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Study on the Characteristics of Cross-Domain knowledge Diffusion from Science to Policy: Evidence from Overton data

Proceedings of the Association for information Science and Technology 2023年第1期60卷 368-378页

作者： Ren, Chao Yang, Menghui School of Information Resource Management Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China

The cross-domain knowledge diffusion from science to policy is a prevalent phenomenon that demands academic attention. To investigate the characteristics of cross-domain knowledge diffusion from science to policy, this study suggests using the citation of policies to scientific articles as a basis for quantifying the diffusion strength, breadth, and speed. The study reveals that the strength and breadth of cross-domain knowledge diffusion from scientific papers to policies conform to a power-law distribution, while the speed follows a logarithmic normal distribution. Moreover, the papers with the highest diffusion strength, breadth, and fastest diffusion speed are predominantly from world-renowned universities, scholars, and top journals. The papers with the highest diffusion strength and breadth are mostly from social sciences, especially economics, those with the fastest diffusion speed are mainly from medical and life sciences, followed by social sciences. The findings indicate that cross-domain knowledge diffusion from science to policy follows the Matthew effect, whereby individuals or institutions with high academic achievements are more likely to achieve successful cross-domain knowledge diffusion. Furthermore, papers in the field of economics tend to have the higher cross-domain knowledge diffusion strength and breadth, while those in medical and life sciences have the faster cross-domain knowledge diffusion speed. 86 Annual Meeting of the Association for information Science & Technology | Oct. 27 – 31, 2023 | London, United Kingdom. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

关键词： Diffusion

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Approximation for a scheduling problem with application in wireless networks

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Science China Mathematics 2010年第6期53卷 1643-1655页

作者： ZHU QingHua & LI DeYing Key laboratory of data engineering and knowledge engineering,MOE,School of information,Renmin University of China,Beijing 100872,China 1. Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing 100872 China

A network of many sensors and a base station that are deployed over a region is *** sensor has a transmission range,an interference range and a carrier sensing range,which are r,αr and βr,*** this paper,we study the minimum latency conflict-aware many-to-one data aggregation scheduling problem:Given locations of sensors along with a base station,a subset of all sensors,and parameters r,α and β,to find a schedule in which the data of each sensor in the subset can be transmitted to the base station with no conflicts,such that the latency is *** designe an algorithm based on maximal independent sets,which has a latency bound of(a+19b)R + Δb-a + 5 time slots,where a and b are two constant integers relying on α and β,Δ is the maximum degree of network topology,and R is the trivial lower bound of *** Δ contributes to an additive factor instead of a multiplicative one,thus our algorithm is nearly a constant(a+19b)-ratio.

关键词： approximation algorithm conflict-aware many-to-one data aggregation latency wireless sensor network

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Frequency-Assisted Temporal Upsampling Artifacts Representation Learning for Face Forgery Detection

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2025年

作者： Sun, Rui Yu, Xiaolu Wang, Fei Da, Zikai Zhang, Yifan Gao, Jun Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) School of Computer and Information Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei 230009 China

Recently, the highly realistic deepfake images have aroused the potential for deepfake abuse, and both the detection and its generalization remain challenging. Existing deepfake video detection methods strive to capture distinguishing features between fake and real faces through temporal modeling. However, these works provide local supervision between sampled video frames, but overlook intra-frame generative artifacts, which can function as an useful indicators for detection. To mitigate the issue, we propose a temporal-frequential convolutional network based on the representation of upsampling artifacts, which can automatically capture incoherent artifacts representation in different ends. Specifically, we design the Upsampling Artifacts Representation Module (UARM), which captures upsampling artifacts introduced in generation end based on the relationship between pixels, and then we probe the deep coupling of intra-frame global frequency information with inter-frame incoherence, and design the Frequency-Assisted Temporal Incoherence Module (FATIM), which can facilitate the detection of temporal information in RGB domain by frequency domain information, and the Self-tuned Convolutions Module (SCM) is designed to supplement information and adjust it automatically in the learning process. In addition, the information Fusion Module (IFM) is designed to couple multiple information and establish a more comprehensive representation. With the above modules, our proposed method can learn robust features in both generation end and detection end. Extensive experiments demonstrate that our method outperforms other state-of-the-art video detection methods by a large margin on the challenging DFDC and FF++ in low-quality. © 2019 IEEE.

关键词： DeepFake detection Multiple end Temporal-frequential convolution Upsampling artifacts

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One tag to bind them all: Measuring term abstractness in social metadata?

One tag to bind them all: Measuring term abstractness in soc...

