Reviewerly/exHarmony huggingface dataset:
Objective Determination of the Graduating Students’ Competencies Level

The use of the competency-based approach in the educational process results in the need to formulate a quantification and objective evaluation of students’ competencies that they mastered in the course of their studies.The article proposes a methodology and algorithm for calculating the achieved competencies level formed during training of university graduates. The methodology is based on a structural description of competencies through the results of training in individual disciplines and the number of credits allocated for the subject mastery.To display the links between disciplines and learning outcomes, an index designation system is introduced. Connected sets are a mathematical representation of a curriculum. Learning outcomes are evaluated through controls, each of which is characterized by a specific labor intensity. The use of labor intensity as weighting factors enables an objective assessment of learning outcomes and the competencies level.The results can be used to justify the objectivity of using a point-rating system both within the individual disciplines, and the curriculum as a whole.
Study on the Path Planning Algorithm Based on Dueling Deep Q Network

Abstract In traditional path planning methods based on Deep Q-Network (DQN) algorithm for mobile robots, the target Q value was usually obtained by a greedy algorithm, and the estimation of Q value was usually high, which would result in slow training speed of the algorithm. To solve this problem, a Dueling DQN algorithm based on Dueling network was proposed in this paper, which was mainly realized by the structure optimization of neural network. The initial algorithm structure was divided into two states and a higher return is obtained through competitive comparison. Then, the long training time and slow speed convergence problem could be effectively solved than the traditional DQN. The simulation results show that the DQN algorithm based on Dueling DQN has high efficiency in Q value obtaining. Compared with the traditional DQN method, it has the better anti-interference ability and decision-making ability, less training time, and stable convergence.
Development of the Creativity of Students at the Study of Filled Sets of Julia with Mathematical Methods and Information Technologies

In the article, the formation of the creative qualities of students is analyzed using the example of the Mandelbrot set and the Julia sets. The connection between the points of the Mandelbrot set and the Julia sets for the degree of complex argument 2 is considered. A program that demonstrates this link is developed. An algorithm for constructing the Mandelbrot set of an arbitrary natural degree is given.
Innovating Methods: Observing In Situ Technology Experiences Using Novel Visual Research Methods

This paper describes the development of novel visual research methods derived through the application of consumer-grade tools of immersive virtual reality. The research methods highlighted are ideally suited for research in the wild, providing field researchers the means to observe and capture any situated activity within its natural context, including the capability to reexamine or “reexperience” activities observed. The utility of these methods was validated empirically through formal and informal field studies. The methods herein are either completely novel approaches or innovative variations of existing visual research methods, thus expanding the existing arsenal of HCI and UX visual research methodologies for in situ observation of ubiquitous and pervasive technology experiences.
Role of the Environment in the Placement of Apocarbonate Gold Mineralization Chakylkalyan Megablock (Southern Uzbekistan)

Analysis of the available information and the results of many years of research on gold deposits in Uzbekistan made it possible to identify the main unconventional types of deposits. Among them, the most interesting are apocarbonate, crustal, sulfide-carbonaceous and apovolcanogenic quartzite (Upper-Kattakashkasai ore occurrence). The apocarbonate type is widespread in Uzbekistan, has been studied in sufficient detail and information is provided on it in this article. The article examines the existence, distribution and genesis of ores of Karlin type gold deposits. The generalizing characteristics of the Karlin type gold mineralization are given. The issues of geochemical specialization of the Paleozoic strata of the Chakylkalyan megablock are considered, the most favorable stratolevel for the localization of mineralized zones is determined, and the features of carbonate rocks in the process of gold deposition during reactions with silicic solutions are characterized. Based on the materials of regional geochemical profiling, the behavior of the main ore-forming elements in the rocks of both carbonate and volcanogenic-terrigenous strata is analyzed. As a result of the analysis, subclarkic contents of the main ore-forming elements (As, Co, Ni, Pb, Cu, Ag, V, Cr, Sc) were revealed, which create increased concentrations in gold-bearing pyrites of both apocarbonate gold mineralization and related formations.
Lesiones cutáneas en pacientes con enfermedades potencialmente mortales: cuidados en el entorno hospitalario

