Reviewerly/exHarmony huggingface dataset:
Research of the Attitude of Students of Pedagogical Profiles for Future Professional Activity

The article presents the results of studying the relationship of students to future professional activities, describes the dynamics of changes in their relationship to pedagogical activity in the process of studying of pedagogical disciplines, examines the causes affecting the formation of positive attitude of students to this professional activity. The purpose of the article is a quantitative and qualitative analysis of the results of a diagnostic study among 1st – 5th year students enrolled in pedagogical profiles of full – time bachelor’s degree programs of the Novokuznetsk Institute (branch) Kemerovo state University, aimed at identifying their attitude to future teaching activities. Methodology. To achieve this goal, a set of complementary methods was used: conceptual analysis of psychological and pedagogical literature to identify the initial research positions; generalization of scientific information obtained during the research; diagnostic methods (pedagogical observation, conversation, study of products, questionnaires, testing, etc.); methods of mathematical processing of primary data (statistical processing of experimental results and their analysis, graphical interpretation of research results, etc.). Results. It is proved that the formation of a positive attitude of students of pedagogical profiles of preparation for future teaching activities is influenced by their difficulties and disturbing expectations.
Development of supra-professional competencies as a condition for realization of career potential of young specialists

The article is devoted to an urgent issue, namely, the process of developing cross-professional competencies as an integral component of social and personal effectiveness and career development of young specialists. The article provides the primary sociological information – the results of a survey of employers conducted by the author (the number of respondents is 178 people), international statistics and the results of world studies (The World economic forum in the future of jobs report 2018, the World economic forum, the Gaidar Forum, etc.). Based on the thorough review and synthesis of the data presented, results of the survey conducted by the author and the analysis of data from one of the largest job search sites and employees in the world - “Head Hunter” about the most frequently requested professional competencies, the author formed the top 15 flexible skills with definitions and structurally meaningful, to which domestic employers are more oriented. The presented list of cross-professional competencies, according to the author of the article, is not exhaustive: modern terms of technical progress, a rapidly changing business environment with the constant changes and changes in the society, powerful shifts in the global market, based on digital technology can affect the list of relevant competencies and it can be changed and supplemented. The author has developed and proposed the competency development algorithm, allowing realize a personalized educational trajectory, an integrated approach to the development of cross-professional competences. The presented algorithm can be used both by the educational institutions implementing higher and continuing education programs, and by the modern organizations in the context of adaptation and staff training.
Towards Phase-Error- and Loss-Tolerant Programmable MZI-Based Optical Processors for Optical Neural Networks

Mach Zehnder Interferometer-based reconfigurable structures are promising candidates for fast and power-efficient computations in optical neural networks. This work presents the classification performance analysis of a single layer optical neural network implemented by Reck and Clements meshes in the presence of experimental imperfections.
Detection of Web Service Anti-patterns Using Neural Networks with Multiple Layers

Anti-patterns in service-oriented architecture are solutions to common issues where the solution is ineffective and may end up in undesired consequences. It is a standard exercise that initially seems like the best solution; however, it finally ends up having bad results that outweigh any benefits. Research revealed that the presence of anti-patterns leads to the demeaning of the quality and design of the software systems, which makes the process of detecting anti-patterns in web services very crucial. In this work, we empirically investigate the effectiveness of three feature sampling techniques, five data sampling techniques, and six classification algorithms in the detection of web service anti-patterns. Experiment results revealed that the model developed by considering metrics selected by Principal Component Analysis (PCA) as the input obtained better performance compared to the model developed by other metrics. Experimental results also showed that the neural network model developed with two hidden layers has outperformed all the other models developed with varying number of hidden layers.
The Solution of the Question of the Isomorphism of Non-Oriented Trees by the Method of Generating Isomorphic Structures

The method and its software implementation tools for assessing the isomorphism of a pair of non-oriented trees with an arbitrary structure and unnumbered nodes, the total number of which does not exceed the value of 65536, are developed. The user communicates with the tools at the visual-mental (similar to the "LEGO" game) communication level, minimizing his participation in the process of solving the problem. The basis of development is the created method of isomorphic reconfiguration, which, for each node selected as a new root, allows to obtain four different variants of structures isomorphic to the original tree structure. For practical use, the tools do not require the use of adjacency matrices or any other means of a formalized description (representation) of the structure of trees and their subsequent application when performing its various transformations. They also eliminate the need for mastering and using standardized graph description languages, specialized programs for constructing their structure, visualization, etc. If the user wishes, two variants of the adjacency matrix can be generated by the program at the end of the work and written to files with the extensions *.cam, conditionally, the classic option and *.tam, conditionally, the truncated option containing elements only of the upper part of the matrix. All the data of the N-node tree required for solving the problem (without taking into account additional parameters) is stored in a binary file type, with a total volume of 4N char-type characters. The method helps to reduce the total cost of resources and time to solve the problem and the corresponding refinement of the current version of the tools will make it possible to work with tree structures that have an almost unlimited number of nodes and arbitrary orientation of the edges.
Neighbourhood Projection Embedding Based Image Tampering Detection and Localization

