Reviewerly/exHarmony huggingface dataset:
Shortest Distance Lattice Cryptographic Algorithm for Data Points Using Quantum Processors

In this paper, a Lattice cryptographic algorithm has been proposed by computing the shortest distance between points in clusters. It classifies points which are dependent and independent. Subgrouping of these points as clusters are formed comprising of parent and multiple child points. Proximity based on similarity is computed as a measure of distance between points. The distance between parent points and child points are also computed and arranged in increasing order. Another subgrouping is made with the points based on this arrangement. This module is called as the cryptographic unit. Later data points are grouped separately and distances are computed for various combinations and arranged in increased. The points are encrypted and later decrypted based on simple matrix operations. The average processing time of SDL has been 55 s and it gives a reduction of 13% as compared to ECC and 23% as compared to RSA.
TIP: Tangible Interactive Projector with Projection Touch Tracking and Spatial Augmented Reality

Projection interaction is a new method of human-computer interaction which creates viewable screen in the actual space and can be directly touched and interacted by users, which can be universally used in the field of robotics and consumer electronics. In this work, we propose a new device named tangible interactive projector to effectively enable projection touch tracking and spatial augmented reality, in which we use object detection, instance segmentation and keypoints detection to improve the efficiency of touch detection and reduce the probability of misrecognition. We implement the spatial augmented reality method with the combination of the projection unit, computing unit, and perception unit. The perception unit consists of a high definition camera and an infrared camera, providing more modal data for touch recognition, which also makes it possible to display the projection adaptively according to the type and shape of the object in the field view using the bounding box output by object detection. The experiments show that our proposed equipment and interactive method perform significantly better than existing equipment or methods.
An Integrated Time-Aware Collaborative Filtering Algorithm

Collaborative filtering is the type of algorithm that has the most variants and is currently the most widely used in recommender systems. The advantage is that it does not require much domain knowledge, and can help to get better recommendation by machine learning. In this paper, a time-aware user&item-based collaborative filtering algorithm and a time-aware latent factor model recommendation algorithm are proposed. Because the two types of algorithms reflect the time dynamics in different ways, the effects are also different. We then put forward an integrated time-aware collaborative filtering algorithm ITCF by integrating them together to compensate for each other's shortcomings. The experimental results show that the proposed two single time-aware algorithms can both obtain good recommendation effect, and the integrated algorithm ITCF gets higher accuracy than the single models with time factors.
Dista nce learning as a new challenge to the educati on system

Distance learning is currently one of the most topical problems at the intersection of technical and pedagogical disciplines. The widespread introduction of distance learning in secondary and higher education has revealed a significant layer of problems that should be addressed by information technologies specialists, teachers, scientists, psychologists in cooperation with officials. The article discusses the main problems that arose during the transition to distance learning, the solution of which will raise the quality of education to a new level, including after the end of the new type of coronavirus pandemic. The authors highlight the organizational, technical and social problems in the process of organizing distance learning, the totality of which can be considered as a challenge to the education system.
Unsupervised streaming anomaly detection for instrumented infrastructure

Structural health monitoring (SHM) often involves instrumenting structures with distributed sensor networks. These networks typically provide high frequency data describing the spatiotemporal behaviour of the assets. A main objective of SHM is to reason about changes in structures’ behaviour using sensor data. We construct a streaming anomaly detection method for data from a railway bridge instrumented with a fibre-optic sensor network. The data exhibits trend over time, which may be partially attributable to environmental factors, calling for temporally adaptive estimation. Exploiting a latent structure present in the data motivates a quantity of interest for anomaly detection. This quantity is estimated, sequentially and adaptively, using a new formulation of streaming principal component analysis. Anomaly detection for this quantity is then provided using conformal prediction. Like all streaming methods, the proposed method has free control parameters which are set using simulations based on bridge data. Experiments demonstrate that this method can operate at the sampling frequency of the data while providing accurate tracking of the target quantity. Further, the anomaly detection is able to detect train passage events. Finally, the method reveals a previously unreported cyclic structure present in the data.
Benefits of using digital image correlation systems for advanced structural assessment

