Reviewerly/exHarmony huggingface dataset:
Mapas cognitivos difusos como herramienta para el análisis de las causas de violencia intrafamiliar

La violencia en general ha sido una preocupacion general desde siempre, sobre todo la intrafamiliar y de genero. Son muchos los factores causales de la violencia intrafamiliar una revision de la bibliografia y la consulta a varios autores, permitio conocer que la modelacion, causal mediante Mapas Cognitivos Difusos, resulta apropiado para analizar este fenomeno. En este trabajo se realiza el analisis estatico del Mapas Cognitivos Difusos obtenido basado en las metricas de centralidad de la teoria de grafos. Como resultado del analisis se detecto que los factores que mayor nivel de desencadenamiento de acciones violentas en el marco familiar es el consumo de sustancias estupefacientes como el alcohol y las drogas. Esto puede provocar que los ninos y ninas y adolescentes que conviven con este problema puedan ser inducidos al consumo de este tipo de sustancias, incorporando otro problema mas a la sociedad. Adicionalmente se detectaron que todos los factores son clasificados como ordinarios por lo que todos mantienen una confluencia entre si de forma que no se puede analizar un problema por separado.Palabras clave: Violencia intrafamiliar, mapas cognitivos difusos, analisis causal. ABSTRACTViolence in general has always been a general concern, especially intra-family and gender-based violence. There are many causal factors of intrafamily violence. A review of the bibliography and the consultation of several authors allowed us to know that causal modeling through Fuzzy Cognitive Maps is appropriate to analyze this phenomenon. In this work, the static analysis of the fuzzy cognitive map obtained based on the centrality metrics of graph theory is carried out. As a result of the analysis, it was detected that the factors with the highest level of triggering of violent actions in the family setting is the consumption of narcotic substances such as alcohol and drugs. This can cause children and adolescents who live with this problem to be induced to consume this type of substance, incorporating yet, another problem into society. Additionally, it was detected that all the factors are classified as ordinary so that they all maintain a confluence with each other so that a problem cannot be analyzed separately.Keywords: Intrafamily violence, fuzzy cognitive maps, causal analysis.
Detección y Clasificación de Objetos en Movimiento en Espacios Cerrados Utilizando Algoritmos de Visión Artificial

The field of computer-assisted vision has expanded over the years, awakening great interest in its study and research development in different disciplines to achieve the highest performance of the applications developed with these methods.This article summarizes the scope of a larger investigation, presented as a Master's Final Project carried out by the same author at the Universitat Politecnica de Valencia (Hidrobo, 2018) in which some of the main techniques of computer vision video surveillance and its behavior when the guarded covered environment is permanently inhabited by a pet. In this way, with the review of different alternatives, a difference is made between an alert generated by its movement and one generated by an intruder, preventing the alarms from being triggered unwantedly.For this purpose, two video resources were used: 1) the OpenCV computer vision library in a C # environment in order to be able to repeat the experiments in the same circumstances for the different algorithms to be evaluated and, 2) different videos that register animals and people, simulating the environment to be monitored.The objective of this work was to develop a test bench that allows detecting and classifying a moving object inside a closed environment through the use of image processing and classification algorithms.At the end of the experiments, the data obtained were arranged in tables that show the results when executing different video sequences on the test bench, using all the image processing and classification methods developed.
Pedestrian Motion Prediction with Improved ADNet Model

Motion prediction obtains pedestrian moving direction, which is fundamental control parameters for robot tail following. In this paper, a tracker named ADNet-PMP is proposed for pedestrian motion prediction. The ADNet model is improved with interlace sampling and optimized with model- update mechanism. The network is pre-trained with deep reinforcement learning and supervised learning to track the pedestrian by moving the bounding box sequentially. The movements of bounding box are transformed to actual motion behaviors with a prediction strategy. According to the results on OTB-100 datasets, ADNet-PMP achieves 1.6 times speed enhancement while keeps competitive accuracy against original ADNet. Experiment on pedestrian motion videos validates the effectiveness of motion prediction.
The Analysis Of The Key Drivers And Barriers Of Cloud Migration In Companies In Bosnia And Herzegovina