Image tampering detection is a well developed field that not only analyzes the authenticity of image but restores credibility of it. Motive of image tampering is to create false notion about an image in viewers prospective. Image can be forged by modifying various features of image or by adding or eliminating part of it. Copy-move tampering can be defined as the process of inserting or deleting image region from an image such that no proof of alteration is visible. In this paper we propose to use Neighbourhood Projection Embedding (NPE) with regard to detection of copy-move tampering. Neighbourhood information preserving property of NPE can effectively detect and localize tampering in the presence of various post-processing operations like, additive Gaussian noise, JPEG compression, brightness change, colour reduction and blur images effectively.
DIGITAL EDUCATIONAL AND PROBLEM "HUMANIZATION"

The article raises the problem of the ambiguity of the processes of introducing information technology into education, which affects many scientific areas. The author explores the leading trends in global education caused by the introduction of digital technologies. The article proves that they not only expand the educational space. In addition to the well-known positive results of the use of information technology in the educational process, the author also shows the negative consequences of digitalization of the educational environment. One of the important consequences of the dominance of digital technologies in the modern educational process is the process of «humanization.»The author reveals the main areas of development of the educational system under the domination of the latest information technologies. The cult ofgadgets gradually leads to the process of «humanization»of the younger generation. By creating a basis for the formation of a single educational space, digital technologies lead to the transformation of domestic pedagogical traditions. The cult of technological progress at the expense ofspirituality is promoted. The traditional integrity of the educational system of the educational system is being broken. «Humanization»is evident in the rejection of truth as the leading goal of gnoseology. Such trends are characteristic of Western education, but some reformers have also successfully imposed them on national education. The author shows what the educational system does not meet the requirements of the information society. Traditional relations between the subjects of the educational process are being violated. In some students, the illusion of the teacher»s uselessness is formed, as they are able to obtain any information themselves. The article shows that the pursuit of speed leads to a drop in the scientific status of information. The article gives examples of distortion of the past in academic cognition, which reduces the role of history as an ideological basis.
Partnerships in Building a Culture of Reading in South Africa: The case of Nal’ibali

For many years, literacy experts have been concerned about the culture of reading in South Africa. As a result, many reading initiatives have been started in the country. Nal’ibali (isiXhosa for “it starts with a story”) is one such initiative that was started in 2012. This study aims to highlight the way in which Nal’ibali used partnership to significantly influence the culture of reading in South Africa by reaching out to over 159 191 children through reading clubs and 63 per cent of the South African population through media campaigns and initiatives. This qualitative study extracted data from the Nal’ibali’s monitoring and evaluation reports and campaign marketing documentation and internal evaluations to identify the way in which Nal’ibali is creating a culture of reading in South Africa and its impact. It was established that through partnerships, Nal’ibali is able to create the conditions that the campaign believes are important to create a culture of reading in South Africa. These partners include local community members, businesses, government, churches, libraries, and the media. Having seen the impact the Nal’ibali model has had on the people that the campaign reached, it was concluded that if South Africa is to become a reading nation, government and civil society should continue to partner in its literacy initiatives.
An Investigation on Public Cloud Performance Variation for an RNA Sequencing Workflow

Public Infrastructure-as-a-Service (IaaS) clouds abstract various details regarding the implementation of resources provided to users. For example, users are not informed about the exact physical location of their virtual machines (VMs), the specific hardware used, the number of co-resident VMs they reside with, or the workloads that co-resident VMs are running. Detecting when VMs underperform can help identify resource contention from co-resident VMs to spur their replacement. Resource utilization metrics can be used to help classify performance of runs for use in VM performance model datasets to sample the distribution of performance outcomes in the cloud. VM performance models are key to predicting the cost of bioinformatics analyses in the public cloud. This paper investigates the performance variations of running a RNA sequencing workflow in the public cloud. We examine causes of performance variations including VM provisioning, CPU heterogeneity, and resource contention. We leverage Amazon Elastic Compute Cloud (EC2) placement groups, a feature designed to help influence VM placement to help examine how VM placement impacts performance variations. As a use case, we investigate the performance of a multi-stage bioinformatics RNA sequencing (RNA-seq) analytical workflow consisting of four distinct phases, executing in 90 minutes on average using 8-core public cloud VMs. In addition, we investigate whether Linux resource utilization metrics collected by profiling workflow runs can help identify performance implications.
AUSS: An arabic query-based update-summarization system

