Reviewerly/exHarmony huggingface dataset:
Design of the independent evaluation system model for educational results of future teachers

This article presents a structural model of an independent assessment of the educational results of future teachers. The properties, assumptions, and limitations of such a system are highlighted. It is shown that the system of independent evaluation of educational results is a subsystem of the system of higher pedagogical education and should have the properties of objectivity, should be an activity system, be built considering the criterion approach to evaluation, following the principle of cyclicity. The article identifies groups of educational results based on the coordination of competencies and labor actions of a teacher. The form of presentation of evaluation tools is justified. A digital platform was chosen to present the model, the cycles of evaluation of educational results, and the stages of functioning of the model of independent evaluation of educational results, students, and graduates in the field of training “Pedagogical Education” were determined.
Network Planning of the Publishing Process for the Issue of the Magazine

The object of research is the publishing process for the magazine’s release. One of the most problematic areas is the difficulty of competition between print publishers and media that provide information services on the web. Therefore, for publishers, it is especially important to ensure consistency of work within the entire publishing process based on its rational organization. In particular, the development of structural diagrams and relationships at different levels of preparation and implementation of publishing projects is relevant.Network planning methods are used in research.The obtained results suggest that the proposed structural scheme for managing the publishing process is successful. Due to the presence of the proposed links between the participants in the publishing process, the scheme provides the ability to plan each current issue of the magazine with a guarantee of the announced terms of the magazine's release.The influence of two scenarios – pessimistic and optimistic – on the deadline is analyzed. It has been established that the pessimistic forecast does not allow for the specified target date if it is 1 time per quarter. The optimistic forecast provides the possibility of a guaranteed quarterly publication of the magazine – the target date for all publishing processes before the start of the finished circulation is 89–92 days. In the case of an optimistic forecast, the network schedule for publishing the magazine showed that all works within the publishing process, before the distribution of the current issue's circulation, have a reserve of time. While in the implementation of the pessimistic scenario, only two works have a reserve of time – the design and approval of the design of the advertisement with the customer.The peculiarities of the proposed solutions are that the pessimism and optimism of the scenarios are determined in relation to those works for which everything depends only on the performer. In turn, these are exactly the works that do not require external approvals.
Fall Detection and Daily Living Activity Recognition using Machine Learning

Elder people are increasing all over the world as a result certain fall occur in their daily life. This fall lead to several severe problems. The fall may often causes injuries and in many cases it result in death of the individual. The problem should be addressed to reduce the fall. By using some Machine Learning(ML) algorithm the fall and daily living activities are recognized. The acceleration and angular velocity data obtained from the dataset are used to detect the fall and daily living activity. Body movement of the person are collected and stored in the dataset. Acceleration and angular velocity data are used to extract the time and frequency domain feature and provide them to classification algorithm. Here, Logistic regression algorithm is used for detecting the fall and living activity. It is very effective algorithm and does not require too many computational resources. It is easy to regularize and provide well calibrated predicted probabilities as output. The sensitivity, accuracy and specificity of fall detection and activity recognition is obtained as a result. The performance evaluation is made with three classification algorithm. The three classification algorithm are Artificial neural network (ANN), K-nearest neighbours (KNN), Quadratic support vector machine (QSVM). Logistic regression provides highest accuracy compared with other three algorithm.
Региональные инновационные проекты как инструменты социально-экономического развития субъектов Российской Федерации

The article examines forming the regional project “Creating an intercollegiate campus” prepared as a part of the program on training civil servants in Novosibirsk Region under a contract with the Siberian Institute of Management, of RANEPA. The President Decree no. 204 on 07.05.2018 “On national goals and strategic objectives for the period up  to 2024” aims the goal to increase the number of foreign citizens enrolled in higher education organizations and scientific organizations at least twice. The higher schools of Novosibirsk and the region has a high potential to train the regional and foreign youth people. In  Novosibirsk Region as a subject of the Russian Federation, there are 17  state, 5 private universities and 5 university branches implementing  educational programs for more than 106 thousand students including 7,800 foreign citizens. The pace of material base development of higher educational institutions in Novosibirsk Region does not correspond to growing the number of non-resident and foreign students, teachers, and scientists. Universities and academies are  located in more than 92 educational and residential buildings scattered throughout Novosibirsk and region. This fact significantly reduces the  potential of inter-university interaction and international competition. The main restriction of developing the regional scientific and educational complex is the lack of places in dormitories and their non- compliance with modern requirements. Therefore, the project “Campus  Nsk-2024” will greatly correct the existing imbalance.
A Neuro-Symbolic Approach to Structured Event Recognition.