The effective use of cloud technology eliminates the barriers and potential IT obstacles that companies face by using a traditional way of developing a platform for the information systems, as a backbone of modern business. Practical application of any technology and innovation produces distinct advantages and risks that need to be carefully analyzed, identified, and managed within the company. Cloud technology will perform an increasing role in the future (primarily in developing countries) because it will be effective to combat certain problems that cause inefficiencies that threaten company’s competitiveness, operational flexibility, and thus its existence. The research was conducted between February and September, 2019 in the form of online research. The research was sent to 387 participants, but in total 84 questionnaires were filled in full. According to there-search results, the respondents believe the key drivers of cloud migration comprise the following: improving efficiency, enabling more extensive data sharing capabilities, improving security, increasing innovations, reducing costs. The aforementioned research results are correlated with studies conducted by other authors over different time periods. According to the research results, the most significant obstacles of cloud migration include the following: lack of internal experts, privacy, lack of responsibility of cloud providers in case of security incident, costs and complexity of migration to the cloud, etc. The research results point to the conclusion that the use of cloud technology increases the degree of agility, operational flexibility, economic productivity, and competitiveness of companies. An increase in the level of cloud awareness as well as in the education of top managers, with simultaneous internal and external changes within the company, would consequently result in the increase of cloud migration in domestic companie.
Development of a framework to guide enterprises in the adoption of Blockchain solutions in their Supply Chain

Blockchain has gained increasing traction since its first application, the Bitcoin. Its deployment does not characterize anymore only the financial sector, but it became a technology whose use is widespread across industries. One of the most promising applications regards the supply chain as this new technology overcomes the hurdles of supply chain complex and fragmented nature. Even if Blockchain has notable advantages, it is still an immature technology and most of the firms have not the financial capability to assess the feasibility of Blockchain projects. The research carried out wants to analyse the actual applications of Blockchain for supply chain across different sectors in order to understand what the most important characteristics and the key success factors are. The final object is the development and application of a framework companies can use to evaluate the feasibility of different blockchain based solutions for supply chain.
Using Google Analytics for Strategic Decision-Making inMarketing

This article provides information about visitors’ of freelyaccessible electronic journal analysis options via GoogleAnalytics resources. The low-budget magazine which readers canfreely download was selected for identification of causeslowering the interest in similar magazines and make theproposal to support of strategic marketing decisions. GoogleAnalytics was used to acquire data which is not related to theconversion of visitors to obtain some information needed toregulate the strategic decision making of the magazine. Themanagement of the magazine received clear information about thedevelopment of communication with visitors. On the basis of thedata obtained, a specific strategic decision was taken for thelow-budget magazine. An invitation to the internationalscientific conference Media and Education 2018 coorganized byMedia4u Magazine – Journal was sent to the selected segment ofthe market. At the same time, information was provided that thelast conference proceedings were indexed in the Web of Science,and the next edition will be submitted with an indexingrequest. Another piece of information presented during thisperiod was an offer of publishing other articles in a magazineissue. Subsequently, a partial impact survey of the implementedactions was made using Google Analytics and proposed to modifythem. The result is a proposal of a strategy focusing on thesegment of doctoral students and the effort to recognize themagazine as a specific space for the acquisition of doctoralexperience by trainers, schools, the Accreditation Office, etc.
MUSEUM’S AND LIBRARY’S ACTUAL EDUCATIONAL INITIATIVES IN BULGARIA: NEW RESEARCH

Dynamic processes in the contemporary information society and the rapid spread of new technologies necessitate innovative methodological, pedagogical and managerial approaches in the field of education to provoke interest, to achieve lasting knowledge and to create conditions for the manifestation of intrinsic motivation among young people. The concept of a „knowledge-based society” provokes the search for changes in institutional structures that guarantee the establishment of lifelong learning attitudes. There are different definitions of the term „education” that unanimously unite its core mission to preserve and develop culture. Therefore, cultural institutions within the meaning of the current legal framework in the country, in addition to carrying out activities to create, disseminate and preserve cultural and historical values, should develop active educational initiatives to fulfil their role as a mediator between the preserved tradition and the present. It is also one of the mechanisms for achieving the current education goals related to its sustainable development. This paper presents new research project entitled „Research of Contemporary Educational Programs of Cultural Institutions in Bulgaria“, financed by the National Science Fund, part of the Ministry of Education and Science.
Twitter User Profile Analyzer

Twitter is the most requested microblog site with 336million active users. On Twitter, users can instantly express ideas, emotions and reactions with tweets. Data coming from twitter is fast and effective responses can be obtained,whichcanbeusedin political, social and economic areas. It is possible to analyze the characteristics, trends and behaviors of the users by revealing their interaction. In this study, a brief description of methodology used to analyze the Twitter account/Profile of an individual based on the tweets tweeted by user along with sentiment analysis of the same is presented.Twitterprofile analysis by parsing the tweets fetched from Twitter using Python isdiscussed.
Biological Affordances a Aesthetics of Interaction

If you could trap a moment of painful sorrow or a deep joy, what form would you like to keep? This project is exploring how designing with biology can allow new sets of affordances and ways of expr ...
QRGAN: Quantile Regression Generative Adversarial Networks

