Reviewerly/exHarmony huggingface dataset:
AUTHENTIC MATERIAL TO IMPROVE STUDENTS’ READING COMPREHENSION AT SENIOR HIGH SCHOOL LEARNERS

As value of observation in the class room the students have score 6.0. This score is as a poor category. In this research the aim is to solve this problem until the students have a good score, while the standard curriculum is 6.5. This research aimed to improve the students’ reading comprehension at eleventh grade students of SMAN 10 Makassar. This research used Classroom Action Research (CAR). The research was conducted in two cycles. Each cycle consisted of four stages that is planning, acting, observing, and reflecting stages. Data were collected through test namely multiple choices and essay. The subject of research was 32 students of SMA Negeri 10 Makassar. Authentic material was obtained by internet, magazine and newspaper. The result of the study showed that there was improvement of students’ reading comprehension after being taught by using authentic materials. There was a significant improvement of the students’ reading ability, after getting the implementation of action among 2 cycles, and the finding was 18 students (56,25 %) in the first cycle and 28 students (87,5%) in the second cycle who can pass minimum completeness criteria (KKM). Therefore, the application of authentic materials in learning and teaching process can improve the students’ reading comprehension and has reached the instruction targeted in curriculum at that school. The students are more active to participate in learning reading by applying authentic materials.
Expanding innovation capacity in public sector by design projects

The demand of a new generation of public services is leading to a systematic exploration of what design can do for public organizations.The article presents and discusses, with the help of 2 design projects conducted in the Municipality of Turin, a design based theoretical framework for organizational change by conducting long terms process of engagement and exposition of employees from public sector to design culture.The two cases show as the raise of innovation capacity in public sector based on the practice of service design projects must consider the necessity of coping with long-standing challenges, i.e. the innovation of public bodies; the peculiarities of organizational learning processes and of the absorptive capacity of the organizations; and the overall resistance to change in people and organizations instead of trying to circumvent them.
An Efficient Framework for Text Document Security and Privacy

Nowadays, with the help of advanced technologies, an illegal copy of digital content can be shared easily. Which rise copyright and authentication problems. Digital text documents are generated and shared daily through different internet technologies such as the cloud, etc. The protection of these documents is a challenging task for researchers. In the past, steganography, cryptography, and watermarking techniques have been applied to resolve the copyright problem. However, most of the existing techniques are applicable for only plain text or protecting the document on the local paradigm. In the said perspective, we proposed a new technique to solve the problem of copyright and authentication on local and cloud paradigms. In this paper, we utilize some custom components of MS Word Document for concealing the watermark into a text document. These components are not referred to as the main document and will not modify the content and format. The experimental analysis and results prove that the proposed method improves the watermark capacity, imperceptible, and robust against formatting attacks.
ANALYSIS OF THE USE OF CLOUD SERVICES FOR FISHING ATTACKS

Phishing, as a type of information attack, has been used by intruders for selfish purposes for quite some time. They are very popular in the criminal world because it is much easier for a person to make certain profitable actions than a program. With the advent of new technologies, this type of attack has gradually adapted to the new conditions of engagement with its victim. Cloud services have become a great modern and widespread tool for phishing campaigns. The use of such services has given to malicious actors a number of significant advantages over the use of their own computing resources. The relative cheapness and ease of exploitation of these technologies has played an important role. The problem of information security with using cloud technologies is that this type of attack is difficult to detect, even more to prevent, without significantly affecting the comfort of using end users of information systems. The article analyzes the relevance of this type of attacks based on real data. We considered the algorithm of their work during a life cycle and analyzes the use of the basic available security methods of protection, their feasibility and problems of use. The analysis showed that not all modern security methods are capable of detecting and preventing phishing attacks, which use public cloud services. Even a combination of several or all methods cannot guarantee high protection for users against phishing threats. In the article were mentioned some examples of phishing campaigns that took place during 2019 and used such popular public cloud services as Azure Blob storage created by Microsoft and Google Drive developed by Google. A basic list of tips was also provided that would increase the level of security for internet users in order to reduce the risk of potential data compromise or its consequences.
Neural Pairwise Ranking Factorization Machine for Item Recommendation

