Reviewerly/exHarmony huggingface dataset:
STATISTICAL STEGANALYSIS OF PHOTOREALISTIC IMAGES USING GRADIENT PATHS

Purpose of the article is experimentally test the efficiency of the feature vector based on gradient paths in the spatial domain of the image. Research method is a comparison of steganalytical feature vectors based on the mean square error and the coefficient of determination obtained using SVM-regression in Matlab. The dataset is formed by automating the freeware steganoprograms that implement embedding into the spatial area of the image with sequential and pseudorandom selection of a pixel for embedding. Results of the study: the optimal parameters of the algorithm for seeking gradient paths from the point of view of embedding detection are experimentally obtained. The results of applying machine learning models are obtained and analyzed, the optimal scale of the SVM-regression kernel is determined. The computation durations of feature vectors obtaining, models training, recognizing containers are calculated. It is shown experimentally that the gradient paths feature vector is expedient to use for solving problems where it is necessary to vary the detection accuracy depending on functioning capacity of system, because the proposed feature vector allows to determine the dimension / accuracy ratio. Also, by experiment, a complex 20D vector is selected from several one-dimensional quantitative steganodetectors and the gradient paths feature vector. The effectiveness of result vector is comparable to the 686D feature vector SPAM.
NEW HARDWARE TESTING METHODOLOGY AT IHEP DATA CENTER

The modern computing center is not only about a production capacity, it is also about a stable work.Stability means not only software, the reliability of the software component but also assemblycomponents. It is important to ensure this before the production. One of the ways is testingcomponents for performance, reliability, assembly defects. In this work we will present themethodology for identifying problem points, the specific tests and some results of their work based onIHEP data center
Measurement of interpersonal trust in virtual software teams: A systematic literature review

The progress of Information and Communication Technologies has significantly promoted the relationships among people of different geographical regions. Under this novel context, new settings of software development teams arise, known as virtual teams. The objective is to identify, evaluate and synthesize reported research about the measurement of interpersonal trust (IpT) in virtual software teams (VST). We conducted a systematic review of the peer-reviewed literature published in the research area until July 2019. We reviewed 747 papers, of which 11 primary studies were considered relevant for the investigation. Most studies (8 of 11) use instruments of direct measurements (interviews and questionnaires) to measure interpersonal trust. Studies that use indirect measurements focus on different characteristics of the virtual software team (agreeableness, affective lexicon, delegation, positive tone, acceptance of knowledge, etc and others). Recomiendo: Most studies (10 of 11) report that interpersonal trust measurements improve performance aspects of virtual software teams. Among them the willingness to share information, goodwill towards others, motivation, collaboration, and effectiveness. Most studies use questionnaires or interviews, but we believe that software repository mining to obtain IpT levels will be an auspicious research trend in the future. The attribute more used to assess IpT is the developer opinion. Other attributes used are emotions, interactions between developers, biography, acceptance of knowledge, and assignment of tasks. IpT measurement is a useful tool for decision-making in VTS management, especially in agile software processes, but there is little evidence of its use.
Towards Better Visualization of Permutation Evolution

Visualization of evolutionary and swarm computation plays an important role in understanding the problem-solving strategies they represent. It allows the description of how they explore solution spaces of the underlying problems and selection of the most suitable algorithms, algorithm variants, and hyperparameters. In this work, we describe the design and conduct an initial assessment of a novel method for the visualization of permutation evolution. It shows which parts of the solution space are explored and how intensively. The proposed approach is demonstrated on a real-world permutation problem solved by a popular population-based metaheuristic algorithm, the differential evolution.
Efficacy of Macroeconomic Policies to Achieve Inclusive Growth: Evidence from Developing Countries

This paper aims to analyse the interactive role of fiscal and monetary policies in promoting inclusive growth.In this attempt, we use a panel data of 51 developing countries for the period 1995-2017 and employ state-of-the-art panel data estimation methods.The paper concludes that expansionary fiscal and monetary policies both affect economic inclusiveness in the developing region.However, it is observed that high expenditures in developing countries, which lead to debt crises, not only directly affect economic inclusiveness but also reduce the effectiveness of monetary policy.Therefore, the governments in these countries may consider cutting their spending.Thus, an increase in the money supply with a low to median level of government expenditure is a favourable policy option.
DEVELOPING A TOOLKIT FOR TASK CHARACTERISTICS PREDICTION BASED ON ANALYSIS OF QUEUE’S HISTORY OF A SUPERCOMPUTER

