Reviewerly/exHarmony huggingface dataset:
Web Server Security Analysis Using The OWASP Mantra Method: Web Server Security Analysis Using The OWASP Mantra Method

Higher Education has been using web-based academic information system, for all academic administration process in this academic system such as study plan, academic transcipt, lecturers and Curriculum and student data. So that required maintenance in database and system management whith well-maintained and scheduled. It is necessary to apply the system  to determine the level of vulnerability in order to avoid attacks from irresponsible parties. OWASP (Open Web Application Security Project) is one of the methods for testing the web-based applications released by owasp.org. Using OWASP may indicate that authentication management, authorization and session management.The STMIK Jakarta website often has problems on the web and the loss of some important data that interferes with lectures. At the end of 2016, around September when preparing for the first semester of the Study Plan, the website experienced programmed data loss, consequently the academic system was disrupted.  The STMIK Jakarta has used a web-based academic information system, for all academic administrative processes such as study plans, academic transcripts, lecturers, curriculum and student data.This system requires data base and system management. It is important to implement a security system to determine the level of vulnerability to avoid attacks from irresponsible parties. OWASP (Open Web Application Security Project) is one method for testing web-based applications released by owasp.org. The results of the research have been carried out with the results reaching around 90% management authentication, authorization, and session management not being implemented properly.
Threat perception in online anti-migrant speech: a Slovene case study

The aim of this article is to describe the perception of refugees as a threat in Slovene online discourse, based on a critical analysis of commenters’ re­sponses to popular media posts at the height of the European migrant crisis. The proposition of the study is that the perception of migration as a threat is at the core of socially unacceptable discourse (SUD), portraying refugees and migrants as an undesirable and potentially dangerous presence. Within the framework of a comprehensive project examining public responses to media coverage of the arrival of migrants to Slovenia, online comments classified as SUD targeting refugees were extracted and annotated to reveal the recur­ring themes of threat perception. The analysis focused on describing the main categories of threat, as well as the various discursive features and strategies employed. Although the approach to observing this subject is essentially qual­itative, a general case-specific overview of the frequency and distribution of identifiable categories is also given.
Undecidability of Future Timeline-based Planning over Dense Temporal Domains?

Planning is one of the most studied problems in computer science. In this paper, we consider the timeline-based approach, where the domain is modeled by a set of independent, but interacting, components, identified by a set of state variables, whose behavior over time (timelines) is governed by a set of temporal constraints (synchronization rules). Timeline-based planning in the dense-time setting has been recently shown to be undecidable in the general case, and undecidability relies on the high expressiveness of the trigger synchronization rules. In this paper, we strengthen the previous negative result by showing that undecidability already holds under the future semantics of the trigger rules which limits the comparison to temporal contexts in the future with respect to the trigger.
AdvExpander: Generating Natural Language Adversarial Examples by Expanding Text

Adversarial examples are vital to expose the vulnerability of machine learning models. Despite the success of the most popular substitution-based methods which substitutes some characters or words in the original examples, only substitution is insufficient to uncover all robustness issues of models. In this paper, we present AdvExpander, a method that crafts new adversarial examples by expanding text, which is complementary to previous substitution-based methods. We first utilize linguistic rules to determine which constituents to expand and what types of modifiers to expand with. We then expand each constituent by inserting an adversarial modifier searched from a CVAE-based generative model which is pre-trained on a large scale corpus. To search adversarial modifiers, we directly search adversarial latent codes in the latent space without tuning the pre-trained parameters. To ensure that our adversarial examples are label-preserving for text matching, we also constrain the modifications with a heuristic rule. Experiments on three classification tasks verify the effectiveness of AdvExpander and the validity of our adversarial examples. AdvExpander crafts a new type of adversarial examples by text expansion, thereby promising to reveal new robustness issues.
OPTIMASI PENILAIAN PADA E-LEARNING UNIVERSITAS MADURA DENGAN MENGGUNAKAN METODE SCAFFOLDING

