Reviewerly/exHarmony huggingface dataset:
Virtual forums as motivational tools to build meaningful learning in times of COVID 19

The development of the virtual forum in remote education in rural areas of the district of Abancay, Apurimac region, during the COVID 19 health emergency has been presented as an opportunity for students to increase not only the cultural and cognitive level, but to mobilize competencies and capabilities characterized by being timely, reflective and self-regulated. Therefore, the objective is to demonstrate the influence of the use of the virtual forum in the construction of meaningful learning The research design in the present article corresponds to descriptive research, is simple correlational and transversal. The results show that the virtual forum as a motivational tool is motivating in 53.6%, promotes interaction in 51.8% and promotes self-learning in 42.9%, while significant learning does develop communicative capacities and competences. The conclusions determine that the virtual forum stimulates communicative skills and abilities, strengthens autonomy and self-regulation skills, allows feedback and promotes cooperative work and interaction between students and teachers.
REVIEWS FROM TRIPADVISOR ON SERVICE QUALITY IN SELECTED BED AND BREAKFAST IN TAGAYTAY CITY

Exceeding the customer's expectations and making them feel like they received their money's worth is the key to providing high-quality service. This study sought to gather reviews from TripAdvisor regarding their experiences with the chosen bed and breakfast in Tagaytay (Casa Maria y Martha, The Boutique Bed and Breakfast, and Joaquin’s Bed and Breakfast). TripAdvisor is a travel information and booking website with reviews, photos, and discussions about hotels and resorts all around the world. Users leave reviews for other users about the places they have stayed, so you can learn everything you need to know about a place before booking your stay. Using the explanatory with the use of thematic analysis, it was concluded that the chosen B&amp;B is still in need of some factors with regards to providing quality service to its guests or customers. Ten (10) relevant reviews were picked by the researchers in order to achieve the authenticity of data. Themes were further analyzed using the data analysis and transcription that was provided in order to justify the placement of the sentences. Event Management Facilities, Personalized Stuff, and In-need of Effective Managerial Team were the major key take-away of this study. Therefore, these key take-aways were the highlighted concerns of the customers who have stayed in their places.
Virtual teaching, a global educational need

With the arrival of COVID-19, the world suffered unexpected impacts. It forced countries to make accelerated changes in different aspects; of course, education was affected too. On this latter point, UNESCO reveals that approximately 94% of students in the world were harmed by the suspension of face-to-face classes. To counteract the effect of this conjuncture, the education sector implemented virtual teaching through existing technological tools. The speed with which the educational system had to adapt to this modality raised questions about the management and functionality of virtuality in education. The purpose of this document is to analyze the need of adopting virtual teaching with the change of pedagogical strategies and to evaluate the digital skills that teachers need to adapt to virtual education, taking advantage of technological resources for the motivation of the student and their access to education. The descriptive method of documentary type and the analysis of the contents were used to process the information. Finally, we conclude on the importance of teaching work that, with a positive attitude towards technology, this has to rethink its role as a facilitator of the educational process and the fundamentals of the appropriation of digital tools.
Aplikasi Rumah Laundry Berbasis Web Menggunakan Rapid Application Development

Laundry house is a business engaged in laundry services. Every day the laundry house can receive about 20 to 25 laundry per day. Most data management processes are still carried out manually, starting from customer data registration, recording transaction data, and even for making reports still written in the general ledger. This causes various problems such as, the difficulty of finding customer data, making complicated reports because they have to recap transaction data that takes a long time and is not efficient. The creation of a Web-Based Laundry Application at a Laundry House using Rapid Application Development (RAD) models that aims to make it easier for employees to serve costamers, with this Web-Based Laundry Application, employees can more quickly handle transactions quickly and accurately so that transaction calculations can be done quickly based on the service chosen by the customer. With the Web-Based Laundry Application, it is also easier for laundry business actors to monitor their daily income in real time, see the busiest time in the field and make accurate reports automatically.
Improving visual quality of blurred digital images

