Reviewerly/exHarmony huggingface dataset:
The Construction of College English Network Technique Platform Based on Computer Aided System

Abstract With the progress of computer technology and the prosperity of information age, the society has higher and higher requirements for English learners. Due to the limitation of English standard, many university students’ employment problems become more serious. This situation poses a greater challenge to university English technique. What we should do to improve the efficiency of university students’ English instruction has become a major concern in academia. At present, the establishment of College English network online technique platform has improved the passion of university students to learn English. Researchers found that online technique platform can help students improve their English level. This paper carefully studies the establishment of College English online teaching terrace under the background of computer-aided system, and finally comes to a conclusion.
The problem of virtual reconstructions of “places of commemoration” in light of the spatial cultural turn

This article examines the problem of historical-cultural significance of virtual reconstructions of places of commemoration and creation of digital museums in the context of spatial turn in cultural sciences. &amp;ldquo;Places of commemoration&amp;rdquo; are viewed not only as the objects of virtual reconstruction, but also as the sources of social constructs and social practices, as the elements of cultural capital of the city. In this vein, the projects of digital museums can be viewed as the program for creation of commemorative environment, which in turn, contributes to formation and development of the sense of solidarity in the society. An interdisciplinary approach is realized through the use of methodology of historical and culturological studies, with consideration of the so-called spatial turn in cultural sciences, as well as technologies of virtual design and mathematical modeling. Formation of commemorative environment and civil identity is intensified by any form of reference (including virtual space) to the places of commemoration as to the landmarks of social memory and tradition, key components of historical capital of the city. The projects of virtual reconstruction (creation of digital museum) of memorial places using software platforms with an open code OpenSimulator should be perceived as &amp;ldquo;augmented reality&amp;rdquo; of historical capital of the city.
REVIEWING DEFINITIONAL AMBIGUITIES AND SIGNIFICANCE OF TEXT AUTHENTICITY IN ENGLISH LANGUAGE TEACHING

Purpose of the study: The premise of this paper is to define and address the ambiguities surrounding the concept of text authenticity in the field of English Language Teaching (ELT).&#x0D; Methodology: It represents a critical review of a series of research studies aimed at defining the concept of text authenticity and investigating the effect of text authenticity on ESL/EFL learners’ individual differences, namely ESL/EFL learner motivation and communicative competence. However, wherever possible, for the purpose of maintaining criticality, data associated with pedagogic/contrived materials are also discussed.&#x0D; Main Findings: The aspects of text authenticity may be situated in the text itself, the participants, social or cultural situations and purposes of the communicative act, or some combination of these elements. In addition, deficiency in learners’ overall communicative competence in the English language can be attributed to teachers’ exclusive reliance on contrived text materials presented in the form of textbooks.&#x0D; Applications of this study: It is strongly recommended that teacher training courses aim to develop classroom teachers’ practical knowledge and skills necessary for designing and evaluating TESOL materials. Reaching a consensus among researchers on the issue of the effects of authentic materials on ESL/EFL students’ motivation and overall communicative competence can have fundamental implications not only for developing language curricula but also for promoting learner autonomy.&#x0D; Novelty/Originality of this study: This study addressed the ambiguities surrounding the concept of text authenticity by proposing a typology encompassing eight possible inter-related definitions of text authenticity emerging in the ELT literature. More importantly, the paper structured a triangulation framework for introducing authentic materials into language classrooms:1) careful implementation of learner need-analysis, 2) criteria-based selection of authentic texts in the light of learner need-analysis, 3) utilization of task-based learning approach stressing the importance of activating learner schemata, awareness-raising activities, and task differentiation. This triangulation methodology is likely to reduce the difficulty of text authenticity and realize comprehensible input.
Continuing Professional Development in ICT for Primary School Teachers, Reflections and Issues

With regard to the main directions of the education policy of the Czech Republic 2030+ , it is clear that the development of information and communication technologies (ICT) in the society brings the necessity to detect the specifics of the teaching profession in the context of current conditions at school. In the future, education should be more integrated with the digital world and respond to technological developments. The upcoming changes will affect virtually all teachers. Computers should be used in the classroom of other subjects, and this may make teachers unwilling to change their practices. Another obstacle to the modernization of teaching is also the lack of school equipment. The Czech School Inspection Authority states that only 9.5% of big primary schools meet the standards for digital education. In small primary schools, it is less than five percent. The aim of our research is to detect, what the real situation is like in the area of professional preparedness in teaching ICT subjects at different types of school with special attention to the perception of the situation by professional teachers. The results of the research confirmed that although teachers have support at school, most of them lack Continuing Professional Development both in the methodology of teaching ICT subjects and in the field of professional ICT subjects. Teachers are rather dissatisfied with the current offer of Continuing Professional Development (CPD) and they would welcome corresponding support.
Automatic Skimming of Web Pages on a Single click Efficiently

