Reviewerly/exHarmony huggingface dataset:
The Future of Distance Education: Challenges and Opportunities

The article discusses the problems encountered during the transition to the distance learning format due to the introduction of quarantine measures for Covid-19. At the same time, it was the mass transition to a remote learning format that allowed us to determine the advantages and feel all the disadvantages of distance learning. Research methods. In this paper, we conducted sociological research in the form of a survey among parents of school leavers in Moscow. Students and teachers of the university of Ryazan on their satisfaction with the organization of distance learning, on the change in the level of academic load and on the possible consequences for the quality of education due to the transition to a remote learning format. According to the results of the study, problems of distance learning of a technical and organizational nature were identified, among which are: interruptions in the operation of online platforms due to overload; the lack of proper software for students, as well as the lack of direct face-to-face contact between students and the teacher; the requirement of strict self-discipline, motivation. The study made it possible to draw the conclusion that the distance form of education in its current implementation leads to a decrease in the quality of training of students of higher educational institutions and school students. To improve the effectiveness of distance education, it is necessary to intensify work on the development of new educational standards, on the application of new methods and tools of teaching.
Bert-Pair-Networks for Sentiment Classification

BERT has demonstrated excellent performance in natural language processing due to the training on large amounts of text corpus in an unsupervised way. However, this model is trained to predict the next sentence, and thus it is good at dealing with sentence pair tasks but may not be sufficiently good for other tasks. In our paper, we introduce a novel representation framework BERT-pair-Networks (p-BERTs) for sentiment classification, where p-BERTs involve adopting BERT to encode sentences for sentiment classification as a classic task of single sentence classification, using the auxiliary sentence, and a feature extraction layer on the top. Results on three datasets show that our method achieves considerably improved performance.
Guidance Cleaning Network for Sketch-Based 3D Shape Retrieval

Abstract The sketch-based query for 3D shapes has drawn growing attention from the academic community due to its flexibility and accessibility. However, most recent works focus on reducing the cross-modality discrepancy between 2D sketches and 3D shapes, which neglect the influence caused by noise information in some low-quality free-hand sketches. To address this issue, we proposed a novel network to decrease the impact in two ways: 1) an attention weighting module to detect the noisy samples by a self-attention mechanism; 2) a data cleaning module to clear up low-quality sketches according to a ranking regularization. Experiments on two widely-used datasets demonstrate the effectiveness of our method.
The Influence of Personas’ Gender in Design

In mobile application design, the construction of personas is a well-known technique that helps designers to empathise with users and to understand their needs. However, there are several issues that lead to stereotyped personas and men’s needs being prioritised in design projects. The under-representation of women in design teams, insufficient user research, the assumption that the default user is a man, and the influence of personal beliefs in the decision making process all obstruct the inclusion of women’s needs in the design process. Therefore, we propose the creation of gender-neutral personas that merge both the needs of men and women using two different approaches, and we compare them with traditional gendered personas. The results of the study confirm our hypothesis — the gender-neutral personas culminated in more gender-inclusive design requirements.
Unified adaptive deep classification for industrial real‐time situation awareness in edge environment

Summary The rapidly growing information technologies such as cloud computing, edge computing, artificial intelligence offer cutting‐edge technical means for virtual reality of industrial manufacturing process. An efficient and accurate method for situation awareness (SA) to monitor the process of marginal layer plays an essential role to enhance the safety of industrial equipment. In this study, a manufacturing life cycle is described and characterized as operation and remote controlling of industrial machineries. The edge servers are first employed to track physical devices. Then a novel unified adaptive deep classification framework for SA with semi‐supervised classification method is developed to find unknown patterns in a huge amount of industrial data. Furthermore, a robust model based on the selection of adaptive local neighbor is proposed to find out an optimal weight and high similarity of neighbors. The combination of the proposed framework and model can realize real‐time SA of modern manufacturing the execution system. Plentiful experiments have been conducted to demonstrate the high accuracy and reliability of the proposed framework.
Halftone Image Steganography Based on Minimizing Distortion with Pixel Density Transition

