Reviewerly/exHarmony huggingface dataset:
NEW PEDAGOGICAL THINKING: THE ESSENTIAL CHARACTERISTICS

Summary. Purpose. The article seeks to reveal the essential characteristics of the new pedagogical thinking. Methodology. The article uses the methodology of dialectics as the logic and theory of knowledge, which involves consideration of the problem in the domain of general (concrete-general, but not abstract-general), theoretical analysis of the movement of concepts as the form of the implementation of thought, as a way of its immersion in the essence of the process. The understanding of the essence of the educational process as the formation of human subjectivity in the process of active development of the experience of previous generations is the theoretical basis of the study is. Scientific novelty. The article argues that pedagogical thinking should be understood not as a certain professional expression of intellectual activity with its specificity, but as the ability to perform activities in accordance with the essence of the educational process. The new pedagogical thinking is not interpreted in the search for abstract features of a certain «novelty», the creation of «new pedagogical technologies», but as such thinking, which can effectively and adequately solve the contradictions of the current state of education. Therefore, the new pedagogical thinking positions itself as thinking (with the necessary reflection in its categorial system) and as a pedagogical one, focusing on the essence of the educational process as the most complicated reality in the world. Its characteristic features include: the implementation of pedagogical communication as a subject-subject relations, the activation of the content of learning as an actual culture and thus – as a «third entity» of the educational process, ensuring the real integrity of education (in particular, not «moral education», But the morality of the entire educational process). Conclusions: for the mastering of new pedagogical thinking, a certain minimum of philosophical culture of teachers is needed, and therefore the system of training future teachers needs a radical change.
Multi-grain Representation Learning for Implicit Discourse Relation Recognition

We analyze narrative text spans (also named as arguments) in this paper, and merely concentrate on the recognition of semantic relations between them. Because larger-grain linguistic units (such as phrase, chunk) are inherently cohesive in semantics, they generally contribute more than words in the representation of sentence-level text spans. On the basis of it, we propose the multi-grain representation learning method, which uses different convolution filters to form larger-grain linguistic units. Methodologically, Bi-LSTM based attention mechanism is used to strengthen suitable-grain representation, which is concatenated with word-level representation to form multi-grain representation. In addition, we employ bidirectional interactive attention mechanism to focus on the key information in the arguments. Experimental results on the Penn Discourse TreeBank show that the proposed method is effective.
Application and Development of Computer Artificial Intelligence Technology

In the development of modern society, Internet technology has been popularized and applied. Artificial intelligence technology is not only found in science fiction movies, but has been widely used in industry, tertiary industry and people’s livelihood. Under the background of rapid advancement of science and technology, computer artificial intelligence technology will play an important role in the future. Due to a series of problems in the development of computer artificial intelligence technology, it is necessary for relevant personnel to strengthen research on the application and development of computer artificial intelligence technology. The paper mainly studies the application and development of computer artificial intelligence technology, and hopes to bring more convenience to the daily life of the people.
Address Randomization for Dynamic Memory Allocators on the GPU

GPUs have been widely used in multi-user environments such as the cloud due to its rich thread-level parallelism. In such an environment, multiple kernels can execute in parallel. However, it is possible that a kernel leverages buffer overflow to attack other kernels on the same GPU. The limited existing work focuses on the detection of buffer overflows instead of prevention. Address randomization is an effective approach to preventing memory-related attacks on the CPU. However, current GPUs lack similar support to defend the increasing threats of memory overflow. In this paper, we propose an address randomization method for dynamic memory allocation on the GPU. We have implemented and compared different pseudo-random algorithms on the GPU, and integrated the address randomization into an existing allocator. Elaborate discussions are presented to analyze the security of our proposed address randomization. Experimental evaluations show that the overhead incurred by our randomized algorithm is less than 20% on top of existing memory allocators.
Error type disfluencies in consecutively interpreted and spontaneous Hungarian speech

