Reviewerly/exHarmony huggingface dataset:
Kullanıcının Bağlamının Önemi ve Bağlamsal Sorgulama

A work of innovation will not achieve its purpose for a user who is disconnected from the context.Since the user is not a robot, he has many worries, troubles, and expectations related or unrelated to the designed product.The strength of the bond between the design and the user is the ability to incorporate these feelings.In this article, contextual inquiry is explained with outlines and examples as one of the most effective ethnographic methods to reveal the context of users.Contextual inquiry means mapping the journey based on interviews, observations, and activities in the user's natural environment.Yes/ no, short answer, general comments, and closed questions are not acceptable.The idea behind market research is based on a central idea and opinion.However, user experience is based on behavior.This study searches the stories or observations that support behavior.The aim is not to reveal a general truth or to prove the correctness of a thesis, but to reveal the wishes and dissatisfaction of the user in-depth, to pursue creative thoughts and to bring out new opportunities from this point.It does not have as a target the creation of a set of recommendations for action but it only produces hypotheses for testing.The difference between what people do and what they say is an opportunity for innovation.The form used in contextual inquiry describes the activities, the humans, the objects, the environment, the messages, the services, the user's physical, cognitive, social, cultural, and emotional aspects and the motivation behind the activities.
Towards a Ledger-Assisted Architecture for Secure Query Processing over Distributed IoT Data

Massive amount of IoT data poses unique challenges in centralized data management systems. Specifically, IoT data can originate from heterogeneous and distributed sources, and commonly regulations forbid data from different IoT stakeholders to be managed via central governance. To serve for IoT applications, recent proposals leverage distributed ledgers (e.g., blockchains) to function on top of distributed data storage with improved data interoperability. Unfortunately, most of them do not consider data security and privacy in the first place. Along with this transformative paradigm, in this paper, we propose a ledger-assisted architecture for secure distributed IoT data management. This architecture adapts searchable encryption to decentralized storage network to enable encrypted query processing. Meanwhile, it is designed to leverage the distributed ledger to harden both data and query integrity. To deal with continuously generated IoT data, we further devise an efficient secure data insertion protocol, and employ a recent variant of blockchain for users to validate updated query results in a scalable manner. Evaluations on Azure blockchain service confirm the practicality of our proposed architecture.
Multimatrix rate-compatible reconciliation for quantum key distribution

Key reconciliation of quantum key distribution (QKD) is the process of correcting errors caused by channel noise and eavesdropper to identify the keys of two legitimate users. Reconciliation efficiency is the most important figure for judging the quality of a reconciliation scheme. To improve reconciliation efficiency, rate-compatible technologies was proposed for key reconciliation, which is denoted as the single-matrix ratecompatible reconciliation (SRCR). In this paper, a recently suggested technique called multi-matrix reconciliation is introduced into SRCR, which is referred to as the multi-matrix rate-compatible reconciliation (MRCR), to further improve reconciliation efficiency and promote the throughput of SRCR. Simulation results show that MRCR we proposed outperforms SRCR in reconciliation efficiency and throughput.
The Impact of Economic Freedom on Economic Growth

This study investigated the relationship between economic growth and economic freedom.Fourty two countries were covered for the period 1996-2017.The dependent variable is the real GDP per capita and the independent variables are; the index of economic freedom, real fixed capital formation, real government spending and average number of hours worked.The sources of data is the World Bank Group (WBG) and the Center for Growth and Development (GGDC).The sample of 42 countries was divided into two groups, based on the score of the country on the index of economic freedom for 2017.While the economic freedom index, which ranges from 100 to 70, has 13 countries.The second group having 69.9 to 50 points, consisted of 29 countries.The data was subjected to cross sectional panel analysis; the unit root indicated that all variables in the two groups were stable at the level, and The Pedroni Residual Co-integration Test showed that there was no co-integration between the variables.The normal least squares method was applied for each group of countries.The results of the analysis showed that the fixed effects model (Fixed Effect) was the appropriate model for data analysis.The study showed that there was a positive and statistically significant relationship between the economic freedom index and the real GDP per capita.In addition, the relationship was positive and statistically significant between real GDP per capita, and both fixed capital formation and government expenditure.The study recommended that more attention should be directed to the factors that have an impact on economic freedom, as these factors might have a positive impact on economic growth.
RANLP 2019 Multilingual Headline Generation Task Overview

