Reviewerly/exHarmony huggingface dataset:
Phygital-Technologies as a Tool for Developing Efficient Communication with Today’s Customer

A key aspect of marketing strategy today is an ability to attract new generations of customers. The present day generation - digitalnatives (digital aboriginals, generation Z) is becoming a fast growing target group of consumer market and future global online-commerce. On the basis of Russian and overseas research on social – psychological and behavioral characteristics of generation Z the author studies the approach to building communication through which brands could broadcast their messages to customers and reveal their values. Phygital-technologies mean communications showing specificity, interests and preferences of generation Z, which provide an opportunity to create a new eco-system by using key advantages of traditional and digital tools. The new generation digitalnatives was born and grew up at the age of the Internet, gadgets and digital technologies. It is more useful to get in touch with this groupd through on-line platforms by developing a unique entertaining, laconic content, which could get within the eye-sight of Z-customers and comply with their interests and behavioral characteristics. However, the efficient two-sided communication implies not only on-line space, it includes off-line environment. The author shows that applying the approach, which could provide mutual integration of different channels of communication into a unique strategic system is an effective way of attracting the attention of new generations.
Modal completeness of sublogics of the interpretability logic $\mathbf{IL}$

We study modal completeness and incompleteness of several sublogics of the interpretability logic $\mathbf{IL}$. We introduce the sublogic $\mathbf{IL}^-$, and prove that $\mathbf{IL}^-$ is sound and complete with respect to Veltman prestructures which are introduced by Visser. Moreover, we prove the modal completeness of twelve logics between $\mathbf{IL}^-$ and $\mathbf{IL}$ with respect to Veltman prestructures. On the other hand, we prove that eight natural sublogics of $\mathbf{IL}$ are modally incomplete. Finally, we prove that these incomplete logics are complete with respect to generalized Veltman prestructures. As a consequence of these investigations, we obtain that the twenty logics studied in this paper are all decidable.
Peer Influence and Performance Task of Senior High School Students

The Peer group is composed of equal status, where students within a given peer group are of the same age and have the same social status. The performance task is any learning activity or assessment that asks students to perform to demonstrate their knowledge, understanding, and proficiency. Performance tasks as part of the student’s performance in school, probably this might be influenced by peers of the students. The study used a descriptive design to determine the influence of peer on the performance task of the students. A total of 243 Senior High students of Jagobiao National High School answered the Likert scale questionnaire, an instrument used by the researcher to conduct the study. The study used weighted mean and chi-square to determine whether peer influence has an association with students’ performance tasks. The results show that peers can influence students’ performance tasks. It was shown in the study that students get a low grade from skipping classes together with their peers. The study concluded that there is an association between peer influence and academic performance of the students. Therefore, peer plays a big role in student’s academic engagement and academic success.
Environmental safety management of used packaging of pesticides and other dangerous substances

In Ukraine, there is a problem of environmentally hazardous handling of used packaging of pesticides and other toxic chemicals and hazardous waste which can pose a serious threat to human health and the environment.The paper presents modern methods of disposal of pesticide packaging in the world and the situation in Ukraine.The Ukrainian and world environmental legislations regulating the problem of pesticide packaging management are analyzed.The scheme of processing and disposal of pesticide containers is proposed.Types and materials from which pesticide packaging is made are analyzed.Methods and equipment for washing of pesticide containers and handling of washing solutions are given.As a result, it is recommended to intensify the process of organizing a system for collecting, certifying and setting up licensed pesticide and other hazardous waste disposal facilities, as well as ensuring state support and the use of the best international experience.
Semi-supervised Extractive Question Summarization Using Question-Answer Pairs

Neural extractive summarization methods often require much labeled training data, for which headlines or lead summaries of news articles can sometimes be used. Such directly useful summaries are not always available, however, especially for user-generated content, such as questions posted on community question answering services. In this paper, we address an extractive summarization (i.e., headline extraction) task for such questions as a case study and consider how to alleviate the problem by using question-answer pairs, instead of missing-headline pairs. To this end, we propose a framework to examine how to use such unlabeled paired data from the viewpoint of training methods. Experimental results show that multi-task training performs well with undersampling and distant supervision.
Sentiment Analysis of User Responses of Tourist Services