espanolObjetivo principal: Conocer la percepcion de los profesionales de enfermeria sobre la prevencion y tratamiento de lesiones en pacientes hospitalizados con enfermedades potencialmente mortales. Metodologia: Estudio con enfoque cualitativo y cuantitativo, descriptivo y exploratorio, realizado en unidades clinicas, quirurgicas, unidad de cuidados intensivos y unidad de urgencias de un hospital del sur de Brasil. Datos recogidos en 2014 mediante entrevistas semiestructuradas con 118 profesionales de enfermeria. Resultados principales: Se identificaron tres categorias tematicas: La percepcion de la atencion; Prevencion y tratamiento de lesiones; y Dificultades y estrategias para implementar la atencion. Conclusion principal: El escenario de investigacion no cuenta con unidad de cuidados paliativos. Realidad de la mayoria de los servicios, aumentando la posibilidad de desconocimiento del tema. Sin embargo, se cree que los resultados de este estudio pueden contribuir a reforzar la necesidad de esta linea de atencion. EnglishObjective: To know the perception of nursing professionals about the prevention and treatment of injuries in patients with life-threatening diseases in the hospital environment. Methods: Study with qualitative and quantitative, descriptive and exploratory approach, performed in clinical, surgical and adult inpatient units, intensive care unit and emergency care unit of a hospital in southern Brazil. Data were collected in 2014 through semi-structured interviews with 118 nursing professionals. Results: Three thematic categories have been identified: The perception of care; Prevention and treatment of injuries; and Difficulties and strategies to implement care. Conclusions: The research scenario does not have a specific palliative care unit. Reality of most health services. This situation increases the possibilities of lack of knowledge about the theme. However, it is believed that the results of this study may contribute to reinforce the need for emphasis and prioritization of this line of care.
Finding Subgraphs with Side Constraints

The subgraph isomorphism problem is to find a small “pattern” graph inside a larger “target” graph. There are excellent dedicated solvers for this problem, but they require substantial programming effort to handle the complex side constraints that often occur in practical applications of the problem; however, general purpose constraint solvers struggle on more difficult graph instances. We show how to combine the state of the art Glasgow Subgraph Solver with the Minion constraint programming solver to get a “subgraphs modulo theories” solver that is both performant and flexible. We also show how such an approach can be driven by the Essence high level modelling language, giving ease of modelling and prototyping to non-expert users. We give practical examples involving temporal graphs, typed graphs from software engineering, and costed subgraph isomorphism problems.
Edge-assisted Viewport Adaptive Scheme for real-time Omnidirectional Video transmission

The omnidirectional application is immersive and highly interactive. In industrial scenarios, it can be used to improve the efficiency of remote collaborative work between workers. The transmission of omnidirectional video (OV) is a key step. Compared with ordinary video transmission, OV transmission requires more bandwidth, which is still a huge burden even in 5G networks. The tile-based scheme can reduce bandwidth consumption, but it cannot accurately obtain the user's field of view (FOV) area. In this paper, we propose an edge-assisted viewport adaptive scheme (EVAS-OV) to reduce bandwidth consumption. First, EVAS-OV uses a Gated Recurrent Unit (GRU) model to predict the user's viewport. Then, the users are divided into multicast clusters according to their viewports. EVAS-OV reprojects the OV frame to accurately obtain the user's FOV area from the pixel level. At the same time, a redundancy strategy is adopted to reduce the influence of viewport prediction errors and improve the robustness of the system. All computing tasks are transferred to the edge server to reduce transmission delay and improve bandwidth utilization. Experimental results show that compared to the viewport adaptive two-layer scheme, EVAS-OV can save 30% of bandwidth.
Fast Multi-Scale Residual Fusion Network for Stereo Matching

Recent deep convolution-based stereo matching methods have shown significant progress. However, most state-of-the-art models achieve high accuracy by using 3D convolutions during cost aggregation, which come with more floating-point computations and makes it difficult for deployment in real-time applications. In this paper, we propose an effective and efficient multi-scale aggregation module (without 3D convolution) to build our fast Multi-Scale Residual Fusion Network (MSRFNet). Three lightweight components are involved in our aggregation module: combination block, attention block, and residual fusion block. The combination block combines features with different receptive fields and the attention block emphasizes salient regions in various cost volumes. The residual fusion module focuses on extracting the differences between adjacent cost volumes, using progressively residual aggregation instead of simply stacking or adding. Extensive experiments on Scene Flow and KITTI benchmarks demonstrate that our method achieves competitive accuracy com-pared with state-of-the-art methods while running at 56ms.
A New Embedding and Extraction Algorithms for Robust Video Watermarking in Wavelet Domain

Watermarking is an innovation that is utilized for giving the security to the multimedia/digital information and digital media copyright assurance. Late years, there are numerous scientists who had created watermarking algorithms based on LeaSe noteworthy Bit (LSB), Discrete Fourier Transform (DFT), Discrete Sine Transform (DST), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Principle Component Analysis (PCA) and so on., Here, we proposed “a new embedding and extraction algorithms for robust video watermarking in wavelet domain”. In terms of consistency, the proposed algorithm indicates that the proposed algorithm was superior to the current algorithms. such as Structural Similarity Index (SSIM), CoVariance (CV) and Peak Signal To Noise Ratio (PSNR).
Extraction of Text From Image

