Reviewerly/exHarmony huggingface dataset:
DEVELOPING PROFESSIONAL COMPETENCE OF PEDAGOGICAL UNIVERSITY STUDENTS MAJORING IN FOREIGN LANGUAGES WHILE ORGANIZING CLASSROOM AND OUT-OF-CLASS ACTIVITIES

The article researches into the possible ways and need for developing professional competence of would-be teachers of foreign languages both in class and while preparing for classes. It has been revealed that while organizing class and out-of-class language activities the emphasis is on developing students’ communicative competence. Professional competence of would-be-teachers of foreign languages is developed in the framework of the discipline Language Teaching Methods, which is studied from the third year at university. Would-be-teachers must be able to use professional terminology, to model professional communication situations in class, to motivate, to explain, and to assess in the foreign language they are studying and are going to teach. Language practice classes very seldom include materials containing professionally-oriented texts. The professionally-oriented component of students’ language competence is not part of the syllabus. It is not assessed at any level, nor it is reflected in the teaching materials. As a result, when having their first teaching practice students experience personal informational problems with classroom management and organizing classroom interaction, which are caused by students’ low level of professional competence. It is proposed to include didactic simulation into the educational process so that foreign language would-be teachers could develop their professional competence from the very first year at pedagogical university. To achieve this goal, it is necessary to create conditions and situations where students could be actively involved and could show their creativity, to shift the focus from the learner to the teacher.
Wind Speed Recovering from Lidar Sensing Data by Solving of Inverse Problem

The wind characteristics measurements by use Doppler wind lidars is actively developing. Unlike measuring with anemometers, remote sensing allows you to get only an average value of the wind speed. In the case of essential spatial variability, the average value can differ significantly from the anemometer measurements. We consider the problem of improving the accuracy of determining wind characteristics from remote lidar sensing data in the case of a non-uniform wind field. Determination of the wind speed is considered as the inverse problem of recovering a function from the values of its integrals. The unknown function of the wind speed is represented in the form of its spline approximation. Various conditions of conjugate splines are considered. The analysis of the influence of the parameters of the problem on the quality of restoration is carried out. As a result, rational values of the parameters of the problem are obtained, which give a stable solution.
Ecological Impacts of Coral Reefs, Egyptian Red Sea Coast: Elemental Characterization and Spatial Distribution with INAA

Instrumental neutron activation analysis (INAA) is a well-accepted technique that has the ability to simultaneously determining 30–40 elements. INAA is also capable of determining major, minor, and trace elements as well as rare earth elements. It was utilised to determine the concentration of heavy elements and elemental contents of coral reef samples collected from three locations on the Red Sea coast of Egypt. The coral reef samples were prepared with their standard materials and together irradiated in Egypt Second Research Reactor (ETRR-2). For short and long irradiations, samples were separately irradiated at a convenient thermal neutron flux 4 × 1011cm−2s−1 for 2 min in a pneumatic rabbit system and samples were irradiated at a thermal neutron flux 1.5x1014cm−2s−1 for 32 mins, respectively. All samples were counted two times in a HPGe detector with a relative efficiency of 100% for short and long irradiations. The following elements have been determined Al, Ti, Na, K, Mg, V, Cl, Ca, Mn, Cr, As, Co, Zn, Ni, Fe, Br, Tb, Zr, Hf, Sb, Ba, Sc, Nd, La, Sm, Ce, Eu, Yb, Ta, U, and Th. In the Quseir harbour, the maximum concentration of Al, Fe, Mn Zn, U, and Th were 3487 ± 74, 2007 ± 22, 33 ± 0.5, 119 ± 0.5, 4 ± 0.3, and 0.52 ± 0.04 mg/kg, respectively. These concentrations were higher for these elements compared to other areas. The present work is concerned to achieve precise information about the elemental content in coral reef samples along the Red Sea coast in specific locations and the effect of detected concentrations on the surrounding environment.
Electronic culture in the information system

