Reviewerly/exHarmony huggingface dataset:
A complementary webometric ranking system based on the website quality and traffic measures: A study focusing on top-ranked world universities

Ranking of universities regarding their web-based activities plays a pivotal role in promoting scientific advancement since it motivates the open access accessibility to scientific results. In this study, a new ranking system based on the website quality factors and traffic evaluation was proposed. Since top-ranked universities are usually considered as the standard models for lower ranked ones, the focus of this study was on top-ranked universities. The proposed ranking was compared with well-known Webometrics ranking system. The website traffic and quality assessment were acquired for websites of top-ranked world universities and the correlation between these indices and the Webometrics ranking was evaluated. The summation of the weighted value of obtained measures according to an optimal weight vector obtained by a genetic algorithm framework was used for ranking purposes. The results showed that the website total traffic size was correlated with Webometrics rank (R≈-0.6, p&lt; 0.01). Also, using the weighted value of website quality and traffic measures, the proposed ranking system could predict Webometrics ranking by the accuracy of up to 69%. Even though the method was proposed for universities, it could be applied for ranking other types of centers or companies, provided that the suitable cost function for the genetics algorithm framework was defined.
Election Tweets Prediction Using Enhanced Cart and Random Forest

Political system of a country has always been complicated in nature, and this complexity can be due to various factors such as number of parties, policies and, most notably, mixed public opinion. The advent of social media has given people around the world the ability to converse and discusses with a very wide audience; the sheer amount of attention gained from a tweet or a post is unimaginable. Recent advances in the area of profound learning have contributed to their use of many different verticals. Techniques such as long-term memory (LSTM) allow a sentiment analysis of the posts to be carried out. This can be used to determine the masses’ overall feelings towards a political party or person. Several experiments have shown how to forecast public sentiment loosely by examining consumer behaviour in blogging sites and online social networks, such as in national elections. Machine learning has a rapid growth in recent years and has been applied from self-driving cars to e health sectors in every technology. A model of machine learning is proposed to predict the chances of winning the upcoming election based on consumer or supporter views on the web of social media. The supporter or user share their opinion or suggestions for the group or opposite group of their choice in social media. The text posts are needed to be collected about election and political campaigns; the models of machine learning are developed to predict the outcome.
Current Political News Translation Model Based on Attention Mechanism

China’s foreign exchanges are becoming increasingly frequent, among which current political news, as the recent or ongoing relevant reports of facts in the national political life, plays a great role in the field of information transmission. However, there are a large number of proper nouns as well as long and complex sentences in current political news, so the news translation through traditional machine translation tends to have low accuracy and poor usability. Based on this situation, this paper proposes a translation model of Chinese current political news based on Attention. It uses the classic Long Short Term Memory (LSTM) model and introduces the Attention Mechanism to improve the traditional Encoder-Decoder framework. Through the training of parallel corpora, constraints are established for the proper nouns of current political news, thereby improving the overall translation accuracy. The experiment shows that the translation model used in this paper has higher accuracy than neural machine translation (NMT).
On the Countermeasures for the Training of Language Sense in Vietnamese Teaching

The sense of language in Vietnamese language teaching is very important, which can improve the quality and efficiency of students ’language learning. The formation of language sense is a long-term practical process. In order to cultivate students’ language sense, in actual teaching activities, teachers should carry out the training of language sense according to the practical condition and characteristics of students to adopt scientific and effective methods to reach the teaching target. Based on the background, this article discusses approaches to cultivating students’ sense of language in Vietnamese teaching from three aspects.
On Expansion and Contraction of DL-Lite Knowledge Bases