In the research the method of tonality analysis was used to find thoughts in the text and determine their properties. The tonality is considered the position of the author on the topic (usually "positive" or "negative") Content analysis of user's responses using corps managers has been applied. This gives grounds to claim that based on the proposed technology it is possible to track the users responses of tourist services and to formulate recommendations for tourism firms for improvement of their activity. The application of sentiment analysis is aimed to improve the tourism industry.
Use of Big Book Learning Media to Improve Students’ Beginning Reading Skills in Primary Schools

This study is motivated by the fact that there are still many lower grade students who have not been able to read fluently. Big book learning media can be used by teachers to teach beginning reading to lower grade students. The purpose of this study is to describe the use of big book learning media to improve beginning reading skills. The study employed a qualitative descriptive method. The subjects were second-grade primary school students who were still unable to read. The research was conducted in one of the primary schools in Tasikmalaya City. The results revealed that students were more enthusiastic in learning to read using big book learning media, which could be seen from an increase in students' reading skills in each meeting. By using big book learning media, in addition to learning to read through a series of letters, students were able to learn to read through images followed by the writing. Therefore, big book learning media can be used to improve students' reading skills.
Sistem Informasi Layanan Puskesmas Berbasis Web

Puskesmas must always improve their quality so that the services provided are faster and more effective. One of the efforts in improving the quality is the construction of information systems regarding types of services, programs, operating hours, and availability of inpatient rooms. Puskesmas Keranggan is one of the Puskesmas where the use of inpatient rooms is still done manually, without a system, so that medical personnel in different health services find it difficult to know the exact number of rooms that are still available. Therefore, Puskesmas Keranggan built a web-based information system that can be accessed quickly and easily using internet. The information system was built using the Laravel framework, Bootstrap, and MySQL. The web application is ready to use and has been built in accordance with the requirements.
Efficient rain–fog model for rain detection and removal

Rain streaks blur and degrade the color fidelity of images. This affects road segmentation accuracy in advanced driver assistance systems. To address this problem, a method combining the gradient property with the rain–fog model is proposed to remove rain streaks in single images. The rain detection model is used to detect rain streaks, which aids training for rain removal and determines if rain streaks exist in images. The rain removal model is based on trained patches with the highest proportion of rain streaks in the high-frequency layer for low-cost computation. In order to recover nonrain images without oversmoothing, the gradient property is used prior to handling the overlapping rain streaks in the background layer. The rain–fog model is employed to remove veiling effects and moderately enhance background scenes. Our results showed that this method outperforms existing methods in regard to visual performance and quantitative aspects.
Multi-Scale Aggregation Using Feature Pyramid Module for Text-Independent Speaker Verification

Currently, the most widely used approach for speaker verification is the deep speaker embedding learning. In this approach, we obtain a speaker embedding vector by pooling single-scale features that are extracted from the last layer of a speaker feature extractor. Multi-scale aggregation (MSA), which utilizes multi-scale features from different layers of the feature extractor, has recently been introduced and shows superior performance for variable-duration utterances. To increase the robustness dealing with utterances of arbitrary duration, this paper improves the MSA by using a feature pyramid module. The module enhances speaker-discriminative information of features from multiple layers via a top-down pathway and lateral connections. We extract speaker embeddings using the enhanced features that contain rich speaker information with different time scales. Experiments on the VoxCeleb dataset show that the proposed module improves previous MSA methods with a smaller number of parameters. It also achieves better performance than state-of-the-art approaches for both short and long utterances.
Modification of Fisher-Hayter Test

Pair wise comparison are applied to answer which group differs when the null hypothesis in ANOVA test is rejected.This study modified the conventional Fisher-Hayter test to test the significant difference between the means when ANOVA test is significant.The conventional Fisher-Hayter test use MSE due to error while the modified Fisher-Hayter test use MSE due to treatment with different degrees of freedom.The result from modified Fisher-Hayter test are then compared with Fisher least significant difference, conventional Fisher-Hayter test and Tukey Honest Significant Different test.The result reveals that the modified Fisher-Hayter test showed comparable performance compared to the conventional Fisher-Hayter test and can be used as alternative of the pairwise comparison procedures.
Polynomial Identification of $$\omega $$-Automata

Abstract We study identification in the limit using polynomial time and data for models of $$\omega $$ -automata. On the negative side we show that non-deterministic $$\omega $$ -automata (of types Büchi, coBüchi, Parity or Muller) can not be polynomially learned in the limit. On the positive side we show that the $$\omega $$ -language classes $$\mathbb {IB}$$ , $$\mathbb {IC}$$ , $$\mathbb {IP}$$ , and $$\mathbb {IM}$$ that are defined by deterministic Büchi, coBüchi, parity, and Muller acceptors that are isomorphic to their right-congruence automata (that is, the right congruences of languages in these classes are fully informative) are identifiable in the limit using polynomial time and data. We further show that for these classes a characteristic sample can be constructed in polynomial time.
ZIBA: A Novel Algorithm Based on Zero-Sum Game For Document Image Binarization

