Reviewerly/exHarmony huggingface dataset:
Redesign of a Questionnaire to Assess the Usability of Websites

Usability is taking an essential role in the software development process. Nowadays, many teams incorporate methods to verify if the graphical user interfaces of a software product in development, meet the required degree of usability. However, most of these methods, instead of providing a numerical value about the accomplishment of this quality attribute, only give support to obtain a list of aspects that can be redesigned to increase the usability. Quantitatively measuring the ease of use is useful for companies which need to compare themselves with their main competitors, or when there are several choice design proposals for their website. In this study, we present the redesign of a questionnaire developed from the theoretical basis of two highly recognized sources, which define the concept of usability in a set of principles. The new assessment tool is a five-point version of the previous work with 60 items which attempt to cover all the spectrum of usability.
Path Planning of Anti ship Missile based on Voronoi Diagram and Binary Tree Algorithm

The path planning of anti-ship missile should be considered both cruising in safety and striking in quick, which is an intractable problem. In particular, it is difficult to consider the safety of each missile path in the path planning of multiple missiles. To solve this problem, the “AREA Algorithm” is presented to divide the relative relations of areas：relative security area of the threat areas and fast-attack area of target approaching. Specifically，it is a way to achieve area division through the relationship between the target and the center of the operational area. The Voronoi diagram topology network, Dijkstra algorithm and binary tree algorithm have been used in the above process as well. Finally, Simulations have verified the feasibility and obvious advantages of “AREA Algorithm” compared with the single algorithm, and the tactical meaning in path planning of multiple missiles.
A First Step Towards an ISO-Based Information Security Domain Ontology

The need for Information Security Management Systems (SIEMs) has increased the effort requested to companies to improve the security level of their systems and their compliance with national and international standards. Unfortunately, the existence of several different security standards to comply with and the lack of well-defined guidelines related to documents preparation and reporting, may result into a bad security management and may cause several security issues. In this paper, we introduce a modeling approach to the definition of a SIEM that leverages a double-layered ontology: it is composed of a highlevel ontology, used to model complex relations among domains, and of a low-level, domain-specific ontology, aimed at modeling the ISO 27000 family of standards.
Remarks on Traffic Signal Coordination

The co-ordination between traffic signals is assumed to be important for the good organization of a transport system. By using an artificial approach to create and analyze a multitude of transportation systems, a few different simple traffic signals programs has been put to the test and compared to each other. The result is that a well co-ordinated system can be outperformed by a non-coordinated signal set-up, where all signals controlers run in (single intersection) actuated mode. Clearly, these results are preliminary and require more investigation.
Modelling and simulation of the quantum ranging and positioning system

An integrated simulation model of the quantum ranging and positioning system (QRPS) is analyzed and built as a means for illustrating the inner structure and working principle of the system. The QRPS is composed of an acquisition, tracking and pointing (ATP) module and a coincidence counting module. In the ATP module, an accurately controlled position system model with noise and disturbance is built, and a model reference adaptive control (MRAC) strategy with state filtering is designed to estimate the system state, the noise and disturbance, and accurately track the position state. In the coincidence counting module the time difference of arrival (TDOA) of the entangled photon pairs is dealt with and obtained. Numerical experiments are implemented to verify the system performance. With a user-friendly interface, the model we proposed can provide theoretical guidance for system integration of the QRPS.
Interaction Design for Selection and Manipulation on Immersive Touch Table Display Systems for 3D Geographic Visualization

Geographic visualizations are, due to the limited need for vertical navigation, suitable for touch tables. In this poster we consider the design of interaction design for selection and manipulation through touch on the screen used for the display of 3D geographic visualization-in our case the visualization of and interaction with drone traffic over rural and urban areas-focusing on moving from a monoscopic to a more immersive, stereoscopic touch table, and how this move affects the interaction design. With a monoscopic display our stereoscopic vision uses the graphics to perceive the location of the surface, and touch interaction can naturally and intuitively be performed on top of 3D objects. Moving to stereocopic display, for increased sense of immersion, the graphics no longer provide visual cues about the location of the screen. We argue that this motivates modification of the design principles, with an alternative interaction design as a result.
SELF-DETERMINATION OF MODERN SCIENCE: PROBLEM OF DEMARCATION AND ITS SOCIAL MEANING

