Reviewerly/exHarmony huggingface dataset:
The scientific and pedagogical potential of the university: essence and structure

The article provides a theoretical underpinning for the concepts of «academic and teaching potential of the university», «potential of the academic and teaching environment of the university» and «potential of academic and teaching workers». The academic and teaching potential of the university is a set of informational, intellectual and intangible resources, conditions and opportunities established at the university for the production and accumulation of new knowledge, ideas, technologies, competencies and other productive properties that contribute to high quality of education, research and management, the image of the university and its competitiveness at the international level.&#x0D; The potential of the academic and teaching environment of the university is a set of informational, intellectual, intangible resources, conditions and opportunities created at the university for production and accumulation of new knowledge, development of competencies and other productive properties that are purposefully used in scientific and educational sphere and contribute to high quality of educational, scientific and managerial activities.&#x0D; The potential of academic and teaching staff is a set of accumulated knowledge, competencies and other productive properties that are necessary for professional (teaching, research and management) activities, and can be developed during formal, non-formal and informal education to improve the quality of teaching, research and management activities.&#x0D; The structure of academic and teaching potential of the university in which intellectual, emotional and spiritual components are specified is given.
On synthesizing Skolem functions for first order logic formulae

Skolem functions play a central role in the study of first order logic, both from theoretical and practical perspectives. While every Skolemized formula in first-order logic makes use of Skolem constants and/or functions, not all such Skolem constants and/or functions admit effectively computable interpretations. Indeed, the question of whether there exists an effectively computable interpretation of a Skolem function, and if so, how to automatically synthesize it, is fundamental to their use in several applications, such as planning, strategy synthesis, program synthesis etc. In this paper, we investigate the computability of Skolem functions and their automated synthesis in the full generality of first order logic. We first show a strong negative result, that even under mild assumptions on the vocabulary, it is impossible to obtain computable interpretations of Skolem functions. We then show a positive result, providing a precise characterization of first-order theories that admit effective interpretations of Skolem functions, and also present algorithms to automatically synthesize such interpretations. We discuss applications of our characterization as well as complexity bounds for Skolem functions (interpreted as Turing machines).
A Novel SRTSR Model for Cross-Resolution Person Re-Identification

Compared to the general person re-identification (re-id), pedestrian images in cross-resolution person re-id tasks always have variant resolutions, thus the resolution mismatching problem between low-resolution (LR) images and high-resolution (HR) images significantly limits the performance of neural networks. The general solution is to introduce interpolation methods or super-resolution (SR) module to normalize all the images to the same resolution. Unfortunately, current SR methods can only upscale the entire image to a certain scale, but fail to unify the resolution of images with different width-to-height ratio. To solve this problem, we propose a Specific-Resolution-Targeted Super-Resolution (SRTSR) embedded person re-id method. Instead of using deep CNN-based SR methods, our SRTSR network consists of a fast and light-weight feature learning module with a self-adaptive Swish (SA-Swish) as its activation function to improve its performance, and a specific-resolution-targeted upscale module to upscale the images to any specific resolution. Compared to the traditional upscale module which can only accept the upscale factor for the entire image, our upscale module can either accept two independent and arbitrary upscale factors for either height and width, e.g., $2.4\times $ for height and $3.1\times $ for width, or any specific resolution, e.g., $256\times 128$ , helping to unify the resolution of images with different width-to-height ratio. Extensive experiments are conducted and the result shows that the performance of our method is over the SR-based state-of-the-art person re-id methods on common re-id benchmarks.
Adversarial Evasion Noise Attacks Against TensorFlow Object Detection API

TensorFlow Object Detection API is an open-source object detection machine learning program that has gained recent popularity and is being used in a variety of applications. Region-Based Fully Convolutional Network (R-FCN) and Faster Region-Based Convolutional Neural Network (Faster R-CNN) are two models of the API that are very popular in object detection. This paper compares the responses of the 2 models when trained and tested under the same datasets for the detection of potholes. The 2 models are compared in their results of evaluating datasets superimposed with simple additive noises such as impulse noise, Gaussian noise and Poisson noise. These models are also tested against different noise density levels of impulse noise to see the percentage of adversarial success. This paper shows the positive effect of low-density additive noise in terms of improving the performance of the ML models such that they could be considered to be added as a new feature vector. The datasets from the referenced paper are examined to find that some improvements such as using a higher resolution camera and placing the camera on the hood of the car with no window pane in between could be done to improve the performance of the API.
CorDial: Coarse-to-fine Abstractive Dialogue Summarization with Controllable Granularity