Many advanced halftone steganographic schemes focus only on the distortion of human visual perception or the distortion according to statistics. In this paper, a halftone image steganography based on minimizing distortion with density transition is proposed which aims at utilizing the entropy model and pixel density to resist potential steganalysis. First, the entropy model is established on the image database to describe the texture content and transformed to the preliminary distortion score map. Because the form of texture presentation is distinct between halftone images and ordinary binary images, the feature of pixel density is introduced to represent the local intensity in images. Then the pixel density transition adjustment based on the entropy model is presented, which makes the distortion score more reliable. The final additive distortion map is generated by combining the entropy model and the strategy of density transition. To play the advantage of distortion measurement, syndrome-trellis code (STC) is applied with the distortion map to minimize the embedding distortions. Experimental results demonstrate that compared with other halftone steganographic schemes, the proposed method achieves high statistical security and great visual quality with considerable embedding capacity.
Penalized Versus Constrained Approaches for Clusterwise Linear Regression Modeling

Several approaches exist to avoid singular and spurious solutions in maximum likelihood (ML) estimation of clusterwise linear regression models. We propose to solve the degeneracy problem by using a penalized approach: this is done by adding a penalty term to the log-likelihood function which increasingly penalizes smaller values of the scale parameters, and the tuning of the penalty term is done based on the data. Another traditional solution to degeneracy consists in imposing constraints on the variances of the regression error terms (constrained approach). We will compare the penalized approach to the constrained approach in a simulation study, providing practical guidelines on which approach to use under different circumstances.
Fuzzy sliding mode control for trajectory tracking of an electric powered wheelchair

<abstract> Various controllers have been applied to control the dynamics of Electric Powered Wheelchair (EPW) for people whose walking are difficult or impossible, due to illness or disability. This paper deals with the nonlinear control of an electric wheelchair based on the hybridization between fuzzy logic and sliding mode control called Fuzzy Sliding Mode Control (FSMC). The EPW is powered by two Permanent Magnet Synchronous Motors (PMSM) due to some advantageous features, such as high efficiency, high torque to the current ratio, low noise and robustness. This research aims to present the dynamic modelling of both EPW motors with Lagrangian method in the first step, and the application of fuzzy sliding mode control in the second. This control technique was presented in order to consider the full dynamic model while alleviating the chattering phenomenon and to increase trajectory tracking performance of the EPW in the presence of disturbances. However, the reference trajectory used is that generated by the fifth-degree polynomial interpolation, which ensures a regular trajectory that is continuous in positions, velocities and accelerations. Finally, numerical simulations are presented to show the evolution of electrical and mechanical quantities in order to verify the effectiveness of the control strategy. </abstract>
Using a Question Answering System to Enhance Knowledge and Improve the Exchange of Information among Physicians

Due to limited time, physicians often find it challenging to find the exact answers to their questions among search engine results; however, question and answer (Q&A) systems can facilitate more rapidly identify accurate solutions. This study aims to develop and evaluate a Q&A system for physicians at Tabriz University of Medical Sciences. Four clinical and informatics experts and the two health information managers agreed on 19 features and themes throughout two focus group meetings. Subsequently, a system was developed on a MySQL database using the PHP web development language and then uploaded to the web. Finally, the system was opened up to 40 users and, over three months, evaluated using a community evaluation questionnaire and the six-dimension Users’ Experience Questionnaire. The focus group results in determining the features of the Q&A system consisted of 19 requirements. The average attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty were equal to 1.76, 1.625, 1.9, 1.425, 1.475, and 1.375, respectively. The Q&A system improved the tasks such as share of knowledge, transfer of information, social partnership, and cooperation among users. The physicians were able to obtain the information they required through contact with their co-practitioners over the system. https://dorl.net/dor/20.1001.1.20088302.2021.19.2.14.2
A Few-shot Learning algorithm based on attention adaptive mechanism