The article deals with the actualization of the sociocultural meanings of the demarcation of scientific and non-scientific knowledge in modern philosophy. The intellectual and social prerequisites of this actualization are revealed. The main variants of solving the problem of demarcation in the history of philosophy (Bacon, Descartes, neopositivism, postpositivism) are characterized. The author analyses parascience, pseudoscience and deviant science as the main components of the array of non-scientific knowledge. The paper notes the social significance of the demarcation of science associated with its growing prestige and role in solving modern sociocultural, anthropological and civilization problems. Rationality is defined as the leading criterion of scientific knowledge and the value of modern social development.
A Novel Algorithm for Exemplar-based Image Inpainting (S)

In traditional exemplar-based image inpainting algorithm, the confidence value will rapidly decrease to zero as the inpainting process progresses.As a consequence, it will lead to unreliable result of the priority calculation and wrong direction of the process.In addition, traditional methods usually use the sum of squared differences (SSD) criterion to search the optimal matching block.Since the matching criterion is single and the precision is limited, the process is easy to produce mismatch.In order to solve the above problems, an improved algorithm has been proposed in this paper.First, we proposed a new confidence update algorithm through replacing the previous linear function form by using a logarithmic function form, which can suppress the phenomenon that the confidence attenuation is too fast and improve the accuracy of guiding and inpainting direction.Then, we combine the physical distance between blocks and traditional SSD matching criterion to improve matching accuracy.The experimental results show that the algorithm overcomes the shortcomings of the traditional algorithm and provides higher quality image restoration effects and better visual effects.
Research on Highway Slope Stability Based on Hierarchical Fuzzy Comprehensive Evaluation Method

Abstract The highway slope stability monitoring based on the Internet of Things technology can effectively realize the accurate and real-time three-dimensional monitoring of highway slopes and reduce the loss caused by road slope disasters. From the perspective of the system, based on the geological structure, topography and hydrology of highway slopes in Guangxi, this study uses the analytic hierarchy process to establish the stability of highway slopes based on the analysis of the characteristics of highway slopes, survey data, and factors affecting stability. This paper analyzes the structural model of the influencing factor layer, calculates the weights of the influencing factors of the road slope stability, combines the fuzzy relationships between the factors, and uses the comprehensive evaluation principle in fuzzy mathematics to realize the evaluation of slope stability. The results of an example analysis show that the evaluation method is reliable and effective.
&lt;b&gt;Hierarchical Structure Learning in a Bayesian Network for the Analysis of Purchasing Behavior&lt;/b&gt;

In the original Bayesian network model, the hierarchical structure of the variables is not assumed.When modeling the relation between the sales of products in a retail industry, it is better to consider a hierarchical structure of items (e.g., first, second, and third classifications).To apply a Bayesian network to such data, we have to focus on one hierarchy only in order to acquire a Bayesian network model.However, focusing only on the first or second classification provides a high-level view, but makes it difficult to understand customers' purchasing behavior in detail.On the other hand, focusing on the third classification results in a considerable number of nodes and a complicated network structure.Thus, capturing the overall relationship between products is not straightforward.Therefore, we propose a hierarchical Bayesian network model and a new learning method based on max-min hill-climbing learning algorithm.In the proposed method, we focus on the hierarchical structure of products, which enables us to construct a lower layer that considers the causal relationships in the upper layer.Furthermore, we investigate a case study using actual hierarchical data on consumer purchases, and demonstrate the proposed model using a simulation analysis.
Towards a Universal Semantic Dictionary

A novel method for finding linear mappings among word embeddings for several languages, taking as pivot a shared, multilingual embedding space, is proposed in this paper. Previous approaches learned translation matrices between two specific languages, while this method learns translation matrices between a given language and a shared, multilingual space. The system was first trained on bilingual, and later on multilingual corpora as well. In the first case, two different training data were applied: Dinu’s English–Italian benchmark data, and English–Italian translation pairs extracted from the PanLex database. In the second case, only the PanLex database was used. The system performs on English–Italian languages with the best setting significantly better than the baseline system given by Mikolov, and it provides a comparable performance with more sophisticated systems. Exploiting the richness of the PanLex database, the proposed method makes it possible to learn linear mappings among an arbitrary number of languages.
Improved cloud computing model of internet pricing schemes based on Cobb-Douglas utility function

