Academic literature on the topic 'Topic evolution networks'
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Journal articles on the topic "Topic evolution networks"
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Liu, Yanni, Dongsheng Liu, and Yuwei Chen. "Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City." Complexity 2020 (June 4, 2020): 1–13. http://dx.doi.org/10.1155/2020/9789431.
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With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.
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Wang, Xiang Yang. "Hot Topic Detection in News Blog." Applied Mechanics and Materials 513-517 (February 2014): 1114–18. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1114.
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Topic detection in news blogs needs to carefully arrange the information and analyze the characteristics of topics. However, there are some difficulties for hot topic detection in blogs. On one hand, information overload and dynamic change of web pages are obstacles of information arrangement. On the other hand, there are different hotness evaluation norms for web topics. The proposed method first analyzes the characteristics of the news blog and recognizes the factors which can influence the evolution of a topic. Then a word network is constructed, and candidate topics are extracted from the word network based on the complex networks theory. Finally, hot topics in the news blog are identified by measuring the user participation, opinion communication between users and user forgetting degree.
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Jung, Sukhwan, and Aviv Segev. "Analyzing the generalizability of the network-based topic emergence identification method." Semantic Web 13, no. 3 (2022): 423–39. http://dx.doi.org/10.3233/sw-212951.
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Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.
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Xu, Xiaoyan, Wei Lv, Beibei Zhang, Shuaipeng Zhou, Wei Wei, and Yusen Li. "A Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks." Complexity 2021 (August 26, 2021): 1–14. http://dx.doi.org/10.1155/2021/8859225.
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With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.
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Liang, Wei, Zixian Lu, Qun Jin, Yonghua Xiong, and Min Wu. "Modeling and Analyzing of Research Topic Evolution Associated with Social Networks of Researchers." International Journal of Distributed Systems and Technologies 7, no. 3 (2016): 42–62. http://dx.doi.org/10.4018/ijdst.2016070103.
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Research trends keep evolving along the time with certain trackable patterns. Mining academic literature and discovering the latent research trends evolution is an interesting and important problem. Few of previous studies focusing on academic topic evolution modeling have addressed the temporal topic evolution patterns. In addition, researchers' profile and their social networks are valuable complementary to the research trends tracking. In this study, to analyze the underlying research trends evolution along with the scientific collaborations of researchers, a novel temporal research trends evolution model associated with researchers' social networks is proposed and built. Specifically, the detected research topics are classified into different clusters in each timeslot, and the evolution patterns are deduced among these topic clusters. The effectiveness of our approach is evaluated based on a real academic dataset. The experimental results can help users to discover the major research trends for specific fields. Besides, the tracked statuses of the corresponding scientific groups are helpful for searching research trends or finding collaboration opportunities according to researchers' different requirements.
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Jensen, Scott, Xiaozhong Liu, Yingying Yu, and Staša Milojevic. "Generation of topic evolution trees from heterogeneous bibliographic networks." Journal of Informetrics 10, no. 2 (2016): 606–21. http://dx.doi.org/10.1016/j.joi.2016.04.002.
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Brunilde Sanso, Lorela Cano, and Antonio Capone. "On the evolution of infrastructure sharing in mobile networks: A survey." ITU Journal on Future and Evolving Technologies 1, no. 1 (2020): 141–57. http://dx.doi.org/10.52953/nbqh9604.
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Infrastructure sharing for mobile networks has been a prolific research topic for more than three decades now. The key driver for Mobile Network Operators to share their network infrastructure is cost reduction. Spectrum sharing is often studied alongside infrastructure sharing although on its own it is a vast research topic outside the scope of this survey. Instead, in this survey we aim to provide a complete picture of infrastructure sharing both over time and in terms of research branches that have stemmed from it such as performance evaluation, resource management etc. We also put an emphasis on the relation between infrastructure sharing and the decoupling of infrastructure from services, wireless network virtualization and multi-tenancy in 5G networks. Such a relation reflects the evolution of infrastructure sharing over time and how it has become a commercial reality in the context of 5G.
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Lee, Won Sang. "Analyzing the Evolution of Interdisciplinary Areas." Journal of Global Information Management 30, no. 1 (2022): 1–23. http://dx.doi.org/10.4018/jgim.304062.
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Recently, various new areas of research have been of great interest to researchers. As these areas are highly based on academic and industrial needs, it is necessary to examine the change and evolution in research. This study proposed a framework for identifying emerging areas and their evolution. The proposed framework suggests that latent Dirichlet allocation is applied to identify emerging topics and their networks in such interdisciplinary areas. The simulation for empirical network analysis was then applied to the identified topic networks to terminate continuous evolution. The proposed framework is applied to a smart city, which is one of the most interdisciplinary and fast-evolving areas. These findings indicate that the evolution of smart transportation and smart grids is likely to be the focus. The findings also indicate that newly emerging research may lack openness and diversity. This study contributes to further investigate research trends and planning research strategies for new and interdisciplinary areas.
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Cai, Meng, Han Luo, and Ying Cui. "A Study on the Topic-Sentiment Evolution and Diffusion in Time Series of Public Opinion Derived from Emergencies." Complexity 2021 (December 2, 2021): 1–23. http://dx.doi.org/10.1155/2021/2069010.
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With the development of the Internet, social media has become an important platform for people to deal with emergencies and share information. When a public health emergency occurs, the public can understand the topics of the event and perceive the sentiments of others through social media, thus building a cooperative communication network. In this study, we took the public health emergency as the main research object and the natural disaster, accident, and social security event as the secondary research object and further revealed the law of the formation and evolution of public opinion through the analysis on temporal networks of topics and sentiments in social media platforms. Firstly, we identified the derived topics by constructing the topic model and used the sentiment classification model to divide the text sentiments of the derived topics into two types: positive sentiment and negative sentiment. Then, the ARIMA time series model was used to fit and predict the evolution and diffusion rules of topics and sentiments derived from public opinions on temporal networks. It was found that the evolution law of derived public opinions had similarities and differences in various types of emergencies and was closely related to government measures and media reports. The related research provides a foundation for the management of network public opinion and the realization of better emergency effects.
