Reviewerly/exHarmony huggingface dataset:
Blockchain Agreement for Self-identification of Online Test Cheating: Improvement of Algorithm Performance

Due to the recent Coronavirus (COVID-19) outbreak, it is not easy for all schools to catch students’ irregularities during online classes. Research is conducted based on a transparent Blockchain that prevents such irregularities. We also want to verify the nodes participating in the test using the latest neuron engine and Blockchain technology. Nodes participating in the network for testing are reinforced using safe algorithms. This study also presents these research models and implements the test environment through the cloud environment of AWS (Amazon Web Service), a network environment. Nodes take the P2P environment and serve as online nodes during more real-time testing. The result data are also derived. These experimental environments later validate more node data. In addition, the experiment showed that the similarity and distribution levels were very good, close to "0," and the performance of the Blockchain was about 4,000 TPS, so the actual testable study was conducted. In this paper, we propose artificial intelligence neurons and verification Blockchain consensus algorithms to verify the evaluation of these online environments. In addition, proposals are made to study verification and performance data over a neural network. In the future, many diseases, etc. are expected to cause a paradigm shift in universities and educational institutions.
A Knowledge-based Express Model of Operational Plan Containing Uncertainties

Under the new confrontational mode of "Multi-Domain Operations", "change" becomes the key to victory. Uncertainty becomes an important challenge to operational command decision making. Traditional operational plans are made according to deterministic assumptions and has very limited ability to adapt to changes. Only human can adapt according to his own understanding to plans. This study proposes a knowledge-based express model of operational plan containing uncertainties. It can formally describe operational plans with uncertain starting time, optional strategies and dynamic calculated parameters, and can be executed by machine. It may be used in wargames, autonomous action monitoring, and other intelligent applications.
Creative Problem Solving as Overcoming a Misunderstanding

Solving or attempting to solve problems is the typical and, hence, general function of thought. A theory of problem solving must first explain how the problem is constituted, and then how the solution happens, but also how it happens that it is not solved; it must explain the correct answer and with the same means the failure. The identification of the way in which the problem is formatted should help to understand how the solution of the problems happens, but even before that, the source of the difficulty. Sometimes the difficulty lies in the calculation, the number of operations to be performed, and the quantity of data to be processed and remembered. There are, however, other problems – the insight problems – in which the difficulty does not lie so much in the complexity of the calculations, but in one or more critical points that are susceptible to misinterpretation , incompatible with the solution. In our view, the way of thinking involved in insight problem solving is very close to the process involved in the understanding of an utterance, when a misunderstanding occurs. In this case, a more appropriate meaning has to be selected to resolve the misunderstanding (the “impasse”), the default interpretation (the “fixation”) has to be dropped in order to “restructure.” to grasp another meaning which appears more relevant to the context and the speaker’s intention (the “aim of the task”). In this article we support our view with experimental evidence, focusing on how a misunderstanding is formed. We have studied a paradigmatic insight problem, an apparent trivial arithmetical task, the Ties problem. We also reviewed other classical insight problems, reconsidering in particular one of the most intriguing one, which at first sight appears impossible to solve, the Study Window problem. By identifying the problem knots that alter the aim of the task, the reformulation technique has made it possible to eliminate misunderstanding, without changing the mathematical nature of the problem. With the experimental versions of the problems exposed we have obtained a significant increase in correct answers. Studying how an insight problem is formed, and not just how it is solved, may well become an important topic in education. We focus on undergraduate students’ strategies and their errors while solving problems, and the specific cognitive processes involved in misunderstanding, which are crucial to better exploit what could be beneficial to reach the solution and to teach how to improve the ability to solve problems.
Summary of Object Recognition

Abstract Object recognition is one of the classic problems in computer vision. It is very important for computers to recognize the common objects in life like the human brain and the human eye. This is also an important step in the development of computers in the direction of intelligence. This article summarizes the current research on object recognition, and elaborates on several aspects of object feature extraction, object feature matching and object recognition methods.
Quantum Robust Fitting

Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by the analysis of monotone Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.
Mathematics teacher’ perceptions and adaptations in developing online classes - ideas for teacher training

