Reviewerly/exHarmony huggingface dataset:
Development of students’ information skills during project-based learning

Abstract The aim of the study was to experimentally test the effectiveness of pedagogical conditions for the development of information skills in students in project-based learning. The research methodology was presented by the competence-based approach, from the position of which information skills were considered as an important component of students’ information competence, the project activity of students was a way of forming information skills. The leading research methods were pedagogical experiment and expert assessment. It has been experimentally proven that the process of developing students’ information skills in project-based learning can be effective if: the subject of projects will be formed based on the content of academic disciplines, taking into account the direction of professional training; the type of project will characterize the dominant goal of the students’ activity; the structure of the project will be determined by its type; students’ project activities will be carried out in the form of group independent work. The following methodologies were used: the methodology for assessing the projects completed by students, as well as the methodology for assessing the development of information skills among students. For the first time, the comparative analysis results of the various groups of information skills development among students-future teachers of different areas of professional training are presented. The results of the research can be used in the design of training sessions both in the social and humanitarian, and in the natural sciences and engineering disciplines at the university.
A new two‐stage method for single image rain removal

Compared with video de-raining, single image de-raining is more technically difficult due to the lack of temporally redundant information. This paper proposes a new two-stage method for single image de-raining. In the first stage, the authors develop an effective two-step model to detect the rain streaks by taking pixel intensity, and direction of rain streaks as priors. In the second stage, the rain repair process is performed at the patch level. The authors first define a way to search for similar patches of each patch, and then group the similar patches together to form a matrix. Finally, a low-rank matrix completion technique is utilized to recover the rain-stained pixels based on the rain map obtained from the first stage. Compared with several state-of-the-art methods, authors' proposed method is competitive in terms of the abilities of removing rain streaks, and preserving image details.
Ensemble Method to classify multi class with concept drift

Abstract Real world scenarios is expected to have more number of classes, evolution of new class, change in concept, different distribution of data etc., This paper focus on the joint problem of multi class imbalance problem and different types of concept drift detection. The proposed work Enhanced Concept drift based Resampling ensemble (ECDRE) algorithm addresses the combined issues of Multi Class Imbalance problem with different types of Concept Drifts using the Ensemble Relearning Model. In this framework, EMUOB and EMOOB algorithm helps to re-balance the multi class distribution of the latest block. Window based strategy is used to classify the multi class imbalance dataset with different types of drifts. A group of classifiers form an ensemble and members belonging to the ensembles are updated frequently, where weights are calculated to emphasis misclassification cost. Meanwhile, ECDRE is swiftly adaptable to new conditions. Observed studies proof that ECDRE algorithm is more effective for learning non-stationary environment which suffers from multi class imbalance with different types of concept drift. CDRE algorithm was implemented to deal with multi-class problem and concept drift, but it is not much efficient to handle all types of concept drift. Even in situations of Data Streams which suffer from drifts such as real, virtual and recurring types, ECDRE works well and shows overall better results of above 80% accuracy.
A Theory of Occlusion for Improving Rendering Quality of Views

Occlusion lack compensation (OLC) is a multiplexing gain optimization data acquisition and novel views rendering strategy for light field rendering (LFR). While the achieved OLC is much higher than previously thought possible, the improvement comes at the cost of requiring more scene information. This can capture more detailed scene information, including geometric information, texture information and depth information, by learning and training methods. In this paper, we develop an occlusion compensation (OCC) model based on restricted boltzmann machine (RBM) to compensate for lack scene information caused by occlusion. We show that occlusion will cause the lack of captured scene information, which will lead to the decline of view rendering quality. The OCC model can estimate and compensate the lack information of occlusion edge by learning. We present experimental results to demonstrate the performance of OCC model with analog training, verify our theoretical analysis, and extend our conclusions on optimal rendering quality of light field.
Medical Image Enhancement using Salp Swarm Algorithm

The article suggests the idea of digital image enhancement using Salp Swarm Algorithm (SSA). The input image is enhanced by increasing the pixel intensity via cost function containing local and global information. The output analysis of the suggested algorithm is presented with respect to the Number of Detected Edges, Entropy, Peak Signal to Noise Ratio (PSNR), Detail Variance (DV), and Background Variance (BV). The simulation results demonstrated that the suggested SSA algorithm is superior to other meta-heuristic techniques.
Features of High-Quality Mathematical Training in the Context of Online Education of Students Oriented to Work in the Nuclear Industry