Dialogue summarization is challenging due to its multi-speaker standpoints, casual spoken language, and limited labeled data. In this paper, we propose CorDial, aiming to improve the abstractive dialogue summarization quality and at the same time enable granularity controllability. We propose 1) a coarse-to-fine generation strategy that generates a summary draft followed by a final summary in an autoregressive way. The summary draft, which provides weakly-supervised signals, is composed of pseudo-labeled interrogative pronoun categories and noisy key phrases extracted with a constituency parser. 2) A simple strategy to control the granularity of the final summary. CorDial can predict and control the number of summary sentences for a given dialogue by predicting and highlighting different text spans from the source text. Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum. We conduct comprehensive error analysis and show competitive human evaluation results to annotated summaries.
COVID-19 and Final (State) Examination: Strategies of International and Russian Universities

Abstract COVID-19 pandemic has changed the functioning of all levels of education systems. Schools and universities had to quickly adapt and change their procedures according to conditions created by the epidemic. Long perceived as additional and backup solutions, different distance learning technologies, particularly internet-based, became the main solution for the continuation of education on all levels. Several issues have stood out particularly with this seismic change – one of those is the issue of final examinations in universities. This issue is especially crucial in Russia, where the successful passing of a state final examination, which combines majority of the topics from the study program, is a prerequisite for graduation. This paper aims to analyze and present different strategies which were used for final university examinations in different countries and universities of the world and compare it with the Russian practices.
Farsi Font Detection using the Adaptive RKEM-SURF Algorithm

Farsi font detection is considered as the first stage in the Farsi optical character recognition (FOCR) of scanned printed texts.To this aim, this paper proposes an improved version of the speeded-up robust features (SURF) algorithm, as the feature detector in the font recognition process.The SURF algorithm suffers from creation of several redundant features during the detection phase.Thus, the presented version employs the redundant keypoint elimination method (RKEM) to enhance the matching performance of the SURF by reducing unnecessary keypoints.Although the performance of the RKEM is acceptable in this task, it exploits a fixed experimental threshold value which has a detrimental impact on the results.In this paper, an Adaptive RKEM is proposed for the SURF algorithm which considers image type and distortion, when adjusting the threshold value.Then, this improved version is applied to recognize Farsi fonts in texts.To do this, the proposed Adaptive RKEM-SURF detects the keypoints and then SURF is used as the descriptor for the features.Finally, the matching process is done using the nearest neighbor distance ratio.The proposed approach is compared with recently published algorithms for FOCR to confirm its superiority.This method has the capability to be generalized to other languages such as Arabic and English.
PEMANFAATAN POWERPOINT TERINTEGRASI DENGAN I-SPRINGPRESENTER SEBAGAI MEDIA PEMBELAJARAN ICT

The use of technology in the field of education has become a fundamental necessity. One such technological involvement can be seen in the learning activities in the classroom. Many applications/software that can be used as a learning tool, two of which are Microsoft Powerpoint and ispring presenter. Microsoft Powerpoint Application integrated ispring presenter can be used as an alternative in the creation of ICT learning media. Learning Media is produced by obsermving the right design supported by various animations, audio/video making the learning process more interesting, the presentation of the material becomes more effective so that it will be easier To be understood by learners.
Modelling of Grover’s quantum search algorithms: implementations of Simple quantum simulators on classical computers

Models of Grover’s search algorithm is reviewed to build the foundation for the other algorithms. Thereafter, some preliminary modifications of the original algorithms by others are stated, that increases the applicability of the search procedure. A general quantum computation on an isolated system can be represented by a unitary matrix. In order to execute such a computation on a quantum computer, it is common to decompose the unitary into a quantum circuit, i.e., a sequence of quantum gates that can be physically implemented on a given architecture. There are different universal gate sets for quantum computation. Here we choose the universal gate set consisting of CNOT and single-qubit gates. We measure the cost of a circuit by the number of CNOT gates as they are usually more difficult to implement than single qubit gates and since the number of single-qubit gates is bounded by about twice the number of CNOT’s.
An Improved Deep Supervised Hashing Method for Hamming Space Retrieval