Abstract The wide availability of digital tools, as well as the current global pandemic has transformed the way in which teachers and students interact. This of course will have consequences on the learning of in physics and mathematics in the medium and long term. The objective of this research is to explore what are the feelings, difficulties and adaptations of the teachers to develop online classes. We reach this goal with support on the tetrahedron model for a didactical situation, which describes the interactions between student, teacher, content, and technology. Following a qualitative methodology, a survey of 48 in-service teachers was conducted to explore how teachers’ knowledge and their use of digital tools influence teacher-student-technology interactions. The results showed that 65% of the participants feel prepared for online teaching, while 35% feel. However, there is not a big difference in the interactions they promote with the students. Teachers experiment a high level of negative emotions such as frustration and anxiety, many of these emotions were recognised in teachers feeling online teaching. The findings explain how emotional and infrastructural factors are related to the interactions in online teaching. The results have implications in physics and mathematics teacher training programs, the ways we teach physical and mathematical content, insofar we highlight those aspects into account to improve the students’ learning.
Attractiveness of Single-Agent Game: Case Study Using Sliding Puzzle

The 8-sliding puzzle game, with its characteristic, is beneficial to human logical training and well-known single-agent game worldwide. This study is interested in determining the attractiveness of the game by analyzing the inner mechanism of the game. By adopting total steps to solve the game and the success rate as the game progress model, the game sophistication can be quantified by utilizing the game refinement theory and law of motions in mind. The game's sophisticated zone, engagement, movement freedom, and entertainment depths for different levels of player can be determined for both AI (via A* algorithm) and human players. The 8-sliding puzzle game was also analyzed as a board game and sports game for its attractiveness. Such findings could contribute to the evolutionary changes in the sliding puzzle games.
How Can Hyperledger Fabric Blockchain Platform Secure Power Plants Remote Monitoring

Security and reliability are important issues in industrial remote monitoring systems. Traditional remote monitoring systems, due to their centralized structure and challenges, could not fully guarantee the security and non-manipulation of monitored data. Blockchain technology with its exclusive features in creating distributed and decentralized networks is a suitable solution for secure power plants remote monitoring. Hyperledger Fabric blockchain (HFB) as a permissioned blockchain has capabilities that can secure power plants remote monitoring. This paper describes HFB, its architecture and most prominent implementation aspects. In this study, we present a secure power plant remote monitoring platform based on HFB technology. The most critical aspects for implementation of presented HFB- based platform such as transaction size, block size, throughput of transactions per second, latency, scale and number of nodes in the network have been discussed.
AdvPL: Adversarial Personalized Learning

The data generation sources are increasing in the past few years, such as mobile devices, embedded sensors, various intelligent equipment and so forth. These increasing data sources push the deployment of deep learning models in a distributed manner. However, the traditional distributed deep learning is to build a global model over all collected data and may overlook specific components which are of vital importance to personalized users. In this paper, we propose a learning framework that allows an individual user to build a personalized model. Our framework consists of two stages, including efficient similar data selection from other users and adversarial training. Instead of selecting similar data by computing hand-designed similarity metrics, we train an auto-encoder and a GAN on individual user's data, and use them to request similar data from other users. To further improve the personalized model performance, we apply adversarial training to minimize the distribution discrepancy between requested data and user's own data. Experimental results demonstrate the effectiveness of the proposed framework.
(BV,<i>L</i><sup><i>p</i></sup>)-decomposition,<i>p</i>= 1,2, of functions in metric random walk spaces

In this paper we study the $(BV,L^p)$-decomposition, $p=1,2$, of functions in metric random walk spaces, a general workspace that includes weighted graphs and nonlocal models used in image processing. We obtain the Euler-Lagrange equations of the corresponding variational problems and their gradient flows. In the case $p=1$ we also study the associated geometric problem and the thresholding parameters.
One-Time Delegation of Unlinkable Signing Rights and Its Application

Delegation of signing rights can be useful to promote effective resource sharing and smooth cooperation among participants in distributed systems, and in many situations, we often need restricted delegation such as one-timeness and unlinkability rather than simple full delegation. Particularly, one-timesness cannot be achieved just by deploying cryptographic measures, and one needs to resort to some form of tamper-proofness or the assistance from external cloud servers for “key-disabling”. In this work, we extend the latter such that a delegatee can sign a message without the delegator’s involvement with the assumption that there exists at least one honest cloud server with secure erasure to achieve one-timeness. In this setting, if the delegator just shares their signing key between the delegatee and cloud servers, it may be problematic. It is because in the worst case, the delegator cannot know whether or not a signing key theft occurred because the signatures generated illegally are indistinguishable from the ones generated legally. To solve this, first we propose an efficient one-time delegation scheme of Okamoto-Schnorr signing. Further we combine the basic delegation scheme with anonymous credentials such that the delegator can detect the signing key theft even if one-time delegation is broken while also achieving unlinkability for both the delegator and cloud servers. Further we show its application to an e-cash scheme, which can prevent double-spending.
Moving shadow detection via binocular vision and colour clustering