Learning high-dimensional probability distributions by competitively training generative and discriminative neural networks is a prominent approach of Generative Adversarial Networks (GANs) among generative models to model complex real-world data. Nevertheless, training GANs likely suffer from non-convergence problem, mode collapse and gradient explosion or vanishing. Least Squares GAN (LSGANs) and Wasserstein GANs (WGAN) are of representative variants of GANs in literature that diminish the inherent problems of GANs by proposing the modification methodology of loss functions. However, LSGANs often fall into local minima and cause mode collapse. While WGANs unexpectedly encounter with inefficient computation and slow training due to its constraints in Wasserstein distance approximation. In this paper, we propose Quantile Regression GAN (QRGAN) in which quantile regression is adopted to minimize 1-Wasserstein distance between real and generated data distribution as a novel approach in modification of loss functions for improvement of GANs. To study the culprits of mode collapse problem, the output space of discriminator and gradients of fake samples are analyzed to see if the discriminator guides the generator well. And we found that the discriminator should not be bounded to specific numbers. Our proposed QRGAN exposes high robustness against mode collapse problem. Furthermore, QRGAN obtains an apparent improvement in the evaluation and comparison of Frechet Inception Distance (FID) for generation performance assessment compared to existing variants of GANs.
LibGuides: Getting Started with Archives: Preparing to Search

This guide serves as an introduction to locating and evaluating collection description, and beginning archival research at NYPL These pointers will help craft searches, edit filters, and analyze archival description in the search results
Lunchtime learning: Avoiding the empathy trap

Does empathy really lead to good design? Nicole Symington is a PhD researcher developing a design method. In this Lunchtime Learning, she shares a process based on a reflexive practice, to help you uncover where your empathy might either help or hinder your designs.Free to attend and fun-for-all - Lunchtime Learning is the home of interesting people talking about interesting things. Hosted by Wintec's Design Factory NZ - this Lunchtime Learning is our first global event - with Design Factories from around the world joining in!Nicole is a researcher at Design Factory Melbourne, and will provide a fun and fast-paced learning experience for all.If you're interested in solving customer problems, and understanding their needs better - then you won't want to miss out on this Lunchtime Learning.
T. Kuhn on Interpretation and Understanding

The author of the article emphasizes that, although before Kuhn, philosophers talked about theories, fundamental principles, or laws, about research methods, all these concepts expressed only one side of science. The concept of “paradigm”, despite its natural vagueness, very successfully unites all these aspects: a paradigm is a set of theories, laws, methods, and research models recognized by the scientific community at a certain period. The article considers the question of how T. Kuhn interpreted in­terpretation and understanding in the natural and human sciences. It is shown that Kuhn identified the interpretation of sensory impressions though natural language with the interpretation of human behavior, and therefore did not see the fundamental difference between the natural sciences and the humanities. From his point of view, the astronomer interprets the objects he observes in exactly the same way as the hu­manist interprets text or human behavior. However, the interpretation of human be­havior is ascribing to the observed actions of a persona certain intention – motive, goal, motivation. We do not ascribe any intentions to natural objects, and this is fun­damental difference between the natural sciences and humanities. Kuhn does not see this difference, since he identifies the “natural interpretation”of sensory impressions through everyday language with the theoretical interpretation of observable objects through myth, religion, scientific theory.
Error feedback denoising network

Recently, deep convolutional neural networks have been successfully used for image denoising due to their favourable performance. This paper examines the error feedback mechanism to image denoising and propose an error feedback denoising network. Specifically, we use the down-and-up projection sequence to estimate the noise feature. By the residual connection, the clean structures are removed from the noise features. The essential difference between the proposed network and other existing feedback networks is the projection sequence. Our error feedback projection sequence is down-and-up, which is more suitable for image denoising than the existing up-and-down order. Moreover, we design a compression block to improve the expression ability of the general 1 ×1 convolutional compression layer. The advantage of our well-designed down-and-up block is that the network parameters are fewer than other feedback networks and the receptive field is enlarged. We have implemented our error feedback denoising network on denoising and JPEG image deblocking. Extensive experiments verify the effectiveness of our down-and-up block and demonstrate that our error feedback denoising network is comparable with the state-of-the-art. The code will be open source. The source codes for reproducing the results can be found at: https://github.com/Houruizhi/EFDN.
LibGuides: A Guide for Reluctant Teen Readers: Adaptations

This guide is a resource for teens (ages 13-18) who may be reluctant to read, who struggle to enjoy books, or who need a starting place for rekindling their previous love for reading.
A Supervised Named Entity Recognition Method Based on Pattern Matching and Semantic Verification