The article discusses the impact of modern electronic media on the culture of the individual. The negative impact of the global system on national traditions and values is considered. This charter is a clear indication that information and communication technologies are an important factor in the development of the world community in the new century. This is when tensions arise in relationships, which often turn into an electronic culture struggle. Ensuring e-culture in open information systems is an urgent need of today.
Conceptual Foundations of Code Rationalization Through a Case Study in Haskell

The syntagm “code rationalization” denotes, in this paper, the approach by which the programmer chooses from the multitude of known alternatives that transpose the algorithmic solution of a problem into the target language of a software system, maximizing the benefits that the user pursues through the utilization of the software system. This paper emphasizes the importance of streamlining the code considering a case study in Haskell.
Webinar explores strategic communications planning

Nonprofit Business AdvisorVolume 2022, Issue 392 p. 9-9 Resources Webinar explores strategic communications planning First published: 26 April 2022 https://doi.org/10.1002/nba.31224Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2022, Issue392May 2022Pages 9-9 RelatedInformation
High-Utility Pattern Mining Using ULB-Miner

Mining high-utility itemset (HUI) is one of the key data mining tasks. It aims to discover a set of items which will produce remarkable return in database transactions. A major complexity with algorithms for high-utility itemset is very time-consuming although it returns a nontrivial large number of HUIs. Likewise, the database with high-quality itemsets conquers the huge market demand, especially in the research community. Therefore, it attracts the researchers to conduct multilevel research in obtaining a high-quality pattern. In this paper, there are three high-utility pattern mining algorithms used to investigate the performance by considering the run-time and consumption of memory. For experimental evaluation, the reputed and well-known algorithms, namely UP-Hist, UFH, and ULB-Miner were used. An applied empirical evaluation shows that ULB-Miner provides better performance of the UP-Hist and UFH algorithms in terms of running time as well as memory consumption.
Heterogeneous Graph Convolution Based on In-Domain Self-Supervision for Multimodal Sentiment Analysis

The inability to fully exploit domain-specific knowledge and the lack of an effective integration method have been the difficulties and focus of multimodal sentiment analysis. In this paper, we propose heterogeneous graph convolution with in-domain self-supervised multi-task learning for multimodal sentiment analysis (HIS-MSA) to solve these problems. Firstly, HIS-MSA carries out the second pre-trained with different self-supervised training strategies to fully mine the unique knowledge of the in-domain corpus, and give Bert the awareness of professional field. Secondly, HIS-MSA uses heterogeneous graph, which is good at integrating heterogeneous knowledge, to fuse feature from multiple sources. Finally, a unimodal label generation module is used to jointly guide multimodal tasks and unimodal tasks to balance independent and complementary information between the modalities. We conducted experiments on the datasets MOSI and MOSEI, which have 2199 and 23454 video segments respectively. The results show an average improvement of approximately 1.5 points in all metrics compared to the current state-of-the-art model.
Composition and Polymorphism Support in the OpenAPI Ontology

An OpenAPI [5] description details the actions exposed by a REST API. A service is represented by a JSON document describing requests, responses, and security information such as authentication and authorization rules for an API action. Schema objects describe the request and response messages and can be combined to form composite or specialized objects using composed or polymorphic expressions. However, Schema properties can be vague. To clarify their meaning, Schema properties may be associated with a semantic model. Further, to resolve ambiguities in service descriptions, OpenAPI descriptions are instantiated to a reference ontology. However, the mapping of composed or polymorphed Schema objects to the ontology generated additional problems to the mapping process. The mapping of composite Schema objects to the ontology is the focus of this work.
Visual relationship extraction in images and a semantic interpretation with ontologies