Abstract Deep learning has achieved remarkable results in image recognition, semantic segmentation and other fields. However, the primary premise of training deep learning model is the support of a large number of data sets and labeled samples. Therefore, deep learning is still faced with difficulties in medical, military and other areas where high-quality samples are scarce. Based on the image classification task as the application background, this paper improves a measure learning algorithm based on covariance representation. In the original Few-shot Learning algorithm based on measure learning, Firstly, from the perspective of second-order statistics, the covariance matrix between the feature vectors of each sample is constructed to realize the class representation; Then, an attention adaptive module is introduced to adjust the feature vectors of the class representation and query samples to make them closer to the class representation of the corresponding class. Finally, the experiment is carried out on the public data set(Omnight) to verify the effectiveness of the model. The experimental results show that the classification accuracy of the model designed in this paper is 6% higher than that of the Few-shot learning algorithm based on metric learning.
Real-time Vehicle Speed Enforcement System

Traffic injuries and deaths are important road traffic issues worldwide, road accidents are a major reason of death, killing around 1.24 million people yearly and leaving 20–50 million be distressed with injuries, traffic accidents are still considered the prime factor in cause of mortality between 5–14 years old children and among 15–29 years old young adults [1]. These numbers could have been reduced if the vehicles had been travelling at more appropriate speed. The most commonly reported factors linked to the severity of accidents and deaths are due to reckless speeding or drivers may drive within the speed limits, but they are not aware of the sudden changes on road condition that could affect their safety and the safety of other road users. In response to the needed speed limit enforcement to save lives, health, and economy, this work presents a system that helps to minimize the speed-related accidents. The system is remotely operated and instantly preventing vehicles from speeding. The traffic management authorities can enforce the optimum safe speed limit whenever and wherever necessary in real time while the vehicles are running on the roads.
Exam form automation using facial recognition

The way Data Science and Machine Learning have set modern trends for automation. It is thoughtful to simplify our day to day activities which are related to these domains. One such task is the repetitive submission of the same personal details while submitting any online form regarding academic or other purposes. The repetitiveness seems a bit dilatory. Plus, the involvement of machine learning further espouses the concept to automate re-iteration of personal details each time we fill a form, instead of manual entry. To obviate the above redundancy, we have proposed to implement “Exam Form Automation Using Facial Recognition” which includes real time face recognition with the help of webcam, pre-storing data on web servers with Panda and NumPy library of Python and then automating the entries on form using selenium library of python. The system captures a real time image of a user and collects the particular details from data hosted on a web server. Thus, precluding the monotonous procedure of manual entry.
Predicting disease progress with imprecise lab test results

Clinical lab tests play an important role in disease diagnose and medical treatment, the test results have been utilized widely in predictive modeling tasks in healthcare. However, in most existing works, the loss function implicitly assumes that the value of the sample used to be predicted is the only correct one. This assumption fails to hold for lab test data, which usually are within respective tolerable ranges or imprecision ranges. In addition, the historical lab test data is always organized based on their sequential position, the timestamps between the data are often neglected. In this paper, we study the issue of building robust models while simultaneously taking imprecision and timestamp of the data into account with better generalization. In particular, "IR loss" is proposed in which each data in imprecision range space has a certain probability to be the real value, participating in the loss calculation. The loss is then defined as the integral of the error of each point in the impression range space. The sampling and discretization methods are proposed for loss calculation. A heuristic learning algorithm is developed to learn the model parameters. We further apply IR loss for disease progress prediction while the input data is organized as sequence. We reformulate the prediction task with timestamp based on Long Short-Term Memory (LSTM) network. At the same time, the timestamp is readily combined with the proposed IR loss to avoid the change of predicted result caused by the change of the test values in small time range. We conducted the experiments based on two real world datasets. Experimental results show that the prediction method based on IR loss can provide more accurate prediction result for different kinds of task and diverse learning methods. Our method can also provide more stable and consistent results when test samples are generated from imprecision range and small time range.
Some Information Geometric Aspects of Cyber Security by Face Recognition

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.
Digital literacy and foreign language learning