Knowledge bases (KBs) are not static entities: new information constantly appears and some of the previous knowledge becomes obsolete. In order to reflect this evolution of knowledge, KBs should be expanded with the new knowledge and contracted from the obsolete one. This problem is well-studied for propositional but much less for first-order KBs. In this work we investigate knowledge expansion and contraction for KBs expressed in DL-Lite, a family of description logics (DLs) that underlie the tractable fragment OWL2 QL of the Web Ontology Language OWL2. We start with a novel knowledge evolution framework and natural postulates that evolution should respect, and compare our postulates to the well-established AGM postulates. We then review well-known model and formula-based approaches for expansion and contraction for propositional theories and show how they can be adapted to the case of DL-Lite. In particular, we show intrinsic limitations of model-based approaches: besides the fact that some of them do not respect the postulates we have established, they ignore the structural properties of KBs. This leads to undesired properties of evolution results: evolution of DL-Lite KBs cannot be captured in DL-Lite. Moreover, we show that well-known formula-based approaches are also not appropriate for DL-Lite expansion and contraction: they either have a high complexity of computation, or they produce logical theories that cannot be expressed in DL-Lite. Thus, we propose a novel formula-based approach that respects our principles and for which evolution is expressible in DL-Lite. For this approach we also propose polynomial time deterministic algorithms to compute evolution of DL-Lite KBs when evolution affects only factual data.
Ensemble Method for Identification and Automatic Production of Related Words for Historical Linguistics

Language change throughout time and space is one of the major issues in linguistic history. The paper deals with new methods for the study of language evolutions to help researchers and experts. Firstly, a method is used to determine, if the words are cognate or not. A linguistic information algorithm is proposed to derive cognates from online dictionaries. Then a dataset is created of similar terms and machine learning techniques are used to focus on spelling to classify the cognates. The aligned subsequences are used to identify standards and guidelines for language change in newly created languages mainly to distinguish between non-cognate and cognates which are used for classification algorithms. Secondly, for identifying the sort of association between those words that humans expand the method to a simpler level. Discriminating cognates and debts gives an insight into a language’s history and allows a clearer understanding of the linguistic relationship. The spelling characteristics have discriminative features and analyze the linguistic factors underlying this classification task. This is considered as the first such effort, to linguistic knowledge. Thirdly, a machine learning technique is developed for producing similar words automatically. One should concentrate on proto-word reconstruction to address issues related to it to generate the modern words which are not synonyms and another one is generating cognates. The task of reconstruction of proto words is to recreate words from its modern daughter languages in an ancient language. The method is based on the regularity of words and uses knowledge from many modern languages to build an ensemble method for proto-word reconstruction. This method is applied to multiple datasets to improve from the previous dataset accuracies.
Розроблення системи розпізнавання людських облич для відеоспостереження

In the article were researched the principles of building systems for observation and recognition of objects. Also we have given the classification of human faces recognition methods. Authors have analized the features of operetion for the progressive calibration network (PCN) for human face recognition. And finally has been created and tested the developed face recognition algorithm as the realized software system.
User Experience in Virtual Environments: Relationship Between Cybersickness Issues and the Optical Aspects of the Image by Contrast Levels

This paper provides a comparative study between problems caused by cybersickness with users in virtual environments, and the studies on the influence of the optical aspects of the image via its levels of contrasts in the quality of the user experience in virtual environments. This article was organized in two parts, the first consists of surveying the results obtained in each research and the second has the objective of crossing these data and verifying the level of relationship between research on cybersickness and studies on the influence of optical aspects quality of the user experience. The results indicate relevant connection with the results of studies on cybersickness. With that, we conclude that in future research researchers can produce much more relevant results in the solution of persistent problems from the negative experience with virtual devices.
Smart Collar and Chest Strap Design for Rescue Dog through Multidisciplinary Approach

Rescuers escorted search dogs into the disaster area, using their unique sense of smell to find the injured.First, researchers summarize the design requirements in the search process from interviews with rescuers, and construct a conceptual prototype to confirm the interaction mode between the user and the dog.User central design invited people melt into the situation to identify product features.The ideas were selected based on the viability which increases efficiency.The main design proposal includes a strap and a smart collar.Smart sensing (heartbeat, speed, temperature, and GPS) can improve communication and increases the efficiency of rescue.The search area is large in many cases; therefore, we selected the WiFi or Ultra-wideband module as the wireless transmission medium when the rescue team enters this domain.The pre-deploy nodes connect and position with the smart collars.The instructor sends voice commands remotely to prompt the dog to return when the temperature is high.The smart collar design includes an elastic O-ring waterproof shell.Rescuers click the recall button, and the remote device sends a signal of dog returning.This proposed work looks more at user's needs through multi-disciplinary aspects of view, which enhance usability.The case consists of customer interviews, observation, concepts, evaluation (science/ device/ electronic packaging/ and App software); the design process also demonstrated a possible teamwork perspective in the industry.This scenario encourages cross-field extension for design education.
Research on the Design of Sound Horn Based on Optimizing the Transmission of Human Voice

