scholarly journals Network model of interpersonal links of Russian empire high bureaucracy. Problems and solutions

2020 ◽  
Author(s):  
O. Golovashina ◽  
◽  
K. Kunavin ◽  
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
Interpersonal Relations ◽  
Detection Algorithm ◽  
Russian Empire ◽  
Historical Research ◽  
Model Ambiguity ◽  
Problems And Solutions

In the article some heuristic perspectives of usage of the methods of network analysis of interpersonal relations in the high bureaucracy of Russian empire network analysis are listed. The main problem of building such a model – ambiguity of the concept “link” – is stated. The ways of conceptual comprehension of this term are suggested to be acceptable for concrete-historical research. The interpersonal links detection algorithm, based on different quality sources with no need of microanalysis, is described.

Nerdalicious scientainment: A network analysis of English libfixes

2019 ◽  
Vol 12 (3) ◽  
pp. 353-384 ◽  
Author(s):  
Muriel Norde ◽  
Sarah Sippach
Keyword(s):  
Network Analysis ◽  
Network Model ◽  
Phonological Similarity ◽  
High Type ◽  
Type Frequency ◽  
Semantic Properties

Libfixes are parts of words that share properties with both blends, compounds and affixes. They are deliberate formations, often with a jocular character, e.g. nerdalicious ‘delicious for nerds’, or scientainment ‘scientific entertainment’. These are not one-off formations – some libfixes have become very productive, as evidenced by high type frequency in a single corpus. Libfix constructions are particularly interesting for a network analysis for three reasons: they do not always have discrete morpheme boundaries, they feature a wide variety of bases (including phrases, as in give-me-a-break-o-meter), and they may be the source of back formations such as infotain. In this paper, we present a corpus-based analysis of eight English libfixes (cracy, fection, flation, gasm, licious, (o-)meter, tainment, and tastic), detailing their formal and semantic properties, as well as their differences and similarities. We argue that libfixes are most fruitfully analysed in a Bybeean network model, in which nodes are connected on the basis of phonological similarity, which allows for both fully compositional and non-compositional constructions to be linked without an exhaustive analysis into morphemes.

scholarly journals Women and universities in El País (1977-2011): A methodological proposal for use of the ITCS for historical analysis

2019 ◽  
Vol 8 (1) ◽  
pp. 009
Author(s):  
Carlos G. Figuerola ◽  
Tamar Groves ◽  
Francisco J. Rodríguez
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Text Mining ◽  
Historical Analysis ◽  
Historical Research ◽  
Software Systems ◽  
Practical Case ◽  
Computer Tools ◽  
Analysis Techniques

The practice of historical research in recent years has been substantially affected by the emergence of the so-called digital humanities. New computer tools have been appearing, software systems capable of processing vast quantities of information in ways that until recently were inconceivable. Text mining and social network analysis techniques are sophisticated instruments that can help render a more enriching reading of the available data and draw useful conclusions. We reflect on this in the first part of this article, and then apply these tools to a practical case: quantifying and identifying the women who appear in university-related articles in the newspaper El País from its founding until 2011.

Preparing Trainee Translators for the Job Market

2018 ◽  
pp. 137-167
Author(s):  
Bryan J. Robinson ◽  
M. Dolores Olvera-Lobo
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Survey ◽  
Interpersonal Relations ◽  
Team Cohesion ◽  
Job Market ◽  
Friendship Relations ◽  
Professional Workplace

Competence-based learning contrasts radically with content-focused education. Today's undergraduate programmes take a multidisciplinary approach that imbues learning with input from the professional workplace. This chapter describes possibly the first social network analysis of trainee translators participating in an intensive, randomised teamwork experience centred on project-based, cooperative learning. An online survey gathered data and perceptions of the teamwork experience and of interpersonal relations. Participants describe friendship relations, the quality of their peers' performance in professional roles, and their preferences with regard to the roles, and these are contrasted within the teams. These indicators of intra-team cohesion are compared with course-final achievement. Results indicate that the strengthening of friendship ties accompanies greater cohesion in teams and may be associated with higher achievement. This suggests that a multidisciplinary focus on teamwork competences enhances learners' professional prospects.

scholarly journals How advocacy affects Twitter migraine conversations: A pilot cross-sectional survey of Northeast American “migraine” landscape on Twitter from May to June 2020

2020 ◽  
Vol 3 ◽  
pp. 251581632097208
Author(s):  
Pengfei Zhang ◽  
Santosh Bhaskarabhatla
Keyword(s):  
United States ◽  
Social Media ◽  
Network Analysis ◽  
Network Model ◽  
Large Scale ◽  
The United States ◽  
Closeness Centrality ◽  
Control Group ◽  
Annual Conference ◽  
Sources Of Information