Events are structured entities with multiple components: the event type, the participants with their roles, the outcome, the sub-events etc. A fully end-to-end approach for event recognition from raw data sequence, therefore, should also solve a number of simpler tasks like recognizing the objects involved in the events and their roles, the outcome of the events as well as the sub-events. Ontological knowledge about event structure, specified in logic languages, could be very useful to solve the aforementioned challenges. However, the majority of successful approaches in event recognition from raw data are based on purely neural approaches (mainly recurrent neural networks), with limited, if any, support for background knowledge. These approaches typically require large training sets with detailed annotations at the different levels in which recognition can be decomposed (e.g., video annotated with object bounding boxes, object roles, events and sub-events). In this paper, we propose a neuro-symbolic approach for structured event recognition from raw data that uses shallow annotation on the high-level events and exploits background knowledge to propagate this supervision to simpler tasks such as object classification. We develop a prototype of the approach and compare it with a purely neural solution based on recurrent neural networks, showing the higher capability of solving both the event recognition task and the simpler task of object classification, as well as the ability to generalize to events with unseen outcomes.
Follow the collaboration compass

Many different mechanisms are available to project managers who wish to improve collaboration in the relationship between client and contractor in a project. However, it is not necessarily clear wh...
PENGARUH LITERASI INFORMASI DAN PENGUATAN PENDIDIKAN KARAKTER TERHADAP KINERJA GURU SMP NEGERI DI KECAMATAN KUANTAN MUDIK KABUPATEN KUANTAN SINGINGI

This study aims to analyze; 1) the effect of information literacy on teacher performance at SMP Negeri Kecamatan Kuantan Mudik Kabupaten Kuantan Singingi; 2) the effect of strengthening character education on teacher performance at SMP Negeri Kecamatan Kuantan Mudik Kabupaten Kuantan Singingi; 3) the effect of information literacy and strengthening character education on teacher performance at SMP Negeri Kecamatan Kuantan Mudik Kabupaten Kuantan Singingi. This study uses a quantitative approach with the type of ex post facto research. This study examines three variables to be studied to determine how much influence the independent and dependent variables have. The population in this study were 116 teachers and the sample in this study was 90 teachers SMP Negeri Kecamatan Kuantan Mudik Kabupaten Kuantan Singingi. The data analysis technique used is descriptive statistical analysis and inferential statistical analysis. The results of the study found that; 1) there is a significant and positive influence between information literacy variables on teacher performance at SMP Negeri Kecamatan Kuantan Mudik Kabupaten Kuantan Singingi; 2) there is a significant and positive effect between strengthening character education on teacher performance at SMP Negeri Kecamatan Kuantan Mudik Kabupaten Kuantan Singingi; and 3) obtained a significant effect between the variables of information literacy and strengthening character education on teacher performance at SMP Negeri Kecamatan Kuantan Mudik Kabupaten Kuantan Singingi.
Consistency-Aware Graph Network for Human Interaction Understanding

Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much more challenging, the main cause is that recent approaches learn human interactive relations via shallow graphical models, which is inadequate to model complicated human interactions. In this paper, we propose a consistency-aware graph network, which combines the representative ability of graph network and the consistency-aware reasoning to facilitate the HIU task. Our network consists of three components, a backbone CNN to extract image features, a factor graph network to learn third-order interactive relations among participants, and a consistency-aware reasoning module to enforce labeling and grouping consistencies. Our key observation is that the consistency-aware-reasoning bias for HIU can be embedded into an energy function, minimizing which delivers consistent predictions. An efficient mean-field inference algorithm is proposed, such that all modules of our network could be trained jointly in an end-to-end manner. Experimental results show that our approach achieves leading performance on three benchmarks.
Full- Wave Modeling of the Emission of a Microwave Frequency Single Photon Source

Single photon sources are an important component in many quantum information processing systems. Despite their importance, current modeling methods predominantly focus on developing optimistic performance bounds, and are not conducive to performing engineering optimization studies. To enhance the capability of modeling methods, we propose a process to utilize the classical electromagnetic dyadic Green's function of a system as an integral component of determining the emitted photon states produced by a single photon source. Importantly, the Green's function can be readily computed using standard fullwave numerical methods, making it a viable analysis tool. We demonstrate this process by characterizing the emission of a microwave frequency single photon source that uses a transmon as a quantum emitter, and find qualitative agreement with a fabricated device that has similar design characteristics.
On the k-resultant modulus set problem on varieties over finite fields