Delivery of learning material in courses that are efficient in lectures at the University Madura, especially in the Faculty of Engineering and the Faculty of Science Teaching and Education is a difficult thing to do achieved, to be able to provide a good understanding and efficient in learning both mastery and understanding concepts, theories, methods or philosophy of a particular field of science obtained through in-process reasoning student learning and work experience. Some research has attempted to propose a solution in the delivery of learning, but very difficult to find a good method to use. In this research proposed a media optimization method learning by using scaffolding in the application e-learning students in understanding the definition of material lectures, which is by using the method scaffolding will provide increased capabilities students in understanding the learning material, 6 strategies the scaffolding used in e-learning includes: modeling, instructing, explaining, questioning and hints, contingency management, and feeding back. Through e-learning system optimization planning, test results system conducted on 50 respondents for maleand female students produced a percentage of 63.82% for female students and 81.47% for male students as benefit values on the evaluation of e-learning system optimization planning.
STUDENTS’ ABILITY IN ANALYZING AMBIGUOUS PERSONAL REFERENCE IN WOMEN’S MURDER CLUB: THE TRIAL NOVEL

This research discussed students’ ability in analysing ambiguous reference in written text. The objectives of this research were to find out students’ ability, and the way of the seventh-semester students of English and Literature Department (2016) in analysing the given extracts. The Researchers used quantitative and qualitative methods for collecting and analysing data. The data source was the seventh-semester students of English and Literature Department (2016). The Researchers used paper test (close-ended and open-ended question) as the instrument to get the data. As the procedure of collecting data, the Researchers gave the students the test and the instruction to answer the test. The Researchers then collected the paper after the students answered the tests. The Researchers used Halliday and Hasan’s reference theory which focusing on personal reference. Based on the findings and discussion, the Researchers found that 1 student possessed excellent ability, 2 students got good ability, 5 students belonged to fair ability, and 2 students obtained poor ability. As the conclusion, the seventh-semester student of English and Literature Department (2016) had fair ability (67.09) in analysing ambiguous personal references. It was proved by their mean score through the paper test (multiple choice). And the Researchers also concluded that analysed the given extracts by 3 ways. Those were by the context, good sense, and guessing. It was proved by their answers got through open-ended questionnaire.
Pedagogical Realization of Proper Names Translation

The relevance of the study is due to the need for an in-depth analysis of onomastic vocabulary and the possibility of its use within English classes for future translators.Three main types of translation exercises have been studied: non-communicative, conditionally communicative and communicative.The study developed a number of visual exercises (according to each of the types) that can be used by the teacher in English classes for future translators.The exercises were based on the series of novels "Witcher" by Andrzej Sapkowsky, including the books "The Last Wish" and "The Sword of Destiny".It is established that the use of all three types of exercises is important for the complex development of translation skills because all of them should debe used at different stages of elaboration of a new lexical topic; all of them have different directions, goals and methods of implementation.Thus, non-communicative exercises do not depend on the context and are aimed mainly at methodical sharpening of the studied material and bringing the student's knowledge to automatism.Conditionally communicative exercises are characterized by proximity to the real language situation, but there is an unnatural repetition of vocabulary and grammar being studied.At this stage, the student can translate individual phrases, determine the type of translation transformations, etc.The stage of communicative exercises is the most difficult, because it involves direct work of the student with the text or its individual fragments (without prompts from the teacher).
A Cascaded Residual UNET for Fully Automated Segmentation of Prostate and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images

Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer. Automated segmentation of the prostate eliminates the need for manual annotation by a radiologist which is time consuming. This improves efficiency in the extraction of imaging features for the characterization of prostate tissues. In this work, we propose a fully automated cascaded deep learning architecture with residual blocks, Cascaded MRes-UNET, for segmentation of the prostate gland and the peripheral zone in one pass through the network. The network yields high Dice scores ($0.91\pm.02$), precision ($0.91\pm.04$), and recall scores ($0.92\pm.03$) in prostate segmentation compared to manual annotations by an experienced radiologist. The average difference in total prostate volume estimation is less than 5%.
Construction of User’s Navigation Sessions from Web Logs for Web Usage Mining

Web Usage Mining applies fewer techniques in record data to pull out the behavior of users. The knowledge mined from the web log can be utilized in web personalization, Prediction, prefetching, restructuring of web sites etc. It consists of three steps in preprocessing, pattern detection and analysis. Web log information is typically noisy and uncertain and preprocessing is a significant process ahead of mining. The Patterns discovered after applying the mining techniques are dependent on the accuracy of the weblog which in turn depends on the preprocessing phase. The output of preprocessing should be the user’s navigation session file. In this paper the techniques of preprocessing and the method for construction of user’s navigation session file is proposed.
Embedding Meta-Textual Information for Improved Learning to Rank