Due to its storage and computation efficiency, hashing has attracted extensive research on large-scale image retrieval tasks in recent years. This work focuses on Hamming space retrieval which enables the most efficient constant-time search by hash table lookups. In this paper, a novel deep supervised hashing method is proposed to generate highly concentrated hash codes based on a redesigned cross-entropy loss function. We also employ a regularizer term to mitigate the discrepancy between the Euclidean distance and the Hamming distance. Extensive experimental results demonstrate the superior performance of our method compared with existing hashing methods on two large-scale image datasets.
Research on on-line Monitoring Method of Automatic Production Line based on Industrial Internet of Things

The traditional monitoring method can only monitor part of the industrial production, and the continuity is poor, which is easy to cause the quality gap of different batches of products. To this end, this article designs a new online monitoring method for automated production lines based on the Industrial Internet of Things. First, establish an industrial field device-level network architecture, determine the functions and transmission methods between each layer of the framework, and ensure that the received data can be parsed, processed, and stored normally. Then the PLC technology is introduced to realize the logic control, analog control and motion control in the monitoring method stably, and the abnormal control chart is identified and corrected in time. The experimental results show that the quality of the products is more stable and more in line with the requirements of product standards after the application of the method in this paper.
Comparison of Current Year (2019) Meteorological Parameters on the Basis of Long Term Climate Data of Bastar District

In year 2019 may be sais as an unique year which registered rainfall of 2313 mm. Weather parameter trends of Jagdalpur (19.0854° N Latitude, 82.0183° E Longitude, elevation 552 m mean sea level) is analysed by MS – excel software package. Analysis of weather parameters viz. maximum and minimum temperature, rainfall, rainy days and relative humidity was done from 1980 to 2018 (39 years) and 2019. The results revealed that total annual rainfall and rainy days are comparatively more in 2019 as compared to past 39 years with a difference of 896 mm in rainfall with 8 more rainy days in year 2019. In case of mean maximum and mean minimum temperature, slight but effective changes are seen.
Visualizing MuZero Models

MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model, achieved state-of-the-art performance in Chess, Shogi and the game of Go. In contrast to standard forward dynamics models that predict a full next state, value equivalent models are trained to predict a future value, thereby emphasizing value relevant information in the representations. While value equivalent models have shown strong empirical success, there is no research yet that visualizes and investigates what types of representations these models actually learn. Therefore, in this paper we visualize the latent representation of MuZero agents. We find that action trajectories may diverge between observation embeddings and internal state transition dynamics, which could lead to instability during planning. Based on this insight, we propose two regularization techniques to stabilize MuZero's performance. Additionally, we provide an open-source implementation of MuZero along with an interactive visualizer of learned representations, which may aid further investigation of value equivalent algorithms.
Technical Challenges for Training Fair Neural Networks

As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging). To respond to these concerns, the community has proposed and formalized various notions of fairness as well as methods for rectifying unfair behavior. While fairness constraints have been studied extensively for classical models, the effectiveness of methods for imposing fairness on deep neural networks is unclear. In this paper, we observe that these large models overfit to fairness objectives, and produce a range of unintended and undesirable consequences. We conduct our experiments on both facial recognition and automated medical diagnosis datasets using state-of-the-art architectures.
Improved segmentation by adversarial u-net

Medical image segmentation has a fundamental role in many computer-aided diagnosis (CAD) applications. Accurate segmentation of medical images is a key step in tracking changes over time, contouring during radiotherapy planning, and more. One of the state-of-the-art models for medical image segmentation is the U–Net that consists of an encoder-decoder based architecture. Many variations exist to the U–Net architecture. In this work, we present a new training procedure that combines U–Net with an adversarial training we refer to as Adversarial U–Net. We show that Adversarial U–Net outperformes the conventional U–Net in three versatile domains that differ in the acquisition method as well as the physical characteristics and yields smooth and improved segmentation maps.
Comparative study of the Performance of Machine Learning Text Classifiers Applied to Afaan Oromo Text

The aim of this study is to find the optimal method that can be used to classify Afaan Oromo text among different classifier by using the same number of text document. Automatic text classification has been needed in many fields for a long time. Many methods are used to classify text. The performance of this classifier we used in this study is measured in terms of recall, precision and F-measure. Finally we compare the efficiencies of the Bayesian Network, Naïve Bayesian, IBK and SMO to classify Afaan Oromo text. Experimental results on the same set of Afaan Oromo documents used before show that SMO slightly outperforms the other methods. Comparison reported in this paper shows that the SMO classifier exceeds the other four Machine learning classifier.
Application of Case Teaching Method in Computer English Teaching under OBE Mode