Nowadays, three challenges should be considered in order to build a strong model that is used to extract and semantically interpret the relationship between objects in images namely: long-tail problem, large intra-class divergence, and the semantic dependency or semantic gap. In order to overcome those challenges, we propose three main contributions: 1) an ontological semantic model to filter false negatives/positives using a statistical ranking module; 2) a combination of semantic ontological module and visual relationship module that both takes as input the results of the statistical ranking module and produces as output classification of < human - predicate - object >; 3) a semantic model for the visual relationship module that ranks each prediction of relation classes by transferring the spatial relationship onto a high dimension spatial feature. We use HCVRD that highlights two important practical problems, the long-tail distribution issue, and the zero-shot problem. The experimental results on the HCVRD dataset demonstrate the superior performance of the proposed approach.
Natural Language Processing (NLP)

No AccessThe Handbook of Data Science and AIApr 2022Natural Language Processing (NLP)Katherine MunroKatherine MunroSearch for more papers by this authorhttps://doi.org/10.3139/9781569908877.010SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 273-316Print ISBN: 978-1-56990-886-0eISBN: 978-1-56990-887-7 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
ECIR 2022 Tutorial: Technology-Assisted Review for High Recall Retrieval

Human-in-the-loop (HITL) IR workflows are being applied to an increasing range of tasks in the law, medicine, social media, and other areas.
Efficient Human Activity Recognition System Using Long Short-Term Memory

Human activity recognition (HAR) is a popular and challenging area of research, driven by a diverse set of applications. This paper aims to build a system with the fewest sensors in locations thoughtful enough to distinguish six activities using a Long Short-Term Memory (LSTM) approach to give high performance. We used two wearable Inertial Measurement Units (IMU) sensors with a gyroscope, accelerometer, and magnetometer fixed in the waist and the right ankle of the subject body. For this purpose, ten random subjects were asked to do six activities (walking, sitting, walking upstairs, standing, walking downstairs, and laying) indoors for data acquisition. Then we analyzed these data and used LSTM to classify the labelled dataset with a different number of hidden units, and the best result was with the number of 150 hidden units. After that, we trained the dataset and registered the test accuracy of 98.44%. Thus, the performance of the proposed method achieved the highest accuracy with low computing costs.
A Design Thinking Approach on Information Security

More than ever before, the economic success of companies depends on the use of information and communication technologies. Along with this development, cyber security plays a vital role to ensure the continuous and secure operation of critical applications and IT-services. The human factor represents one especially important aspect for ensuring cyber security in organizations, which has taken a turn for the worse in recent time. Security awareness activities, such as security training, newsletters or quizzes, are often performed to try to improve the situation, but the effects are slow to materialize and often do not bring lasting change. This paper therefore gets to the root of the problem using a different approach, which is centered around the people involved. The introduced framework combines the domains of design thinking and information security and presents a creative and human-centered way towards cyber security. We highlight building blocks, tools and techniques, which support the implementation of the presented framework. In order to demonstrate the applicability of the approach, we present our evaluation results of start-up company, which used our approach.
The Role of Augmented Reality in the Development of BIM for Construction Visualization

The notion of augmented reality (AR) has been around for a long time, and the widespread availability of new mobile technology, such as Smartphone and portable devices, has aided in the realization of the concept. Various industries, including the Architecture, Engineering, and Construction (AEC) business, are taking use of the expanding possibilities that the notion of AR may provide. The use of augmented reality in civil infrastructure can help to avoid costly mistakes, increase efficiency, and save money. Building-Information-Models (BIMs) are also used in AR to provide precise 3D information about the structure for display. AR has also been investigated for Structural-Health-Monitoring (SHM), regular and problem identification, energy effectiveness evaluation, cracks examination, digging, and subterranean utility servicing. The goal of this article is to make the operation of supervising infrastructure projects a little easier. The traditional method of measuring building progress is to use paper reports, which requires a significant amount of human data collecting as well as the effort of visualizing the real progress from the paperwork. This research highlights a novel approach for utilizing Smartphones to track building progress. This is accomplished by offering a new system that includes a newly created application known as “AEC-AR”. “AEC-AR” is an Android application that is utilized throughout the building phase by integrating a 4D “as-planned” phased layout including an augmented video that shows actual or projected development. The project’s outcomes are then analyzed and evaluated in order to predict the potential of these and other time and cost tracking approaches in building activities.
Effect of online mindfulness program on stress, anxiety, and psychological well-being of college students in the era of COVID-19