The factorization machine models attract significant attention from academia and industry because they can model the context information and improve the performance of recommendation. However, traditional factorization machine models generally adopt the point-wise learning method to learn the model parameters as well as only model the linear interactions between features. They fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this paper, we propose a neural pairwise ranking factorization machine for item recommendation, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, the pair-wise ranking model is adopted to learn the relative preferences of users rather than predict the absolute scores. Experimental results on real world datasets show that our proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models.
Forecasting Bitcoin Volatility Using Two-Component CARR Model

In this paper, we propose an extension of the range-based CARR model, the two-component CARR (CCARR) model to model and forecast the Bitcoin volatility. The extension inherits the strength of the original range-based CARR model, its capability of exploiting intraday information from the high and low prices to estimate volatility. Moreover, the CCARR model has the capacity to accommodate the long memory volatility. Empirical results show that the CCARR model outperforms the CARR model and the return-based GARCH and twocomponent GARCH (CGARCH) models in forecasting the Bitcoin volatility.The results highlight the value of using price range and including a second component of the conditional range for forecasting the Bitcoin volatility.
GitHubNet: Understanding the Characteristics of GitHub Network

Web 2.0 technologies have not only raised microblogs, but also social software development and collaboration platforms. GitHub is the most popular software development platform that provides social collaboration. Within the scope of this study, a novel graph-based analysis model is proposed which targets to reveal (1) the characteristics of the GitHub in order to shed light on social software development in general, and (2) the most popular programming languages, repositories, and developers in order to shed light on the trending software development technologies. To this end, a subset of the GitHub network, which contains 84, 737 developers and 209, 100 repositories, was collected through the GitHub API and stored on a graph database namely neo4j to be later analyzed. The result of the analysis shows that (1) the connections in GitHub are not mutually linked, (2) JavaScript, Python, and Java are currently the most popular three programming languages, (3) You-Dont-Know-JS, oh-my-zsh, and public-apis are the most popular three repositories, and (4) TarrySingh (Tarry Singh), indrajithban-dara (Indrajith Bandara), and rootsongjc (Jimmy Song) are the most popular three developers. Furthermore, the proposed novel analysis model can be easily applied to other social networks.
Deriving Theorems in Implicational Linear Logic, Declaratively

The problem we want to solve is how to generate all theorems of a given size in the implicational fragment of propositional intuitionistic linear logic. We start by filtering for linearity the proof terms associated by our Prolog-based theorem prover for Implicational Intuitionistic Logic. This works, but using for each formula a PSPACE-complete algorithm limits it to very small formulas. We take a few walks back and forth over the bridge between proof terms and theorems, provided by the Curry-Howard isomorphism, and derive step-by-step an efficient algorithm requiring a low polynomial effort per generated theorem. The resulting Prolog program runs in O(N) space for terms of size N and generates in a few hours 7,566,084,686 theorems in the implicational fragment of Linear Intuitionistic Logic together with their proof terms in normal form. As applications, we generate datasets for correctness and scalability testing of linear logic theorem provers and training data for neural networks working on theorem proving challenges. The results in the paper, organized as a literate Prolog program, are fully replicable. Keywords: combinatorial generation of provable formulas of a given size, intuitionistic and linear logic theorem provers, theorems of the implicational fragment of propositional linear intuitionistic logic, Curry-Howard isomorphism, efficient generation of linear lambda terms in normal form, Prolog programs for lambda term generation and theorem proving.
To tell the truth sometimes it pays to lie

Thou shalt not lie. Kant famously said one must always tell the truth. Even with a murderer at the door one cannot lie. Probably no one else holds this extreme view. There are times a lie is appropriate, ethical. When might it be permissible to lie? Are there times when it might be not only OK to lie but be appropriate to lie? But others remind us that a business cannot succeed in the long run by lying, In education we help people learn how to make effective and ethical choices. Specific examples and minicases related to these issues help get classroom or online conversations started. Question are discussed, usually in dyads. The discussion does not end here. Our classroom experiences and feedback from students convinces us: to tell the truth, sometimes it pays to lie.
19‐4: Research on Reducing Motion Sickness of Playing First Person Shooting VR Game with Texture Blur

In order to reduce motion sickness (MS), texture blur (TB) was used on the 3D objects in the First Person Shooting VR game. Simulator Sickness Questionnaire and ECG data were analyzed in our study, and significant differences were found by comparing “with TB” and “without TB” conditions.
Comparative Study on Eye Gaze Estimation in Visible and IR Spectrum