Picture Text is the content data implanted or written in picture of various structure. Picture text can be found in caught pictures, filtered records, magazines, papers, banners and so on These picture messages are profoundly accessible these days and they are vital in addressing, depicting and moving data which help people groups in correspondence, tackling issues, accessibility, formation of new sorts of occupations, cost viability, efficiency, globalization and social hole and so forth The data from these picture archives would give higher proficiency and straightforward entry on the off chance that it is changed over to message structure. The cycle by which Image Text changed over into plain content is Text Extraction. Text Extraction is helpful in data recovering, looking, altering, recording, filing or detailing of picture text. In any case, variety of these writings because of contrasts in size, direction style, and arrangement, text is installed in complex hued archive pictures, corrupted reports picture, inferior quality picture, as well as low picture differentiation and complex foundation make issue text extraction incredibly troublesome what's more, testing one. Various strategies like Connected Component Method, Mathematical Morphology Method, Edged Based Method and Texture Based Method have been utilized beforehand, however those all have their own constraints when estimated by various boundaries like exactness, review and f- score. In this paper, text extraction from picture reports, utilizing blend of the two amazing techniques Connected Component and Edge Based Method, to improve execution and exactness of text extraction is talked about and execution is finished by incorporated MATLAB code with MATLAB/Simulink device and the proposed framework is tried by Digital Image Binarization Competition (DIBCO) 2017 dataset. At long last, the separated and perceived is changed over to discourse for legitimate use for outwardly hindered individuals.
Clustering Research Papers: A Qualitative Study of Concatenated Power Means Sentence Embeddings over Centroid Sentence Embeddings

Mathematical average of word embeddings is a common baseline for sentence embedding techniques which typically fall short of the performance of more complex models such as BERT and InferSent. There has been significant improvement in the field of sentence embeddings and especially towards the development of universal sentence encoder that can be used for transfer learning in a wide variety of downstream tasks. Academic paper retrieval systems are widely used in academic institutions to store and categorise scientific papers and find connections between them using citation links, but these methods do not account for the content of the papers. For unsupervised clustering of these papers, a new approach of sentence embeddings is proposed using concatenated power means sentence embeddings and centroid sentence embeddings. The sentence embeddings so created are clustered using K-means clustering algorithm. The results show a clear increase of 47.94% in cosine distance of nearest papers using concatenated power means sentence embeddings with respect to baseline centroid embeddings for the highest performing GloVe models proving that the computationally inexpensive P-Means clustering sentence embeddings can be used for unsupervised clustering of scientific research papers using their abstracts.
Image encryption based on improved chaos and hash function

Based on the traditional Kent mapping, an improved Kent mapping is proposed, which increases its sequence complexity, key space and parameter range. The image is divided into equal parts and scrambled by Rubik's cube function. The initial value and control parameters are generated by the hash function, and two improved Kent chaotic mapping sequences and Logistic sequences are generated. Finally, the encrypted image is generated by the chaotic sequence. In-depth analysis and simulation of image gray distribution, adjacent pixel correlation, information entropy, key space and robustness show that the algorithm has high security and strong anti-attack ability.
Skew Correction Techniques for Scanned Documents

This work manages viable partition of foreground and background in low quality pictures such as old documents experiencing different kinds of debasements which incorporates effects of aging, uneven foreground and background, noise in scanning, separating the content from the picture, and so forth. The methods of image restoration of these outdated documents are proposed. The process is started by scanning documents page by page using a HD scanner and as an output HD images are obtained. And the different kinds of techniques of processing are acquired which includes skew correction, tilt correction, black hole removal and text extraction. The proposed techniques demonstrate a phenomenal flexibility to handle with these issues of uneven brightening and neighbourhood changes or non-consistency in foreground and background view hues.
La formación entre iguales del Pdi: un reto en la universidad