The goal of image deblurring is to recover a sharp image with the necessary edge structure and sharp details from the input blurred image. Blurring occurs due to various reasons, such as motion or focal length, camera shake, subject movement, or out of focus. Since, in practice, the blur parameters are unknown or known only approximately by the nature of the visible distortions, this study discusses methods for eliminating image blur blindly. The article explores the latest blind blur removal algorithms developed in the last five years: SVRNN, SRN, RCGAN, Gao et al., DMPHN, DeblurGAN, MBMF, MSCNN, DeblurGAN-v2, EDPN, RCAN, MSRN, GFN, SelfDeblur. The task of the study is to choose the most effective method for removing blur, for further application in the development of software to improve the visual qualities of digital images. Experiments have shown that the newly developed methods converge faster than older algorithms and are able to extract an image that is more consistent with the subjective opinion of experts. DeblurGAN-v2, EDPN and DBGAN(+) are currently the best method for restoring blurry images.
Algebraic methods for generating optimal s-blocks

The paper presents a review of algebraic methods for generating cryptographic S-boxes. In this paper, we consider methods for generating S-boxes based on the use of polynomial transformations (linear, quadratic, cubic), fractional linear transformations, and other special types of algebraic transformations. A number of examples of generating S-boxes using quasigroups and other algebraic structures are also given. The considered methods for generating S-boxes make it possible to obtain new S-boxes that have the necessary cryptographic properties that are the same or even better than those of the S-box built for the Rijndael algorithm.
Exploring the Characteristics of White-Collar Criminals: A Cross-Case Analysis

Many countries have faced the issue of white collar crime long time ago. With concern to the crime, this study conducted to explore the characteristics of white-collar criminals in Malaysia. A qualitative method has been implemented in conducting this study by using case study approach. Ten selected officers from two agencies namely Malaysian Anti-Corruption Commission (MACC) and Royal Malaysia Police (RMP) have been interviewed to know their views and opinions towards this issue. To have a rich finding, a semi-structured interview was used as an instrument. Besides, a cross-case analysis also added to compare the similarities and differences of the results. From the interviewed, results revealed that, exploits his/her position, lack of religiosity, lack of integrity, lack of value, rule violator, smart and strategize, careless, does not know about the punishment, educated, ego, habit and professional were the characteristics mentioned by the participants. Among the characteristics, lack of integrity was noted and agreed by both agencies. The findings showed that the characteristics of individual involve in white collar crime were varied. It also become the factor contribute to white collar crime.
Progressive web applications capabilities to become an alternative to mobile applications

These days, almost every successful company owns a web application for their business. Essentially, companies try to develop a website that is easy to navigate, so that a user could have a great experience regardless of a used device. But, a web app, even if developed to be used on any device, can be constraining for a user as it does not have features that have been reserved to native apps. Thus, companies are forced to develop a native application for their product and two applications are needed for two different OS IOS and Android to reach all potential users and match the modern criteria of a successful product. However, such a strategy of developing a web and two native applications are time and money consuming, which is far from ideal from a business point of view. In 2015, Google introduced Progressive Web Apps (PWA), which aims to close the gap between web and native applications by combining the best features from web and native apps. In this article I am going to describe several APIs that can make PWA feel like a native app.
Common Merit List Generation Method for Multi Shift Exam using Difficulty Index Value of Question Paper

The competitive examinations conducted in multiple shifts have different question papers for different shifts. The assigned difficulty index of the entire question papers are kept same for the entire shift, however the calculated difficulty index may vary. The major challenge faced in the conduction of the multiple shift examination is the generation of the common merit list. This research paper concentrates on the study of various merit list generation techniques used for multi shift examinations. It also proposes a new merit list generation method taking in to consideration the difficulty index of the question paper. This paper also compares various merit list generation techniques such as actual score method, normalized score method, percentile score method and the proposed difficulty index based score method. The merit list generated by percentile score method gives equal number of selection from each shift and does not consider the difficulty index of the question paper. Whereas in normalization method, the score is normalized by considering the mean, standard deviation of the score of each shift and of all shifts. This method also equalises the selection count. However, the proposed techniques take in to account the difficulty index of the question paper as well, which may vary the selection count in each shift. This assures that the deserving and the eligible candidate does not get affected due to the difficulty level variation of question paper.
What are the full capabilities of relativistic quantum cryptography?