In today's world, access to information through websites is sought to be the most convenient. The research on efficient skimming on websites was done to further ease the information accessible to the users of the internet through a button automatically. The button named as “quick info” makes it easy for visitors of the website to traverse through the pages after every three seconds automatically to the next page without any user action after initiating the process that means after clicking on the button. People are becoming busier nowadays and have less time to view every web page minutely, so our paper will make it easier for the user to automatically view the web pages. Recommendation of skimming can be implemented on websites that are to be viewed on mobile and other devices. It is important to have a web page with general guidelines followed such that titles, headings are visible to make skimming in three seconds successfully.
DIGITALISASI AKUNTANSI PENGELOLAAN KEUANGAN DENGAN METODE ACCRUAL BASIS PADA KLINIK AS SHIFA KENDAL

The purpose of this study is to implement the digitization of accounting through the adoption of financial management accounting information systems. Accounting digitalization is needed for automated financial transaction management and results in a fast, accurate, and transparent reporting system. The approach used in building financial management accounting information systems is the accrual basis method, the research method used is the method of research and development (R&amp;D) with a prototype system development model. The results of this study indicate that the digitalization of accounting by implementing financial management information systems at As shifa clinics shows that the information system can work as expected and is able to meet needs. Based on the results of testing the effectiveness of the old system compared to the new system it can be concluded that the performance of financial management in the As Shifa clinic using the new system is more effective than using the old system.
Sight-Man

Visual Impairment is something that most people experience in their day to day life. With the rise of technology, more hardware and software can be studied and created to assist those people with such disability. Visual Impairment can also be referred to as vision impairment or vision loss; Infrared radiation is invisible to human eyes, but people can feel it as heat. With today's technology, it can be used to make it useful and assist us in our daily life. Infrared radiation is a type of electromagnetic radiation and part of the electromagnetic spectrum. Infrared energy or infrared light is not visible to the human eye but if can be seen using special cameras that translate light into visible colors. In fact, our own body radiates far infrared energy through our skin at between 3 and 50 microns. This study developed an application that can help guide the visually impaired using mobile devices and in the same way, making it more accessible to them. The study also aims to find a different approach on how IR sensors can be used. It can also be used in detection and navigational purposes. The developed system named "Sight-Man", has the purpose of applying infrared technology to aid those with visual imparity using mobile phones and an application that runs using Android-based smartphone.
EmailDetective: An Email Authorship Identification And Verification Model

Abstract Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another situation is to verify if an email was written by the sender. This paper extracts features from the email header and email body and analyzes the writing style and other behaviors of email authors. The behaviors of email authors are extracted through a statistical algorithm from email headers. Moreover, the author’s writing style in the email body is extracted by a sequence-to-sequence bidirectional long short-term memory (BiLSTM) algorithm. This model combines multiple factors to solve the problem of anonymous email author attribution. The experiments proved that the accuracy and other indicators of proposed model are better than other methods. In email authorship verification experiment, our average accuracy, average recall and average F1-score reached 89.9%. In email authorship identification experiment, our model’s accuracy rate is 98.9% for 10 authors, 92.9% for 25 authors and 89.5% for 50 authors.
Least Squares K-SVCR Multi-class Classification

The support vector classification-regression machine for K-class classification (K-SVCR) is a novel multi-class classification method based on “1-versus-1-versus-rest” structure. In this paper, we propose a least squares version of K-SVCR named as LSK-SVCR. Similarly as the K-SVCR algorithm, this method assess all the training data into a “1-versus-1-versus-rest” structure, so that the algorithm generates ternary output \( \{-1, 0, +1\}\). In LSK-SVCR, the solution of the primal problem is computed by solving only one system of linear equations instead of solving the dual problem, which is a convex quadratic programming problem in K-SVCR. Experimental results on several benchmark data set show that the LSK-SVCR has better performance in the aspects of predictive accuracy and learning speed.
Object-Aware Centroid Voting for Monocular 3D Object Detection