Eye gaze estimation aims to find the point of gaze which is basically," where we look".Estimating the gaze point plays an important role in many applications with varying usage.Gaze estimation is used in automotive industry to ensure safety.In the field of retail shopping and online marketing gaze estimation is used to analyse the consumer's interest and focus.Gaze estimation is also used for psychological tests and in healthcare for diagnosing some of the neurological disorders.This also has a significant role to play in the field to entertainment.There are multiple ways by which eye gaze estimation can be done.This paper is about a comparative study done on two of the popular methods for gaze estimation using eye features.An infra-red camera is used to capture data for this study.Method 1 tracks corneal reflection centre w.r.t the pupil centre and method 2 tracks the pupil centre w.r.t the eye centre to estimate gaze.There are advantages and disadvantages with both the methods which has been looked into.Choosing the right method for gaze estimation hence depends on the type of application, precision required and many other factors including environmental conditions.This paper can act as a reference for researchers working in the same field
Covid-19: Glitch leaves users unable to link test results to new contact tracing app

Thank you for your interest in spreading the word about The BMJ.NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address. Username * Your Email * Send To * You are going to email the following Covid-19: Glitch leaves users unable to link test results to new contact tracing app Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
(Invited) Domain Adaptation for the Semantic Segmentation of the Unmanned Surface Vehicle

Recently, deep learning has great development and is applied in various fields. But the performance of the network depends on a large amount of label data training. After training in a specific domain, a network model can have a good performance. But it may have a poor performance when transferred into a new domain. This paper addresses the visual semantic segmentation model transfer for the unmanned surface vehicle (USV) system. An adaptive cross-domain incremental learning method is proposed, which enables USV adaptive to navigate in unknown new environments. In our work, the knowledge of the trained segmentation network model and the feature information of the input image is used for the network incremental learning. First, the trained semantic segmentation network is used to classify each pixel of the input image. Then, pixels with high prediction probability are selected as seed points. At the same time, a convolution network for feature extraction is used to obtain the embedded feature space vector of each pixel. A mask centered on the seed point is generated based on the similarity between pixels in the feature space. The mask is labeled based on the segmentation network prediction. Multiple masks are fused to generate the label of the input image. Finally, the automatically generated labels are used to update the network to realize the adaptability of the network in the new environment.
Mont Saint-Hilaire: History, Geology, Mineralogy

Book Review| July 01, 2020 Mont Saint-Hilaire: History, Geology, Mineralogy Mont Saint-Hilaire: History, Geology, Mineralogy. By László Horváth, Robert A. Gault, Elsa Pfenninger-Horváth and Glenn Poirier. The Canadian Mineralogist, Special Publication 14, 2019. IX + 634 pages. ISBN 978-0-921294-61-0. Alf Olav Larsen Alf Olav Larsen Stathelle, Norway Search for other works by this author on: GSW Google Scholar The Canadian Mineralogist (2020) 58 (4): 543–545. https://doi.org/10.3749/canmin.BR00005 Article history first online: 25 Sep 2020 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Search Site Citation Alf Olav Larsen; Mont Saint-Hilaire: History, Geology, Mineralogy. The Canadian Mineralogist 2020;; 58 (4): 543–545. doi: https://doi.org/10.3749/canmin.BR00005 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentBy SocietyThe Canadian Mineralogist Search Advanced Search The authors are the couple László and Elsa Horváth, who have spent the last 20 years working on this monograph, along with the mineralogist Robert Gault and geologist Glenn Poirier. Interestingly, both the Horváths as well as Gault are autodidacts in mineralogy and have gained deep knowledge in this field. The now retired Robert Gault worked as a research assistent at the Canadian Museum of Nature in Ottawa and has since 1991 been operator of the institution's electron microprobe. This allowed him to investigate the chemical composition of MSH minerals firsthand. Glenn Poirier works as a senior research assistant and... You do not currently have access to this article.
Visualizing Facets of Text Complexity across Registers

In this paper, we propose visualizing results of a corpus-based study on text complexity using radar charts. We argue that the added value of this type of visualisation is the polygonal shape that provides an intuitive grasp of text complexity similarities across the registers of a corpus. The results that we visualize come from a study where we explored whether it is possible to automatically single out different facets of text complexity across the registers of a Swedish corpus. To this end, we used factor analysis as applied in Biber’s Multi-Dimensional Analysis framework. The visualization of text complexity facets with radar charts indicates that there is correspondence between linguistic similarity and similarity of shape across registers.
Normative regulation of vocational education during the epidemiological situation in the Russian Federation