Update summarization is a relatively recent summarization task concerned with creating a short summary from news articles, assuming the user has already read a number of previous articles. It is useful for users who want to know the latest information about a specific topic. The availability of systems that provide update summaries saves user time and effort. Unfortunately, such resources are lacking for the Arabic language. This paper aims to provide an update summarization system that generates an update summary containing the latest information requested by a user from multiple documents. This paper provides an Arabic query-based Update-Summarization System (AUSS). We use a graph-based ranking model to represent the similarity, through a combination of lexical and semantic relations between words. Our experiments show that AUSS achieves promising results, achieving a best F-Measure of 0.5405. To test AUSS, we created a new corpus especially for Arabic Update Summaries, which we call (AUS-DB). AUS-DB contains 183 articles and their corresponding reference summaries.
Towards a Predictive Framework for Power Consumption of Jobs in HPC Facilities

As the mainstream computing technology is entering into a post petascale era, the number and complexity of their computational components is on a sharp increase. With the increased pressure to pack more components per rack, the power and system densities are growing. Recently many researchers are focusing on Power Capping to address the power challenges in current and future computing systems. The power capping can be achieved by proactively estimating the power consumption of High Performance Computing (HPC) Jobs. In this study, we present our proposed machine learning framework to predict the power consumption of Lawrence Berkeley National Laboratory (LBNL) National Energy Scientific Computing Center (NERSC) Cori supercomputer workloads. We evaluate our framework using historical data of real production jobs executed on Cori to predict the amount of power required by a given job and to apply the predictions for enabling power capping in power-limited future systems to be commissioned at LBNL or other installation sites.
Algebraic and Coalgebraic Perspectives on Interaction Laws

Monad algebras, turning computations over return values into values, are used to handle algebraic effects invoked by programs, whereas comonad coalgebras, turning initial states into environments (“cocomputations”) over states, describe production of coalgebraic coeffects that can respond to effects. (Monad-comonad) interaction laws by Katsumata et al. describe interaction protocols between a computation and an environment. We show that any triple of those devices can be combined into a single algebra handling computations over state predicates. This method yields an isomorphism between the category of interaction laws, and the category of so-called merge functors which merge algebras and coalgebras to form combined algebras. In a similar vein, we can combine interaction laws with coalgebras only, retrieving Uustalu’s stateful runners. If instead we combine interaction laws with algebras only, we get a novel concept of continuation-based runners that lift an environment of value predicates to a single predicate on computations of values. We use these notions to study different running examples of interactions of computations and environments.
Speech recognition of spoken Italian based on detection of landmarks and other acoustic cues to distinctive features

Modeling the process that a listener actuates in deriving words intended by a speaker, requires setting a hypothesis on how lexical items are stored in memory. Stevens’ model (2002) postulates that lexical items are stored in memory according to distinctive features, and that these features are hierarchically organized. The model highlights the importance of abrupt acoustic events, named landmarks, in the perception process. In this model, the detection of landmarks is primary in human perception, corresponding to the first phase of recognition. The temporal area around the landmark is then further processed by the listener. Based on the above model, the Speech Communication Group of the Massachusetts Institute of Technology (MIT) developed a speech recognition system—for spoken English—over a span of more than 20 years. In the current work (LaMIT project, Lexical access Model for Italian) the above model is applied to Italian. Exploring a new language will provide insight into how Stevens' approach has universal application across languages, with relevant implications for understanding how the human brain recognizes speech. K. N. Stevens “Toward a model for lexical access based on acoustic landmarks and distinctive features,” J. Acoust. Soc. Am., 111(4), 1872–1891 (2002).
Structural Design of Convolutional Neural Network-Based Steganalysis