Due to the COVID-19 coronavirus infection pandemic, the world is faced with the need for social isolation and a forced rapid transition to a “home office” and online learning. The Russian education system has almost completely switched to distance learning. Working in the new conditions turned the daily routine of both teachers and students who not only faced difficulties of various nature but also discovered new opportunities for professional growth. Traditionally, it is customary to discuss the disadvantages of distance education, but this paper considers the advantages and new opportunities of this form of education. This is especially important in the process of mathematical training of university students focused on work in the nuclear industry due to the specifics and importance of studying this subject and due to the fact that at present online training is widely implemented in the training of employees of nuclear power plants where digitalization and everything which is associated with remote access technologies, thanks to the pandemic, are in demand and irreplaceable.
Pornographic Video Detection with Convolutional Two-Stream Network Fusion

This paper describes a pornographic video detection system with convolutional two-stream network fusion. Both spatial and temporal information are utilized to detect obscenity in videos by fusing a spatial stream network and a temporal stream network. Each network is based on the 121-layered DenseNet. The input to the temporal stream network is stacked dense optical flows. 2D convolution and 2D global average pooling are applied for the two-stream network fusion.
The Adaptive Kalman Filter in Aircraft Control and Navigation Systems

This paper describes our approach to tackle the task of achieving optimal estimation while the ongoing changes in accuracy characteristics of measuring instruments are not known in advance. Imprecise data on characteristics of measurement uncertainties can significantly impair accuracy of the state vector's elements estimation. Since accuracy of the measuring complex may vary during a dynamic system operation, the a priori data on measuring instruments' statistical characteristics do not always correspond to actual current values. In this connection, it is reasonable to supplement the Kalman filtering algorithm by a procedure enabling to make these characteristics more precise. Such procedure determines characteristics of the updatable sequence aiming to improve accuracy of statistical characteristics of the measurement uncertainties and consider in the Riccati matrix differential equation. This paper also provides the modelling results confirming effectiveness of the proposed approach.
Uwaridi kwani? (‘Why ‘Uwaridi’?’) Digital Literary Networks and the App-Propriation of Swahili Popular Novels

This paper examines the current dynamics of publishing Swahili novels in digital media. In August 2016, a collective of writers formed the literary association “Umoja wa Waandishi wa Riwaya wenye Dira” (Union of Novelists with a Compass/Target”), Uwaridi, which in March 2017 launched a homonymous smartphone app for popular fiction. Based on several conversations with Hussein Tuwa, writer and co-founder of Uwaridi, I explore the double nature of this new digital literary network: A network of writers and their texts gone digital. A neat mobile phone network connection tends to replace not only the book shop or newspaper stall, but even the social media experience. Will the recent innovations in digital publishing outstage printed books of Swahili literature in Tanzania? Is this new ‘app-propriation’ likely to extend its networks towards Kenya, and further abroad? What are the main opportunities and challenges for writers and readers of Swahili fiction?
PLC course performance evaluation based on machine learning and image feature retrieval

PLC is an indispensable technology for modern automation. The future social development will require a large number of PLC technical talents, so higher requirements are put forward for the teaching of PLC courses in colleges and universities. The intelligence and practical effect of the PLC course evaluation system are particularly important. Based on this, this article combines machine learning and image feature retrieval to construct a PLC course performance evaluation system. Moreover, this paper introduces the smoothness of the multispectral image, the smoothness of the blur function and the smoothness between the blur functions of adjacent spectral images as constraints and uses the gradient of the image blur kernel to express the smoothness of the image blur kernel itself. In addition, this article constructs the model system architecture according to the teaching requirements of the course and analyzes its realization process. Finally, in order to verify the performance of the model, this paper conducts system performance verification experiments through practical teaching methods and analyzes the results with statistical methods. The research results show that the PLC performance evaluation system constructed in this paper has a certain effect.
Mapping metamorphic hydration fronts with field-based near‑infrared spectroscopy: Teakettle Junction contact aureole, Death Valley National Park (California, USA)