Neural approaches to learning term embeddings have led to improved computation of similarity and ranking in information retrieval (IR). So far neural representation learning has not been extended to meta-textual information that is readily available for many IR tasks, for example, patent classes in prior-art retrieval, topical information in Wikipedia articles, or product categories in e-commerce data. We present a framework that learns embeddings for meta-textual categories, and optimizes a pairwise ranking objective for improved matching based on combined embeddings of textual and meta-textual information. We show considerable gains in an experimental evaluation on cross-lingual retrieval in the Wikipedia domain for three language pairs, and in the Patent domain for one language pair. Our results emphasize that the mode of combining different types of information is crucial for model improvement.
Implementing Video Media to Improve Speaking Skill

English is one of international language in the world.  There are four skills  in English  those are  writing skill, listening skill. Reading skill, and speaking skill.  This research take speaking skill which  is focused on the conversation or communication.  Language is a means to communicate among many people in the society.  From the obtained evidence by the researcher there were some result that the members had the lower score, from 10  members basic class of ECA Course there were 10members that had a value below the average, the obtained result was gotten from the result of oral interview with the members. Obtained result of interview test can be concluded that there were some members who is still difficult in speaking skill.  This type of research uses Classroom Action Research because researcher act directly in research, starting from the beginning to the end of the action. From  the research  finding,  students become more active and not bored,  Students memorize  faster than the previous method,  Students become more enthusiastic to do speaking practice,  Little by little improvement is better than before,  In the fourth stage of meeting students are able to apply speaking  skill in classroom,  The student’s memorization  is getting stronger.  From 10 students, 9 students achieve the criteria of succes, while the rest student didn’t achieve the criteria of succes.
Trade, Income Distribution and Inequality: Sudan and Turkey in Comparative Perspective

This paper investigates the impact of trade on country’s income distribution and inequality also controlling for a set of trade related factors made a comparison between Sudan and Turkey during the years 1987-2018. Using 2SLS models and for examining the long run relationship we are conducting autoregressive distributed lag ARDL model with F bounds test approach. The empirical results show that trade has a positive impact on income distribution and increases in trade leads to reduce inequality in Sudan and Turkey.
Aprendizaje geométrico hiperciclo activado por la realidad aumentada

The development of scientific and technological knowledge can produce areas of ignorance, when no attempt is made to accelerate learning processes in the modern environment. From this approach, the ways of obtaining geometric learning through real activities and augmented virtual reality (RVA), provides combined visual elements, maximizing motivating three-dimensionality (3D), which facilitates the discovery of all geometric bodies in an inductive and subjective way, promoting accelerated changes in learning. In the cyclical structure, it is made up of loops, creating the hyperstructure, considering RVA as an additive to activate learning. The interpretive phenomenological hermeneutical method was established, based on qualitative research, considering the theoretical observables provided by the research question, How does RVA in the hypercyclic structure contribute to potentiate learning ?, obtaining that learning in a cyclical way has to be in relation to the advancement of science so that AVR activates it more quickly.
Spread of Covid-19: a Study Case of Honduras, Forecasting with Logistic Model and SIR Model

In this compilation, the logistic model and the SIR model are tested to forecast the spread of Covid-19 virus in Honduras by using the software Matlab. The mathematics topics involved are presented in a friendly way to facilitate their reading and to identify some possible didactic practices derived from their application. Certainly, some graphs, data arrangements and comments about the results obtained are given. The forecasts in this document depended on the handling of the data provided by the Hondurean's authorities.
Neural Network Analysis of Electroencephalograms Graphical Representation

The article is devoted to the problem of recognition of motor imagery based on electroencephalogram (EEG) signals, which is associated with many difficulties, such as the physical and mental state of a person, measurement accuracy, etc. Artificial neural networks are a good tool in solving this class of problems. Electroencephalograms are time signals, Gramian Angular Fields (GAF), Markov Transition Field (MTF) and Hilbert space-filling curves transformations are used to represent time series as images. The paper shows the possibility of using GAF, MTF and Hilbert space-filling curves EEG signal transforms for recognizing motor patterns, which is further applicable, for example, in building a brain-computer interface.
ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performance