espanolEl traslado del proceso de ensenanza aprendizaje a los entornos virtuales trajo consigo una intensa demanda de formacion en recursos digitales por parte del profesorado universitario. Para abordar este reto, el equipo decanal de la Facultad de Derecho impulso un programa formativo dirigido al personal docente e investigador (PDI). No obstante, debido a la sobrecarga de la unidad de docencia virtual de la Universidad no fue posible encomendar la tarea a expertos en recursos virtuales para la docencia. Por ello, se contacto con docentes de la Facultad que conocian, en la mayoria de los casos a nivel de usuario, metodologias basadas en el uso de tecnologias digitales y se les invito a compartir sus conocimientos mediante la imparticion de talleres. En particular, se diseno un programa formativo de dos semanas durante las que se impartieron sesiones sobre distintos recursos digitales para la docencia y la evaluacion virtual. Estas acciones formativas tuvieron gran acogida y evidenciaron que la ensenanza entre iguales genera un ambiente informal y accesible que favorece el exito en la adquisicion de las competencias digitales. El grado de satisfaccion ha sido tan amplio que se ha solicitado la continuacion de los talleres durante el proximo curso. EnglishThe transfer of the teaching-learning process to virtual environments brought with it an intense demand for training in digital resources on the part of university teachers. To address this challenge, the College´s Law direction team promoted a training program aimed at teaching and research staff. However, due to the overload of the University’s virtual teaching unit, it was not possible to entrust the task to experts in virtual resources for teaching. For this reason, teachers of the Faculty who knew methodologies based on the use of digital technologies were contacted and they were invited to share their knowledge by giving workshops. In particular, a two-week training program was designed during which sessions on different digital resources for teaching and virtual assessment were given. These training actions were well received and showed that peer teaching creates an informal and accessible environment that favors success in the acquisition of digital skills. The degree of satisfaction has been so wide that the continuation of the workshops during the next academic year has been requested
Towards Association Rule-Based Item Selection Strategy in Computerized Adaptive Testing

One of the most important stages of Computerized Adaptive Testing (CAT) is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this chapter, the integration of Association Rule Mining is proposed as an item selection criterion in a CAT system. Specifically, we present the analysis of association rule mining algorithms such as Apriori, FPGrowth, PredictiveApriori, and Tertius into three data sets obtained from the subject Databases, to know the advantages and disadvantages of each algorithm and choose the most suitable one to employ in an association rule-based CAT system that is being developed as a Ph.D. project. We compare the algorithms considering the number of rules discovered, average support and confidence, lift, and velocity. According to the experiments, Apriori found rules with greater confidence, support, lift, and in less time.
Internet of Things and Its Impact on Financial Services

The internet of things (IoT) is proving to be a seminal development amongst this century's most productive and pervasive high-tech revolutions. Increased reliance on the internet of things (IoT) is one of the foremost trends, and the financial services industry is a major contributor to that trend. IoT's influence on our daily lives is noteworthy, and it has become imperative for financial services organizations to evolve to adapt to these changes. Digital devices have started to interconnect with each other and possibly with other peripheral entities. Owing to the explosion of these devices and digitization in the banking and financial services industry, businesses are discovering the possibility of IoT in finance to control data and to minimize the risk. This chapter focuses on the impact of internet of things on financial services. It discusses the various applications, trends, challenges, and risks associated with adoption of IoT by financial services institutions. This chapter also discusses Indian and global cases of application of internet of things by financial services institutions.
A Research and Design of Lightweight Convolutional Neural Networks Accelerator Based on Systolic Array Structure

With the wide application of convolutional neural networks(CNNs) in the field of artificial intelligence, more attention has been paid to the architecture design of CNNs accelerator. But even so, there is little research on hardware acceleration of light-weight CNNs, and there is a lack of systematic and in-depth exploration of lightweight CNNs accelerator design space. In this paper, we propose a design scheme for the lightweight CNNs accelerator based on the systolic array structure. Taking the MobileNet series, the typical representative of lightweight CNNs, as the test benchmark, we carry out detailed experiments and research analysis on the accelerator performance under different data-flow modes and different core computing array scales. Based on the systematic and comprehensive experiments, we provide powerful experimental data supporting and scientific guidance for the design space of the systolic array based lightweight CNNs accelerator and the trade-off of various indicators including operational efficiency, acceleration ratio, cycle time and so on, which makes up for the blank of current research in this field, and makes great convenience for subsequent designers to develop lightweight CNNs accelerators. Through our research, MobileNet V1 is speeded up nearly 1.2 times under certain conditions.
Time-varying Obstacle Avoidance Assist Control by Using Exact Differentiator

Control barrier functions (CBFs) have been successfully implemented in various control strategies; one of those is a human assist control for moving obstacle avoidance by using time-varying CBFs. However, the human assist control contains the complete information on the motion of environments; in general, a derivative of moving obstacle states needs estimating. In this paper, we apply an exact differentiator to estimate derivatives of obstacle state signals in real time. Then, we propose a human assist control by using both a time-varying CBF and an exact differentiator. Moreover, the effectiveness of the proposed method is confirmed by computer simulation and experiments of an electric wheelchair.