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Gomez, Manuel J., José A. Ruipérez-Valiente, and Félix J. García Clemente. "Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study." Sensors 23, no. 23 (2023): 9303. http://dx.doi.org/10.3390/s23239303.
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Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area.
Dissertations / Theses on the topic "Topic evolution networks"
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Li, Ke. "Exploring Topic Evolution in Large Scientific Archives with Pivot Graphs." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS043.
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Il existe une demande croissante d'outils pratiques pour explorer l'évolution de la recherche scientifique publiée dans des archives bibliographiques telles que le Web of Science, arXiv, PubMed ou ISTEX. L’extraction de topics et de leur évolution à partir de ces archives documentaires a de nombreuses applications et peut être étendue pour synthétiser des récits à partir d'ensembles de documents dans plusieurs domaines, y compris les flux d’actualités, les articles de recherche, les textes juridiques et les œuvres littéraires. Dans cette thèse, nous proposons un modèle de données et un langage d'interrogation pour la visualisation et l'exploration de réseaux d'évolution de sujets de recherche scientifiques. Notre modèle est indépendant d'une méthode particulière d'extraction et d'alignement de topics et propose un ensemble de métriques sémantiques et structurelles pour caractériser et filtrer des motifs d'évolution de domaines scientifiques. Ces métriques sont particulièrement utiles pour la visualisation et l'exploration de grands réseaux d'évolution de topics. Nous présentons également une implémentation de notre modèle sur Apache Spark et les résultats expérimentaux obtenus pour quatre archives de documents représentatives<br>There is an increasing demand for practical tools to explore the evolution of scientific research published in bibliographic archives such as the Web of Science, arXiv, PubMed or ISTEX. Revealing meaningful evolution patterns from these document archives has many applications and can be extended to synthesize narratives from datasets across multiple domains, including news stories, research papers, legal cases and works of literature. In this thesis, we propose a data model and query language for the visualization and exploration of topic evolution networks. Our model is independent of a particular topic extraction and alignment method and proposes a set of semantic and structural metrics for characterizing and filtering meaningful topic evolution patterns. These metrics are particularly useful for the visualization and the exploration of large topic evolution networks. We also present a prototype implementation of our model on top of Apache Spark and experimental results obtained for four representative document archives
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Teixeira, Diana Von-Haff Lopes. "Spatio-temporal distribution analysis of brand interest in social networks." Master's thesis, 2018. http://hdl.handle.net/10071/18604.
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Social Networks applications such as Facebook and Twitter became part of many people’s
lives and are used daily by millions of users. In such platforms, users share their emotions,
opinions, experiences, and thoughts. Twitter, in particular, is used to discuss diverse topics,
including brands, their products and services. In this thesis, we analyse how brand interest is
reflected on Twitter and how this platform can be used to monitor what people say about specific
brands, as an indicator of brand interest. Brand interest can be defined as the level of interest
one has in a brand, and the level of curiosity one has to learn more about a brand. For this work,
the volume of tweets is used as a measure of brand interest. Our methodology is based on time,
location, and the number of brand-related tweets to perform a spatio-temporal analysis.
Additionally, we propose a framework for discovering latent patterns (topics) from a large
dataset of grouped short messages to analyse brand interest, using Twitter as a data source. We
applied a well-known Text Mining technique called Topic Modelling, which is an unsupervised
learning technique used when dealing with text data, useful to uncover topics in a collection
of documents. This technique provides a convenient way to retrieve information from unstructured text. Topic Modelling tasks have been applied to track events/trends and uncover topics
in domains such as academic, public health, marketing, and so forth. The framework consists of training LDA (Latent Dirichlet Allocation) topic models on aggregated tweets, and then
applying the model on different documents, also composed by grouped Twitter posts. Furthermore, we describe a set of pre-processing tasks that helped to improve the performance of topic
models, enabling us to obtain a better output, thus performing a better analysis of it. The experiments demonstrated that Topic Modelling can successfully track people’s discussions on Social
Networks even in massive datasets such as the one used in the current work, and capture those
topics spiked by real-life events<br>Actualmente, plataformas como Twitter e Facebook fazem parte do dia-a-dia de muitas pessoas e são usadas por milhões de utilizadores. Nestas plataformas, denominadas Redes Sociais,
os utilizadores partilham informações incluindo opiniões, sentimentos, experiências e pensamentos. A plataforma Twitter, em particular, e usada para partilhar diversos tópicos, que podem
incluir dicussões sobre marcas, seus produtos e/ou serviços. O presente estudo analisa como o
interesse numa marca e reflectido na Rede Social Twitter e apresenta uma metodologia que permite utilizar o Twitter como fonte de informação para monitorizar o que os utilizadores dizem
acerca de determinadas marcas. O interesse numa marca pode ser definido como o nível de
interesse que um indivíduo tem por uma marca, e o nível de curiosidade que um indivíduo tem
e que o leva a aprender mais acerca dessa marca. Neste estudo, o número de tweets publicados
e usado para medir o interesse nas marcas escolhidas. A metodologia seguida baseia-se na data
em que o tweet foi publicado, localização, e número de publicações, para efectuar uma análise
espacio-temporal.