Monocular 3D object detection aims to detect objects in a 3D physical world from a single camera. However, recent approaches either rely on expensive LiDAR devices, or resort to dense pixel-wise depth estimation that causes prohibitive computational cost. In this paper, we propose an end-to-end trainable monocular 3D object detector without learning the dense depth. Specifically, the grid coordinates of a 2D box are first projected back to 3D space with the pinhole model as 3D centroids proposals. Then, a novel object-aware voting approach is introduced, which considers both the region-wise appearance attention and the geometric projection distribution, to vote the 3D centroid proposals for 3D object localization. With the late fusion and the predicted 3D orientation and dimension, the 3D bounding boxes of objects can be detected from a single RGB image. The method is straightforward yet significantly superior to other monocular-based methods. Extensive experimental results on the challenging KITTI benchmark validate the effectiveness of the proposed method.
Bottom-Up Temporal Action Localization with Mutual Regularization

Recently, temporal action localization (TAL), i.e., finding specific action segments in untrimmed videos, has attracted increasing attentions of the computer vision community. State-of-the-art solutions for TAL involves evaluating the frame-level probabilities of three action-indicating phases, i.e. starting, continuing, and ending; and then post-processing these predictions for the final localization. This paper delves deep into this mechanism, and argues that existing methods, by modeling these phases as individual classification tasks, ignored the potential temporal constraints between them. This can lead to incorrect and/or inconsistent predictions when some frames of the video input lack sufficient discriminative information. To alleviate this problem, we introduce two regularization terms to mutually regularize the learning procedure: the Intra-phase Consistency (IntraC) regularization is proposed to make the predictions verified inside each phase; and the Inter-phase Consistency (InterC) regularization is proposed to keep consistency between these phases. Jointly optimizing these two terms, the entire framework is aware of these potential constraints during an end-to-end optimization process. Experiments are performed on two popular TAL datasets, THUMOS14 and ActivityNet1.3. Our approach clearly outperforms the baseline both quantitatively and qualitatively. The proposed regularization also generalizes to other TAL methods (e.g., TSA-Net and PGCN). code: https://github.com/PeisenZhao/Bottom-Up-TAL-with-MR
A Graph-based Interactive Reasoning for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection devotes to learn how humans interact with surrounding objects via inferring triplets of < human, verb, object >. However, recent HOI detection methods mostly rely on additional annotations (e.g., human pose) and neglect powerful interactive reasoning beyond convolutions. In this paper, we present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs, in which interactive semantics implied among visual targets are efficiently exploited. The proposed model consists of a project function that maps related targets from convolution space to a graph-based semantic space, a message passing process propagating semantics among all nodes and an update function transforming the reasoned nodes back to convolution space. Furthermore, we construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Beyond inferring HOIs using instance features respectively, the framework dynamically parses pairwise interactive semantics among visual targets by integrating two-level in-Graphs, i.e., scene-wide and instance-wide in-Graphs. Our framework is end-to-end trainable and free from costly annotations like human pose. Extensive experiments show that our proposed framework outperforms existing HOI detection methods on both V-COCO and HICO-DET benchmarks and improves the baseline about 9.4% and 15% relatively, validating its efficacy in detecting HOIs.
Power Consumption Estimation in Data Centers Using Machine Learning Techniques

Large data centers consume large amounts of electricity. Estimating the energy consumption in a data center can be of great importance to data centers administrators in order to know the energy-consuming tasks and take actions for reducing the total energy consumption. Smart workflow mechanisms can be built to reduce the energy consumption of data centers significantly. In this paper, we are investigating the factors that affect the energy consumption of scientific applications in data centers. We also use eight machine learning methods to estimate the energy consumption of multi-threaded scientific applications. Extensive computational results on a computer with 20 cores show that the CPU usage is the most important parameter in the power consumed by an application. However, better results can be obtained when the CPU utilization is combined with other parameters. We generate various regression models that predict the energy consumption of an application with an average accuracy of 99%. Simpler models with one and two parameters can achieve comparable accuracy with more complex models. We also compare various machine learning methods for their ability to obtain accurate predictions using as few parameters as possible.
Data augmentation enhanced speaker enrollment for text-dependent speaker verification