Empirical studies have repeatedly shown that in High-Performance Computing HPC users’ resource estimations lack accuracy. Therefore, resource underestimation may remove the job at any step ofcomputing and subsequently allocated resources will be wasted. Moreover, resource overestimationalso will waste resources. In this work, to effectively utilize the overall HPC system, we proposed anew approach to predict the required resources such as; number of required CPUs, time slots etc. fornewly submitted job. The study focused on predictive analytics tasks including regression andclassification. A supervised machine learning system, comprising several models, was trained basedon the collection of statistical data including per-job and per-user features collected from the referencequeue systems. Results indicated that adding more features to the dataset improves the predictionaccuracy. The possibility of designing a plugin to apply our machine learning system in practicalapplications was studied. A dynamically connected SLURM SPANK plugin was created that adds the“--predict-time” option and takes control on srun and sbatch commands while they are executed. Itwas found that the plugin enables practical use of our proposed machine learning system.
거리 줄이기 : 가상공간에서 음악의 사회적 역량 및 연결 능력 재형성

Ethnomusicologists have long recognized the integrative function of music performance. Studies of music performance examine social histories, politics, spiritual beliefs, among others, that underscore meaning in music performance as well as examine the acoustics and physical experience which shape the value and impact of a performance. To ethnomusicologists, the contexts of performance and identities of performers are as essential as the aesthetics and production of sound. Transformations in sociocultural contexts lead to transformations in perceptions and interpretations of musical performance and understanding these is essential to ethnomusicology’s ongoing development. In 2020, the COVID-19 pandemic forced into survival mode industries and events dependent on the movement and gathering of people. The Association for Asian Studies, for example, had to reconceptualize its annual conference, which, historically, brings together 3~4,000 individuals for in-person networking and academic presentations. Music performance at these conferences largely has been peripheral, engaging audiences in passing and enhancing, rather than centralizing, the significance of the overall event. Without downplaying its devasting impact worldwide, it is important to note that the pandemic has created opportunities to re-imagine ways by which we interact and the role of music performance therein. In this paper, I contend that this new virtual reality into which we were all forced has altered perceptions regarding the use of music, the significance of hybrid music performance space, and music performance’s impact and function. Here, I examine pre-recorded performances of Chindon’ya and contemporary Japanese fusion music at the 2021 AAS Virtual Annual Conference. Within this specific virtual space, music performances merged with organizers’ intellectual framing and the virtual ‘chuimse’ of participants. Through an examination of these performances, I will uncover the ways by which the physical location of performance worked in tandem with the virtual space to both dissolve space and time and reshape the meaning of music performance within the conference context. My goal is to demonstrate how such online music performances can eradicate perceptions of physical distance while simultaneously augmenting connectivity of participants. Such performances engage all participants—performers, producers, spectators—in an active, multisensory immersion in a space both real and imaginary.
Research objectives in teaching mathematics to students of pedagogical universities

The development of society requires modern graduates to be ready for continuous self-education, updating their knowledge and skills, the ability to solve research problems and to scientific research. One of the most important goals of the implemented educational programs that meet the requirements of the federal state educational standards of higher education is the formation of research competencies. This is especially relevant for future teachers, as they will be able to apply the acquired skills in their future professional activities. The article actualizes the issue of using research problems in the study of mathematics as a forming means of research competencies, since in the process of solving them, all components of research activity are mastered. The content features of various mathematical disciplines make it possible to do this especially effectively. The article describes the method of using research problems in the study of the discipline «Algebra and number theory», which is one of the components of mathematical and professional pedagogical education. The listing of the material is presented by a detailed description of the study of indefinite equations solved by the method of searching the remainders of dividing an algebraic expression which is containing a variable by a certain number. It is presented how a change in some parameters of the problem leads to a change in the result. The solution of such an equation is already a full-fledged research task. In addition, experience has shown that a positive cognitive result is obtained by a method that allows, based on the analysis of ready-made solutions, to learn how to construct new equations with predefined properties, which brings students to a new level of creative work. In conclusion, it is noted that, despite a number of objective difficulties associated with the low level of training of some students, the need for additional time expenditures, this is significant, not only from the point of view of forming the necessary research competencies, but also contributes to the activation of educational and cognitive activities, the motivation growth to study the discipline.
Senior High Schools Teachers' Perception of Integrating ICT into Social Studies Lessons in the New Juaben Municipality

