Reviewerly/exHarmony huggingface dataset:
Review of Online Teaching Resources Integration in the New Situation

Integration of online teaching resources is an important way to achieve optimal allocation of resources. This paper aims at the problem that the existing online teaching resources are overloaded and dispersed, which makes it difficult for users to obtain resources accurately. Beginning with the concept and goal of online teaching resources integration, this paper summarizes the research progress of online teaching resources integration, compares the advantages and disadvantages of various integration methods, and finally the future research direction of online teaching resources integration is put forward.
Efficiency in Simulating Information Networks

Recent successes in quantum technology have provided a number of powerful tools for viewing nature in terms of information. In particular, quantum information science has proved to be a useful way to greatly simplify the way the universe is viewed as well as treating the subject, an observer, and the object, the observed universe, on an equal footing. Several important applications in quantum technology such as communication, information processing, etc., use a distinctive property of entanglement. In this article, a numerical simulation is applied to a swapping measurement protocol involving two 3-level entangled states. A different condition, in non-maximal states, that approximates the optimal result (i.e., the weaker link between the two initial states) will be shown through a numerical method. This different class of states is distributed just as wide as the class yielding the optimal result. The result may be useful in establishing a long distance maximal entanglement that is often fragile and unstable due to noise.
Action Recognition Happens Very Quickly in the Human Brain

What is that person doing?"Without even thinking about it, your brain is processing what you see and using it to recognize other people's actions, like running and walking.Recognizing actions comes easily to humans, but it is an extremely challenging problem for computers to solve.In particular, it is difficult for computers to recognize the same actions from different viewpoints, like front or side views, or against different backgrounds.As a result, scientists and engineers have not been able to develop computer algorithms that can recognize actions as well as humans can.Our research goal is to better understand how the human brain recognizes actions so we can replicate those functions in computer systems.Using MEG, a device that allows us to read brain signals, and machine learning, a branch of computer science, we were able to decode the signals that occur in the human brain when it recognizes an action.
Novel Artificial Human Optimization Field Algorithms – The Beginning

New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms.Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity.In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts.These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field.There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc.But there are no Hybrid PSO articles which based on concepts like Human Disease, Human Kindness and Human Relaxation.This paper proposes new AHO Field algorithms based on these research gaps.Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms.A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions.Results obtained are compared with PSO algorithm.
Study on ESP Course Design of ‘Wide-angled’ and ‘Narrow-angled’ Based on Demand Analysis

This paper is concerned with the ESP course design, its theoretical analysis and comparison between the narrow and wide angles, based on demand analysis. By practical examples overseas, we can understand their differences and characteristics.
Evaluation of the Impact of Using Electronic Marketing in Marketing Illegal Products by Applying to Marketing of Digital Drugs in the Kingdom of Saudi Arabia

Just as electronic marketing has advantages, it also has its disadvantages. Through it, illegal products can be marketed, which are prosecuted by governments in traditional markets. These products have found a safe haven in the electronic marketing to market them. One of these products is drugs, which are a major threat to any government and fought against by it. Therefore, its marketers have circumvented this situation and turned them into digital addiction fighting the young people known for their keen interest in the Internet.&#x0D; &#x0D; The study observed the misuse and illegal use of electronic marketing. Thus, the problem of the study was the spread of marketing illegal products through the Internet, such as digital drugs, exploiting the spread and popularity of electronic marketing, especially among adolescents and young people.&#x0D; &#x0D; The study used the descriptive analytical approach. It was also based on the simple correlation coefficient and the stepwise regression coefficient to test hypotheses for one of the methods included in the SPSS.&#x0D; &#x0D; The study concluded that there was a strong relationship between the use of electronic marketing components and the spread of digital drugs. Without electronic marketing, there would be no presence of this type of drugs. It also found the skill of their marketers in using the marketing mix components to market their deadly product. The study also found the possibility of using the means of curbing the spread of these drugs via electronic means over the Internet and in the same way of their marketers. Finally, the study concluded that this harmful marketing could affect the future of electronic marketing; rather, the electronic commerce as a whole.&#x0D; &#x0D; The study recommended the use of the electronic counter-promotion strategy against these websites in line with raising awareness of young people, blocking websites promoting them, and prosecuting them if created again, penetrating the websites marketing the digital drugs and change their content. A comprehensive national plan that includes the Ministries of Health, Education, Interior and Justice is needed.
LibGuides: Love Data Week: Home