Data augmentation is commonly used for generating additional data from the available training data to achieve a robust estimation of the parameters of complex models like the one for speaker verification (SV), especially for under-resourced applications. SV involves training speaker-independent (SI) models and speaker-dependent models where speakers are represented by models derived from an SI model using the training data for the particular speaker during the enrollment phase. While data augmentation for training SI models is well studied, data augmentation for speaker enrollment is rarely explored. In this paper, we propose the use of data augmentation methods for generating extra data to empower speaker enrollment. Each data augmentation method generates a new data set. Two strategies of using the data sets are explored: the first one is to training separate systems and fuses them at the score level and the other is to conduct multi-conditional training. Furthermore, we study the effect of data augmentation under noisy conditions. Experiments are performed on RedDots challenge 2016 database, and the results validate the effectiveness of the proposed methods.
Organização do conhecimento na arquivologia: desafios e perspectivas no âmbito do tratamento documental

It addresses research methodologies and scenarios in Brazilian Archivology and their relationship with the Knowledge Organization. It consists of a review article under the inductive logic, with qualitative approach and content analysis strategy. It is understood that in the universe of digital transformation, the dialogue between Knowledge Organization and Knowledge Engineering provides for research in Archivology and its capillarity in the information market by means of Digital Information Systems.
Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning

Implicit feedback data is extensively explored in recommendation as it is easy to collect and generally applicable. However, predicting users' preference on implicit feedback data is a challenging task since we can only observe positive (voted) samples and unvoted samples. It is difficult to distinguish between the negative samples and unlabeled positive samples from the unvoted ones. Existing works, such as Bayesian Personalized Ranking (BPR), sample unvoted items as negative samples uniformly, therefore suffer from a critical noisy-label issue. To address this gap, we design an adaptive sampler based on noisy-label robust learning for implicit feedback data. To formulate the issue, we first introduce Bayesian Point-wise Optimization (BPO) to learn a model, e.g., Matrix Factorization (MF), by maximum likelihood estimation. We predict users' preferences with the model and learn it by maximizing likelihood of observed data labels, i.e., a user prefers her positive samples and has no interests in her unvoted samples. However, in reality, a user may have interests in some of her unvoted samples, which are indeed positive samples mislabeled as negative ones. We then consider the risk of these noisy labels, and propose a Noisy-label Robust BPO (NBPO). NBPO also maximizes the observation likelihood while connects users' preference and observed labels by the likelihood of label flipping based on the Bayes' theorem. In NBPO, a user prefers her true positive samples and shows no interests in her true negative samples, hence the optimization quality is dramatically improved. Extensive experiments on two public real-world datasets show the significant improvement of our proposed optimization methods.
Automatic global path generation for large-scale 3D scene exploration

Large-scale virtual scene exploration is still a challenging task. The novice users can easily get distracted and disorientated, which results in being lost in space. Assisted camera control technology is the most effective solution for virtual environment exploration problems which requires viewpoint computation and path planning. In this paper, a novel approach for large-scale virtual scene based on viewpoint scoring is proposed. First, the scene was adaptively divided into several meaningful and easily analyzed subregions according to the optimal view distance criterion. Second, a novel viewpoint scoring method based on visual perception and information entropy fusion was developed for optimal viewpoint determination and greedy N-Bestviewpoint selection algorithm was utilized for visual perceptibility calculation. Then evolutionary programming approach for the Traveling Salesman problem was applied for intra-subregion and inter-subregion exploring path optimization. Finally, the Cubic Hermite Curve was introduced to smoothen the inflection point on the exploration path. The experimental results demonstrate that the proposed method can effectively generate an automatic smooth, informative, aesthetic and non-intersecting path, with the characteristics of good exploring comfort, strong immersion and high scene information perception.
A Comprehensive Hybrid Ensemble Method with Feature Selection Techniques

Data Mining is the process of examining huge pre-existing databases in order to produce new information. Decision Tree is a very popular and practical approach in Data Mining. Decision Trees plays a crucial role in medical field. Ensemble methods create multiple models and produce more accurate solutions than a single model. Feature Selection improves the standard of the data by removing irrelevant attributes, due to that accuracy will increase. In this study experiments are conducted on a Hybrid Method i.e., C4.5 Decision Tree with MultiBoostAB Ensemble technique with different Feature Selection Techniques on Tumor datasets for finding out accuracy, execution time to build a decision tree and size of the tree.
A Modern Tool for Students’ Self-organization in the Digital Economy

The article presents the results of an empirical study of the level of self-organization of students of the Samara State University of Economics (SSEU). The purpose of the study is to determine the level of self-organization of students in the digital economy. It was supposed to diagnose parameters that reflect the degree of self-organization of modern students. The survey was used as a research method. A sample of 120 students. The results of the study demonstrate the degree of application of self-organization tools for students aimed at developing the key competencies of future specialists and the fundamental competencies of future employees.