Let [Formula: see text] be a regular variety, [Formula: see text] be an integer and [Formula: see text]. Covert et al. proved the following generalization of the Erdős–Falconer distance problem that if [Formula: see text], then we have [Formula: see text] In this paper, we provide improvements and extensions of their result.
THE INFLUENCE OF PROMOTION AND QUALITY SERVICES ON PURCHASING DECISION FOR ELECTRONIC TRANSACTION SERVICE AT ALFAMIDI KUTABUMI

The title of this research is “the impact of promotion and quality services on purchasing decision for electronic transaction service at alfamidi kutabumi”. this study aims to determine the significance of the effect of promotion and service quality partially and simultaneously on electronic transaction decisions at Alfamidi Kutabumi. This study uses quantitative methods, the number of populations is not known for sure so that based on the Rao Purba formula, the number of samples is 96.4 and is rounded up to 100 respondents by the researcher. Technical analysis of instrument test data (validity test, reliability test), classical assumption test (normality test, multicollinearity test, heteroscedasticity test), hypothesis testing (t-test, F test). From the results of the research, the X 1 variable has a significant effect on the Y variable with the regression equation Y = 9.450 + 0.230 X 1, the correlation value of 0.579 means that the two variables have a moderate level of relationship with a coefficient of determination of 33.5%. Hypothesis test obtained t arithmetic &gt; t table or (7.033 &gt; 1.984). Thus H 0 is rejected and H 1 accepted. Variable X 2 has a significant effect on variable Y with the regression equation Y = 9.294 + 0.375 X 2, the correlation value is 0.570, meaning that both have a moderate level of relationship with a coefficient of determination of 32.4%. Hypothesis test obtained t count &gt; t table or (6.859 &gt; 1.984). Variables X 1 and X 2 simultaneously have a significant effect on variable Y with the regression equation Y = 4.407 + 0.158X 1 + 0.249X 2 . The correlation value of 0.667 means that the independent variable and the dependent variable have a strong relationship with a coefficient of determination of 44.5% while the remaining 55.5% is influenced by other factors. Hypothesis test obtained value of F arithmetic &gt; F table or (38.891 &gt; 2,700). Thus H 0 is rejected and H 3 is accepted
Information Package = Message + Message Carrier. Information New Reality. Artificial Message Carrier.

This article presents the idea that a meaningful for human understanding portion of information includes the message and the carrier of that message. Such a portion of information I call here an information package.Further, this article pursues the view that the human understanding of the information package depends on both the message and on the message carrier.The artificial modification of the message carrier can essentially influence and modify the understanding and perception of the information package by a human.An informational life model is an information package wherein the message and the message carrier are so selected to create the predetermined effect on the human perception/understanding of the information which is carried by the information package.
Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks

In this paper we introduce Local Logo Generative Adversarial Network (LL-GAN) that uses regional features extracted from Faster R-CNN for logo generation. We demonstrate the strength of this approach by training the framework on a small style-rich dataset of real heavy metal logos to generate new ones. LL-GAN achieves Inception Score of 5.29 and Frechet Inception Distance of 223.94, improving on state-of-the-art models StyleGAN2 and Self-Attention GAN .
Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation

Image generation has rapidly evolved in recent years. Modern architectures for adversarial training allow to generate even high resolution images with remarkable quality. At the same time, more and more effort is dedicated towards controlling the content of generated images. In this paper, we take one further step in this direction and propose a conditional generative adversarial network (GAN) that generates images with a defined number of objects from given classes. This entails two fundamental abilities (1) being able to generate high-quality images given a complex constraint and (2) being able to count object instances per class in a given image. Our proposed model modularly extends the successful StyleGAN2 architecture with a count-based conditioning as well as with a regression sub-network to count the number of generated objects per class during training. In experiments on three different datasets, we show that the proposed model learns to generate images according to the given multiple-class count condition even in the presence of complex backgrounds. In particular, we propose a new dataset, CityCount, which is derived from the Cityscapes street scenes dataset, to evaluate our approach in a challenging and practically relevant scenario.
Innovation Strategy on Online-Based Library Services to Achieve Freedom Learning Program during Covid 19