Adicionalmente, apresenta-se uma framework que possibilita a exploração de um vasto
conjunto de dados, com o objectivo de revelar padrões latentes, bem como analisar o interesse
nas marcas seleccionadas, usando o Twitter como fonte dados. Para o efeito, aplicou-se Topic
Modelling, uma técnica de Text Mining bastante utilizada para descobrir tópicos em texto não
estruturado. Algoritmos de Topic Modelling têm sido amplamente utilizados para monitorizar
eventos e tendências e descobrir tópicos em áreas como educação, marketing, saúde, entre outras. A framework consiste em treinar o modelo de tópicos LDA (Latent Dirichlet Allocation)
usando tweets agrupados (considerando determinado critério) e posteriormente aplicar o modelo treinado noutro conjunto de tweets agrupados (considerando outro critério). Descreve-se um
conjunto de tarefas de pré-processamento dos dados que ajudaram a melhorar o desempenho dos modelos, a obter melhor resultados e, consequentemente, a efectuar uma melhor análise. As experiências revelam que atravês de Topic Modelling e possível rastrear dicussões de utilizadores
de Redes Sociais durante um longo período de tempo, e capturar alterações relacionadas com acontecimentos reais.
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Chen, Jia-Yu, and 陳佳瑜. "A Novel Citation Network for Research Topic Evolution." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/d5j5cc.
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碩士<br>國立臺灣大學<br>資訊管理學研究所<br>107<br>In the past, people consumed related research thesis, journal papers or patent specifications to conclude the research trend of a topic area. However, with the coming of information explosion, consuming such a large number of data only by human seems to be inefficient. Therefore, it is important to develop a method to help users observe the evolution of research topics. In this study, we propose a novel citation network for detecting research trend to replace the past citation network that use a single document as a node and the citation relationships between documents as links. We extract the keywords which were specified by authors from the documents and redefine the citation relationships between nodes as well as the weight of edges. After deleting edges whose weight is lower than designated threshold, nodes in the final citation network would be clustered to find the research topics. Each cluster of nodes is considered as a research topic. The details of a research topic can be realized from the keywords in the cluster. Finally, we are able to observe the evolution of a topic area by checking the relationships among research topics in different years. The experiment shows that the proposed novel citation network can extract useful research topics from numerous literature and assist domain experts to observe the change of research topics.
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"Topics in dynamical processes in networked objects." 2008. http://library.cuhk.edu.hk/record=b5896855.
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Lee, Kwan Ho = 探討網絡上的若干動態過程 / 李鈞浩.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.<br>Includes bibliographical references (leaves 114-118).<br>Abstracts in English and Chinese.<br>Lee, Kwan Ho = Tan tao wang luo shang de ruo gan dong tai guo cheng / Li Junhao.<br>Chapter 1 --- Overview --- p.1<br>Chapter 2 --- Networks --- p.5<br>Chapter 2.1 --- Describing Networks --- p.5<br>Chapter 2.1.1 --- Adjacency Matrix --- p.6<br>Chapter 2.1.2 --- "Degree, Degree Distribution and Mean Degree" --- p.6<br>Chapter 2.1.3 --- Clustering Coefficient --- p.7<br>Chapter 2.1.4 --- "Shortest Path, Shortest Distance and Diameter" --- p.7<br>Chapter 2.1.5 --- Betweenness --- p.9<br>Chapter 2.2 --- Barabasi-Albert (BA) Network --- p.10<br>Chapter 2.2.1 --- Construction of BA Network --- p.10<br>Chapter 2.2.2 --- Analytical Study of Degree Distribution --- p.11<br>Chapter 2.2.3 --- Numerical Study of Degree Distribution --- p.12<br>Chapter 2.2.4 --- Shortest Distance --- p.13<br>Chapter 2.3 --- Summary --- p.14<br>Chapter 3 --- Routing in Networks: A Review --- p.16<br>Chapter 3.1 --- Introduction --- p.16<br>Chapter 3.2 --- Dijkstra´ةs Algorithm --- p.17<br>Chapter 3.2.1 --- Algorithm --- p.17<br>Chapter 3.2.2 --- Running Time --- p.18<br>Chapter 3.2.3 --- Routing Table Based on Shortest Path Algorithm --- p.19<br>Chapter 3.3 --- Routing Model --- p.19<br>Chapter 3.3.1 --- General Setup --- p.19<br>Chapter 3.3.2 --- Phase Transition and Evaluation of Network Performance --- p.20<br>Chapter 3.4 --- Using Shortest Path as Routing Algorithm on BA Networks --- p.21<br>Chapter 3.5 --- Other Routing Algorithms --- p.22<br>Chapter 3.5.1 --- Efficient Path --- p.23<br>Chapter 3.5.2 --- Routing based on Local Structural Information --- p.24<br>Chapter 3.5.3 --- Routing based on Dynamical Information --- p.25<br>Chapter 3.6 --- Summary --- p.26<br>Chapter 4 --- Optimization of Routing Efficiency through Redistributing Limited Resources --- p.28<br>Chapter 4.1 --- A Reallocation Rule - Short to Long (S2L) --- p.29<br>Chapter 4.2 --- Performance Enhancement After Applying S2L --- p.33<br>Chapter 4.3 --- Optimized Capability Distribution in Detail --- p.36<br>Chapter 4.4 --- Summary --- p.44<br>Chapter 5 --- N-person Evolutionary Snowdrift Game: A Review --- p.47<br>Chapter 5.1 --- Snowdrift Game (SG) and Evolutionary Snowdrift Game (ESG) --- p.47<br>Chapter 5.2 --- N-person Evolutionary Snowdrift Game --- p.49<br>Chapter 5.2.1 --- Payoffs of C-character and D-character Agents --- p.49<br>Chapter 5.2.2 --- Replicator Dynamics --- p.50<br>Chapter 5.2.3 --- Numerical Simulations --- p.52<br>Chapter 5.3 --- Summary --- p.55<br>Chapter 6 --- NESG on Complex Network --- p.56<br>Chapter 6.1 --- Models --- p.57<br>Chapter 6.2 --- Results of Model A (varying N) --- p.58<br>Chapter 6.2.1 --- Correlation of Characters in Degree --- p.60<br>Chapter 6.2.2 --- Correlation of Characters in Distance --- p.62<br>Chapter 6.3 --- Results of Model B (Fixed-N) --- p.63<br>Chapter 6.3.1 --- Correlation of Characters in Degree --- p.64<br>Chapter 6.4 --- Summary --- p.69<br>Chapter 7 --- Synchronization: A Review --- p.71<br>Chapter 7.1 --- Kuramoto Model --- p.72<br>Chapter 7.1.1 --- Analytical Method --- p.74<br>Chapter 7.1.2 --- Numerical Method --- p.78<br>Chapter 7.1.3 --- Summary of Kuramoto Model --- p.81<br>Chapter 7.2 --- Integrate-and-Fire Model --- p.81<br>Chapter 8 --- Kuramoto Model with Spatially Distributed Oscillators --- p.84<br>Chapter 8.1 --- Model --- p.84<br>Chapter 8.2 --- Numerical Results --- p.85<br>Chapter 8.3 --- Analytic Results --- p.87<br>Chapter 8.4 --- Summary --- p.90<br>Chapter 9 --- Integrate-Fire-and-Run Model --- p.92<br>Chapter 9.1 --- Model --- p.92<br>Chapter 9.2 --- Two-Body System --- p.94<br>Chapter 9.2.1 --- Case I: Oscillators A and B are in different sites --- p.94<br>Chapter 9.2.2 --- Case II: Oscillators A and B are in the same site --- p.101<br>Chapter 9.2.3 --- Comparison of Analytic Results and Numerical Simulations of Two-Body system --- p.103<br>Chapter 9.3 --- N-Body System --- p.105<br>Chapter 9.4 --- Summary --- p.111<br>Chapter 10 --- Outlook --- p.112<br>Bibliography --- p.114
Books on the topic "Topic evolution networks"
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Verloo, Nanke, and Luca Bertolini, eds. Seeing the City. Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463728942.