Quantum cryptography bases its security proofs on physical assumptions. A longstanding observation in the field is that we may be able to do more cryptographic tasks when we assume not only the laws of quantum mechanics but also the impossibility of superluminal signaling (i.e., that information cannot travel faster than the speed of light). Relativistic quantum cryptography takes into account the spatial locations of the parties involved and uses the impossibility of superluminal signaling as a basis for security. Previous efforts in this field have been fruitful, both theoretically and experimentally.
Features of constructing a solution clausen-type systems

We study the features of constructing solutions near a singularity of a regular homogeneous system consisting of two partial differential equations of the third order. A number of properties of the product of Clausen functions constructed near these singularities are proved. The features of the construction of the general solution of the Clausen system are investigated. Recently, special attention has begun to be removed on the solution of such systems, in connection with the research of multidimensional degenerate differential equations.
Analysis of Online Toxicity Detection Using Machine Learning Approaches

Social media and the internet have become an integral part of how people spread and consume information. Over a period of time, social media evolved dramatically, and almost half of the population is using social media to express their views and opinions. Online hate speech is one of the drawbacks of social media nowadays, which needs to be controlled. In this paper, we will understand how hate speech originated and what are the consequences of it; Trends of machine-learning algorithms to solve an online hate speech problem. This study contributes by providing a systematic approach to help researchers to identify a new research direction and elucidating the shortcomings of the studies and model, as well as providing future directions to advance the field.
Keyframe Extraction Using Linear Rotation Invariant Coordinates

Keyframe extraction is a widely applied remedy for issues faced with 3D motion capture -based computer animation. In this paper, we propose a novel keyframe extraction method, where the motion is represented in linear rotation invariant coordinates and the dimensions covering 95% of the data are automatically selected using principal component analysis. Then, by K-means classification, the summarized data is clustered and a keyframe is extracted from each cluster based on cosine similarity. To validate the method, an online user study was conducted. The results of the user study show that 45% of the participants preferred the keyframes extracted using the proposed method, outperforming the alternative by 6%.
Language in Artificial Neural Network Training

Now, in the era of Industry 4.0, the connection between engineering and language is especially topical. We can talk not only about machine translation of a text from one language to another, but about a live conversation between a person and a robot. With the advent of modern technologies, the problem of human speech recognition by a robot appears. Electronic computers generally understand formalized, unambiguous words and expressions, logical commands, however, for an everyday user it is much more accustomed to have a more natural dialogue, with various kinds of synonyms, interjections, pauses, emotions. Engineering linguistics is a branch of applied linguistics that studies the automation of processes such as machine translation, voice search, and the creation of tools for human-machine communication. Voice assistant is artificial intelligence, it not only follows pre-written algorithms, but also is able to learn and make its own decisions. A neural network is one of the ways to implement artificial intelligence, which resembles the human brain in its functions, simulating our nervous system. As a neural network is created on the basis of the human brain, its training is based on similar principles as human training.
Effective and Scalable Manifold Ranking-Based Image Retrieval with Output Bound

Image retrieval keeps attracting a lot of attention from both academic and industry over past years due to its variety of useful applications. Due to the rapid growth of deep learning approaches, more better feature vectors of images could be discovered for improving image retrieval. However, most (if not all) existing deep learning approaches consider the similarity between two images locally without considering the similarity among a group of similar images globally , and thus could not return accurate results. In this article, we study the image retrieval with manifold ranking (MR) which considers both the local similarity and the global similarity, which could give more accurate results. However, existing best-known algorithms have one of the following issues: (1) they require to build a bulky index, (2) some of them do not have any theoretical bound on the output, and (3) some of them are time-consuming. Motivated by this, we propose two algorithms, namely Monte Carlo-based MR ( MCMR ) and MCMR+ , for image retrieval, which do not have the above issues. We are the first one to propose an index-free manifold ranking image retrieval with the output theoretical bound. More importantly, our algorithms give the first best-known time complexity result of \(O(n \log n)\) where \(n\) is the total number of images in the database compared with the existing best-known result of \(O(n^2)\) in the literature of computing the exact top- \(k\) results with quality guarantee. Lastly, our experimental result shows that MCMR+ outperforms existing algorithms by up to four orders of magnitude in terms of query time.
Оценка уровня эмоционального интеллекта в межкультурной образовательной среде