Despite the unresolved 'dying ReLU problem', the classical ReLU activation function (AF) has been extensively applied in Deep Neural Networks (DNN), in particular Convolutional Neural Networks (CNN), for image classification. The common gradient issues of ReLU pose challenges in applications on academy and industry sectors. Recent approaches for improvements are in a similar direction by just proposing variations of the AF, such as Leaky ReLU (LReLU), while maintaining the solution within the same unresolved gradient problems. In this paper, the Absolute Leaky ReLU (ALReLU) AF, a variation of LReLU, is proposed, as an alternative method to resolve the common 'dying ReLU problem' on NN-based algorithms for supervised learning. The experimental results demonstrate that by using the absolute values of LReLU's small negative gradient, has a significant improvement in comparison with LReLU and ReLU, on image classification of diseases such as COVID-19, text and tabular data classification tasks on five different datasets.
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network

In this work, we aim to learn an unpaired image enhancement model, which can enrich low-quality images with the characteristics of high-quality images provided by users. We propose a quality attention generative adversarial network (QAGAN) trained on unpaired data based on the bidirectional Generative Adversarial Network (GAN) embedded with a quality attention module (QAM). The key novelty of the proposed QAGAN lies in the injected QAM for the generator such that it learns domain-relevant quality attention directly from the two domains. More specifically, the proposed QAM allows the generator to effectively select semantic-related characteristics from the spatial-wise and adaptively incorporate style-related attributes from the channel-wise, respectively. Therefore, in our proposed QAGAN, not only discriminators but also the generator can directly access both domains which significantly facilitates the generator to learn the mapping function. Extensive experimental results show that, compared with the state-of-the-art methods based on unpaired learning, our proposed method achieves better performance in both objective and subjective evaluations.
Pen Trace Reconstruction with Skeleton Representation of a Handwritten Text Image

This paper presents a new approach for the pen trace reconstruction task in handwritten text images. The proposed approach is based on the skeleton representation of a binary image which provides sufficient information about the pen movement trajectory during the writing. As such, the initial problem can be viewed as a problem of constructing a specific walk in the skeleton graph covering all its edges. The proposed approach consists of splitting the graph into structural elements and estimating the pen movement direction in each of them with the resulting trace being composed of the traces of these elements.
Forming Linguistic Students’ Compensatory and Pragmatic Competences on the Basis of Verbal Cognitive Activity

The research objective includes identifying the role of verbal cognitive activity when forming linguistic students’ compensatory and pragmatic competences. The article analyses correlation of linguistic competences and speech activity (language usage) from the viewpoint of foreign language teaching. Specificity of the competences manifestation depending on a type of thinking is revealed. Scientific originality of the research involves justifying the thesis that students’ verbal cognitive activity stimulation allows simultaneous development of their pragmatic and compensatory competences taking into account similarity of intellectual models. The authors introduce a definition of verbal cognitive activity and reveal the educational potential of the case-study method. The research findings are as follows: the authors identify the stages of verbal cognitive activity development with a focus on forming students’ compensatory and pragmatic competences on the basis of the case-study technology.
UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction

Definition Extraction is the task to automatically extract terms and their definitions from text.In recent years, it attracts wide interest from NLP researchers.This paper describes the unixlong team's system for the SemEval 2020 task6: DeftEval: Extracting term-definition pairs in free text.The goal of this task is to extract definition, word level BIO tags and relations.This task is challenging due to the free style of the text, especially the definitions of the terms range across several sentences and lack explicit verb phrases.We propose a joint model to train the tasks of definition extraction and the word level BIO tagging simultaneously.We design a creative format input of BERT to capture the location information between entity and its definition.Then we adjust the result of BERT with some rules.Finally, we apply TAG ID, ROOT ID, BIO tag to predict the relation and achieve macro-averaged F1 score 1.0 which rank first on the official test set in the relation extraction subtask.
Designing the educational space of the university

The relevance of the problem of youth education in modern conditions is due to changes in the social environment. The educational process at the university is currently focused on personal development, which implies an individual approach to each student, taking into account their needs, aspirations, desires, and abilities. The article describes the main parameters of the traditional system of education, which in the current conditions of modernization of education requires changes. Modernization of the education system leads to changes in the educational activities of teachers. The dominant feature of upbringing, which allows students to realize themselves at all stages of their personal development, is the formation of the personal success of pupils in society. The role of the teacher who carries out the educational process is changing: stimulating independence and creativity, focusing on initiative and responsibility. The application of modern pedagogical technologies in the design of the educational space of the university is relevant