COVID-19를 겪으면서 여러 상담 기관에서 온라인 상담에 대한 요구가 증가하고 있다. 본 연구는 온라인 마음챙김 프로그램이 대학생의 스트레스와 불안, 심리적 안녕감에 미치는 영향을 검증하는데 목적이 있다. 이를 위해 강원도 소재 4년제 대학 재학생 28명을 대상으로 프로그램에 참여하는 학생 14명은 실험집단으로, 참여하지 않는 학생 14명은 통제집단으로 배정하여 연구를 실시하였다. 온라인 마음챙김 프로그램은 6주 6회기 프로그램으로 각 회기는 120분의 시간이 소요되었다. 프로그램의 효과를 확인하기 위해 집단 간 스트레스와 불안, 심리적 안녕감 척도를 사전-사후로 실시하였다. 효과를 검증하기 위해 먼저, 실험집단과 통제집단의 사전 점수로 동질성 검증을 하였다. 그리고 집단 간 차이를 검증하기 위해 공분산분석(ANCOVA)을 실시하였다. 그리고 양적 결과의 제한점을 보완하기 위해 참여자의 소감문을 바탕으로 질적 분석을 하였다. 연구결과 첫째, 온라인 마음챙김 프로그램이 대학생의 스트레스(신체화, 우울, 분노)에 유의한 영향을 주었다. 둘째, 온라인 마음챙김 프로그램이 대학생의 불안(특성불안)에 유의한 영향을 주었다. 셋째, 온라인 마음챙김 프로그램이 대학생의 심리적 안녕감(자기수용)에 유의한 영향을 주었다. 끝으로 본 연구의 함의와 제한점에 대해 논의하였다.With COVID-19, the demand for online counseling at various counseling institutions is increasing. The purpose of this study is to verify the effect of online mindfulness programs on stress, anxiety, and psychological well-being of college students. To this end, 14 students participating in the program were assigned as experimental groups and 14 students who did not participate as control groups for 28 students from four-year universities in Gangwon-do. The online mindfulness program was 6 weeks and 6 sessions, and each session took 120 minutes. In order to confirm the effectiveness of the program, pre-post measures of stress, anxiety, and psychological well-being between groups were conducted. In order to verify the effectiveness, first, homogeneity verification was performed with the pre-scores of the experimental group and the control group. And covariance analysis (ANCOVA) was conducted to verify differences between groups. In addition, a qualitative analysis was conducted based on the participants impressions to compensate for the limitations of quantitative results. As a result of the study, first, the online mindfulness program had a significant effect on the stress (bodyization, depression, anger) of college students. Second, the online mindfulness program had a significant effect on the anxiety (characteristic anxiety) of college students. Third, the online mindfulness program had a significant influence on the psychological well-being (self-acceptance) of college students. Finally, the implications and limitations of this study were discussed.
Models for Detecting Frauds in Medical Insurance

Health insurance is important for many people, but unfortunately it is susceptible to frauds, therefore expenditures for covering the funds show exponential growth. The victims of this kind of scams are not only the institutions that provide the funds and treatments, but also are the ones who really need that help, except they have lost their priority due to a committed fraud. In order to rationally provide funds and minimize losses, there is a need for fraud detection systems. In this paper, this issue is considered as a binary classification problem, using data inherent in the nature of the field. The whole data science pipeline process is considered in order to elaborate our results that are higher than the published ones on the same problem: 0.95, 0.96 and 0.98 AUC scores with different models. The data is integrated from three interconnected databases, which are pre-processed and then their cross-section is undertaken. The dataset is unbalanced concerning the records of both classes, therefore certain balancing techniques are applied. Several models are built using traditional Machine Learning models, classifiers with Deep Neural Networks and ensemble algorithms and their performance is validated according to several evaluation metrics.
Determining the Degree of Fuzzy Regularity of a String