The objective of the 2019 RANLP Multilingual Headline Generation (HG) Task is to explore some of the challenges highlighted by current state of the art approaches on creating informative headlines to news articles: non-descriptive headlines, out-of-domain training data, generating headlines from long documents which are not well represented by the head heuristic, and dealing with multilingual domain.This tasks makes available a large set of training data for headline generation and provides an evaluation methods for the task.Our data sets are drawn from Wikinews as well as Wikipedia.Participants were required to generate headlines for at least 3 languages, which were evaluated via automatic methods.A key aspect of the task is multilinguality.The task measures the performance of multilingual headline generation systems using the Wikipedia and Wikinews articles in multiple languages.The objective is to assess the performance of automatic headline generation techniques on text documents covering a diverse range of languages and topics outside the news domain.
Ontology Database Construction for Medical Knowledge Base

The rapid development of computer science and technology represented by artificial intelligence is fundamentally affecting man's lifestyle. Big data is pushing forward the development of artificial intelligence to unprecedented levels. The typical application, as one small element of the big data, refers to the knowledge engineering which sets the knowledge map as the core. A knowledge map is the knowledge base of the Semantic Web. Semantic web stands for a network describing things in the way that is understood by computers. The purpose of it is to enable computers to understand data and build a knowledge base of computers. By modeling the things that can be described, filling the attributes of things and expanding the connection with other things, the knowledge base can be built. This paper aims to propose a semantic information metadata model based on the ontology of traditional Chinese medicine and construct an ontology database with metadata model as the knowledge description method to realize keyword search by using a combination of ontology and Chinese medical knowledge data. Finally, achieving ontology database construction and visualization of ontologies.
Language Recognition Method of Convolutional Neural Network Based on Spectrogram

Language recognition is an important branch of speech technology. As a front-end technology of speech information processing, higher recognition accuracy is required. It is found through research that there are obvious differences between the language maps of different languages, which can be used for language identification. This paper uses a convolutional neural network as a classification model, and compares the language recognition effects of traditional language recognition features and spectrogram features on the five language recognition tasks of Chinese, Japanese, Vietnamese, Russian, and Spanish through experiments. The best effect is the ivector feature, and the spectrogram feature has a higher F value than the low-dimensional ivector feature.
University as a specific type of organization, its functions and relationships with the external environment

The relevance of the analyzed problem is associated with the need to determine the guidelines for the organizational development of universities.The source of organizational development is a dynamic external environment.The article analyzes the problems of the relationship of universities with the external environment.The authors consider various approaches to the interpretation of the organizational specifics of the university.Recreating organizational specifics, special attention is paid to the resulting product of the university.It is on the definition of the resulting product and its customers that the choice of optimal adaptation methods and, consequently, the guidelines for organizational development depends.Next, the forms of interaction of the organization with the external environment are analyzed.The authors do not doubt that the university can be considered as a specific type of formal organization.However, its specificity (activities, type of personnel, environmental characteristics, etc.) does not imply the development of flexible structures.Taking into account the tasks of the universitythe training of specialists and the development of sciencethe possibility of using forms and methods of adaptation associated with customer orientation as the main factor in changing the content of educational programs is being questioned.Considering customers on the resultant product of the university's activities, the authors believe that the university's orientation toward the production of educational services and the client (consumer of these services) contradicts the fulfillment of the university's most important social functionsthe training of qualified specialists and the development of science.Keywords: university as an organization; the external environment of the university; the result of the activities of universities.
Translation-Based Matching Adversarial Network for Cross-Lingual Natural Language Inference

Cross-lingual natural language inference is a fundamental task in cross-lingual natural language understanding, widely addressed by neural models recently. Existing neural model based methods either align sentence embeddings between source and target languages, heavily relying on annotated parallel corpora, or exploit pre-trained cross-lingual language models that are fine-tuned on a single language and hard to transfer knowledge to another language. To resolve these limitations in existing methods, this paper proposes an adversarial training framework to enhance both pre-trained models and classical neural models for cross-lingual natural language inference. It trains on the union of data in the source language and data in the target language, learning language-invariant features to improve the inference performance. Experimental results on the XNLI benchmark demonstrate that three popular neural models enhanced by the proposed framework significantly outperform the original models.
Blameworthiness in Security Games