Abstract Mineral distribution in a previously undescribed contact aureole in siliceous dolomite at Teakettle Junction, Death Valley National Park (California, USA), was mapped with a handheld visible and near-infrared (Vis-NIR, 0.35–2.5 μm) spectrometer. With increasing distance from a small Jurassic(?) pluton, the following mineral zones occur: periclase (hydrated to brucite), forsterite (variably hydrated to serpentine and typically accompanied by clinohumite), tremolite, and talc. Airborne Vis-NIR imaging spectrometer data with 5 m ground resolution shows serpentine and tremolite distribution in close agreement with the field data. Field measurements show dolomite and calcite throughout the study area, along with minor amounts of phlogopite and illite. Hydrous minerals were detected in field measurements at levels on the order of 1 vol%. Phlogopite, talc, and illite crystals in many samples are &amp;lt;20 μm in size and difficult to identify without scanning electron microscopy examination. Sensitivity of Vis-NIR spectroscopy to hydrous minerals is especially important in the context of metamorphic fluid flow. Significant hydration of wall rocks is limited to samples between the pluton contact and the serpentine (forsterite) isograd as shown by mineral distribution and variation in the depth of the OH absorption feature near 1.4 μm. This boundary is interpreted to represent a metamorphic hydration front beyond which there was minimal infiltration of wall rocks by H2O-rich fluid. Within this zone of hydration, however, the extent of hydration is highly variable, even at individual sample sites with samples spaced on the order of 1 m or less. Heterogeneity of fluid-rock interaction at this scale must be considered in models of heat and mass transfer accompanying contact metamorphism.
Communicative component of professional competence &#x0D; of a musician-performer

At the present stage, musical and performing activity requires rethinking key concepts and identifying its new content. The article contains an analysis of modern professional training of a musician-performer at a university. Special attention is paid to the significance of interpretation in modern musical and performing activities. Long-term practice of concert performance creates a significant number of performing interpretations. A musician-performer must not only get acquainted and try to interpret the composer's text, but also try to understand this value content. The article offers an understanding of interpretation as a reinterpretation of the content of a work based on the artist's personal creative beliefs, which implies a different experience. This topic is also supplemented by the consideration of the act of joint activity of the participants in the performance, during which a common view of the ideas and images of a musical work is developed, as a result of which the audience's consciousness is transformed. This requires communicative competence. Thus, a musician-performer combines both communicative and interpretative abilities. The article considers the structure of professional competence of a musician-performer, taking into account the specifics of professional activity and based on the competence approach. The existing approaches to defining the essence of the concept of competence are analyzed. The structure of professional competence integrates musical and performing, interpretive, communicative, pedagogical, organizational and managerial competencies. Communicative competence is considered as a professionally significant personal property of a future musician-performer in the course of professional training at a university.
A Jitter Elimination and Data Compression Algorithm for Pressure Sensor Array

This paper proposes an algorithm to process data of high-precision sensor array with data compression. To eliminate the jitter in pressure sensing, the Speeded-up Robust Features(SURF), Fast Approximate Nearest Neighbor Search Library(FLANN), RANdom SAmple Consensus(RANSAC), Gaussian filter and motion compensation are implemented. Moreover, data compression is conducted based on JPEG-LS and Run-length coding. As a result, the proposed algorithm achieves the jitter eliminating and high compression ratio.
Robust Train Component Detection with Cascade Convolutional Neural Networks based on Structure Knowledge

Recently, convolutional neural network (CNN) based methods have achieved superior results in generic object detection and have become the de-facto standard in the domain. However, potential adaptations to industrial areas are not well studied yet. A case worth exploring is the train component detection, in which the components may have strong relationships and some components (e.g., screws and nuts) are very small. Nevertheless, the detection performance of small train components significantly affects the efficiency of overall train component detection. In this work, we propose a novel robust train component detection(RTCD) framework, built on cascading CNNs and utilizing prior structure knowledge of the relationships between train components. The core idea of RTCD is to detect the big and easily detectable component first, and then find the areas that may contain small and challenging to detect components for following fine-grained exploitation. Our proposed attention region mechanism can find regions deserving of further analysis based on the region-of-interest (ROI) detected by the previous CNNs with the known structure knowledge. Then, these areas are cropped, zoomed in and fed into the following deep learning models for further detection. In order to verify the effectiveness of RTCD, 1, 130 high-resolution images of moving trains are captured and collected, from which 17, 334 critical train components are manually annotated. Extensive experiments therein have demonstrated that RTCD outperforms the existing state-of-the-art baselines significantly. The dataset and corresponding source code will be released to facilitate more future work.
Towards Criminal Sketching with Generative Adversarial Network

Criminal sketching aims to draw an approximation portrait of the criminal suspect by details of the criminal suspect that the observer can remember. However, even for a professional artist, it would need much time to complete sketching and draw a good portrait. It therefore motivates us to study forensic sketching with a generative adversarial network based architecture, which allows us to synthesize a real-like portrait of the criminal suspect described by an eyewitness. The proposed work contains two steps: sketch generation and portrait generation. For the former, a facial outline is sketched based on the descriptive details. For the latter, the facial details are completed to generate a portrait. To make the portrait more realistic, we use a portrait discriminator, which can not only learn the discriminative features between the faces synthesized by the generator and the real faces, but also recognize the face attributes. Experiments have shown that this work achieves promising performance for criminal sketching.
Employees' Attitudes and Perceptions Towards Electronic Monitoring Surveillance Affecting Employee Behavior