&lt;p&gt;New developments in civil engineering in the area of innovative high-performance materials and sophisticated geometrical designs generate the need for advanced measurement and monitoring systems. Based on years of utilizing digital image correlation (DIC) in a diverse field of applications, the advantages and disadvantages of DIC measurements are highlighted, by means of a selection of various experimental tests conducted by the authors. Recommendations for a general methodology for the utilization of DIC systems are presented. For this purpose, the beneficial advantages in the case of a variety of different chosen test setups, as well as the originated evaluation data, are shown. Such a type of non invasive optical measurement system enables the user to gather data even in challenging conditions, where a brittle failure of the specimen has to be expected and conventional measurement equipment, like LVDTs or displacement transducers, reach their limit. In addition, a simplified model for a 3D measurement setup calculation is presented.&lt;/p&gt;
A systematic approach to literature analysis: traveling through stories

Abstract Travel literature has captured humanity’s imagination ever since the emergence of famous works such as The Wonders of The World by Marco Polo and The Journal of Christopher Columbus. Authors in this genre must process large and diverse volumes of data (visual, sensory, and written) obtained on their trips, before synthesizing it humanly in such a way as to move and communicate personally with the reader, without losing the factual nature of the story. This is the ultimate goal of the natural language processing (NLP) field: to process and generate human–machine interaction as naturally as possible. Hence, this article’s purpose is to analyze and describe a nonfictional literary text, which is a type of documentary text that contains objective, qualitative, and quantitative information based on evidence. In this analysis, traditional methods will not be used. Instead, it will leverage NLP techniques to process and extract relevant information from the text. This literary analysis is a new kind of approach that encourages further discussions about the methodologies currently used. The proposed methodology enables exploratory analysis of both individual and unstructured corpus databases while also allowing geospatial data to complement the textual analysis by connecting the people in the text with real places.
Measuring Mobile Experience in Chinese Language Learning

After mapping dimensions and directions of experience in applying mobile technologies to Chinese language learning, another key task is to measure different levels of experience in terms of its dimensions and directions of learners' experience in leveraging mobile technologies, mobile learning apps in particular. Equipped with the proposed 6E-r framework of mobile experience, this chapter provides indicators and criteria in measuring mobile learning experience in applying mobile technologies to Chinese language learning around the world. This chapter also developed an online questionnaire for future research reference in order to successfully gauge the different levels of experience in applying mobile technologies to Chinese language learning. Ultimately, this chapter attempted to resolve the research question, that is, how mobile learning experience is enhanced by narrowing the gap between the normative experience and the empirical experience.
A Neural Network Based Image Steganography Method using Cyclic Chaos and Integer Wavelet Transform

Steganography is the technique of hiding confidential information inside a cover in order to deceive attackers. Neural Networks (NN) become very popular and effective to train a model and get accurate result. In this article we used unsupervised NN to select and train model for cover image selection. Chaotic structures and transformation techniques like the integer wavelet transform in Steganography have good result due to minimum data loss. In this paper, we propose a cyclic chaos algorithm that produces Pseudorandom Number Generator (PRNG), use seeds for embedding purpose. We also used the Integer Wavelet Transform (IWT) to minimize data loss and return a high-quality encoded image. Our proposed system can embed colored image. Furthermore, evident and easily understandable as well as executable. We found that our approach outperforms other recent steganography approaches in terms of improved embedding time, reduced complexity, and improved decoded image quality with increased hiding capability.
An improved sine cosine algorithm for assignment problem

Abstract The assignment problem is a NP-hard combinatorial optimization problem, where assignees are being assigned to perform tasks. This paper presents an improved sine and cosine algorithm (ISCA) to solve this problem. Nonlinear decreasing inertia weight, chaotic map and greedy strategy are added to the original sine cosine algorithm (SCA) to enhance the ability of focusing on optimal and avoiding local optima. Simulation results show that the proposed algorithm can get more competitive solutions when compared with differential evolution (DE), particle swarm optimization (PSO) and original SCA.
Digital Competence of the Educational Environment of the St. Petersburg Public School