Security games are an example of a successful real-world application of game theory. The paper defines blameworthiness of the defender and the attacker in security games using the principle of alternative possibilities and provides a sound and complete logical system for reasoning about blameworthiness in such games. Two of the axioms of this system capture the asymmetry of information in security games.
Science And Education In The Conditions Of Formation Of The Global World

One of the main social transformations is that the formation of the global world is taking place and this world is reflected in science and education, and these last begin to form it. It is shown that to a large extent a global direction of research has already been formed, which includes globalistics, global scientific disciplines, globalization research, global evolutionism and a number of other areas of scientific research. Although this global cluster of scientific knowledge is most intensively developed only in the second half of the last century, its origins are found in the works of V. I. Vernadsky at the beginning of the same century. The global cluster of scientific knowledge is associated with the globalization of education and the formation of global education. The main integrative characteristics and features in the forms and content of these global evolutionary processes in education are revealed. It is shown that integrative tendencies connected with global-evolutionary changes of civilization and its interaction with nature are developed in education. It is noted that the globalization of education is focused mainly on the formal and organizational methods of integration of different systems and forms of education; the most important directions of this process are highlighted. And global education is characterized by qualitative and meaningful transformations of the educational process, the subject field of which is "filled" with universal-integrative knowledge obtained in the course of global studies. It is assumed that with the globalization of science and education begins a new global revolution in the scientific and educational space.
Collective Intelligence, the Future of Internet and the IEML: Interview to Pierre Lévy by Art Farley and Massimo Lollini

Collective Intelligence, the book in French, that Pierre Levy wrote before the existence of the worldwide web, when only the Internet existed, it's a philosophical vision of the future, a philosophical vision of what could be a global civilization based on the digital and the general interconnection of all the computers. In the interview, Levy addresses the creation o the WWW by Tim Berners Lee as a form of collective intelligence. He then discusses Berners Lee's proposal for a reform of the WWW to avoid its confiscation by the big platforms, such as Google, Facebook, and Amazon. Then, he introduces his most recent project the IELM as a tool for semantic metadata and a code between the natural languages and the algorithms, and all the apparatus of computations.
ENCRYPTION SECURITY SHARING DATA CLOUD COMPUTING BY USING AES ALGORITHM: A SYSTEMATIC REVIEW

Technology cloud computing is the revolution can share resources, services and data between users through a network of. Because millions of users use the same rights on the network to transfer data, and data to a vulnerable to attack security of the do not have the right of access or an intruder. System for security of data now concentrate on the provision of security the internet in data storage, cloud but little regard on security data while data transfer consider security as a very influential, the proposed concentrate on providing security to transfer data using a technique encryption. Research conducted show that the approach proposed boost security whole system make an intruder difficult to take the data in the transfer. To prevent it proposes encryption writer methods in cloud computing with algorithm AES (Advanced Encryption Standard) the process of security in the process of data transfers and data storage. The method is in search of the research uses systematic review, because method SLR able to give suggestions and opportunities for research on the future.
Visual Relationship Detection with Low Rank Non-Negative Tensor Decomposition

We address the problem of Visual Relationship Detection (VRD) which aims to describe the relationships between pairs of objects in the form of triplets of (subject, predicate, object). We observe that given a pair of bounding box proposals, objects often participate in multiple relations implying the distribution of triplets is multimodal. We leverage the strong correlations within triplets to learn the joint distribution of triplet variables conditioned on the image and the bounding box proposals, doing away with the hitherto used independent distribution of triplets. To make learning the triplet joint distribution feasible, we introduce a novel technique of learning conditional triplet distributions in the form of their normalized low rank non-negative tensor decompositions. Normalized tensor decompositions take form of mixture distributions of discrete variables and thus are able to capture multimodality. This allows us to efficiently learn higher order discrete multimodal distributions and at the same time keep the parameter size manageable. We further model the probability of selecting an object proposal pair and include a relation triplet prior in our model. We show that each part of the model improves performance and the combination outperforms state-of-the-art score on the Visual Genome (VG) and Visual Relationship Detection (VRD) datasets.
Transfer Value Iteration Networks