Background: Twitter is a leading microblogging platform, with over 126 million daily active users as of 2019, which allows for large-scale analysis of tweets related to migraine. June 2020 encompassed the National Migraine and Headache Awareness Month in the United States and the American Headache Society’s virtual annual conference, which offer opportunities for us to study online migraine advocacy. Objective: We aim to study the content of individual tweets about migraine, as well as study patterns of other topics that were discussed in those tweets. In addition, we aim to study the sources of information that people reference within their tweets. Thirdly, we want to study how online awareness and advocacy movements shape these conversations about migraine. Methods: We designed a Twitter robot that records all unique public tweets containing the word “migraine” from May 8th, 2020 to June 23rd, 2020, within a 400 km radius of New Brunswick, New Jersey, United States. We built two network analysis models, one for the months of May 2020 and June 2020. The model for the month of May served as a control group for the model for the month of June, the Migraine Awareness Month. Our network model was developed with the following rule: if two hashtag topics co-exist in a single tweet, they are considered nodes connected by an edge in our network model. We then determine the top 30 most important hashtags in the month of May and June through applications of degree, between-ness, and closeness centrality. We also generated highly connected subgraphs (HCS) to categorize clusters of conversations within each of our models. Finally, we tally the websites referenced by these tweets during each month and categorized these websites according to the HCS subgroups. Results: Migraine advocacy related tweets are more popular in June when compared to May as judged by degree and closeness centrality measurements. They remained unchanged when judged by between-ness centralities. The HCS algorithm categorizes the hashtags into a large single dominant conversation in both months. In each of the months, advocacy related hashtags are apart of each of the dominant conversation. There are more hashtag topics as well as more unique websites referenced in the dominant conversation in June than in May. In addition, there are many smaller subgroups of migraine-related hashtags, and in each of these subgroups, there are a maximum of two websites referenced. Conclusion: We find a network analysis approach to be fruitful in the area of migraine social media research. Migraine advocacy tweets on Twitter not only rise in popularity during migraine awareness month but also may potentially bring in more diverse sources of online references into the Twitter migraine conversation. The smaller subgroups we identified suggest that there are marginalized conversations referencing a limited number of websites, creating a possibility of an “echo chamber” phenomenon. These subgroups provide an opportunity for targeted migraine advocacy. Our study therefore highlights the success as well as potential opportunities for social media advocacy on Twitter.

scholarly journals Preventing Patent Risks in Artificial Intelligence Industry for Sustainable Development: A Multi-Level Network Analysis

2020 ◽  
Vol 12 (20) ◽  
pp. 8667
Author(s):  
Xi Yang ◽  
Xiang Yu
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Network Analysis ◽  
Network Model ◽  
Industrial Development ◽  
Research Collaboration ◽  
New Technology ◽  
Technological Field ◽  
Electric Equipment ◽  
Multi Level

In recent years, assessing patent risks has attracted fast-growing attention from both researchers and practitioners in studies of technological innovation. Following the existing literature on risks and intellectual property (IP) risks, we define patent risks as the lack of understanding of the distribution of patents that lead to losing a key patent, increased research and development costs, and, potentially, infringement litigation. This paper aims to propose an explorative approach to investigating patent risks in the target technology field by integrating social network analysis and patent analysis. Compared to previous research, this study makes an important contribution toward identifying patent risks in the overall technological field by employing a patent-based multi-level network model that has not appeared in existing methodologies of patent risks. In order to verify the effectiveness of this approach, we take artificial intelligence (AI) as an example. Data collected from the Derwent Innovation Index (DII) database were used to build the patent-based multi-level network on patent risks from market, technology, and assignee perspectives. The results indicate that the lack of international collaborations among assignees and industry–university–research collaboration may lead to patent collaboration risks. Regarding patent market risks, the lack of overseas patent applications, especially the lack of distribution in the main competitive markets, is a key factor. As for patent technology risks, most of the leading assignees lack awareness of the distribution in the following technological fields: industrial electric equipment, engineering instrumentation, and automotive electrics. In summary, assignees from the U.S. with first mover advantages are still powerful leaders in the AI technology field. Although China is catching up very rapidly in the total number of AI patents, the apparent patent risks under the perspectives of collaboration, market, and technology will obviously hamper the catch-up efforts of China’s AI industry. We conclude that, in practice, the proposed patent-based multi-level network model not only plays an important role in helping stakeholders in the AI technological field to prevent patent risks, find new technology opportunities, and obtain sustainable development, but also has significance for guiding the industrial development of various emerging technology fields.

Studies on a multidimensional public opinion network model and its topic detection algorithm

2019 ◽  
Vol 56 (3) ◽  
pp. 584-608 ◽  
Author(s):  
Guanghui Wang ◽  
Yuxue Chi ◽  
Yijun Liu ◽  
Yufei Wang
Keyword(s):  
Public Opinion ◽  
Network Model ◽  
Detection Algorithm ◽  
Topic Detection

scholarly journals Image target detection algorithm compression and pruning based on neural network

2021 ◽  
Vol 18 (2) ◽  
pp. 499-516
Author(s):  
Yan Sun ◽  
Zheping Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Network Model ◽  
Detection Algorithm ◽  
Image Feature ◽  
Propagation Mode ◽  
Learning Technology ◽  
Image Detection ◽  
Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

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