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The city is a complex object. Some researchers look at its shape, others at its people, animals, ecology, policy, infrastructures, buildings, history, art, or technical networks. Some researchers analyse processes of in- or exclusion, gentrification, or social mobility; others biological evolution, traffic flows, or spatial development. Many combine these topics or add still more topics beyond this list. Some projects cross the boundaries of research and practice and engage in action research, while others pursue knowledge for the sake of curiosity. This volume embraces this variety of perspectives and provides an essential collection of methodologies for studying the city from multiple, interdisciplinary, and transdisciplinary perspectives. We start by recognizing that the complexity of the urban environment cannot be understood from a single vantage point. We therefore offer multiple methodologies in order to gather and analyse data about the city, and provide ways to connect and integrate these approaches. The contributors form a talented network of urban scholars and practitioners at the forefront of their fields. They offer hands-on methodological techniques and skills for data collection and analysis. Furthermore, they reveal honest and insightful reflections from behind the scenes. All methodologies are illustrated with examples drawn from the authors own research applying them in the city of Amsterdam. In this way, the volume also offers a rich collection of Amsterdam-based research and outcomes that may inform local urban practitioners and policy makers. Altogether, the volume offers indispensable tools for and aims to educate a new generation of interdisciplinary and transdisciplinary-minded urban scholars and practitioners.
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The Myc/Max/Mad Transcription Factor Network (Current Topics in Microbiology and Immunology). Springer, 2006.
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Prescott, Tony J., and Leah Krubitzer. Evo-devo. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0008.
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This chapter explores how principles underlying natural evo-devo (evolution and development) continue to inspire the design of artificial systems from models of cell growth through to simulated three-dimensional evolved creatures. Research on biological evolvability shows that phenotypic outcomes depend on multiple interactions across different organizational levels—the adult organism is the outcome of a series of genetic cascades modulated in time and space by the wider embryological, bodily, and environmental context. This chapter reviews evo-devo principles discovered in biology and explores their potential for improving the evolvability of artificial systems. Biological topics covered include adaptive, selective, and generative mechanisms, and the role of epigenetic processes in creating phenotypic diversity. Modeling approaches include L-systems, Boolean networks, reaction-diffusion processes, genetic algorithms, and artificial embryogeny. A particular focus is on the evolution and development of the mammalian brain and the possibility of designing, using synthetic evo-devo approaches, brain-like control architectures for biomimetic robots.
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Halassa, Michael M., ed. The Thalamus. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108674287.
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The thalamus is a key structure in the mammalian brain, providing a hub for communication within and across distributed forebrain networks. Research in this area has undergone a revolution in the last decade, with findings that suggest an expanded role for the thalamus in sensory processing, motor control, arousal regulation, and cognition. Moving beyond previous studies of anatomy and cell neurochemistry, scientists have expanded into investigations of cognitive function, and harness new methods and theories of neural computation. This book provides a survey of topics at the cutting edge of this field, covering basic anatomy, evolution, development, physiology and computation. It is also the first book to combine these disciplines in one place, highlighting the interdisciplinary nature of thalamus research, and will be an essential resource for students and experts in biology, medicine and computer science.
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Chow, Peter C. Taiwan in the Global Economy. Greenwood Publishing Group, Inc., 2002. http://dx.doi.org/10.5040/9798216022121.
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A role model for late industrializing countries, Taiwan provides unique and interesting development lessons for third world countries. Once a poverty-stricken, resource-poor, technologically backward nation, Taiwan has become the hub of a global production network in many high tech industries with increasing significance in the world economy. In ten outstanding essays, written by highly respected economists, this book analyzes Taiwan's postwar economic development path, providing a valuable case study of its structural transformation from a labor-intensive to a technology-intensive economy. The book addresses three major topics. First it recaptures the lessons of Taiwan's experience. Then it considers the role of foreign investment on structural transformation and globalization. Finally, it examines Taiwan's economy in a global perspective, evaluating its role in the world market from the past to the future and its evolution from a colony to a newly industrialized country.
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Child, John, David Faulkner, Stephen Tallman, and Linda Hsieh. Cooperative Strategy. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198814634.001.0001.