The author of this paper analyzes and studies the problems in the current computer English teaching. At the same time, this article also discusses the application strategy of case teaching method in computer English teaching under OBE mode. Therefore, the method proposed by the author in this article can improve the teaching efficiency and teaching level of computer English to a certain extent.
From the concept of technogenic civilization to the concepts of post-culture and culture-congruent technology

&amp;nbsp; This article examines the concept of technogenic civilization outlined by the academician V. S. Stepin. The factors of its ponderability and disadvantages are subjected to analysis: F. Vacon's historical project of mastering natural processes, focus of social institutions on implementation of this project, negative consequences thereof. The author discusses the methods for overcoming such consequences. According to Stepin, the way out consists not in rejection of the scientific and technological progress, but in attribution of humanistic dimension to it; another opinion (expressed by the biologist Timofeev-Resovsky) &amp;#8210; maximum usage of modern sciences and engineering, as well as reasonable and rational management. The author formulates the original concept: from his perspective, modern culture is being replaced by a new culture, which he names &amp;ldquo;post-culture&amp;rdquo;, forming a culture-congruent technology necessary for solving problems and tasks of the post-culture. A brief description is given to the idea of post-culture and the alternatives that may have to be addressed in the course of its establishment, for example, to problem of life preservation on the Earth. The author articulates and discusses the problem, as well as constructs the pattern of transition from the concept of technogenic civilization to the concepts of post-culture and culture-congruent technology. &amp;nbsp;
Evaluation Model of Asphalt Mixture Road Performance Based on Data Mining

In order to solve the problem of evaluation error in traditional road performance evaluation model, the optimization design of asphalt mixture pavement performance evaluation model is realized by using data mining technology. Design and set up evaluation standards for the use of asphalt pavement, and use data mining technology in the network environment to obtain relevant data on asphalt mixture road performance, and use this as the input of the model. The asphalt mixture is classified, and the corresponding influencing factors are analyzed for different types, combined with the asphalt mixture type and influencing factors, the road performance evaluation index is selected, and the corresponding weight value is calculated. Combined with the input value of the model and the test results of asphalt mixture pavement performance, the comprehensive road performance evaluation data is obtained, and the final evaluation result of asphalt mixture pavement performance is obtained by comparing with the evaluation standard. Compared with the traditional evaluation model, the error is reduced by 27.4 points.
XNOR-YOLO: The High Precision of The Ball and Goal Detecting on The Barelang-FC Robot Soccer

The essential part in developing the humanoid robot which is able to play soccer is the vision system. The vision system needs to be fast and accurate in detecting the surrounding objects on the field such as the ball, goal, teammates, or even the opponent. One of the powerful methods which was able to generate the object detection quickly with high accuracy was the deep learning. However, this method proceeded a huge computation. Even if it was generated on the GPU, it would still generat a low speed of detecting. Therefore, the high precision and fast detecting of the object method need to be considered in this area. In order to overcome this problem, we proposed the combination of the XNOR-Network (XNOR-Net) towards YOLOv3 running on the GPU with the same layer configuration as the tinyYOLO. To testify the performance of this method, some experiments has been carried out in real-time application by implementing it in the NVDIA Jetson TX1 GPU. From the experiment results, this method is able to detect the object faster than other object detection in detecting the ball and goal colored by white and generated 30 FPS in detecting each object.
Development of Maritime Insight Learning Tools for Ocean Literacy in Children Aged 5-6 Years Old

Indonesia as an archipelagic country should be introduced to early childhood. Therefore, it is necessary to develop maritime insight learning tools for children in coastal areas. The objectives of this research were to develop maritime insight learning tools for ocean literacy in children aged 5-6 years old and to analyze the effectiveness of the learning tools. The learning tools were in the form of semester program, weekly and daily lesson plan, teaching materials, and learning media and were developed by following the conditions and characteristics of the coastal areas. Research &amp; Development with one group pretest-posttest design were conducted in six kindergartens with 80 students at Konawe Utara Regency, Southeast Sulawesi. The research also involved education experts, practitioners, and teachers of that areas as research subjects. Data collection instruments used were observation sheets, questionnaires, tests, and documentation. The results showed that the maritime insight learning tools is valid for use. The result of trials with t-test showed there were an increase in the ocean literacy skills of students after using maritime insight learning tools with n-Gain scores of 0.69 with the category medium on a limited scale trial and 0.70 with the high category in wide scale trial. It concluded that development of the maritime insight learning tools was effective for improving ocean literacy in children aged 5-6 years old