Value iteration networks (VINs) have been demonstrated to have a good generalization ability for reinforcement learning tasks across similar domains. However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained. In this paper, we propose a transfer learning approach on top of VINs, termed Transfer VINs (TVINs), such that a learned policy from a source domain can be generalized to a target domain with only limited training data, even if the source domain and the target domain have domain-specific actions and features. We empirically verify that our proposed TVINs outperform VINs when the source and the target domains have similar but not identical action and feature spaces. Furthermore, we show that the performance improvement is consistent across different environments, maze sizes, dataset sizes as well as different values of hyperparameters such as number of iteration and kernel size.
Extended Vulnerability Feature Extraction Based on Public Resources

The focus of this research is to define a framework that automatically analyses Common Vulnerabilities and Exposures (CVE) from public and disclosed resources and makes mapping to the target computer system. The current framework calculates risk assessment and estimates vulnerabilities impact according to the features of the target platform. In this paper, we describe the main vulnerability feature set, provide approaches for automatic extraction from databases and open resources. We evaluated and improved each obtaining approaches on the recent set of security vulnerabilities (2018 year database). Comparison obtained results with results of manual expert analysis is proved.
A Framework for Interactive Virtual Tours

This paper introduces a framework for the creation, management and deployment of interactive virtual tours. A panoramic image acquisition unit is adapted to acquire panoramic images and video streams from a wide variety of sources. The acquired panoramic images and video streams are fed into a transform engine that performs any required transformations on the input data in a uniform and seamless manner. A package generator coupled with the transform engine synthesizes complete virtual tour packages by combining the transformed data with a broad range of multimedia resources to create navigable virtual tours. Perspective correction and related transformations on the data are carried out by a viewing engine allowing a plurality of viewers to independently and simultaneously interact with the virtual tours. User interaction with the virtual tour packages is enhanced by a control engine that facilitates bi-directional communication between various elements of the system.
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation

In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content. In detail, the input is a set of structured records and a reference text for describing another recordset. The output is a summary that accurately describes the partial content in the source recordset with the same writing style of the reference. The task is unsupervised due to lack of parallel data, and is challenging to select suitable records and style words from bi-aspect inputs respectively and generate a high-fidelity long document. To tackle those problems, we first build a dataset based on a basketball game report corpus as our testbed, and present an unsupervised neural model with interactive attention mechanism, which is used for learning the semantic relationship between records and reference texts to achieve better content transfer and better style preservation. In addition, we also explore the effectiveness of the back-translation in our task for constructing some pseudo-training pairs. Empirical results show superiority of our approaches over competitive methods, and the models also yield a new state-of-the-art result on a sentence-level dataset. 1
GaeltechVR: Measuring the Impact of an Immersive Virtual Environment to Promote Situated Identity in Irish Language Learning

This study investigates how a design-based research methodology is best suited to measuring the impact of a designed virtual reality experience to improve situated identity in Irish learners focusing on their attitudes, motivation, and confidence as Irish language learners. This paper describes the design of GaeltechVR: an immersive Irish language VR experience designed for the VIVE Pro. It also gives the results of a mixed-methods study to measure the impact in a local adult Irish language learner context. A questionnaire on situated attitudes and motivation to language learning (Ushioda &amp; Dörnyei, 2009) was adapted for the Irish context to investigate a small scale sample of the local context’s attitudes to Irish language learning. The participant’s gameplay was recorded for analysis along with questionnaires on presence (Witmer &amp; Singer, 1998), simulator sickness and an adapted questionnaire on their attitudes after the intervention.Using best practice in design-based research experiments (Nelson, Ketelhut, Clarke, Bowman, &amp; Dede, 2013) the study had two main goals: To investigate the usability of the design of GaeltechVR and to measure the impact of the intervention on attitudes, identity and motivation in the local Irish language learning context.