Modern language teaching is oriented towards the use of new technologies, and in this regard it is necessary to work on the development of digital literacy, both for students and teachers. Knowing how to find, select, analyze and adequately use digital information are important segments of digital literacy. In this regard, in foreign language teaching, digitally literate teachers and students can further contribute to their own development, learning and acquisition of foreign languages. With the help of digital literacy, students are able to use digital tools, digital information as well as create online projects in foreign languages. However, in order for students to adequately work on their tasks in an online environment, teacher moderators are needed who strive for continuous improvement in this field, who cultivate positive attitudes towards the use of new technologies and recognize their pedagogical value. Therefore, the focus of this paper is, first, on defining the concept of digital literacy, on pointing out the connection between digital literacy and learning and teaching foreign languages, on student work and the role of teachers in the digital environment, as well as on research results.
Similar Patch Selection in Embedding Space for Multi-View Image Denoising

This paper proposes an image patch selection that finds similar patches in multiple images so that image denoising can suppress noise more effectively by exploiting the identified similar patches from the multi-view images. We encode all image patches in multi-view images into a low-dimensional space, and it allows for a denoiser to find similar patches effectively from the space. Our approach enables existing patch-based denoisers, which often find similar patches within an image window, to identify more similar patches by extending the limited search space into the entire space (i.e., all input images). We integrate our technique into state-of-the-art single-view denoising (block-matching and 3D filtering (BM3D)), and demonstrate that the BM3D combined with our approach is able to conduct multi-view image denoising effectively, without a major alteration to the existing algorithm.
A Jordan curve theorem for 2-dimensional tilings

The classical Jordan curve theorem for digital curves asserts that the Jordan curve theorem remains valid in the Khalimsky plane. Since the Khalimsky plane is a quotient space of R2 induced by a tiling of squares, it is natural to ask for which other tilings of the plane it is possible to obtain a similar result. In this paper we prove a Jordan curve theorem which is valid for every locally finite tiling of R2. As a corollary of our result, we generalize some classical Jordan curve theorems for grids of points, including Rosenfeld's theorem.
An Empirical Study on Insertion and Deletion Mutation in Cartesian Genetic Programming

The most commonly used genetic operator in Cartesian Genetic Programming (CGP) is the genotypic point mutation. Since CGP suffers from a lack of knowledge about the possibilities and effectiveness of advanced genetic operators, the point mutation is usually the sole genetic operator when CGP is used. To improve the state of knowledge, this work is devoted to the investigation of the effectiveness of two phenotypic mutation techniques. takes another step towards the use of advanced phenotypic mutations in CGP. The functionality of the proposed mutations is inspired by biological evolution, where DNA sequences are mutated by inserting and deleting nucleotides. This behavior is adapted by activating and deactivating function nodes in the genotype. In the first place, the experimental part of this paper focuses, on experiments with sets of well-known Boolean functions and symbolic regression problems are performed and the results show an improved search performance when these phenotypic mutations are used. The observed improvement of the search performance indicates that the insertion and deletion mutation techniques are beneficial for the use of CGP. The effectiveness of both mutation techniques is underlined with a comparison to another state-of-the-art technique in the field of graph-based genetic programming. Another part of this work is devoted to the analysis and interpretation of the effects which are caused in fitness and phenotype space when both mutation techniques are used. For the interpretation, we analyze and compare the findings of previous work in the field of phenotypic genetic operators for CGP which leads to new ideas about the effects of both mutation techniques on the behavior of CGP.
Neural Precedence Recommender

Abstract The state-of-the-art superposition-based theorem provers for first-order logic rely on simplification orderings on terms to constrain the applicability of inference rules, which in turn shapes the ensuing search space. The popular Knuth-Bendix simplification ordering is parameterized by symbol precedence —a permutation of the predicate and function symbols of the input problem’s signature. Thus, the choice of precedence has an indirect yet often substantial impact on the amount of work required to complete a proof search successfully. This paper describes and evaluates a symbol precedence recommender, a machine learning system that estimates the best possible precedence based on observations of prover performance on a set of problems and random precedences. Using the graph convolutional neural network technology, the system does not presuppose the problems to be related or share a common signature. When coupled with the theorem prover Vampire and evaluated on the TPTP problem library, the recommender is found to outperform a state-of-the-art heuristic by more than 4 % on unseen problems.