Love Data Week is a nationwide campaign designed to raise awareness about research data management, sharing, and preservation.
The Rating Based Recommender System using Textual Reviews: A Survey

Nowadays online shopping is emerging as a growth of business.Customers are getting used to purchasing the items online.Online reviews are an essential resource for users choosing to buy a product, watch a movie or go to a hotel.When it needs to decide the items/products through online, the opinions of other users through review matter a lot.It gives a good idea of the product to be purchasable or not.However, people face the information overloading problem.So the problem is as to how to get valuable information from user reviews so as to understand a user's preference and make an accurate recommendation.Recommender systems become risen as an essential tool to overwhelm the negative result of information overloading problem.The traditional recommendation system examines some factors like the user's buying records, product classification, and user's geographic location.This paper is an attempt to discuss the three social factors with some rating prediction algorithms based on user sentiment similarity, item reputation and user circle influence and review the applicable sentiment dictionary to the recommender system.
Magnetic Anomalies Due To 2-D Cylindrical Structures - An Artificial Neural Network Based Inversion

Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic anomalies caused by a long-2D horizontal circular cylinder is presented.Although, the subsurface targets are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical analysis.ANNCM inversion extract the parameters of the causative subsurface targets include depth to the centre of the cylinder (Z), the inclination of magnetic vector(Ɵ) and the constant term (A) comprising the radius(R) and the intensity of the magnetic field (I).The method of inversion is demonstrated over a theoretical model with and without random noise in order to study the effect of noise on the technique and then extended to real field data.It is noted that the method under discussion ensures fairly accurate results even in the presence of noise.ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana, India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.
Abstract WMP26: Using Machine Learning Pattern Recognition in Aneurysmal Subarachnoid Hemorrhage Patients to Determine Patterns in Central versus Infectious Fever

Introduction: Patients with acute neurologic injury such as aneurysmal subarachnoid hemorrhage (aSAH) are at risk of central fever and infections. Fever has been found to independently increase morbidity and mortality. These patients are at risk of exposure to antibiotic overuse, resistant infections, and extensive fever work-ups as central fever remains a diagnosis of exclusion. Methods: We included 28 patients who underwent treatment for aSAH at the University of California, San Francisco’s Neurointensive Care Units from 2012-2016. We retrospectively reviewed their fever curves, with every 2 seconds temperature data, and used strict CDC infection criteria to delineate between central and infectious fever. For every patient, we extracted one or multiple 12-hours-fever-windows (FW) for a total of 289 FW. We then extracted 40 features in either time or frequency domain such as signal skewness, kurtosis, and variance for each FW, and trained a Random Forest model to classify between central and infectious fever. Our model was trained and cross-validated using 19 patients and tested using the remaining 9 patients. Results: Our initial machine learning model shows a cross-validated ROC AUC score of 0.825 ± 0.150, and a testing ROC AUC score of 0.75 on 30% test patients. Conclusion: These preliminary results suggest that there are combinations of characteristics or patterns that can differentiate between central and infectious fever through a machine learning model. To our knowledge, this has never been done before. Our next steps include the development of a more robust features matrix and the use of other machine learning models such as the Recurrent Neural Network (RNN). If this machine learning model can effectively identify central versus infectious fevers, we can optimize antibiotic stewardship, tailor the work up and treatment of this critically ill population and open the door for prospective central fever and temperature management research.
Retrospective Blind MR Image Recovery with Parametrized Motion Models

In this paper, we present an alternating retrospective MRI reconstruction framework based on a parametrized motion model. An image recovery algorithm promoting sparsity is used in tandem with a numeric parameter search to iteratively reconstruct a sharp image. Additionally, we introduce a multiresolution strategy to restrict the numeric complexity. This algorithm is then tested in conjunction with a simple motion model on simulated data and provides robust and fast reconstruction of sharp images from severely corrupted k-spaces.
parallels between web standards communities, power, progress ...

parallels between web standards communities, power, progress and problems like #w3c, WHATWG, industry group standards, etc with education standards #CSSS, industry groups, professional
CHITOSAN-BASE COAGULANTS IN COAGULATION-FLOCCULATION OF KAOLIN SUSPENSION