In this paper, we proposed an algorithm based on game theory for document image binarization. For designing the algorithm, we formulate document image binarization using zero-sum game in which foreground of the image with textual shapes and background of the image are considered as two players. Then the best distance between the players is calculated. Finally, a global threshold is set for binarization of the image. The proposed algorithm is called Zero-sum Image Binarization Algorithm (ZIBA). The experimental results show that ZIBA has admissible precision for document image binarization.
Monte Carlo Analysis of Gathering and Scattering Behavior Generated by Gravitational Particle Swarm Algorithm

In this study, we propose a novel multimodal optimization algorithm, gravitational particle swarm algorithm (GPSA), by replacing the global feedback term of a classical particle swarm optimization with a term that introduces inverse-square gravitational force between particles. We analyze the search behavior of these particles by Monte-Carlo simulation, demonstrating that the particles often gather around the global optima but seldom scatter away from them. Furthermore, our GPSA’s performance is evaluated on benchmark functions, showing that it found over 95.5% and 78.2% of the global optima over the course of multiple runs for two- and three-dimensional benchmark functions, respectively. At the same time, it found all global optima at least 62% and 5% of the separate runs, whereas the existing methods almost entirely failed to do.
A Curry-style Semantics of Interaction: From Untyped to Second-Order Lazy $$\lambda \mu $$-Calculus

Abstract We propose a “Curry-style” semantics of programs in which a nominal labelled transition system of types, characterizing observable behaviour, is overlaid on a nominal LTS of untyped computation. This leads to a notion of program equivalence as typed bisimulation. Our semantics reflects the role of types as hiding operators, firstly via an axiomatic characterization of “parallel composition with hiding” which yields a general technique for establishing congruence results for typed bisimulation, and secondly via an example which captures the hiding of implementations in abstract data types: a typed bisimulation for the (Curry-style) lazy $$\lambda \mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>λ</mml:mi><mml:mi>μ</mml:mi></mml:mrow></mml:math> -calculus with polymorphic types. This is built on an abstract machine for CPS evaluation of $$\lambda \mu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>λ</mml:mi><mml:mi>μ</mml:mi></mml:mrow></mml:math> -terms: we first give a basic typing system for this LTS which characterizes acyclicity of the environment and local control flow, and then refine this to a polymorphic typing system which uses equational constraints on instantiated type variables, inferred from observable interaction, to capture behaviour at polymorphic and abstract types.
DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation

Depth estimation is an active area of research in the field of computer vision, and has garnered significant interest due to its rising demand in a large number of applications ranging from robotics and unmanned aerial vehicles to autonomous vehicles. A particularly challenging problem in this area is monocular depth estimation, where the goal is to infer depth from a single image. An effective strategy that has shown considerable promise in recent years for tackling this problem is the utilization of deep convolutional neural networks. Despite these successes, the memory and computational requirements of such networks have made widespread deployment in embedded scenarios very challenging. In this study, we introduce DepthNet Nano, a highly compact self normalizing network for monocular depth estimation designed using a human machine collaborative design strategy, where principled network design prototyping based on encoder-decoder design principles are coupled with machine-driven design exploration. The result is a compact deep neural network with highly customized macroarchitecture and microarchitecture designs, as well as self-normalizing characteristics, that are highly tailored for the task of embedded depth estimation. The proposed DepthNet Nano possesses a highly efficient network architecture (e.g., 24X smaller and 42X fewer MAC operations than Alhashim et al. on KITTI), while still achieving comparable performance with state-of-the-art networks on the NYU-Depth V2 and KITTI datasets. Furthermore, experiments on inference speed and energy efficiency on a Jetson AGX Xavier embedded module further illustrate the efficacy of DepthNet Nano at different resolutions and power budgets (e.g., ~14 FPS and &gt;0.46 images/sec/watt at 384 X 1280 at a 30W power budget on KITTI).
Practical Machine-Checked Formalization of Change Impact Analysis