Trumpet is very common in daily life and plays an important role. With the development of science and technology, the research on the physical characteristics and performance of the horn has reached a new level. For a long time, the research on electrical loudspeaker and other electronic products is more popular, while the design of horn which can optimize the human voice transmission is less involved. Aiming at this research direction, this paper introduces in detail the influence of horn parameters on the sound output of human voice frequency. Based on the theoretical calculation results, the sound horn with good performance is manufactured by using 3D printing technology, and relevant experiments are designed. Combined with the theoretical and simulation results, the performance of the horn with different parameters is explored to optimize the voice transmission to distant listeners, which are helpful to further understand the performance of the horn, are applied in various fields.
Delegation of Access Rights

This chapter presents a discussion of the critical issue of transferring access rights from one entity to another in an IoT context. The process of transferring such access rights is commonly known as delegation. It is a significant component of an access control system. There have been considerable works in the area of the delegation but much of it assumes a centralized, well-resourced, system and these solutions have limited applicability in the context of the IoT. Where delegation models for the IoT have been proposed they typically provide only coarse-grained control over the delegation of rights. Moreover, many of them require a centralized trusted authority, which can suffer from being a single point of failure. We demonstrate that the IoT requires a secure, flexible, and fine-grained delegation model.
Visualization Research of People's Livelihood Service Data based on Knowledge Graph

Knowledge graph is a kind of directed graph which is used to reflect the relationship between objective world entities. It can acquire a lot of computer readable knowledge by combining with natural language processing technology. It is suitable for processing and mining large-scale and multi-modal massive data. It is a research hotspot in the field of computer knowledge engineering. People's livelihood issues are related to social stability and political stability. Solving people's livelihood problems is an important task for the Party and the state. At present, how to visualize and retrieve relevant data in the field of people's livelihood services is still in its infancy. for the data in this service platform, knowledge graph technology has been used to realize the association and visualization of all kinds of data. it is of great significance and reference value for data mining and analysis in the field of people's livelihood and providing decision support for the government.
Are Racial Vilification Laws Supported by Free Speech Arguments?

This article examines the relationship between laws regulating racial vilification and arguments for freedom of speech. Commonly, it is argued that racial vilification laws are inconsistent with free speech. This article argues that such laws are consistent with and, indeed, supported by arguments commonly made in favour of free speech.
Interactive Human-Computer Theoretical Model of Editorial Design with Augmented Reality

This research presents an editorial design model with augmented reality that concurrently integrates human-computer interaction with the printed picture book and the mobile application. The editorial proposal is supported by theoretical foundations of design related to composition, intelligent formats for its production, diagramming with and without a grid, typography, color theory, use of materials; In addition, it has three-dimensional figures based on paper engineering. The augmented reality suggested for this model must have images strategically located in the illustrated book that lead the perceiver to enter the mobile device and interact with its content, complementing in a playful way the tangible message of the printed document. The mobile application interacts with the illustrated book integrating: videos, audiobook, video games and links to social networks. For the validation of the editorial model with augmented reality, the mobile application and the human-computer interaction, user experience design metrics will be applied in which usability variables are measured such as: ease of learning, efficiency, memorization, effectiveness, satisfaction, among others. This study presents the theoretical foundations on which this editorial model is based and exposes how it was tested and developed with an educational theme in the city of Quito–Ecuador.
Rising Internet Shutdowns in India: A Legal Analysis