Coagulation-flocculation is one of the waste water treatments using coagulant/flocculants agent. Using coagulant/flocculants based on natural polymers were many used due to many available, renewable source, and environment friendly. One of that coagulant/flocculants was chitosan that can be produced through deacetylation of chitin. Amino groups in the chitosan structure play important role in the coagulant/flocculants process. The aim of research is using chitosan-based coagulants in the coagulation-flocculation of kaolin suspension. In here, there were two coagulants type, first, single coagulant which chitosan with different molecular weight added alone in kaolin suspension. Second, mix coagulants which chitosan aid with alum in kaolin suspension. As the result, functional group analysis showed that chitosan with different molecular weight have the same functional groups. Increasing molecular weight of chitosan increase NH2 groups and degree of deacetylation. The removing turbidity of kaolin suspension showed that increasing molecular weight chitosan increase removing turbidity of kaolin suspension. Using double coagulant (chitosan and tawas) have the removing turbidity greater than single coagulant.
Analysis of Personality Characteristic Using the Naïve Bayess Classifier Algorithm (Case Study Official Twitter of Basuki Tjahaja Purnama's and Anies Baswedan)

Abstract - This research uses data from social media Twitter based on the results of tweets from user_timeline @basuki_btp and @aniesbaswedan. This study uses 2100 tweet data. Data that has been collected is then pre-processed first and labeled manually. The next process is classification using the Naïve Bayess Classifier Algorithm using the Big Five Personality Theory. Based on the test results using 500 tweet data as training data and 1600 tweet data as testing data. The classification results obtained by using the Naïve Bayes Classifier Method and grouped in the "Big Five" personality groups: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism on tweet data in Indonesian.
Beyond Instrumentalism: A Substantivist Perspective on Law, Technology, and the Digital Persona

Law and technology matters have traditionally been researched in discrete categories such as intellectual property (e.g., copyright, patent, or trademark) or intermediary liability and responsibility (e.g., secondary liability and telecommunications regulation). In the last two decades, however, academics have studied the broader interaction between law and technology across legal fields. This Article examines progress to date and discusses two distinct perspectives on law and technology.The dominant approach has been an instrumentalist one that treats technology as a tool for individuals to use while downplaying its broader social implications. However, the fields of philosophy of technology, science and technology studies, and social studies of science are now mature enough to support a rival approach grounded in a deep understanding of the nature—rather than the results—of technological change. This substantivist approach suggests analytical principles to refine and improve technology law and policy in ways that rival, instrumentalist approaches have neglected. For instance, substantivist commitments support a law and technology construct called a “digital persona” to emphasize the need for laws and policies to promote autonomy within the online world. By contrasting instrumentalist and substantivist approaches, we demonstrate new ways to integrate ethics, policy, and law in the digital age.
Deconstruct the Poetic Imagery of Poetry and Improve the Teaching Ability of Chinese Teacher on Poetic Imagery with Qualitative Research Methods

Based on the related knowledge of pedagogy, psychology, aesthetics and literature, this dissertation adopts the method of quantitative research and qualitative research to deconstruct the poetic imagery of poetry. Taking typical Chinese poems as samples, 16 teachers are chosen randomly to deconstruct the poetic imagery of poetry in the form of composing commentary narration, and content analysis method is adopted to make quantitative analysis of the commentary narration, which is the content of streaming media courseware, a language teaching tool, to show the cultural heritage in the teaching process vividly and effectively, and intentionally cultivate the ability of Chinese teachers to design teaching plan about poetry conception, and enhance the applicability of its teaching tools.
Realising Variability in Dynamic Software Product Lines

Like many application domains Big Data has become a very important driver for innovation and growth for e-government which aims to automate the public services to citizens. In general, e-government systems are often characterized as big data systems in which data storage and processing is one of the business-critical concerns. However, different features are required for different e-government systems, and likewise the corresponding big data architectures will need to be different as well. In this chapter, we present a systematic approach for the design of various big data architectures within the e-government domain. In this context, we adopt a big data reference architecture with a variability model for big data systems and e-government systems. We discuss the design decisions, the experiences and the lessons learned for deriving application architectures for different e-government systems.
The Demise of the Criminal Records Bureau

