scholarly journals Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 415
Author(s):  
Jinli Wang ◽  
Yong Fan ◽  
Hui Zhang ◽  
Libo Feng
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Graph Model ◽  
Representation Learning ◽  
Research Direction ◽  
Calculation Model ◽  
Topic Evolution ◽  
Blockchain Technology ◽  
The Status ◽  
Research Hotspots

Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.

Characterizing Individualized Coding Contributions of OSS Developers from Topic Perspective

2017 ◽  
Vol 27 (01) ◽  
pp. 91-124 ◽  
Author(s):  
Zhongjie Wang ◽  
Dewayne E. Perry ◽  
Xiaofei Xu
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Deep Understanding ◽  
Time Dimension ◽  
Team Members ◽  
Source Codes ◽  
Topic Evolution ◽  
History Of ◽  
Temporal Locality ◽  
Project Coordination

Developers participating in an open source software (OSS) project make contributions to the project at different levels and aspects. Their underlying technical interests, expertise, and working habits are indirectly delineated by their personal contributions. This paper is to discover the individualized contribution features of developers by latent Dirichlet allocation (LDA) approach. Dominant latent topics of each developer and the corresponding topic coverage degree are extracted from the source codes committed to the project repository, and such topic model is validated to be feasible for representing the individualized contribution features by statistics tests. Four types of topic evolution patterns are observed from the commit history of a developer. Temporal locality is partially exhibited in the topic evolution but there usually exhibit drastic changes between time-adjacent contributions of a developer. Respective proportions of the four evolution patterns and the degree of temporal locality in the topic evolution delineate a developer’s individualized working habits in the time dimension. It is also proved that the correlation among the topic models of different developers is not equivalent to the real social collaborations among them. The outcome of this study would help OSS project coordinators get deep understanding on the work preferences and behavioral patterns of team members, thus facilitate project coordination activities such as task allocations.

scholarly journals Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Recall Rate ◽  
Defect Classification ◽  
Software Defects ◽  
Classification Approach ◽  
Software Defect ◽  
Model Combining ◽  
Dirichlet Allocation

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

scholarly journals An exploration of text mining of narrative reports of injury incidents to assess risk

2018 ◽  
Vol 251 ◽  
pp. 06020 ◽  
Author(s):  
David Passmore ◽  
Chungil Chae ◽  
Yulia Kustikova ◽  
Rose Baker ◽  
Jeong-Ha Yim
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Surface Mining ◽  
Modeling Processes ◽  
Free Text ◽  
Text Data ◽  
Injury Occurrence ◽  
The Usa ◽  
Musculoskeletal Systems ◽  
Topic Mining

A topic model was explored using unsupervised machine learning to summarized free-text narrative reports of 77,215 injuries that occurred in coal mines in the USA between 2000 and 2015. Latent Dirichlet Allocation modeling processes identified six topics from the free-text data. One topic, a theme describing primarily injury incidents resulting in strains and sprains of musculoskeletal systems, revealed differences in topic emphasis by the location of the mine property at which injuries occurred, the degree of injury, and the year of injury occurrence. Text narratives clustered around this topic refer most frequently to surface or other locations rather than underground locations that resulted in disability and that, also, increased secularly over time. The modeling success enjoyed in this exploratory effort suggests that additional topic mining of these injury text narratives is justified, especially using a broad set of covariates to explain variations in topic emphasis and for comparison of surface mining injuries with injuries occurring during site preparation for construction.

Research on the Production Load Rate of Suppliers in Order Allocation

2011 ◽  
Vol 101-102 ◽  
pp. 851-855
Author(s):  
Fu Lei Liu ◽  
Fei Fan Ye ◽  
Guo Fu Li ◽  
Lan Lan Liu
Keyword(s):  
Calculation Model ◽  
Delivery Time ◽  
Information Need ◽  
Time Analysis ◽  
Order Allocation ◽  
Load Rate ◽  
The Status ◽  
Resource Collection

As for the information need of the supplier production status in order allocation, parameter of production load rate was introduced to represent the status within the order delivery time. Analysis on the characteristics of the production load rate was made at first after its definition was given. Then after a key resource collection was built based on the key process of production, together with introduction of the algorithm of the load as far as the key resource collection and different scheduling modes are concerned, the calculation model of the supplier production load rate was built. Furthermore, the practicability of the algorithm was illustrated by the example.

scholarly journals Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

2016 ◽  
Author(s):  
Timothy N. Rubin ◽  
Oluwasanmi Koyejo ◽  
Krzysztof J. Gorgolewski ◽  
Michael N. Jones ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Brain Activity ◽  
Human Cognition ◽  
Brain Images ◽  
Whole Brain ◽  
Context Sensitive ◽  
Cognitive States ◽  
Small Set

AbstractA central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

scholarly journals Research on the Application of Blockchain Technology in the Management of Financial Transfer Payment

2021 ◽  
Vol 251 ◽  
pp. 01065
Author(s):  
Zhang Jiangen ◽  
Xing Shuxia ◽  
Qi meng
Keyword(s):  
Supply Chain ◽  
Personal Computer ◽  
Financial Management ◽  
Payment System ◽  
Transfer Payment ◽  
Blockchain Technology ◽  
The Status ◽  
Adaptation Scenarios ◽  
Great Development ◽  
Financial Transfer

Blockchain technology is considered to be a subversive innovation of computing mode after mainframe, personal computer and Internet. In the financial, medical, supply chain and other industries show great development potential. As an important part of financial management, financial transfer payment system is an important part of modern financial system. This paper combines blockchain technology with the management of financial transfer payment, analyzes the adaptation scenarios of blockchain technology, summarizes the status and development bottleneck of financial transfer payment informatization, tries to establish a financial transfer payment system based on blockchain technology, describes the advantages of blockchain technology in supporting the management of financial transfer payment, and puts forward suggestions on the possible problems.

scholarly journals Efficient and Secure Strategy for Energy Systems of Interconnected Farmers′ Associations to Meet Variable Energy Demand

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2182
Author(s):  
Maria Simona Raboaca ◽  
Nicu Bizon ◽  
Catalin Trufin ◽  
Florentina Magda Enescu
Keyword(s):  
Energy Demand ◽  
National Level ◽  
National Development ◽  
Clean Energy ◽  
Energy System ◽  
Research Direction ◽  
Auxiliary Equipment ◽  
Strategic Objective ◽  
Blockchain Technology ◽  
Variable Energy

Since ancient times, agriculture has been one of the most important resources of national development. At a national level, clean energy is a strategic objective of Romania, in accordance with the EC directive 2016/30.11.2016 (“Clean Energy for All”). At a European level, the European Commission published in January 2019 the “Towards a Sustainable Europe by 2030” strategy, highlighting the strategic importance of the Internet of Things (IoT) and blockchain technologies. In this context, the synergy between the energy management of a hybrid energy system and blockchain technology, applied to farmers’ associations, represents a priority research direction in the field of information and communication technology, blockchain, and security. This paper presents the integration of the management of the energy produced by photovoltaic panels owned by farmers’ association, to support the variable energy demand (necessary for water pumps, charging stations of the electric agricultural machines, the animal farms, and the auxiliary equipment) based on the IoT, DLT, blockchain technologies and smart contracts applied to farmers associations registered as users of the SmartFarm platform.

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