Abstract Change impact analysis techniques determine the components affected by a change to a software system, and are used as part of many program analysis techniques and tools, e.g., in regression test selection, build systems, and compilers. The correctness of such analyses usually depends both on domain-specific properties and change impact analysis, and is rarely established formally, which is detrimental to trustworthiness. We present a formalization of change impact analysis with machine-checked proofs of correctness in the Coq proof assistant. Our formal model factors out domain-specific concerns and captures system components and their interrelations in terms of dependency graphs. Using compositionality, we also capture hierarchical impact analysis formally for the first time, which, e.g., can capture when impacted files are used to locate impacted tests inside those files. We refined our verified impact analysis for performance, extracted it to efficient executable OCaml code, and integrated it with a regression test selection tool, one regression proof selection tool, and one build system, replacing their existing impact analyses. We then evaluated the resulting toolchains on several open source projects, and our results show that the toolchains run with only small differences compared to the original running time. We believe our formalization can provide a basis for formally proving domain-specific techniques using change impact analysis correct, and our verified code can be integrated with additional tools to increase their reliability.
Analysis of the Influence of Sensitivity on the Quality of Regulation in Digital Control Systems

The article describes the problem of the influence of sensitivity on the quality of control in digitalcontrol systems at the premier synthesis of approaches used in the theory of electrical circuits and the theory ofautomated control. The analysis of the amplitude-frequency characteristics of the classical system of automaticcontrol, a mathematical model of this system. After that, the obtained model was used to analyze the sensitivityand influence of the initial system settings on the quality control of the industrial process.
Manage big data using optical networks

To complete the demand of efficient, secure, reliable,scalable, secure and multitenancy datacenters .We are presenting an architecture for optical datacenters which provides the solution of above problems along with high speed interconnection between inter and intra data center networks. So it can efficiently fulfill the requirement of “on demand service” by various corporates.Datacenter in optical network will be able to provide a multi tenant datacenter with distributive environment.
Accelerating complex control schemes on a heterogeneous MPSoC platform for quantum computing.

Control and readout of superconducting quantum bits (qubits) require microwave pulses with gigahertz frequencies and nanosecond precision. To generate and analyze these microwave pulses, we developed a versatile FPGA-based electronics platform. While basic functionality is directly handled within the FPGA, guaranteeing highest accuracy on the nanosecond timescale, more complex control schemes render impractical to implement in hardware. To provide deterministic timing and low latency with high flexibility, we developed the Taskrunner framework. It enables the execution of complex control schemes, so-called user tasks, on the real-time processing unit (RPU) of a heterogeneous Multiprocessor System-on-Chip (MPSoC). These user tasks are specified conveniently using standard C language and are compiled automatically by the MPSoC platform when loaded onto the RPU. We present the architecture of the Taskrunner framework as well as timing benchmarks and discuss applications in the field of quantum computing.
DEVELOPMENT OF PID LIKE FLC ALGORITHM FOR INDUSTRIAL APPLICATIONS

Now a day, in many industries different types of controllers (PD, PID, PLC, FLC etc.) are used. One of them is fuzzy logic controller. Here we develop a PID like fuzzy logic controller for industrial application, such application is water purification plant. For developing the PID like FLC, first we have to design a PID algorithm than we develop an algorithm for fuzzy logic controller. By comparing this two of controller we will develop a PID like FLC. A simple PID controller is sum of three type of controller proportional, integral and derivative controller, after simulated on MATLAB. Same cases we can be develop a structure of FLC for water purification plant. In the water purification plant raw water or ground water is promptly purified by injecting chemical rates at rates, related to water quality [13][2]. The feed of chemical rate judged and determined by the skilled operator. Here we try to develop an FLC algorithm so that the feed rate of coagulant is can be judged automatically without any skilled operator, than compose a PID like FLC for water purification plant process.
Students’ Social Interaction Competence: Essence, Structure and Content

The article justifies the necessity to form students’ social interaction competence in educational process. Contrary to previous studies, this phenomenon is examined in the aspect of group activity aimed to achieve a common objective and to develop positive interpersonal relations between the participants. The paper proposes the author’s original interpretation of essence, structure and content of students’ social interaction competence, which includes value-motivational, cognitive, technological and reflexive components.
Tunability of auto resonance network

This paper proposes a new type of Artificial Neural Network called Auto-Resonance Network (ARN) derived from synergistic control of biological joints. The network can be tuned to any real valued input without any degradation of learning rate. Neuronal density of the network is low and grows at a linear or low order polynomial rate with input classification. Input coverage of the neuron can be tuned dynamically to match properties of input data. ARN can be used as a part of hierarchical structures to support deep learning applications.