This article critically analyses the operations of the Criminal Records Bureau (‘the CRB’) and seeks to determine why the Bureau closed abruptly in December 2012. It questions whether the decision to open in March 2002 was expedient, in light of the apparent flaws in the operational assumptions made by Capita Plc, the successful bidder for the CRB contract, and the long-standing problems with the criminal records data. The article then identifies three key failings in the administration of the criminal records checks which it is submitted quickly and decisively eroded public and political confidence in the Bureau. The article concludes by considering who, if anyone, is responsible for the apparent failure of the CRB, and what lessons might be learned by the organisation which replaced it.
Human emotional state recognition using 3D facial expression features

In recent years there has been a growing interest in improving all aspects of the interaction between human and computers. Emotion recognition is a new research direction in human-computer interaction (HCI) which is based on affective computing that is expected to significantly improve the quality of HCI system and communications. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. In light of such problems, two 3D visual feature based approaches are presented in this dissertation. First, we present a recognition method based on the Gabor library for real 3D visual features extraction and an improved kernel canonical correlation analysis (IKCCA) algorithm for emotion classification. Second, to reduce the computation cost and provide a more general approach, we propose using a fiducial points' controlled 3D face model to recognize human emotion from video sequences. An Elastic body spine (EBS) technique is applied for deformation feature extraction and a discriminative Isomap (D-Isomap) based classification is used for the final decision. The most significant contributions of this work are detecting and tracking fiducial points automatically from video sequences to construct a generic 3D face model, and the introduction of EBS deformation features for emotion recognition. The experimental results show the robustness and effectiveness of the proposed methods.
An End-to-End Tree Based Approach for Instance Segmentation

This paper presents an approach for bottom-up hierarchical instance segmentation. We propose an end-to-end model to estimate energies of regions in an hierarchical region tree. To this end, we introduce a Convolutional Tree-LSTM module to leverage the tree-structured network topology. For constructing the hierarchical region tree, we utilize the accurate boundaries predicted from a pre-trained convolutional oriented boundary network. We evaluate our model on PASCAL VOC 2012 dataset showing that we obtain good trade-off between segmentation accuracy and time taken to process a single image.
Clouds for scalable Big Data processing

The last decade has been characterised by an exponential growth of digital data production. This trend is particularly strong in scientific computing. For example, in the biological, medical, astro...
Revista Ciência da Informação : memória da pesquisa científica em ciência da informação no Brasil

: Journal Ciencia da Informacao celebrated 45 years in 2017. There were 113 publishedissues with works in the most varied fields of research of Information Science and of authors ofseveral countries. With almost half a century of uninterrupted activity, it can be said that the journalis the main memory of research in the field at the national level and the largest repository ofInformation Science in Brazil. This research intends to unveil the main themes of researchdisseminated in the journal, which is also the portrait of Information Science in the country in thisperiod. In addition, it will be possible to identify the most productive authors-researchers and theirareas of competence. From foreign authors, the initial premise is that being the oldest journal inLatin America and the only one for more than 20 years, it is possible that established names inInformation Science have published in it. For the same reason, it is possible that several researcheson the journal itself have been produced. The methodology will be based on quantitative studiesthrough statistical analysis with data mining technologies available in Tableau and Knime software.The results intend to demonstrate that the journal Science of Information, even with the setbackssuffered as a result of the financial turbulence of the public sector, remains an important source ofdissemination of scientific research in Information Science and memory of the area in Brazil
Correction to: MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules

In the original version of this chapter there was a typing error in the family name of the first author. This has now been corrected.
Activation of self-employed activities of students after distance learning of higher mathematics

Conclusion.The effectiveness of using an educational site during teaching depends on its structure.Finding, systematizing, analyzing, organizing and transforming the necessary data are possible by using the educational module of the site.With the help of the cognitive module of the site, students are able to implement high-performance computations based on cloud services and with the help of systems of computer mathematics.Using the monitoring module of the site allows students to automate their own workplace, promote self-development of students.Task-case solution facilitates the skills of effective choice of software products for solving the tasks, teaches to analyze the task, to build mathematical models of the studied processes.Educational software placed on the site is intended for the intellectual analysis of the data with the visualization of the results.