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Cooperation has become the leading strategy adopted by business and other organizations. It is taking on new forms that are adapted to changing market expectations and technological possibilities in the rapidly evolving business environment. This new edition of Cooperative Strategy provides a comprehensive view of the practical and theoretical literature concerning cooperative strategies, and the alliance and network organizational forms that are the enablers of these strategies. It takes the reader through the stages of developing a cooperative alliance, from choosing a cooperative form and selecting partners, to establishing an alliance and managing the process of cooperation. It examines cooperative strategies in different sectors as well as internationally, and discusses performance criteria and evolution of cooperation over time. With insights from internationally recognized experts on cooperative strategy, this book presents extensive research on the topic while also addressing practical issues of alliance management. Echoing the words of the famous social psychologist Kurt Lewin that “there is nothing so practical as a good theory,” the authors provide a sound understanding of the theory and research on cooperative strategy so as to inform its practice. In this respect, this new edition follows its predecessor as an essential resource for both students and managers alike.
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Duhaime, Irene M., Michael A. Hitt, and Majorie A. Lyles, eds. Strategic Management. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190090883.001.0001.
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This contributed volume provides the knowledge portfolio for the Strategic Management field. Strategic Management has experienced significant growth as a research discipline and builds on strong theoretical and empirical research to provide valuable knowledge for managerial practice. The book is designed to capture the rich breadth and depth of knowledge in the field as well as insightful examinations of critical topics for the future and opportunities for future research (50 percent of the focus). Such information and knowledge are critical for all current and future scholars in the field and for thoughtful executives as well. The 37 chapters by well-known and highly respected scholars capture the essence of the field. They address the field’s evolution, the primary economic and organization theories underlying the base of knowledge, and the critical methodologies used to conduct research. The chapters cover topics of major importance (and their primary subtopics), such as corporate strategy (diversification, acquisitive growth, divestitures), strategic entrepreneurship (nascent firms, industry emergence, technology entrepreneurship), competitive and cooperative strategies (competitive advantage, alliances, networks), global strategy (multinational firms’ cross-border strategies, strategies employed in emerging economies), strategic leadership (top management teams, CEO succession), governance and boards of directors (corporate and venture boards, ownership effects on governance), knowledge and innovation (organizational learning, knowledge sharing), strategy process and practice (strategic decision making, organizational change), and microfoundations of strategy (strategic human capital, organizational capabilities). The book concludes with chapters on broadly important issues for the future, such as artificial intelligence, sustainability strategy, stakeholder perspective, and business model innovation strategies.
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Macdonald, David W., Chris Newman, and Lauren A. Harrington, eds. Biology and Conservation of Musteloids. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198759805.001.0001.
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The editors of this book have used their combined 90 years of experience working on the behaviour and ecology of wild carnivores to draw together a unique network of the world’s experts on musteloid biology and conservation. The musteloids are the most speciose and diverse super-family among carnivores, ranging from little known, exotic, and highly-endangered species to the popular and familiar, and include a large number of introduced invasives. They feature terrestrial, fossorial, arboreal, and aquatic members, ranging from tenacious predators to frugivorous omnivores, span weights from a 100g weasel to 30kg giant otters, and express a range of social behaviours from the highly gregarious to the fiercely solitary. Their diversity and extensive biogeography inform a wide spectrum of ecological theory and conservation practice. Beginning with a brief account of 93 musteloid species, there follow eight comprehensive review chapters covering topics most relevant to musteloid biology and conservation: evolution, form and function, population dynamics, communication, social organisation, exploitation and conflict with people, study tools and techniques, and disease. Twenty detailed case studies then delve into the very best species investigations worldwide, written by leading figures in the field, and providing a range of geographic and taxonomic coverage. The final chapter synthesises what has been discussed in the book, and reflects on the different and diverse conservation needs of musteloids and the wealth of conservation lessons they offer.
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Hotson, Howard. The Reformation of Common Learning. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780199553389.001.0001.
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Howard Hotson’s previous contribution to this series, Commonplace Learning, explored how a fragmented political and confessional landscape turned the northwestern corner of the Holy Roman Empire into the pedagogical laboratory of post-Reformation Protestant Europe. This sequel traces the further evolution of that tradition after that region’s leading educational institutions were destroyed by the Thirty Years War (1618–1648) and their students and teachers scattered in all directions. Transplanted to the Dutch Republic, the post-Ramist tradition provided ideas, values, and methods which helped to formulate the mechanical philosophy of Descartes and institutionalize it within a network of thriving universities. Within the international diaspora of Protestant intellectuals documented in the archive of Samuel Hartlib, post-Ramist encyclopaedism provided much of the framework for the pansophic programme of Comenius, which assisted the initial spread of Baconianism and related aspirations both in England and abroad. In post-war central Europe, another branch of the tradition helped inspire Leibniz’s life-long vision of a revised combinatorial encyclopaedia as the centrepiece of a wide-ranging reform programme. But as the underlying political, confessional, educational, and intellectual context shifted after 1648, the ancient conception of the encyclopaedia as a cycle of disciplines to be mastered by every scholar exploded into a potentially infinite number of discrete topics organized alphabetically within a mere work of reference. This book weaves together many new lines of inquiry against a huge geographical and thematic canvas to contribute fresh perspectives on the fraught middle years of the seventeenth century in particular and the shape of modern knowledge more generally.
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Burns, Kelli S. Social Media. ABC-CLIO, LLC, 2017. http://dx.doi.org/10.5040/9798216015888.