Electronic monitoring surveillance (EMS) has emerged as a mechanism to monitor the behavior of the employees. Fast-growing organizations are adopting this strategy in an expeditious way. EMS deals with the CCTV, internet surfing control, and check on emails. The chapter is an attempt to diagnose the employees' attitudes and perceptions towards EMS and its impact. For this research purpose, a sample was drawn from the population of the selected airports Chandigarh, Pune, Goa, and New Delhi. Sample data of 237 respondents was done by using Smart-PLS. The findings revealed that employees show that attitude and perception towards EMS were significant. It also influenced their work behavior positively. This chapter suggests managerial implications for organizations in understanding the appropriate implementation and use of EMS strategy as future scope of study. Moreover, this chapter also provides a platform for the researchers to study the different HR practices like commitment, loyalty, job satisfaction, and engagement.
Organizational Model of Personalized Digital Education for Teachers

The research is devoted to the study of the problem of organizing personalized training of teachers in digital technologies in the system of post-diploma teacher education in online format. The paper describes an approach to designing an organizational learning model that provides a set of flexible and practice-oriented educational programs.
Misleading Sentiment Analysis: Generating Adversarial Texts by the Ensemble Word Addition Algorithm

Deep neural networks are vulnerable to the adversarial examples that are generated by adding small perturbations to the original inputs. Similarly, traditional machine learning models such as logistic regression and support vector machine are also threatened by such carefully generated adversarial examples. In this paper, we study the adversarial texts that could mislead the sentiment analysis of traditional machine learning models. We present the Ensemble Word Addition (EWA) algorithm, which filters out a small number of words that have large attack ability and these words are added at the end of the original text. By extensive experiments, we show that the generated adversarial texts could fool some traditional machine learning models with a very high confidence, but they cannot affect human's judgment. The experimental results show that the proposed algorithm has a powerful attack ability to misleading the sentiment analysis, specifically the attack success rate could reach above 95% by only adding 4.6% words.
Knowledge-Enhanced Named Entity Disambiguation for Short Text

Named entity disambiguation is an important task that plays the role of bridge between text and knowledge. However, the performance of existing methods drops dramatically for short text, which is widely used in actual application scenarios, such as information retrieval and question answering. In this work, we propose a novel knowledge-enhanced method for named entity disambiguation. Considering the problem of information ambiguity and incompleteness for short text, two kinds of knowledge, factual knowledge graph and conceptual knowledge graph, are introduced to provide additional knowledge for the semantic matching between candidate entity and mention context. Our proposed method achieves significant improvement over previous methods on a large manually annotated short-text dataset, and also achieves the state-of-the-art on three standard datasets. The short-text dataset and the proposed model will be publicly available for research use.
Estimating the Number of Clusters via Proportional Chinese Restaurant Process

Dirichlet Process Mixture (DPM) models tend to produce some major clusters along with many small clusters. These small confusing clusters are highly overlapped with major clusters. As the size of samples increasing without the change of sample distribution, the small unnecessary clusters would be introduced more and more in the cluster results. Recently, powered Chinese Restaurant Process (pCRP) is purposed to eliminate the counterfactual small clusters. However, it violates the usual and indispensable exchangeability assumption of DPM. In this paper, we propose a new method called proportional Chinese Restaurant Process (pro-CRP) that keeps the property of exchangeability while reduces the number of unnecessary small clusters. We show the experiment results on comparing pro-CRP with CRP and pCRP models and prove the number of clusters reduced by pro-CRP.
CEN

Image classification is a challenging but fundamental task for many computer vision applications, such as self-driving, face recognition, and object tracking. The deep neural network (DNN) is a modern, powerful model to tackle this task, whose representation ability mainly comes from hidden layers. The interpretability of DNN, however, drops rapidly as the inexplicable hidden part becomes deeper and deeper. To make neural networks more explainable, we propose a novel neural network named concept evolution network (CEN), learning explicit concepts of images to help classify. Concepts evolve during training with three stages: emergence, elevation, and elimination. We design three algorithms (one primary and two improved) to train CEN. The experiment results on MNIST show our methods' feasibility and that CEN has both interpretability and adaptive learning capacity for the image classification task. In the last section, we discuss the development prospects of CEN in the future.