A pedestrian segmentation algorithm in the presence of cast shadows is presented in this study. The novelty of this algorithm lies in the fusion of multi‐view and multi‐plane homographic projections of foregrounds and the use of the fused data to guide colour clustering. This brings about an advantage over the existing binocular algorithms in that it can remove cast shadows while keeping pedestrians’ body parts, which occlude shadows. Phantom detection, which is inherent with the binocular method, is also investigated. Experimental results with real‐world videos have demonstrated the efficiency of this algorithm.
Crowd Counting Guided by Attention Network

Crowd Crowd counting is not simply a matter of counting the numbers of people, but also requires that one obtains people’s spatial distribution in a picture. It is still a challenging task for crowded scenes, occlusion, and scale variation. This paper proposes a global and local attention network (GLANet) for efficient crowd counting, which applies an attention mechanism to enhance the features. Firstly, the feature extractor module (FEM) uses the pertained VGG-16 to parse out a simple feature map. Secondly, the global and local attention module (GLAM) effectively captures the local and global attention information to enhance features. Thirdly, the feature fusing module (FFM) applies a series of convolutions to fuse various features, and generate density maps. Finally, we conduct some experiments on a mainstream dataset and compare them with state-of-the-art methods’ performances.
Research on the Determination of Reasonable Cost Range of Power Grid Production Technological Transformation Project

Abstract This paper proposed a method to determine the reasonable cost interval of power grid production technological transformation project based on Kernel Density Estimation (KDE). The general idea of the study of reasonable cost interval is to select sample data, determine the sample estimation method, remove the outlier data, and finally calculate the reasonable unit cost interval of power grid. The general idea of the study of reasonable cost interval is to select sample data, determine the sample estimation method, remove the outlier data, and finally calculate the reasonable unit cost interval of power grid technical transformation project. Taking the actual data of the disconnector replacement project as an example, this paper made statistics of the reasonable cost range of the power grid production technological transformation project, which provides a new idea and method for the reasonable determination of the cost range in the case of large amount of data.
Learnability and positive equivalence relations

Prior work of Gavryushkin, Khoussainov, Jain and Stephan investigated what algebraic structures can be realised in worlds given by a positive (= recursively enumerable) equivalence relation which partitions the natural numbers into infinitely many equivalence classes. The present work investigates the infinite one-one numbered recursively enumerable (r.e.) families realised by such relations and asks how the choice of the equivalence relation impacts the learnability properties of these classes when studying learnability in the limit from positive examples, also known as learning from text. For all choices of such positive equivalence relations, for each of the following entries, there are one-one numbered r.e. families which satisfy it: (a) they are behaviourally correctly learnable but not vacillatorily learnable; (b) they are explanatorily learnable but not confidently learnable; (c) they are not behaviourally correctly learnable. Furthermore, there is a positive equivalence relation which enforces that (d) every vacillatorily learnable one-one numbered family of languages closed under this equivalence relation is already explanatorily learnable and cannot be confidently learnable. In addition, if there is for a positive equivalence relation a confidently learnable class, then there is also a class which is confidently but not finitely learnable.
Integration of Artificial Intelligence as a Tool for an Online Education Model

Currently, universities base all their efforts on improving student learning. To achieve this, they improve their educational methods and work in training the teaching staff in new techniques that transform traditional learning into active learning. Active learning seeks to make the student the main actor of his own education, with this, the student is free to learn what he owes and does so in a time appropriate to his needs. This need grows when the evolution of education conforms to online education models. Online education, although presented as an accessible option for all sectors of society has several complications such as high dropout and low learning rates. New technologies act as ideal assistants to solve these problems, because, through an application, they have the ability to interact with people and improve learning processes. Artificial intelligence interacts with users and simulates a person. These tools allow improving management in educational processes. This work aims to apply a Chatbot applied to a learning management system in the online education model.
Skeletal Similarity based Structural Performance Evaluation for Document Binarization