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Social media is arguably one of the most powerful technology-enabled innovations since the Internet itself. This single-volume book provides a broad and easily understandable discussion of the evolution of social media; related problems and controversies, especially for youth; key people and organizations; and useful social media data. Social media is an integral part of people's lives. More than half of the world's 2.4 billion Internet users sign in to a social network regularly—a figure that continues to grow. More than half of online adults now use two or more social media sites; 71 percent of Internet users are on Facebook. This book surveys the history of social media, addresses the power of social media for positive change, describes the problems and controversies social media have caused, and suggests potential solutions to these issues. Geared toward students and general readers, this accessibly written book covers such topics as the link between social media and body image, the psychological affects of social media use, online conversations about sexual assault, corporate use of social media data, political campaigning through social media, fan tweeting during television shows, and crisis communication through social media. Readers will also gain insights into the range of serious problems related to social media, including privacy concerns, social media addiction, social media hoaxes and scams, the pressure to project an ideal self, the curation of content presented on social media, cyberbullying, sexting, Facebook depression and envy, online shaming, and the impact of social media use on communication skills.
Book chapters on the topic "Topic evolution networks"
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Rahimi, Hamed, Hubert Naacke, Camelia Constantin, and Bernd Amann. "ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives." In Complex Networks & Their Applications XII. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53472-0_28.
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Le Coze, Jean-Christophe. "Safety and Subcontracting." In SpringerBriefs in Applied Sciences and Technology. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35163-1_1.
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AbstractThe aim of this introductory chapter is to provide a description of the evolving operating landscape of safety-critical systems (e.g. aviation, chemical and nuclear industries, railway) in the past two to three decades, towards network configurations based on contracting out. The topic of this book is strongly connected to this evolution and our understanding of its consequences for safety. The chapter situates the rise of these network configurations in the context of the advent of what has been described as globalisation, a phenomenon shaped by the liberalisation of trade and finance; privatisation and deregulation and the development of technology (communication, transport). A distinction between occupational safety and process safety is introduced to remain aware of different situations, depending on their nature, and positions within such networks. The chapter then summarises the different contributions to this book by a range of authors who bring unique lenses to this topic from a diversity of angles.
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Sangiorgio, Matteo. "Deep Learning in Multi-step Forecasting of Chaotic Dynamics." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_1.
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AbstractThe prediction of chaotic dynamical systems’ future evolution is widely debated and represents a hot topic in the context of nonlinear time series analysis. Recent advances in the field proved that machine learning techniques, and in particular artificial neural networks, are well suited to deal with this problem. The current state-of-the-art primarily focuses on noise-free time series, an ideal situation that never occurs in real-world applications. This chapter provides a comprehensive analysis that aims at bridging the gap between the deterministic dynamics generated by archetypal chaotic systems, and the real-world time series. We also deeply explore the importance of different typologies of noise, namely observation and structural noise. Artificial intelligence techniques turned out to provide robust predictions, and potentially represent an effective and flexible alternative to the traditional physically-based approach for real-world applications. Besides the accuracy of the forecasting, the domain-adaptation analysis attested the high generalization capability of the neural predictors across a relatively heterogeneous spatial domain.
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Tangi, Marco. "Dynamic Sediment Connectivity Modelling for Strategic River Basin Planning." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_3.
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AbstractSediment connectivity is a distributed property of river systems that emerges from the connected transfer of sediment between multiple sources and sinks. Its disruption, brought by anthropic disturbances, can have severe and unforeseen consequences on both fluvial ecosystems and human livelihood. Modeling network-scale sediment connectivity provides a foundational understanding of river processes and their response to new pressures and can be used to forecast future system evolutions. In this chapter, we present the basin-scale, dynamic sediment connectivity model D-CASCADE (Dynamic CAtchment Sediment Connectivity And DElivery), which quantifies spatiotemporal patterns of sediment delivery in river networks. D-CASCADE considers multiple factors affecting transport, including heterogeneities in hydrology and sediment supply, different grain sizes, channel morphological evolution, and reservoir presence and management. The model is designed to be flexible, data parsimonious, and computationally efficient. We also present two applications of D-CASCADE in real-world case studies for historic geomorphic evolution reconstruction and future dam impacts forecasting. D-CASCADE is intended for integrated, basin-scale water management efforts, to perform multiple screening of various decision portfolios for hydromorphological impact assessments.
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Stadler, Peter F. "Evolution of RNA-Based Networks." In Current Topics in Microbiology and Immunology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/82_2015_470.
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Zhou, Qingling, Genying Wang, and Haiqiang Chen. "A Topic Evolution Model Based on Microblog Network." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7262-5_89.
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Bioglio, Livio, Ruggero G. Pensa, and Valentina Rho. "TrAnET: Tracking and Analyzing the Evolution of Topics in Information Networks." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71273-4_46.
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Zhang, Lina. "System of Cross-Border E-commerce Network Pattern Evolution on Account of Bayes-BP Algorithm." In Innovative Computing Vol 1 - Emerging Topics in Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2092-1_24.
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Malavena, Gerardo. "Modeling of GIDL–Assisted Erase in 3–D NAND Flash Memory Arrays and Its Employment in NOR Flash–Based Spiking Neural Networks." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_4.
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AbstractSince the very first introduction of three-dimensional (3–D) vertical-channel (VC) NAND Flash memory arrays, gate-induced drain leakage (GIDL) current has been suggested as a solution to increase the string channel potential to trigger the erase operation. Thanks to that erase scheme, the memory array can be built directly on the top of a $$n^+$$ n + plate, without requiring any p-doped region to contact the string channel and therefore allowing to simplify the manufacturing process and increase the array integration density. For those reasons, the understanding of the physical phenomena occurring in the string when GIDL is triggered is important for the proper design of the cell structure and of the voltage waveforms adopted during erase. Even though a detailed comprehension of the GIDL phenomenology can be achieved by means of technology computer-aided design (TCAD) simulations, they are usually time and resource consuming, especially when realistic string structures with many word-lines (WLs) are considered. In this chapter, an analysis of the GIDL-assisted erase in 3–D VC nand memory arrays is presented. First, the evolution of the string potential and GIDL current during erase is investigated by means of TCAD simulations; then, a compact model able to reproduce both the string dynamics and the threshold voltage transients with reduced computational effort is presented. The developed compact model is proven to be a valuable tool for the optimization of the array performance during erase assisted by GIDL. Then, the idea of taking advantage of GIDL for the erase operation is exported to the context of spiking neural networks (SNNs) based on NOR Flash memory arrays, which require operational schemes that allow single-cell selectivity during both cell program and cell erase. To overcome the block erase typical of nor Flash memory arrays based on Fowler-Nordheim tunneling, a new erase scheme that triggers GIDL in the NOR Flash cell and exploits hot-hole injection (HHI) at its drain side to accomplish the erase operation is presented. Using that scheme, spike-timing dependent plasticity (STDP) is implemented in a mainstream NOR Flash array and array learning is successfully demonstrated in a prototype SNN. The achieved results represent an important step for the development of large-scale neuromorphic systems based on mature and reliable memory technologies.