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Symposium on Learning, knowledge, and Adaptivity 2011, LWA 2011

作者： Benz, Dominik Körner, Christian Hotho, Andreas Stumme, Gerd Strohmaier, Markus Knowledge and Data Engineering Group University of Kassel Germany Knowledge Management Institute Graz University of Technology Austria Data Mining and Information Retrieval Group University of Wurzburg Germany

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tagabstrcatsness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of wordabstrcatness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

关键词： Ontology

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Preventing Recommendation Attack in Trust-Based Recommender Systems

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Journal of Computer Science & Technology 2011年第5期26卷 823-828页

作者：张富国 School of Information and Technology Jiangxi University of Finance and Economics Institute of Information Resource Management Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation *** work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these *** argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender *** the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender *** analyze the attack problem,and find that ＂victim＂ nodes play a significant role in the ***,we propose a data provenance method to trace malicious users and identify the ＂victim＂ nodes as distrust users of recommender *** study of the defend method is done with the dataset crawled from Epinions website.

关键词： data lineage ＂victim＂ node attack trust propagation

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Multi-Cluster Feature Selection Based on Isometric Mapping

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

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Processing the v-KNN queries in wireless sensor networks

Processing the v-KNN queries in wireless sensor networks

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36th International Conference on Parallel Processing in Xi'an, ICPP

作者： Lai, Yongxuan Chen, Hong Li, Cuiping School of Information Renmin University Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)076952933X

Recently there have been growing interests in the applications of wireless sensor networks. Given a query point, which is a value, find a set of K nodes whose values are nearest to this point. We call this query the value-based KNN (v-KNN) query. v-KNN is a challenging query in wireless sensor networks because the network is highly distributed and global knowledge of the values are needed before the result set could be constructed. In this paper, we propose a new algorithm called KVC (v-KNN queries based on value clustering ) to process the v-KNN queries in wireless sensor networks in an energy efficient way. KVC takes advantage of the rich resources in the base station to do some optimizations to filter the irrelevant nodes and push down the v-KNN query into the network. The aim of KVC is to reduce the total communication cost as much as possible while processing the v-KNN queries correctly and efficiently. Simulation results on both realistic and synthetical data sets show that KVC outperforms the central processing in both average and hotspot energy consumption in wireless sensor networks. © 2007 IEEE.

关键词： Sensor nodes

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Enhancing Storage Efficiency and Performance: A Survey of data Partitioning Techniques

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Journal of Computer Science & Technology 2024年第2期39卷 346-368页

作者：刘鹏举李翠平陈红 Distinguished Member CCF 1.School of InformationRenmin University of ChinaBeijing 100872China Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Beijing 100872China

data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhancing resource utilization and overall system ***,the design of effective partition schemes faces multiple challenges,including considerations of the cluster environment,storage device characteristics,optimization objectives,and the balance between partition quality and computational ***,dynamic environments necessitate robust partition detection *** paper presents a comprehensive survey structured around partition deployment environments,outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are *** discuss partitioning features pertaining to database schema,table data,workload,and runtime *** then delve into the partition generation process,segmenting it into initialization and optimization stages.A comparative analysis of partition generation and update algorithms is provided,emphasizing their suitability for different scenarios and optimization ***,we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and *** survey aims to foster the implementation,deployment,and updating of high-quality partitions for specific system scenarios.

关键词： data partitioning survey partitioning feature partition generation partition update

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Exploratory subgroup analytics on ubiquitous data 4

Exploratory subgroup analytics on ubiquitous data

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4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013 in conjunction with the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECML-PKDD 2013

作者： Atzmueller, Martin Mueller, Juergen Becker, Martin Knowledge and Data Engineering Group University of Kassel Kassel Germany Data Mining and Information Retrieval Group University of Würzburg Würzburg Germany

ISBN: (纸本)9783319147222

This paper presents exploratory subgroup analytics on ubiquitous data: We propose subgroup discovery and assessment approaches for obtaining interesting descriptive patterns and provide a novel graphbased analysis approach for assessing the relations between the obtained subgroup set. This exploratory visualization approaches allows for the comparison of subgroups according to their relations to other subgroups and to include further parameters, e.g., geo-spatial distribution indicators. We present and discuss analysis results utilizing real-world data given by geo-tagged noise measurements with associated subjective perceptions and a set of tags describing the semantic context. ©Springer International Publishing Switzerland 2015

关键词： Semantics

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Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

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Chinese Journal of Electronics 2023年第6期32卷 1329-1340页

作者： SUN Haoran WANG Yang LIU Haipeng QIAN Biao Department of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of EducationHefei University of Technology

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel finegrained text-image fusion based generative adversarial networks(FF-GAN), which consists of two modules: Finegrained text-image fusion block(FF-Block) and global semantic refinement(GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

关键词： Visualization Fuses Convolution Semantics Linguistics Benchmark testing Generative adversarial networks

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