In the backdrop of India's problematic record with internet shutdowns, this article undertakes a comprehensive analysis of the validity of internet shutdown orders vis-à-vis the right to freedom of speech and expression guaranteed by Article 19(1)(a) of the Indian Constitution. To this end, shutdown orders are separately assessed under the three fundamental requirements of Article 19(2) which exhaustively lays down the manner in which, and the reasons for which, restrictions can be imposed on free speech. First, to assess whether shutdown orders are provided by law, the article studies and compares the various Indian legislations (both primary and secondary) under which shutdowns are imposed by the Central and State Governments, including the Telegraph Act, 1885, the Code of Criminal Procedure, 1973, and the Information Technology Act, 2000. The requirement of publication of shutdown orders – another component of lawfulness/legality – is also discussed. Second, the legitimacy of shutdown orders is assessed against the exhaustive list of aims contained in Article 19(2), with a specific focus on "public order". Third, on reasonableness, the article identifies various factors that ought to be considered by courts in determining whether an internet shutdown is a reasonable and proportionate response to the problem at hand.The article also examines judicial trends with respect to challenges mounted against internet shutdown orders. All relevant judicial orders – right from the Gujarat High Court's abdication of constitutional responsibility in Gaurav Vyas (2015-16) all the way up to the Supreme Court's abdication in Foundation for Media Professionals (2020) – are studied and critically analyzed. Before concluding, we identify possible areas of further research to take forward the discourse on finding the right balance between the demands of free speech and societal security concerns.
An Efficient Object Detection Network for Ammeter Inspection Task on Mobile Devices

The inspection and evaluation of electric metering devices play an important role in the power grid, which is concerned with the safety of millions of households and consumes immense manpower. With the rapid development of the smart grid, there are urgent needs to evaluate the state of these devices in mobile scenarios by artificial intelligence. However, the limited memory resources and computation power of mobile devices present a huge challenge. In this study, we present an efficient object detection network called CSCNet for ammeter inspection on mobile devices, which achieves a good balance on detection precision and speed for the ammeter inspection task. Besides, the CSCNet improves small object detection precision to 90%. The experiment shows that the average detection time for an ammeter image is about 0.2 seconds with 92.87mAP on the chip of Qualcomm Snapdragon 855.
Lightweight Declarative Server-Side Web Programming

Web interfaces are an important part of many applications but their implementation is full of pitfalls due to the client/server nature of web programming. This paper presents a lightweight approach to web programming based on a standard infrastructure, in particular, the common CGI protocol between client and server. No specific additions are necessary on the server side. Our approach exploits declarative programming features to provide a high-level API for server-side web scripting. This API allows to check many programming errors at compile time by using functional (static typing, higher-order functions) as well as logic (free variables) programming features. Together with further abstractions, like session handling, persistence, and typeful database access, it is used for non-trivial web applications.
Bank Marketing Using Intelligent Targeting

In the banking system, bank marketing has become an essential continued existence weapon and is basically dynamic in real world. The rise of bank marketing is reused business relations, and most made banks are those who will really strong their relationships with customers. Knowledge modernization and ferocious rivalry in the midst of current banks have altered a large collection of banking facilities. Technology is neutering the relationship amid banks and its inside and outside customers. This dataset will give you the clear targets for marketing depending upon the age of the customers, salaries, duration of the call, etc. The objective of this paper is to assess the value of data in defining marketing strategies and marketing management. The technological advances in recent years offer many opportunities to marketing practitioners and researchers.
Scalability and precision improvement of neural program synthesis

Mosts of the neural synthesis construct encoder-decoder models to learn a probability distribution over the space of programs. Two drawbacks in such neural program synthesis are that the synthesis scale is relatively small and the correctness of the synthesis result cannot be guaranteed. We address these problems by constructing a framework, which analyzes and solves problems from three dimensions: program space description, model architecture, and result processing. Experiments show that the scalability and precision of synthesis are improved in every dimension.
Introducing Context and Context-awareness in Data Integration

Data integration is the process of selecting, preprocessing, and transforming data from heterogeneous sources in data-driven projects. This process also requires the most time, effort, resources. Data integration is such an involved process due to the many informed decisions one has to make. These decisions are influenced by the complex context of a data-driven project. We argue that using said context could facilitate the decision-making processes and even automate some integration steps. However, the problem we identify in this paper is that the context of a data-driven project is tacit and, therefore, not easily accessible by humans and certainly not by software agents. From the SotA, however, we observe that current models represent the context in crude and simplistic terms. These context models are furthermore built for specific tasks or application domains such as query optimization or a smart home. The current state of affairs is thus is not fit for intelligent data integration. Next to identifying the problem, we postulate that solving this problem requires two steps: formalizing context and using that context for building context-aware agents. We illustrate this notion of "context-aware data integration" with preliminary results obtained with a use case in the domain of GDPR, more specifically the generation of datasets that takes into account informed consent.