The epidemiological situation associated with the coronavirus has affected all spheres of life of Russians, including education. Serious changes have affected all educational organizations without exception. The implementation of vocational education programs, vocational training and additional vocational education, as well as the activities of professional educational organizations have undergone dramatic changes and faced a number of difficulties. Therefore, methodological and legal support of educational activities during a pandemic is an urgent task. The paper considers regulatory documents and recommendations that regulate the activities of educational institutions implementing secondary vocational education programs in a difficult epidemiological situation. The main normative acts, which should be relied on for the correct interpretation of methodological and explanatory documents, are indicated. Regulatory documents and methodological recommendations concerning the following issues are given: implementation of programs using e-learning and distance learning technologies; forms of education; autonomy of an educational organization in the development of educational programs, the adoption of local acts; the student’s right to an individual curriculum, academic leave, during a pandemic. The experience of flexible implementation of educational programs will contribute to the growth of competence and qualifications of management and pedagogical teams of professional educational organizations.
Virtual fisheries through mobile apps: The way forward

Virtual information was used in ambit of compiling different mobile apps in fisheries sector using google search and virtual product snow ball sampling method. Total of 124 mobile apps were located in fields of Angling, Aquaculture, Aquarium Management, Marine Fisheries and Fisheries Governance, Marketing and Biology out of which 22.58% apps were of Indian origin. Apps providing information on marine fisheries were maximum. It is expected that the software market for precision agricultural tools will grow and there is a need for fisheries organizations to partner with Information Technology providers and come out with solutions which will be visionary for both sustainability and profitability.
Early Development and Design of Crowdsourcing Agriculture Mobile Application for Lambsquarters

As big data has become increasingly necessary in modern farming techniques, the dependence on high quality and quantity of ground truth data has risen. Collecting ground truth data is one of the most labor-intensive aspects of the research process. A crowdsourcing platform application to aid lay people in completing ground truth data can improve the quality and quantity of data for growers and agricultural researchers. In this study, a user-centered design process was used to develop a prototype of a mobile application which will teach people how to classify certain characteristics of lambsquarters in the District of Columbia. Focus group results demonstrated that the greatest motivation for the participants was having opportunities to develop their skills and access to educational resources. From the focus groups, design personas were created and wireframe prototypes were produced. The prototypes were evaluated by users using the System Usability Scale and qualitative feedback. The design received an average score of 75.95, which indicates an acceptable design. From the feedback of the users, improvements to the design were made in the mobile application development of the system.
Bringing Roguelikes to Visually-Impaired Players by Using NLP

Although the roguelike video game genre has a large community of fans (both players and developers) and the graphic aspect of these games is usually given little relevance (ASCII-based graphics are not rare even today), their accessibility for blind players and other visually-impaired users remains a pending issue. In this document, we describe an initiative for the development of roguelikes adapted to visually-impaired players by using Natural Language Processing techniques, together with the first completed games resulting from it. These games were developed as Bachelor’s and Master’s theses. Our approach consists in integrating a multilingual module that, apart from the classic ASCII-based graphical interface, automatically generates text descriptions of what is happening within the game. The visually-impaired user can then read such descriptions by means of a screen reader. In these projects we seek expressivity and variety in the descriptions, so we can offer the users a fun roguelike experience that does not sacrifice any of the key characteristics that define the genre. Moreover, we intend to make these projects easy to extend to other languages, thus avoiding costly and complex solutions. KEYWORDS: Natural Language Generation, roguelikes, visually-impaired users
TermEval 2020: Using TSR Filtering Method to Improve Automatic Term Extraction

The identification of terms from domain-specific corpora using computational methods is a highly time-consuming task because terms has to be validated by specialists. In order to improve term candidate selection, we have developed the Token Slot Recognition (TSR) method, a filtering strategy based on terminological tokens which is used to rank extracted term candidates from domain-specific corpora. We have implemented this filtering strategy in TBXTools. In this paper we present the system we have used in the TermEval 2020 shared task on monolingual term extraction. We also present the evaluation results for the system for English, French and Dutch and for two corpora: corruption and heart failure. For English and French we have used a linguistic methodology based on POS patterns, and for Dutch we have used a statistical methodology based on n-grams calculation and filtering with stop-words. For all languages, TSR (Token Slot Recognition) filtering method has been applied. We have obtained competitive results, but there is still room for improvement of the system.