Interpreting can be considered as a form of spontaneous speech, the key differences being that language change is involved in interpreting and the fact that speech production is influenced by several constraints during interpreting.Research has shown that the interpreting task influences the disfluency patterns of target language texts.The aim of this paper is to investigate how the frequency and distribution of error type disfluencies changes in the target language output of trainee interpreters as they progress in their training.Results indicate that there is no considerable change in the frequency and proportion of error type disfluencies in the target language texts recorded at the end of the second, third and fourth semesters of interpreter training.The proportion of error type disfluencies is higher in the consecutively interpreted texts than in the spontaneous monolingual speech of the students.This suggests that the complexity of the task, rather than progress in training, determines the disfluency pattern of consecutively interpreted target language texts.
Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter

Each year, there are about 400'000 new cases of kidney cancer worldwide causing around 175'000 deaths.For clinical decision making it is important to understand the morphometry of the tumor, which involves the timeconsuming task of delineating tumor and kidney in 3D CT images.Automatic segmentation could be an important tool for clinicians and researchers to also study the correlations between tumor morphometry and clinical outcomes.We present a segmentation method which combines the popular U-Net convolutional neural network architecture with post-processing based on statistical constraints of the available training data.The full implementation, based on PyTorch, and the trained weights can be found on GitHub http://github.com/ipa/kits2019.
Attention Guided 3D U-Net for KiTS19

We use a two-stage 3d U-Net model to predict the multi channels segmentations from coarse to fine.The second stage is guided by the predictions from the first stage. MethodWe proposed a two stages method to segment CT image from coarse to fine.The two stages are trained with different learning scope and are assigned with different learning missions.1.1 Stage 1 -Coarse stage Data preprocess.Firstly, we downscale the training data to a normal shape, in order to make sure the model can take a whole image at once.All the images and segmentations are downscale to 128*128*32 (height*width*depth).The segmentation files are transformed to 3-channels arrays, in which the channels-wise pixel values represent kidneys, tumors and the background (without kidneys and tumors) in order.Training.We train the standard 3D U-Net follow with a softmax layer.While training, we apply some data augmentation to the training data, including normalize, random contrast, random flip and random rotate.We input all the 210 cases training data and train the model to regress the multi-channel segmentations.We apply with pytorch, and the learning rate is 0.1 which divide 0.1 in 300000 epochs and 500000 epochs.We use the Binary Cross Entropy Loss as loss function.Predicting.The 90 cases testing images are preprocessed the same with the training images then input to the trained model.The channel-wise predictions are scaled back the original shape.We take the first 2 channels of the predictions, represent as the segmentation of kidneys and tumors, then package as the .nii.gz files.
Uncertainty and Fuzzy Modeling in Human-robot Navigation

The interaction between humans and mobile robots in shared areas requires a high level of safety especially at the crossings of the trajectories of humans and robots.We discuss the intersection calculation and its fuzzy version in the context of human-robot navigation with respect to noise information.Based on known parameters of the Gaussian input distributions at the orientations of human and robot the parameters of the output distributions at the intersection are to be found by analytical and fuzzy calculation.Furthermore the inverse task is discussed where the parameters of the output distributions are given and the parameters of the input distributions are searched.For larger standard deviations of the orientation signals we suggest mixed Gaussian models as approximation of nonlinear distributions.
The Contribution of Forensic Science to Establishing the Truth in Criminal Proceedings

This article is intended to highlight, on the one hand, the role played by forensic science (forensics) among criminal sciences in Romania, and, on the other hand, to present the specifics of forensic science in order to delineate it from other related sciences.Through an interdisciplinary approach, taking into account the fact that forensic technique, forensic tactics and, last but not least, forensic methodology serve the finding of truth in a criminal case, as well as the prevention and/or combating of deeds provided by criminal law, we will emphasise its autonomous nature in relation to other sciences, although the aims of sciences in the field of criminal sciences are common.The delineation of forensic activities from activities specific to other disciplines is essential for the substantiation of criminal policies, for drafting amendments of criminal legislation, putting forward proposals for best interinstitutional/intrainstitutional practices, especially when investigation is carried out by joint teams, and sometimes for transnational crimes.
DATA MASKING & TECHNIQUES