Abstract In this research, cloud computing model on internet pricing scheme based on Cobb-Douglas utility function was formed. The model formed is a combination of cloud computing model, consumer problem model by using Cobb-Douglas utility functions. These model considers the level of customer satisfaction on the sale of a service product and the service quality of the service provider company. The model used is solved by using LINGO 13.0 program to get the optimal solution result. Based on the calculation, the optimal solution was obtained for two types of cases, for the first case is 217.76 and for the second case is 206.97 which is on cloud computing model with usage based pricing scheme and flat fee, respectively. Based on the result of each cases, both Internet Service Provider (ISP) and internet users will get maximum benefit when ISP applied models with utility function compared to the original model.
Named Entity Recognition with Conditional Random Fields on Turkish News Dataset: Revisiting the Features

Named entity recognition is a natural language processing problem that aims to mark entity names, such as person, place, organization, date, time, money and percentage, from different types of text. Various applications such as location estimation, event time estimation, determination of important people in the text can be possible with the solutions to this problem. The number of named entity recognition studies on Turkish texts is quite limited compared to those on English. In this study, the use of the technique Conditional Random Fields for the named entity recognition in Turkish news texts has been reviewed and the effect of new attributes on the model accuracy has been analyzed. The results of the experiments on different data sets are compared with the similar studies and presented.
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution

With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs. However, the lack of high-quality stereo datasets has limited the research in this area. To facilitate the training and evaluation of novel stereo SR algorithms, in this paper, we present a large-scale stereo dataset named Flickr1024, which contains 1024 pairs of high-quality images and covers diverse scenarios. We first introduce the data acquisition and processing pipeline, and then compare several popular stereo datasets. Finally, we conduct crossdataset experiments to investigate the potential benefits introduced by our dataset. Experimental results show that, as compared to the KITTI and Middlebury datasets, our Flickr1024 dataset can help to handle the over-fitting problem and significantly improves the performance of stereo SR methods. The Flickr1024 dataset is available online at: https://yingqianwang.github.io/Flickr1024.
An Evaluation on Robustness and Utility of Fingerprinting Schemes

Fingerprinting of data is a method to embed a traceable marker into the data to identify which specific recipient a certain copy of the data set has been released to. This is crucial for releasing data sets to third parties, especially if the release involves a fee, or if the data contains sensitive information due to which further sharing and potential subsequent leaks should be discouraged and deterred from. Fingerprints generally involve distorting the data set to a certain degree, in a trade off to preserve the utility of the data versus the robustness and traceability of the fingerprint. In this paper, we will thus compare several approaches for fingerprinting for their robustness against various types of attacks, such as subset or collusion attacks. We further evaluate the effects the fingerprinting has on the utility of the datasets, specifically for Machine Learning tasks.
Towards efficient privacy-preserving image similarity detection

Image similarity plays an essential role in many real world applications such as content based image retrieval (CBIR), computer vision, and near duplicate image detection. The state of the art methods are generally assumed that the content of images is not private. This reduces the utilization of these methods to work within only environments where images are publicly access. Essentially, this assumption limits more practical applications, e.g., image matching between two security agencies, where images are confidential. We address the problem of evaluating the similarity between image collections of two parties, who are reluctant to reveal their actual content. The Euclidian distance measure is used to measure distances between global hierarchal color histograms. We conduct several experiments on real image collections to demonstrate the practical value of the proposed schemes.
Adversarial Domain Adaptation for Machine Reading Comprehension

In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. To this end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where ($i$) pseudo questions are first generated for unlabeled passages in the target domain, and then ($ii$) a domain classifier is incorporated into an MRC model to predict which domain a given passage-question pair comes from. The classifier and the passage-question encoder are jointly trained using adversarial learning to enforce domain-invariant representation learning. Comprehensive evaluations demonstrate that our approach ($i$) is generalizable to different MRC models and datasets, ($ii$) can be combined with pre-trained large-scale language models (such as ELMo and BERT), and ($iii$) can be extended to semi-supervised learning.
A New Method for Complex Triplet Extraction of Biomedical Texts

Extracting biomedical triplet is one of the most important tasks in medical knowledge graph construction. Relations in complex biomedical text are overlap heavily. Although existing biomedical relation extraction methods have higher accuracy, they still have two problems. First, most of those methods hardly consider relations overlap problem. A lot of precious biomedical information is neglected. In addition, the entities in biomedical text are intensive, and the contextual information association also affects the understanding of the meaning of biomedical texts. Methods often used to encode sentence, like canonical bidirectional recurrent neural networks (BiRNN) or convolutional neural networks (CNN), are difficult to capture enough information from biomedical text. In this paper, we propose a new end-to-end triplet extraction method to address the complex triplet extraction problem in biomedical text. In our model, sentences are encoded by Recurrent Convolutional Neural Network (RCNN), which combines the advantages of BiRNN and CNN flexibly, containing more information of sentence. Experimental results on biomedical dataset and general field dataset show that our method is effective.
A Model Driven Architecture Approach to Generate Multidimensional Schemas of Data Warehouses