This study employed a descriptive survey design to survey all 107 social studies teachers' perception of integrating Information and Communication Technologies (ICT) into the teaching and learning of social studies using a questionnaire in the public senior high school in the New Juaben Municipality. The results revealed that the teachers had a positive perception that incorporating ICT into the Social Studies classroom makes lessons more engaging, varied, and well-presented, as well as allows the teacher to control the instructional time effectively, aids students in comprehending what they have been taught, makes lessons more concrete, and increases students' attention in class. The findings also showed that ICT tools like computers, internet systems, educational software, printers and overhead projectors were found to be limited in the schools. Significant differences in perception were found among teachers based on age, gender and teaching experience. Multiple regression analysis found that age was a predictor of perceptions of the integration of ICT. It was, therefore, recommended that heads of schools should enable teachers to utilize ICT facilities appropriately in Social Studies lessons. Also, the Ministry of Education should set aside funds each year to retain, replace, and extend ICT infrastructure in schools. Moreover, the Ministry of Education should review the policy of the non-use of mobile phones by senior high students.
Establishment and Verification of Neural Network for Rapid and Accurate Cytological Examination of Four Types of Cerebrospinal Fluid Cells

Fast and accurate cerebrospinal fluid cytology is the key to the diagnosis of many central nervous system diseases. However, in actual clinical work, cytological counting and classification of cerebrospinal fluid are often time-consuming and prone to human error. In this report, we have developed a deep neural network (DNN) for cell counting and classification of cerebrospinal fluid cytology. The May-Grünwald-Giemsa (MGG) stained image is annotated and input into the DNN network. The main cell types include lymphocytes, monocytes, neutrophils, and red blood cells. In clinical practice, the use of DNN is compared with the results of expert examinations in the professional cerebrospinal fluid room of a First-line 3A Hospital. The results show that the report produced by the DNN network is more accurate, with an accuracy of 95% and a reduction in turnaround time by 86%. This study shows the feasibility of applying DNN to clinical cerebrospinal fluid cytology.
Statistical analysis of the distribution of phreatic eruption products in the caldera of the Golovnin volcano (Kunashir Island, Kuril Islands)

The paper presents the results of statistical processing of data on the thickness and size of the tephra fragments of andesidacite composition erupted as a result of a phreatic explosion in the caldera of Golovnin volcano about 1000 years ago. A petrochemical description of the products of volcanic activity of the crustal Golovnin volcano and its evolution process is presented based on geological and geophysical data. The relationship between the thickness of the tephra, the size of its fragments, and the distance to the eruption center was studied using the polynomial regressions of varying degrees and exponential distribution. The adequacy of the constructed models to the initial data is illustrated by determination coefficients. Tephra distribution models were constructed on the basis of a three-dimensional trend analysis. For the first time, a logarithmic model was used to describe the size of tephra fragments. The accuracy of the model used was estimated. A method for estimating the potential dispersion range of tephra fragments of a certain size was obtained. The work demonstrates the potential of mathematical statistics for describing the distribution of products of volcanic eruptions of a certain type. The results of this study are suitable for creating an information database of pyroclastite distribution across the Kuril-Kamchatka volcanic region.
Coupled Generation

Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled instances). In this article, we introduce a novel generative method, called a coupled generator, producing instances given a specific learning outcome, based on indirect and direct generators. The indirect generator uses the inverse principle to yield the corresponding inverse probability, enabling to generate instances by leveraging an unlabeled data. The direct generator learns the distribution of an instance given its learning outcome. Then, the coupled generator seeks the best one from the indirect and direct generators, which is designed to enjoy the benefits of both and deliver higher generation accuracy. For sentence generation given a topic, we develop an embedding-based regression/classification in conjuncture with an unconditional recurrent neural network for the indirect generator, whereas a conditional recurrent neural network is natural for the corresponding direct generator. Moreover, we derive finite-sample generation error bounds for the indirect and direct generators to reveal the generative aspects of both methods thus explaining the benefits of the coupled generator. Finally, we apply the proposed methods to a real benchmark of abstract classification and demonstrate that the coupled generator composes reasonably good sentences from a dictionary to describe a specific topic of interest. Supplementary materials for this article are available online.
Digital watermarking image using three-level discrete wavelet transform under attacking noise