Named entity recognition is a basic task in the field of natural language processing and plays a pivotal role in tasks such as information extraction, machine translation, and knowledge graph construction. It has also received widespread attention in financial, biological and pharmaceutical industries. This paper proposes a method of weakly supervised learning to recognize the complex named entities (commonly composed of multiple small entity sequences, hereinafter referred to as CNEs) in the corpus, which makes it difficult to determine the boundaries of such entities. To improve the recognition accuracy, our method Masked-BiLSTM-CRF is proposed to separate the context semantic relationship determination from the entity boundary confirmation. This method is based on two aspects to solve the above problems: (1) Semantic model based on CNEs mask processing. Before training, the CNEs in the corpus will be masked, and then use the masked corpus training the semantic model through BiLSTM-CRF, which can verify whether the context semantics of the corresponding location entities are correct. (2) A weakly supervised CNEs boundary confirmation model based on sequential patterns. In the small sample data set, the target CNE candidate set is found by sliding window combined with sequence pattern matching, and then it is effectively screened and judged by the semantic understanding model obtained in (1). The experimental results show that compared with the named entity recognition method based directly on BiLSTM-CRF on the weakly-supervised named entity recognition in financial field, our proposed method improves F1-Score in the small data training sample set by nearly 9%, and it has some generalization ability.
국제 테러리즘에 대한 효과적 대응 방안

According to statistics from the Foreign Ministry''s Center for Terrorism and Information, the number of international terrorism cases stood at 1,614 in 2019, down from 2,079 last year, but the death toll rose to 8,025 from 7,916 last year.This shows that terrorism in the Middle East has decreased due to the U.S.-led campaign to repel ISIS, but its size and severity are increasing. In other words, the recent terrorist attacks are indiscriminately occurring not only in public institutions such as the military and police, but also in private facilities such as tourist attractions, hotels and restaurants where people gather.Last year, there were no direct terrorist attacks in Korea, but there were more than 500 suspected bombing threats and terrorist attacks on important national facilities, and 26 foreigners related to terrorist groups were deported. As such, Newterrorism is growing and growing globally, and Korea is no longer safe. It is time to keep in mind that there is a possibility of a terrorist attack in Korea at any time, as foreigners including the Middle East are entering the country due to rapid internationalization, and the dispatch of South Korean troops to Lebanon (Dongmyeong Unit) and South Sudan (Hanbit Unit) is included in the target countries of terrorist groups.Accordingly, this paper investigated and analyzed papers, reports and statistical data published in relation to terrorism, examined background knowledge about terrorism and presented effective countermeasures.
Reinforcement Learning in Industrial Applications

Although reinforcement learning has gained great success in computer games, there are only few yet known implementations in ndustrial applications. This despite the fact that reinforcement learning offers interesting methods to optimise the control of nonlinear processes. In this thesis we have used two model free reinforcement learning algorithms (PPO and DDPG) to control three different simulations of industrial processes, the simplified Tennessee Eastman, original Tennessee Eastman and the Haldex brake valve. Both reinforcement learning algorithms could in almost all cases learn to reach a set point. In addition, hyperparameters were found to have a high impact on training performance. In conclusion, our tests indicate that the model free reinforcement learning algorithms are basically capable of controlling industrial processes. Python code for the PPO algorithm applied to the Original Tennesse Eastman process can be found at Github. 1 1 https://github.com/Heigke/Reinforcement-Learning-In-Industrial-Applications (Less)
Preface for International Conference on Recent Trends in Applied Research (ICoRTAR2020) Proceedings

The 1st International Conference on Recent Trends in Applied Research (ICoRTAR2020) is a virtual conference based on enabling platforms to present research results relating to global issues. ICoRTAR2020 is hosted by Research Acad HUB with channels and human resources in Nigeria, Mexico, India, and Cameroon. Thus, the conference was held virtually from September 18-19, 2020. ICoRTAR2020 aims to bring together faculty members, leading scientists, academicians, research &amp; graduate scholars, industrial professionals, and decision-makers to discuss current trends in research with implications on global phenomena such as COVID-19, economic growth &amp; development, and so on. Basically, the ICORTAR2020 participants discussed extensively vital topics on recent trends and issues in Applied Science, Applied Mathematics, Computational Science, Engineering Science, Physics and Nanoscience, Financial Modeling and, Artificial Intelligence, and Stochastic Dynamics.
Resolving pronouns in Twitter streams: context can help!

Many people live-tweet televised events like Presidential debates and popular TV-shows and discuss people or characters in the event. Naturally, many tweets make pronominal reference to these people/characters. We propose an algorithm for resolving personal pronouns that make reference to people involved in an event, in tweet streams collected during the event.