Over the past decade, the concept of data warehousing has been widely accepted. The main reason for building data warehouses is to improve the quality of information in order to achieve specific business objectives such as competitive advantage or improved decision-making. However, there is no formal method for deriving a multidimensional schema from heterogeneous databases that is recognized as a standard by the OMG and the professionals of the field. Which is why, in this paper, we present a model-driven approach (MDA) for the design of data warehouses. To apply the MDA approach to the Data warehouse construction process, we describe a multidimensional meta-model and specify a set of transformations from a UML meta-model which is mapped to a multidimensional meta-model. The transformation rules are programmed by the Query View Transformation (QVT) language. A case study illustrates our approach. It demonstrates how it reinforces the components traceability and reusability and how it globally improves the modeler’s efficiency. Furthermore, the use of the UML, as a technique to build data warehouses, is an important facilitator which prepares our further work to automate this approach.
On the importance of system-view centric validation for the design and operation of a crypto-based digital economy

Ubiquitous connectivity, networked computation, open technologies and advances in intelligent web approaches enable the third-generation web, Web3.0. Not least due to advancements in crypto technologies adoption in real world applications, it is now possible to convert the internet of things (IoT) into an economy of things (EoT), which basically refers to a heterogeneous digital economy of everything (dEoE). Based on the realization of the deficiencies of Web2.0 and the loss of confidence in a central intermediaries based economy, a societal desire to overcome these deficiencies has emerged. In the beginning, the crypto movement has been enthusiastic about the ability to encode all necessities for a digital economy in algorithms and code. However, in the mean time, insight has gained ground that on the one hand, designing the EoT is a quite complex endeavor and the resulting system might fail to achieve the targeted ideals, even when no intermediaries are present. We emphasize this position by arguing that the EoT in fact corresponds to a complex system operated in open contexts, for which a sophisticated design approach and a set of measures are inevitable, in order to be able to achieve a valid design and operation. In addition, we argue that the high level guiding principles necessary for a valid and effective EoT align well with the ideals of the democratized Web3.0 movement, and are no naive rapture, but rather - adequate adoption provided - can lead to socioeconomic efficiency. Over and above, we postulate that in this kind of EoT, commercial companies not only play a role, but can benefit alongside the society. In more game theoretical jargon, one could state that it is rational for the society and enterprises to undertake the effort to establish a valid EoT based on these ideals.
DIGITAL HUMANITIES IN HIGHER EDUCATION SYSTEM OF CANADA

The urgency of the article is due to the increased impact of information and communication technologies on research in the field of socio-humanitarian cycle sciences, which led to the emergence of interdisciplinary direction of research and projects – Digital Humanities, increasing socially important digital content (electronic collections, archives, libraries, museums) and the need for training specialists for this area of activity. The author relies on the main provisions of the publications by V. Kopanieva, T. Yaroshenko, S. Chukanova, O. Oliinyk, and Manifesto for the Digital Humanities. The purpose of the paper is to analyze the experience of preparing specialists in Library Science and Information Sciences (LIS) in the interdisciplinary Master’s Degree Programs in Digital Humanities in Canada and determining the prospects for introducing such programs in Ukraine. The author uses the systematic approach to determining the prospects of training specialists in Digital Humanities for libraries, educational institutions, museums, archives, and other cultural institutions. Methods of analysis of literary sources and information resources, content analysis of university sites in Canada, comparative analysis of educational programs Digital Humanities are applied. The University of Alberta (Edmonton, Canada) was selected as the main research object, where the 3-year Interfaculty Educational Program upon Digital Humanities and the Master of Science in Library and Information Research (MLIS) is offered by the Digital Humanities Program and the Library for Information Studies School. The comparative analysis of the contents of the Interfaculty Educational Program upon Digital Humanities at the University of Alberta and the educational and professional program on Information, Library and Archives Management at Borys Grinchenko Kyiv University stimulated the decision to develop a new specialization in Digital Humanities for undergraduate Master’s program students in Ukraine. Digital Humanities combine information and communication methods and theories with research and teaching in the field of art and the humanities. The training of specialists in the field of DH is an actual and important task for the higher education system in Ukraine, and the experience of universities in Canada can be extremely useful.