The paper deals with the issue of determining the degree of fuzzy regularity of a crisp string. It is assumed that the concept of fuzzy regularity is formalized by a pattern given as a finite automaton with fuzzy properties of alphabet characters on transitions. Proceeding from this, we replace the problem of determining the degree of fuzzy regularity of a crisp string with the problem of determining the degree of belonging of such a string to the language of the corresponding automaton and propose an effective method for solving it using the dynamic programming approach. The solution to the considered problem makes it possible to fuzzify the set of strings in a given alphabet based on a pattern defining fuzzy regularity. This work is a continuation of the author’s previous works related to finding occurrences of a fuzzy pattern in the text. It may have applications in the field of pattern recognition, data clustering, bio-informatics, etc.
Correlating &lt;i&gt;pre-search&lt;/i&gt; and &lt;i&gt;in-search&lt;/i&gt; context to predict search intent for exploratory search

Modern information systems are expected to respond to a wide variety of information needs from users with diverse goals. The topical dimension ('what' the user is searching for) of these information needs is well studied; however, the intent dimension ('why' the user is searching) has received relatively less attention. Traditionally, the intent is an immediate reason, purpose, or goal that motivates the user search, and captured in search contexts (pre-search, in-search, pro-search). An ideal information system would be able to use. This article proposed a novel intent estimation strategy, based on the intuition that captured intent proactively extracts potential results. Captured pre-search context adapts query term proximities within matched results beside document-terms statistics and pseudo-relevance feedback with user-relevance feedback for in-search. The assessment asserts the superior performance of the proposed strategy over equivalent on trade-offs, e.g., novelty, diversity (coverage, topicality), retrieval (precision, recall, F-measure) and exploitation vs. exploration.
“Philosophical Congress. Pilot” The Conversation of A.Yu. Antonovsky, O.V. Nikiforov and Academician A.V. Smirnov

Conversation of A.Yu. Antonovsky and O.V. Nikiforov with Academician of the Russian Academy of Sciences A.V. Smirnov about the problems and features of modern Russian philosophy, about the history of Russian and foreign philoso­phy, about polycentricity in the context of the VIII All-Russian Philosophical Congress (“Philosophy in a Polycentric World”)
Improvements in User Targeted Offline Advertising Using CNN and Deviation-Based Queue Scheduling

Offline digital advertising has opened up new avenues of promotion for businesses. The advantages of offline advertising can be realized more effectively if user targeting is done, similar to online advertising. The previous research, proposed by Malhotra et al. (International Journal of Engineering and Advanced Technology 2020 [1]), presents a technology that uses recognition-based demographics and queue scheduling for user targeted offline advertising. The technology uses cameras on billboards to collect demographics of the viewing population and quantitatively finds the centroid to show the most appropriate advertisement on every billboard. This paper proposes an improved demographic generation technique which uses an enhanced convolutional neural network (CNN) and deviation-based scheduling to display the advertisements on billboards. Deviation-based scheduling helps in improving coverage and increases the relevant viewership of the advertisement. Also, a higher deviation reduces the distance between static advertisement coordinates and dynamic billboard coordinates. This distance represents suitability of the advertisements on the respective billboard. Higher deviation allows advertisers to promote among rare target groups, prevents the overlapping target audience problem, and offers high returns on investment. The earlier multimedia and intelligent system was named Target. Advertise. Revolutionize. Promote. (T.A.R.P.). This multimedia system shall be referred to as T.A.R.P. 2.0 throughout the paper. T.A.R.P. 2.0. is more potent than T.A.R.P., as it is optimized, highly scalable, and handles several cases that were beyond the scope of T.A.R.P.