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Zheng, Yipu, Zhuqian Zhou, and Paulo Blikstein. "Towards an Inclusive and Socially Committed Community in Artificial Intelligence in Education: A Social Network Analysis of the Evolution of Authorship and Research Topics over 8 Years and 2509 Papers." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11644-5_34.
Full textConference papers on the topic "Topic evolution networks"
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Jung, Sukhwan, and Aviv Segev. "Semantic Similarity Analysis between Future Topics and Their Neighbors in Topic Networks for Network-based Topic Evolution." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020287.
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Zehnalova, S., Z. Horak, M. Kudelka, and V. Snasel. "Evolution of Author's Topic in Authorship Network." In 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.208.
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Liang, Wei, Zixian Lu, Qun Jin, Yonghua Xiong, and Min Wu. "Modeling of Research Topic Evolution Associated with Social Networks of Researchers." In 2015 IEEE 12th Intl. Conf. on Ubiquitous Intelligence and Computing, 2015 IEEE 12th Intl. Conf. on Autonomic and Trusted Computing and 2015 IEEE 15th Intl. Conf. on Scalable Computing and Communications and its Associated Workshops (UIC-ATC-ScalCom). IEEE, 2015. http://dx.doi.org/10.1109/uic-atc-scalcom-cbdcom-iop.2015.213.
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Guo, Tinghao, Jiarui Xu, Yue Sun, Yilin Dong, Neal E. Davis, and James T. Allison. "Network Analysis of Design Automation Literature." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67361.
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In this paper we present a study of citation and co-authorship networks for articles from the ASME Design Automation Conference (DAC) during the years 2002–2015. We identify key authors, show that the co-authorship network exhibits the small world network property, and reveal other insights from network structure. Results from two topic modeling methods are presented. A frequency-based model was developed to explore DAC topic distribution and evolution. Citation analysis was also conducted for each core topic. A correlation matrix and association rule mining were used to discover topic relations and to gain insights for research gaps and recommendations. A recently developed unsupervised learning algorithm, propagation mergence (PM), was applied to the DAC citation network. Influential papers and major clusters were identified and visualizations are presented. The resulting insights may be beneficial to the engineering design research community, especially with respect to determining future directions and possible actions for improvement. The data set used here is limited. Expanding to include additional relevant conference proceedings and journal articles in the future would offer a more complete understanding of the engineering design research literature.
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Zhao, Yunwei, Can Wang, Chi-Hung Chi, Willem-Jan van den Heuvel, Kwok-Yan Lam, and Min Shu. "Beyond the Power of Mere Repetition: Forms of Social Communication on Twitter through the Lens of Information Flows and Its Effect on Topic Evolution." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852290.
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Besharatloo, Mohammad, Atiye Rahimizadeh, and Masoud Besharatloo. "Hybrid Intrusion Detection Model for Computer Networks." In 11th International Conference on Signal Image Processing and Multimedia. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130906.
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Intrusion detection is an important research topic in network security because of increasing growth in the use of computer network services. Intrusion detection is done with the aim of detecting the unauthorized use or abuse in the networks and systems by the intruders. Therefore, the intrusion detection system is an efficient tool to control the user's access through some predefined regulations. Since, the data used in intrusion detection system has high dimension, a proper representation is required to show the basis structure of this data. Therefore, it is necessary to eliminate the redundant features to create the best representation subset. In the proposed method, a hybrid model of differential evolution and firefly algorithms was employed to choose the best subset of properties. In addition, decision tree and support vector machine (SVM) are adopted to determine the quality of the selected properties. In the first, the sorted population is divided into two sub-populations. These optimization algorithms were implemented on these subpopulations, respectively. Then, these sub-populations are merged to create next repetition population. The performance evaluation of the proposed method is done based on KDD Cup99. The simulation results show that the proposed method has better performance than the other methods in this context.
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Hakimova, Aida. "Network Approach for Visualizing the Evolution of the Research of Cross-lingual Semantic Similarity." In International Conference "Computing for Physics and Technology - CPT2020". Bryansk State Technical University, 2020. http://dx.doi.org/10.30987/conferencearticle_5fce2773d960b0.37534641.
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The paper is devoted to the problem of the bibliometric study of publications on the topic “Cross-lingual Semantic Similarity”, available in the Dimensions database. Visualization of scientific networks showed fragmentation of research, limited interaction of organizations. Leading countries, leading organizations and authors are highlighted. Overlay visualization allowed us to assess the trends in citing authors. The expansion of the geography of research is shown. For international cooperation, the uniformity of semantic approaches to describing the concepts of critical infrastructure, incidents, resources and services related to their maintenance and protection is important. The stated approaches can be applied for visualization and modeling of technological development in the modern digital world. Semantic similarity is a longstanding problem in natural language processing (NLP). The semantic similarity between two words represents the semantic proximity (or semantic distance) between two words or concepts. This is an important problem in natural language processing, as it plays an important role in finding information, extracting information, text mining, web mining and many other applications.
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Peres, Leandro, Pablo Cecilio, Francielly Rodrigues, Nícollas Silva, and Leonardo Rocha. "An overview of Brazilian researches in the Computer Science field in last years." In VII Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/kdmile.2019.8783.