Introduction. Currently, the training of professionally competent specialists is a priority in the higher education system. One of the productive creative approaches is the development of emotional intelligence (EI/EQ), which contributes to the development of emotional intercultural competence to accelerate adaptation in a new intercultural environment. Aim. The aim of the research is to empirically determine the level of emotional intelligence in the intercultural context for further improving work with students of the humanities and economics specialities and proposing new techniques that develop emotional intercultural competence. Methodology and research methods. The methodological basis of the research is the concept of developing emotional intelligence as an integral part of soft skills. To study the level of emotional intelligence, the authors used the Mark Daniel Self-Scoring Emotional Intelligence Tests, which are a compilation of tests designed to study the level of emotional intelligence and determine the strengths and weaknesses of the test takers. The methods of psycho-diagnostics, comparison and generalisation of the obtained results were used. The test results were analysed using the SPSS.27 statistical package. Results . The research results help to understand the psychological characteristics of students of the humanities and economics specialities, taking into account the cultural context, which makes it possible to predict the difficulties and problems faced by teachers in the process of work with this type of students, to more accurately determine the characteristics and motives of their behaviour and the characteristics of the professional development of the individual. It also helps to select the right curriculum to meet students’ needs. Scientific novelty. The scientific novelty of the research consists in supplementing the existing knowledge in the field of EI about the psychological characteristics of representatives of the humanities and economics specialities in the context of intercultural communication and further forecasting their development based on correctly selected teaching methods. Practical significance. The presented features of students of the humanities and economics specialities can be employed by teachers when interacting with them and organizing the educational process, taking into account their interests and needs, as well as adjusting the level of development of EI. The results obtained can be useful in the preparation of educational and methodological complexes specifically designed for the development of emotional intercultural competence.
Increasing the efficiency of music education with the help of innovative technologies

This article provides full information about the requirements for improving the pedagogical skills of a music teacher, methods of using innovative technologies and various methods for effective organization of music culture lessons by teachers.
Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation

In task-oriented dialogue systems, response generation from meaning representations (MRs) often suffers from limited training examples, due to the high cost of annotating MR-to-Text pairs. Previous works on self-training leverage fine-tuned conversational models to automatically generate pseudo-labeled MR-to-Text pairs for further fine-tuning. However, some self-augmented data may be noisy or uninformative for the model to learn from. In this work, we propose a two-phase self-augmentation procedure to generate high-quality pseudo-labeled MR-to-Text pairs: the first phase selects the most informative MRs based on model's prediction uncertainty; with the selected MRs, the second phase generates accurate responses by aggregating multiple perturbed latent representations from each MR. Empirical experiments on two benchmark datasets, FewShotWOZ and FewShotSGD, show that our method generally outperforms existing self-training methods on both automatic and human evaluations.
DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation

Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses. However, surrogate objectives of correspondence learning in 3D space are a step away from the true ones of object pose estimation, making the learning suboptimal for the end task. In this paper, we address this shortcoming by introducing a new method of Deep Correspondence Learning Network for direct 6D object pose estimation, shortened as DCL-Net. Specifically, DCL-Net employs dual newly proposed Feature Disengagement and Alignment (FDA) modules to establish, in the feature space, partial-to-partial correspondence and complete-to-complete one for partial object observation and its complete CAD model, respectively, which result in aggregated pose and match feature pairs from two coordinate systems; these two FDA modules thus bring complementary advantages. The match feature pairs are used to learn confidence scores for measuring the qualities of deep correspondence, while the pose feature pairs are weighted by confidence scores for direct object pose regression. A confidence-based pose refinement network is also proposed to further improve pose precision in an iterative manner. Extensive experiments show that DCL-Net outperforms existing methods on three benchmarking datasets, including YCB-Video, LineMOD, and Oclussion-LineMOD; ablation studies also confirm the efficacy of our novel designs.
Understanding Embodied Reference with Touch-Line Transformer

We study embodied reference understanding, the task of locating referents using embodied gestural signals and language references. Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line. However, existing human pose representations fail to incorporate the virtual touch line. To tackle this problem, we devise the touch-line transformer: It takes as input tokenized visual and textual features and simultaneously predicts the referent's bounding box and a touch-line vector. Leveraging this touch-line prior, we further devise a geometric consistency loss that encourages the co-linearity between referents and touch lines. Using the touch-line as gestural information improves model performances significantly. Experiments on the YouRefIt dataset show our method achieves a +25.0% accuracy improvement under the 0.75 IoU criterion, closing 63.6% of the gap between model and human performances. Furthermore, we computationally verify prior human studies by showing that computational models more accurately locate referents when using the virtual touch line than when using the elbow-wrist line.