Document image binarization algorithms are usually evaluated by a pixelwise comparison. Such metrics can be misleading and do not assess the overall structure of the text in the image, thus they do not measure the character recognition capability of the binarized image. In this paper we propose the use of metrics, based on skeleton comparisons, to evaluate structural consistency of the strokes that better correspond to character readability in the binarized image. This approach divides the skeleton of two binary images to be compared (e.g. binarization result and ground truth) in small segments and measures curve similarity between those segments. We conducted experiments with manually generated data with small distortions in the image, which greatly affect common pixelwise metrics but do not hinder the readability of the text. We also binarized images of well-known document binarization datasets using classical and state-of-the-art algorithms such as Otsu's and deep learning methods. On both manually generated and real data it could be demonstrated, amongst others, that the skeletal similarity metrics are more consistent than the pixelwise comparison regarding small character distortions and better capture the readability of the binarized image. Skeletal similarity metrics can be used to complement the pixelwise comparison towards multifaceted performance evaluation for document binarization.
Gradient Sparsification Can Improve Performance of Differentially-Private Convex Machine Learning

We use gradient sparsification to reduce the adverse effect of differential privacy noise on performance of private machine learning models. To this aim, we employ compressed sensing and additive Laplace noise to evaluate differentially-private gradients. Noisy privacy-preserving gradients are used to perform stochastic gradient descent for training machine learning models. Sparsification, achieved by setting the smallest gradient entries to zero, can reduce the convergence speed of the training algorithm. However, by sparsification and compressed sensing, the dimension of communicated gradient and the magnitude of additive noise can be reduced. The interplay between these effects determines whether gradient sparsification improves the performance of differentially-private machine learning models. We investigate this analytically in the paper. We prove that, for small privacy budgets, compression can improve performance of privacy-preserving machine learning models. However, for large privacy budgets, compression does not necessarily improve the performance. Intuitively, this is because the effect of privacy-preserving noise is minimal in large privacy budget regime and thus improvements from gradient sparsification cannot compensate for its slower convergence.
Recovery of Oversaturated Pixels in a Low-Cost Solder Paste Inspection Setup

Solder paste inspection (SPI) devices have been used in the SMT assembly line in consideration of advantages such as obtaining good quality products, reducing cost, and cutting diagnosis time. In this regard, we build a low-cost SPI based on the structured light procedure. Subsequently, we have to accept a trade-off because of the economical components cause undesirable effects. One of them is oversaturated occurrence in shiny surfaces such as solder-pastes, which are considered in this article. Our impetus is to attenuate the undesirable consequences of oversaturated pixels in the final step, which assess solder paste quality and detect height defects based on 3D reconstruction. The phase-shifting profilometry, one of the structured light method for 3D reconstruction, is considered in this work. This paper aims to apply image processing algorithms to captured images and assessing phase-map in the interest of conquering undesirable effects and recovery of the oversaturated pixels.
Creation of a Dependency Tree for Sentences in the Kazakh Language

In the semantico-syntactic analysis of great importance is understanding of its formal structure. For this, in the text it is necessary to distinguish units of lexical meaning and designate the types of relations between them. The dependency tree is an indispensable tool for parsing sentences and determination of hierarchical relationships between the main components in it. In this work, an algorithm for constructing a dependency tree for sentences in the Kazakh language using the filter method is proposed. The dependency tree was created on the basis of the spinning tree from the oriented graph constructed according to the rules of syntactic relationship in the Kazakh language.
Determining the vulnerability of educational institutions in terms of the requirements of the program “My city to prepare”

Abstract The article considers the risks that pose a danger to educational institutions in the aspect of the requirements of international programs to improve the sustainability of cities. Risk-generating factors that threaten the sustainability of educational institutions can be both external and internal, associated with the social interaction of the participants of this organization. The authors believe that one of the most significant factors in the sustainability of an educational institution is the state of anti-terrorism security. The main principles for ensuring anti-terrorist security are: elimination of the source of danger from the system; exclusion of conditions for the commission of terrorist acts. The article describes the criteria for anti-terrorism protection, a small number of which, when attesting the anti-terrorism security of an educational institution on its own, is determined by the capabilities of the educational institution itself. Recommendations are given on creating a plan to reduce the vulnerability of an educational institution.