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Recently, most traditional market services have joined online service platforms. Despite the practicality achieved, such services eventually bring a large amount of data to the Web. In this sense, data analysis, data engi- neering, and data science activities have become extremely necessary. In general, they can extract extra information about systems and users, allowing the owners to produce insights and analyze patterns. Then, we propose an evalua- tion methodology to be applied in the online scenario of registration of publications and scientific productions, such as ResearchGate and Lattes Platform of CNPq. This methodology is unsupervised and divided into three main stages: (i) obtaining and representing the data; (ii) application of topic modeling; and (iii) the labeling of topics. This proposal diverges from the literature’s proposes that are based on collaborative networks and supervised techniques. We applied this methodology to a Lattes database and were able to observe the evolution of Computer Science research in Brazil. Based on this analysis, it is possible to identify the most popular and least explored research lines in order to direct public investments according to a certain interest.
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Nguyen, Thuc, and Phuc Do. "Discovering Topic Evolution in Heterogeneous Bibliographic Network." In 2018 10th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2018. http://dx.doi.org/10.1109/kse.2018.8573400.
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Ye, Chunlei, Dongmei Liu, Na Chen, and Li Lin. "Mapping the topic evolution using citation-topic model and social network analysis." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382375.
Full textReports on the topic "Topic evolution networks"
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Payment Systems Report - June of 2020. Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/rept-sist-pag.eng.2020.
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With its annual Payment Systems Report, Banco de la República offers a complete overview of the infrastructure of Colombia’s financial market. Each edition of the report has four objectives: 1) to publicize a consolidated account of how the figures for payment infrastructures have evolved with respect to both financial assets and goods and services; 2) to summarize the issues that are being debated internationally and are of interest to the industry that provides payment clearing and settlement services; 3) to offer the public an explanation of the ideas and concepts behind retail-value payment processes and the trends in retail payments within the circuit of individuals and companies; and 4) to familiarize the public, the industry, and all other financial authorities with the methodological progress that has been achieved through applied research to analyze the stability of payment systems. This edition introduces changes that have been made in the structure of the report, which are intended to make it easier and more enjoyable to read. The initial sections in this edition, which is the eleventh, contain an analysis of the statistics on the evolution and performance of financial market infrastructures. These are understood as multilateral systems wherein the participating entities clear, settle and register payments, securities, derivatives and other financial assets. The large-value payment system (CUD) saw less momentum in 2019 than it did the year before, mainly because of a decline in the amount of secondary market operations for government bonds, both in cash and sell/buy-backs, which was offset by an increase in operations with collective investment funds (CIFs) and Banco de la República’s operations to increase the money supply (repos). Consequently, the Central Securities Depository (DCV) registered less activity, due to fewer negotiations on the secondary market for public debt. This trend was also observed in the private debt market, as evidenced by the decline in the average amounts cleared and settled through the Central Securities Depository of Colombia (Deceval) and in the value of operations with financial derivatives cleared and settled through the Central Counterparty of Colombia (CRCC). Section three offers a comprehensive look at the market for retail-value payments; that is, transactions made by individuals and companies. During 2019, electronic transfers increased, and payments made with debit and credit cards continued to trend upward. In contrast, payments by check continued to decline, although the average daily value was almost four times the value of debit and credit card purchases. The same section contains the results of the fourth survey on how the use of retail-value payment instruments (for usual payments) is perceived. Conducted at the end of 2019, the main purpose of the survey was to identify the availability of these payment instruments, the public’s preferences for them, and their acceptance by merchants. It is worth noting that cash continues to be the instrument most used by the population for usual monthly payments (88.1% with respect to the number of payments and 87.4% in value). However, its use in terms of value has declined, having registered 89.6% in the 2017 survey. In turn, the level of acceptance by merchants of payment instruments other than cash is 14.1% for debit cards, 13.4% for credit cards, 8.2% for electronic transfers of funds and 1.8% for checks. The main reason for the use of cash is the absence of point-of-sale terminals at commercial establishments. Considering that the retail-payment market worldwide is influenced by constant innovation in payment services, by the modernization of clearing and settlement systems, and by the efforts of regulators to redefine the payment industry for the future, these trends are addressed in the fourth section of the report. There is an account of how innovations in technology-based financial payment services have developed, and it shows that while this topic is not new, it has evolved, particularly in terms of origin and vocation. One of the boxes that accompanies the fourth section deals with certain payment aspects of open banking and international experience in that regard, which has given the customers of a financial entity sovereignty over their data, allowing them, under transparent and secure conditions, to authorize a third party, other than their financial entity, to request information on their accounts with financial entities, thus enabling the third party to offer various financial services or initiate payments. Innovation also has sparked interest among international organizations, central banks, and research groups concerning the creation of digital currencies. Accordingly, the last box deals with the recent international debate on issuance of central bank digital currencies. In terms of the methodological progress that has been made, it is important to underscore the work that has been done on the role of central counterparties (CCPs) in mitigating liquidity and counterparty risk. The fifth section of the report offers an explanation of a document in which the work of CCPs in financial markets is analyzed and corroborated through an exercise that was built around the Central Counterparty of Colombia (CRCC) in the Colombian market for non-delivery peso-dollar forward exchange transactions, using the methodology of network topology. The results provide empirical support for the different theoretical models developed to study the effect of CCPs on financial markets. Finally, the results of research using artificial intelligence with information from the large-value payment system are presented. Based on the payments made among financial institutions in the large-value payment system, a methodology is used to compare different payment networks, as well as to determine which ones can be considered abnormal. The methodology shows signs that indicate when a network moves away from its historical trend, so it can be studied and monitored. A methodology similar to the one applied to classify images is used to make this comparison, the idea being to extract the main characteristics of the networks and use them as a parameter for comparison. Juan José Echavarría Governor