Mandal, Pratap ChandraA tailored Convolutional Neural Network (CNN) model aimed at steganalysis has been designed. The model is able to acquire the complicated dependencies which are necessary for steganalysis. The usefulness of the model has been demonstrated on the state-of-the-art method S-UNIWARD at 0.4 bpp. The model achieves F1 score equal to 0.849 and detection error 0.152 which are comparable performance.
Brewing up a storm: developing Open Research culture through ReproducibiliTea

ReproducibiliTea Reading is a group of researchers and students who meet regularly to discuss Open Research practice. Our aim is to reduce the perceived costs of adopting open and reproducible practices by increasing Open Research literacy. We have developed our technical skills and promoted open practices that have been taken up in our research. We are also exploring ways in which Open Research objectives can be integrated into student learning activities and researcher incentives.
Development of Intelligent Companion Robot for Hospitals

Nowadays, the rapid development of technology has increased the use of robots in the health sector in artificial intelligence supported surgeries, personalized treatment, drug development and many other fields. In this study, it is aimed to develop a robotic system that accompanies the incoming patient and escorted the patient to the point of examination. The system will be able to keep the patient’s record and help the patient to complete their work in the hospital in a shorter time to provide sustainable and highquality health care in the hospitals. EvaRobot is used in the study. The robot operating system (ROS) was used in the system. The shortest distance algorithm was also implemented. The robotic system was designed in a way that it could get command either from touch panel or with voice. The robot operation was tested in Gazebo environment.
A Framework for Reinforcement Learning with Autocorrelated Actions

The subject of this paper is reinforcement learning. Policies are considered here that produce actions based on states and random elements autocorrelated in subsequent time instants. Consequently, an agent learns from experiments that are distributed over time and potentially give better clues to policy improvement. Also, physical implementation of such policies, e.g. in robotics, is less problematic, as it avoids making robots shake. This is in opposition to most RL algorithms which add white noise to control causing unwanted shaking of the robots. An algorithm is introduced here that approximately optimizes the aforementioned policy. Its efficiency is verified for four simulated learning control problems (Ant, HalfCheetah, Hopper, and Walker2D) against three other methods (PPO, SAC, ACER). The algorithm outperforms others in three of these problems.
Interference classification and identification of TDCS based on improved convolutional neural network

Abstract A classification and recognition algorithm based on short-time Fourier transform and convolutional neural network (STFT-CNN) is proposed to solve the common interference signal classification and recognition problem in transform domain communication systems. In this algorithm, the time-spectrum diagram of interference signals obtained by short-time Fourier transform is input into the vggnet-16 network model improved according to STFT characteristics for feature learning and training, and the classification and recognition of signals are completed. Simulation results show that the proposed algorithm for comprehensive recognition rate reached 97.7%, 6 kinds of jamming signal in low SNR circumstance still can reach more than 93% recognition rate, compared with the traditional algorithm, this method not only improves the classification recognition rate of single interference, but also improves the recognition of mixed interference ability, has the ability to resist low signal-to-noise ratio, makes the transform domain communication system can choose transform domain for anti-interference, provides theoretical basis and support for the application of convolutional neural network in anti-interference of communication system in transform domain.
A Wearable Devices-Based Home Sports Recording System for Health Management

This paper proposes a wearable devices-based home sports recording system, which is composed of wearable devices, a mobile device app, and a cloud-based management platform. The proposed system can analyze five regular sports behaviors (walking, jogging (running), hula hoops, sit-ups, and push-up) taken by the sportsperson during a day. These five regular sports behaviors can be converted into calorie consumption so that the sportsmen can further understand how much exercise they need to reach the target of calories burned in a day.
Trends of modern education: a look at the preparation of mid-level specialists under conditions of modernization of the education system

Discusses the trends and directions of modernization of the education system in the process of reforming technical education, while implementing training programs for mid-level specialists. Insufficient training of future students in the exact sciences creates difficulties in mastering interdisciplinary courses, and does not allow demonstrating a high level of development of professional modules. This fact especially affects the areas of training, where the exact sciences form the basis of technical disciplines. Modernization of educational processes should contribute to solving the problem of students’ different preparedness for the study of new knowledge and the acquisition of practical skills. A variety of forms and teaching methods used in the implementation of educational programs at various levels, changes in the requirements of regulatory documents related to changes in the structure of secondary vocational education management pose difficult tasks for teachers to find effective ways for future specialists to develop independent thinking based on fundamental professional training and practical skills solve a variety of tasks. Indicated on contradiction between innovative changes implemented in the field of education and the results of pedagogical activity. The most significant trends that affect the education system in terms of training mid-level specialists have been identified and systematized.