

software refactoring is used to reduce the costs and risks of software evolution. Automated software refactoring tools can reduce risks caused by manual refactoring, improve efficiency, and reduce difficulties of software refactoring. Researchers have made great efforts to research how to implement and improve automated software refactoring tools. However, results of automated refactoring tools often deviate from the intentions of the implementer. To this end, in this paper, we proposed and implemented a prototype tool for a software refactoring recommendation system based on previous research. The tool provides users with an optimized software refactoring scheme and users realize refactoring intentions by interacting with the tool. The tool has been evaluated to be effective, especially for users who are inexperienced and non- English speaking. © 2020 Totem Publisher, Inc. All rights reserved.

关键词： Recommender systems

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Caching transient content for IoT sensing: Multi-agent soft actor-critic

arXiv

引用

arXiv 2020年

作者： Wu, Xiongwei Li, Xiuhua Li, Jun Ching, P.C. Leung, Victor C.M. Poor, H. Vincent Department of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong School of Big Data & Software Engineering Chongqing University chongqing401331 China Ministry of Education China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China College of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

Edge nodes (ENs) in Internet of Things commonly serve as gateways to cache sensing data while providing accessing services for data consumers. This paper considers multiple ENs that cache sensing data under the coordination of the cloud. Particularly, each EN can fetch content generated by sensors within its coverage, which can be uploaded to the cloud via fronthaul and then be delivered to other ENs beyond the communication range. However, sensing data are usually transient with time whereas frequent cache updates could lead to considerable energy consumption at sensors and fronthaul traffic loads. Therefore, we adopt age of information to evaluate data freshness and investigate intelligent caching policies to preserve data freshness while reducing cache update costs. Specifically, we model the cache update problem as a cooperative multi-agent Markov decision process with the goal of minimizing the long-term average weighted cost. To efficiently handle the exponentially large number of actions, Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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Reversing Free-Electron Transfer of MoS2+xCocatalyst for Optimizing Antibonding-Orbital Occupancy Enables High Photocatalytic H2Evolution

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Angewandte Chemie 2023年第25期135卷

作者： Duoduo Gao Pinsi Deng Dr. Jianjun Zhang Prof. Liuyang Zhang Prof. Xuefei Wang Prof. Huogen Yu Prof. Jiaguo Yu State Key Laboratory of Silicate Materials for Architectures and School of Chemistry Chemical Engineering and Life Science Wuhan University of Technology 430070 Wuhan P. R. China Contribution: Investigation (lead) Methodology (lead) Software (lead) Visualization (lead) Writing - original draft (lead) Contribution: Formal analysis (lead) Software (equal) Laboratory of Solar Fuel Faculty of Materials Science and Chemistry China University of Geosciences 430074 Wuhan P. R. China Contribution: Data curation (lead) Project administration (lead) Contribution: Data curation (lead) Software (equal) Contribution: Conceptualization (lead) Funding acquisition (lead) Resources (lead) Supervision (lead) Validation (lead) Visualization (lead) Contribution: Conceptualization (lead) Resources (lead) Supervision (equal) Validation (lead)

The interaction between a co-catalyst and photocatalyst usually induces spontaneous free-electron transfer between them, but the effect and regulation of the transfer direction on the hydrogen-adsorption energy of the active sites have not received attention. Herein, to steer the free-electron transfer in a favorable direction for weakening S−H ads bonds of sulfur-rich MoS 2+ x , an electron-reversal strategy is proposed for the first time. The core–shell Au@MoS 2+ x cocatalyst was constructed on TiO 2 to optimize the antibonding-orbital occupancy. Research results reveal that the embedded Au can reverse the electron transfer to MoS 2+ x to generate electron-rich S (2+δ)− active sites, thus increasing the antibonding-orbital occupancy of S−H ads in the Au@MoS 2+ x cocatalyst. Consequently, the increase in the antibonding-orbital occupancy effectively destabilizes the H 1s-p antibonding orbital and weakens the S−H ads bond, realizing the expedited desorption of H ads to rapidly generate a lot of visible H 2 bubbles. This work delves deep into the latent effect of the photocatalyst carrier on cocatalytic activity.

关键词： Antibonding Orbitals Co-Catalysts Electron Reversal Photocatalysis Sulfur–Hydrogen Bonds

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Supramolecular Copolymers Under Kinetic, Thermodynamic, or Pathway-Switching Control

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Angewandte Chemie 2023年第28期135卷

作者： Yan Chen Dr. Yusheng Shi Dr. Zhanming Gao Dr. Lei Wang Yutian Tang Prof. Dr. Jinxuan Liu Prof. Dr. Chun Liu State Key Laboratory of Fine Chemicals Frontier Science Center for Smart Material School of Chemical Engineering Dalian University of Technology Linggong Road 2 Dalian 116024 China Contribution: Conceptualization (lead) Data curation (lead) Methodology (lead) Project administration (lead) Software (lead) Writing - original draft (lead) Contribution: Software (supporting) Contribution: Resources (supporting) Contribution: Funding acquisition (lead) Resources (lead) Supervision (lead) Writing - review & editing (lead)

Supramolecular copolymers have attracted much attention due to their potential functionalities. However, the co-assembly strategies to construct co-assemblies of small molecules with well-defined sequence structures are still limited, especially for more complex crystalline block co-assemblies. Herein, we target this challenge by designing Ir III complexes 1 and 2 , which possess unique self-assembly pathways and are capable of forming crystalline assemblies in aqueous systems. Specifically, block and random co-assemblies of 1 and 2 can be synthesized by kinetic and thermodynamic control, respectively. Meanwhile, by adjusting the water content to orthogonalize the on-pathway and the off-pathway, an unprecedented pathway-switching approach is realized to synthesize block and random co-assemblies. By coupling the kinetic pathways, the present co-assembly strategies are expected to pave the way for the synthesis of crystalline co-assemblies of small molecules and the construction of organic heterostructures.

关键词： Block Copolymers Co-Assembly IrIII Complexes Living Self-Assembly Pathway Switching

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