The authentication, identification, and copyright protection can be obtained by constructing the digital image watermarking technique. Watermark robustness and imperceptibility account for the capability of the hidden watermark to survive the manipulation. The proposed paper is a robust algorithm for digital image watermarking with 3-level discrete wavelet transform (DWT) with some attacks method. The 3-level DWT method was used constants α=0.01 and 0.03 as a function of how depth the watermark inserts to the host image in the insertion and extraction process. The algorithm was evaluated using 8 bits per pixel (bpp) grayscale, 1024x1024 pixels for the host image, and 256x256 pixels for the watermark image. The method is also implemented some experimental with attacks such as gaussian, salt and pepper, blurring, and compression. The algorithm is relatively acceptable of good quality, achieves low-value mean squared error (MSE), high peak signals to noise ratio (PSNR), and structural similarity index metric (SSIM) value approach to 1. It is found that the highest image quality measurements by using α=0.03 with the attacking method of salt and pepper yield MSE=0.01, PSNR=45.6 dB and SSIM=0.95, respectively.
Uncertainty Evaluation in Surface Structured Light Measurement

Surface structured light 3D measurement is a typical non-contact method based on optics, with a wide range of applications in 3D scanning measurement field. In order to evaluate the quality of the results, the measurement uncertainty is analyzed. The principle of the surface structured light 3D measurement method is briefly introduced, and the possible influencing factors of measurement accuracy are fully analyzed. The sources of measurement uncertainty are analyzed by the method based on the statistical characteristics of measurement values, and the uncertainty evaluation models are established. Both the GUM method and MCM method are used to evaluate the measurement uncertainty, and finally, the uncertainty evaluation result for dimensional measurement of the surface structured light 3D measuring equipment is obtained.
Mathematical Model for Handling Unstable Time Series by Using a Linear Approximation Technique

Time series are typically built on basic assumptions that include stationarity, linearity and normality. The three characteristics are crucial for estimating and building time series models. Studies on time series include these assumptions. To deal with unstable time series that are based on its basis, mathematical models that are suitable for such series are adopted in this study. A nonlinear self-regression model, called the rational model, is proposed. This model is a fraction in which the numerator is the complete sine function and the denominator is an exponential self-regression model. The fixed point and limit cycle of the model are simulated and determined, and its stability is studied using a linear approximation technique.
Cybernetic Loops and Fermented Technologies of Participatory Poetry

As a response to “bookless” libraries, The Kimchi Poetry Machine is a new media art installation that reimagines how tangible computing can be utilized for feminist participatory engagement with poetry. Instead of kimchi, the machine produces small pieces of paper inscribed with poetry. When the jar is opened, instead of the pungent smells of fermented cabbage filling your nostrils, your eardrums are lulled by the luminous readings of poetry written by a variety of invited feminist poets. The small “kimchi twitter” paper poems housed inside the jar each have an invitation to tweet a poem back to the machine handle. Eight original feminist “kimchi twitter” poems were written for the machine by invited women, non-binary, and transgender poets. The machine is powered by open-source electronic prototyping platforms and reimagines how poetry can be written, listened to, and interacted with now and in the near future. The project speaks to the haunting qualities of these experiences and what Tara McPherson writes as “a dream to intervene in technological legacy systems that often route our machines away from our feminist desires.” Drawing from McPherson, The Kimchi Poetry Machine explores how machines can be feminist, poetic, and participatory for impactful change.
Spoken Language Fluency Via Live Situational Language Practice On an AI-Driven App

A Foreign Language Learning Platform is provided which uses NLP in situational dialogs and simulates active conversations to facilitate voice-based language acquisition by a user.The platform presents multiple themed situational dialogs that simulates and determines stress and interaction of live dialogs by the user with the platform.For example, the situational dialogs may include a Japanese native language speaker learning to book a taxi in English language or ordering food in English language or asking for directions in English language.At the core of the platform is a proprietary approach that bypasses a requirement for an exact response from the user allowing multiple responses to the dialog which intensifies the live interaction in the dialogs.To simulate a live language exchange as closely as possible, the NLP model references multiple alternatives so there is more than one response to a situation.Multiple language form alternatives include but are not limited to, nouns, verbs, synonyms, vocabulary, grammar, and other parts of speech.The platform can also offer hints to speakers during the dialogs via assisted hints as well as multiple testing options pre-and post-dialog.