Database emerged as a big and important field in software development nowadays and use of database is increasing day by day.The use of database is increasing with the passage of time and the security & privacy issues of data in the database are also increasing so different techniques are emerging to tackle these issues .Data masking is the process of de-identifying (masking) specific data elements within data stores.The main reason for applying masking to a data field is to protect data that is classified as personal identifiable data, personal sensitive data or commercially sensitive data, however the data must remain u sable.There are variety of situations in which users, developers or other personnel may not be allowed to view information that they are not authorized to see such as in development phase, training sessions, testing etc.Many methods are used for data masking such as substitution, shuffling, variance, encryption, nulling out, field masking etc.Data masking is a technique used to hide the original data from developers and end users in development process and t raining sessions.
Clustering Arabic Tweets for Saudi National Vision 2030

Twitter provides a valuable resource for opinion mining, which many applications can take place in investigating peoples' interests and concerns.In this paper, we are interested to investigate users' main concerns regarding the national Saudi vision of 2030.As the vision has many tracks of planning, we investigated the education and health sectors.The aim of this study is to show people's focus and interest in implementing this vision.For this, some unsupervised Machine Learning techniques are implemented.Three clustering algorithms are experimented (K-means, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF)).LDA and NMF give promising results, as both clustered the data into two significant discriminative clusters with a silhouette coefficient equals to 0.639 and 0.538 respectively.While K-means provides overlapped clusters with a silhouette coefficient equals to 0.181.Therefore, our models are able to cluster Arabic tweets in the context of the Saudi national vision.Additionally, they showed that the national vision 2030 is implemented in education more than in healthcare.
ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA

Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.
Obstacle Avoidance Path Planning for Space Manipulator Based on Improved Probability Roadmap Method

Aiming at the point-to-point path planning of apace manipulator, an improved probability roadmap method is proposed in this paper. By introducing the concept of intermediate configuration, the set of intermediate configuration is established, and the configuration-to-point path planning is solved, and the point-to-point path planning can be divided into configuration-to-configuration path planning and configuration-to-point path planning. The correctness and validity of the proposed method are verified with 7-DOF manipulator used as the research object to simulate the experiment.
The Goal Of Educaton In Knowledge Society

Based on the fact that today humanity takes the first steps to the knowledge society, the paper raises the problem of goal-setting in an educational institution under transit conditions. The author considers that the becoming reality opens both unprecedented opportunities (a free choice of forms and ways of an individual’s activity), and unprecedented threats (spiritual and physical degradation) for the person. In this regard, the paper considers the question: which personal qualities will be in demand in the emerging world, which of the visions of the "person of the future" meets the interests of both the human race and the individual development. The aim of the paper is to discover those constitutional features of the personality, the formation of which an educational institution has to focus its efforts on, in the medium and long term ahead. The achievement of this goal is ensured by using a methodology that integrates the research tools of different social sciences and humanities (world-system approach by I. Wallerstein, the theory of modernization, the concept of "knowledge society", the systematic-holistic approach to the study and design of pedagogical reality). In the context of the knowledge society (post – industrial/postmodern society) formation various models of the transit subject are considered, the correspondence of one of them – "homo creator" - to the interests of both the individual and the society development is argued. Attributive features of this individual subject (personality) are post-materialistic motivation and craving for creativity (activity, driven by internal, non-economic interest and aimed at finding something new).
On Hybridizations of Fourth Order Kernel of the Beta Polynomial Family.

The usual second order nonparametric kernel estimators are of wide uses in data analysis and visualization but constrained with slow convergence rate. Higher order kernels provide a faster convergence rates and are known to be bias reducing kernels. In this paper, we propose a hybrid of the fourth order kernel which is a merger of two successive fourth order kernels and the statistical properties of these hybrid kernels were study. The results of our simulation reveals that the proposed higher order hybrid kernels outperformed their corresponding parentâ€™s kernel functions using the asymptotic mean integrated squared error.
Game Theoretic Fund Allocation for Maximizing Throughput of Multiserver Systems in Cloud