The Covid-19 pandemic has had a significant impact on the implementation of teaching and learning activities, especially libraries as critical factor in maintaining good education. The Covid-19 outbreak has impacted the availability of instructional resources, reading materials, literacy, and other information. To ensure successful implementation, libraries must constantly innovate services through the use of information technology advances, including developing e-digital library services, online libraries, and book delivery services, as well as developing big data and digital library applications that involve libraries throughout Indonesia. Even the world through the use of a system that is frequently found in digital e-commerce platforms.
Deep Neural Network based Robust Text independent Speaker Identification

The successful implementation of DNN (Deep Neural Network) in speech recognition encouraged researchers to implement DNN in speaker recognition effectively. A deep neural network is a complex artificial neural network (ANN) which has more than two layers between the input and output. In this work DNN is receiving input as the speech signal feature vector and providing output as the estimated probabilities of the target speaker. Experiments are conducted on ELSDSR database. The inter-session variation, acoustic variability and the presence of noise are the major challenges in speaker recognition task. These issues severely reduce the performance of speaker recognition. DNN shows remarkable improvements in the recognition accuracies in these situations [1]. The acoustic and prosodic features are given as input to DNN classifier. The maximum work has been dominated in speaker recognition by only low level features such as MFCC. In this work the feature vector contains MFCC and prosodic features such as pitch, energy, formants etc., as the feature level fusion shows improved recognition accuracy and prosodic features gives improved performance in presence of noise. The results with low level and high level features are compared with combined system with feature level fusion. The recognition accuracy using combined features with DNN classifier is improved by approximately 5 % compared with only low level features.
CREPO: An Open Repository to Benchmark Credal Network Algorithms.

Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass functions. A Java library called CREMA has been recently released to model, process and query credal networks. Despite the NP-hardness of the (exact) task, a number of algorithms is available to approximate credal network inferences. In this paper we present CREPO, an open repository of synthetic credal networks, provided together with the exact results of inference tasks on these models. A Python tool is also delivered to load these data and interact with CREMA, thus making extremely easy to evaluate and compare existing and novel inference algorithms. To demonstrate such benchmarking scheme, we propose an approximate heuristic to be used inside variable elimination schemes to keep a bound on the maximum number of vertices generated during the combination step. A CREPO-based validation against approximate procedures based on linearization and exact techniques performed in CREMA is finally discussed.
IMPLIKATUR DALAM PEMBELAJARAN DARING

ABSTRACT: This study aims to determine the implications of conversations between students and students in online learning for Indonesian subjects at SMAN 1 Gedeg. To achieve the research objectives, researchers used a documentation method in screenshots of group conversations on the WhatsApp application. After conducting an analysis based on Grice's (1967) conversational implicature theory, the researcher found that during discussions in virtual conversations during learning, the implicatures used are general and special implicature which some include lexical ambiguity. Further those are used as the form of rejection, and reinforcement of opinion were found, and negotiation. Besides, students also transmit meaning through writing and pictures that aim to laugh at a speech.
An Infinitely Sustaining Piano Achieved Through a Soundboard-Mounted Shaker

This paper outlines a demonstration of an acoustic piano augmentation that allows for infinite sustain of one or many notes. The result is a natural sounding piano sustain that lasts for an unnatural period of time. Using a tactile shaker, a contact microphone and an amplitude activated FFT-freeze Max patch, this system is easily assembled and creates an infinitely sustaining piano.
Проблемы проектирования комплексной системы защиты информации облачных ресурсов в Республике Беларусь

The influence of cloud computing on the information security of information resources during their transfer or deployment on cloud platforms is considered. The relevance of the problem of protecting information resources on cloud platforms, including for the Republic of Belarus, and the need to build an integrated system for protecting cloud resources are determined. The main features of cloud computing, affecting the security of information resources, are highlighted, such as: a model of joint responsibility of the provider and cloud clients to ensure information security; protection of the cloud platform infrastructure by the provider; the use of a reliable subsystem for identifying cloud clients; reliable isolation of virtual resources of cloud clients; the need for cloud clients to re-architect their applications and use encryption; Interaction between the cloud provider and clients when organizing audit procedures, incident response and implementing identification and authentication procedures in deployed services. General approaches to the implementation of an integrated information security system for cloud resources are proposed.