As an important tool of large-scale computing, cloud servers have received enough attention in academia research and industry production, which typically used to handle scientific computation. It is critically necessary for the service provider to understand the relationship between task operation and fund in cloud computing system. In other words, from the perspective of the cloud service provider, how to allocate the limited fund to multiple servers is the top issue. This is because the available fund to the service provider is usually limited and it may insufficient for all application servers. To address this problem, we propose a resource scheduling strategy based on the cooperative game for the fund-sensitive cloud computing system. Firstly, we use the cooperative game to model the fund scheduling problem in a fund-sensitive cloud computing system. Secondly, we obtain the appropriate allocating scheme by deriving the Nash bargaining solution (NBS) of this game model. The NBS of the established model is very difficult to formulate due to it's complexity. Therefore, a heuristic method is designed for the service provider to get fund scheduling strategy according to the current system's fund condition. Finally, we conducted numbers of simulation experiments. The simulation results show that our fund scheduling strategy based on the cooperative game is able to achieve a high system throughput while ensuring the basic performance of individual servers.
Web Application Vulnerability Scanner

In today's world the field of cyber security is a hotline.As the digital world is growing to its mass extend the security related to it is also on the peak verge of exploitation and vulnerable.So the domain of Computer security, cyber security has become a major topic to be concerned about. The mass cyber attacks have been detected and has done a great loss of money , capital and various type of resources in that domain .There are very few prevention techniques that are implemented when the model is designed on a big platform and these are not maintained at all. The paper describes a technique that is used for detecting vulnerability (XSS) in the web applications that are poorly constructed and can be easily targeted to a system attack and possible exploitation.
Method-Level Test Selection for Continuous Integration with Static Dependencies and Dynamic Execution Rules

In Continuous Integration (CI) development environment, integration testing (i.e., CI testing) is an important practice to verify the quality of submitted code. With the growth of integration system, running all tests leads to high test cost with slow feedback. Many test case selection techniques have been proposed to tackle this problem. We analyze existing static test selection approaches on method-level, and find there are two main drawbacks, i.e., omission of dependencies and imprecise dependencies, which influence the effectiveness of test selection. Both of them are due to the fact that some dependencies are dynamically determined at runtime, which could not be resolved solely by current static analysis techniques. We propose a Method-level tEst SelecTion approach (i.e., MEST), which utilizes static dependencies and dynamic execution rules (i.e., dynamic invocation in reflection and dynamic binding in inheritance). Evaluation is conducted on 18 open source projects with 261 continuous integration versions from Eclipse and Apache communities. We assess the effectiveness of MEST from reduced test size, fault detection efficiency and test cost, and compare it with the state-of-the-art approach ClassSRTS (as baseline); and further analyze the contribution of dynamic execution rules. Results show that (1) on average, MEST can reduce test size by 92% and 43% compared with actual CI testing and baseline respectively; (2) MEST can fully cover all faults detected by actual CI testing (in 97% versions) and baseline (in 98% versions), and find new faults in 26% and 27% versions respectively; (3) on average, the endto-end time of MEST is 24% and 48% of actual CI testing and baseline respectively; (4) both dynamic execution rules contribute to fault detection through capturing necessary dependencies. This approach can further speed up the feedback of CI testing and improve the fault detection efficiency of CI testing.
Using Wordless Picture Book as a Contributive Learning Media to Enhance the Student's Writing Ability

This research aims to know enhancement the Student's writing ability by using wordless picture book as a contributive learning media. The benefits of using wordless picture book show that the students enhance their writing ability and create their creative writing and thinking skill. The researcher applied quasi-experimental method, with two groups namely experimental and control class. The experimental class consists of 30 students and control clas consists of 30 students. The subjects of the research were 60 students from two classes taken from students of SMP Negeri 10 Parepare. The research instrument was writing test in which the comparison of pre-test and post-test decided the enhancement of the Student's writing ability. The results of the data analysis showed that the Student's writing ability getting significantly improvement. It was proved by the mean score of pre-test was 46.93 and the post-test was 80.00. It indicates that the use of wordless picture book can enhance the Student's writing ability at SMP Negeri 10 Parepare. Besides, after analyzing the data by using the t-test formula, the result of t-test value in post-test was 6.02 and t-table value was 1.671. It meant that the t-test value was higher than t-table value. It clarified H1 was accepted and H0 was rejected and the students who were taught by using wordless picture book were better than the students who were taught by using wordless picture book.