Currently, a new digital educational environment is being formed, changing not only the content, but also pedagogical technologies. There are also new requirements for the digital competence of all participants in the educational process. As a result, there is an increasing need for educational organizations for specialists who are able to organize and support the educational process using the resources of various educational platforms, e-learning, online games, digital textbooks, etc. The purpose of the article is to familiarize readers with the issues of introducing a digital educational environment in a modern general education school, which is used to solve various educational and organizational tasks. During studying the topic, the following methods of the empirical level were used: description, comparison, measurement. The analysis of regulatory documents and a survey among users of existing digital educational environments were also used. At the end, the modeling of the digital educational environment for a specific educational organization was carried out. The result of the study is a designed structure of the environment that reflects the interests of all participants in the educational process. The tools for creating each component of it are also considered. The possibilities of each tool are revealed for students and their parents, teachers and the administration of an educational organization. The composition and user group of the environment components are described. Special attention is paid to the advantages and disadvantages of implementing the environment when implementing mixed and distance learning within the individual training route of students at any stage. Since the openness of the environment should take into account social mechanisms – competition, cooperation, mutual learning and mutual evaluation. In addition, the specific features of the digital educational environment at different levels are reflected. The issues of high-quality content and openness of the digital educational environment are raised.
A sentiment classification algorithm of Bi-LSTM model fused with weighted word vectors

As a hot topic in the field of natural language processing, sentiment classification has always attracted much attention. With more and more comments from users in different fields, it is necessary to build a more accurate and efficient sentiment classification model. The traditional distributed word vector representation method cannot well represent the ability of words to distinguish text and cannot capture the emotional information in sentences. We use Word2vec to obtain semantic information between words, obtains word distribution representation characteristics, and then combines emotional dictionary to judge word emotional information, uses TF -IDF algorithm to construct the word distribution characteristics of weighted word vectors, so as to effectively capture the emotional information of contextual sentences. Finally, Combining the bi-directionallong short-term memory network (Bi-LSTM) model can get more accurate sentiment classification results. The experimental results show that after selecting appropriate model parameters, the weighted word vector method combined with emotional information and the distributed feature vector method based on semantic relations have improved accuracy and other indicators; through the feature representation of the weighted word vector Methods Compared the Bi-LSTM model with other text classification models, it is concluded that the feature representation method of the weighted word vector has improved the classification results in each model, and the classification effect is the best in the Bi-LSTM model.
Hole-Filling Method Using Nonlocal Non-convex Regularization for Consumer Depth Cameras

Recovery of correct depth values in missing regions of depth maps captured using consumer depth cameras is a challenging problem in the field of computer vision. Applications like robotic vision, automatic navigation and imperfection of captured data with existing sensors, hole-filling in depth maps is a significant research area. Imprecision of estimated depth values of missing regions increases especially at depth discontinuities. To overcome this remarkable complication we model the estimated depth map as a non-local extension of discontinuity-adaptive MRF. The non-convex energy function can be optimized by exploiting the graduated non-convexity algorithm. Experiments with both synthetic and real-world data demonstrate the superiority of the proposed approach.
Interacting with More Than One Chart: What Is It All About?

Visual objects made of multiple views, e.g., dashboards and small multiples, are taking the scene in information communication and visual patterns design, but still vague are the studies that try to abstract away from characterizing them at the level of their single charts, and rather focus on their structural characteristics and the resulting interactions with their multi-view ensemble seen as a whole. In this paper, we are proposing this unified view through a multi-dimensional wheel, on the strand of Cairo's wheel made for infographics, devised for the identification, the analysis, and the evaluation of design patterns for multiple views.
Functional verification of cyber-physical systems containing machine-learnt components

Abstract Functional architectures of cyber-physical systems increasingly comprise components that are generated by training and machine learning rather than by more traditional engineering approaches, as necessary in safety-critical application domains, poses various unsolved challenges. Commonly used computational structures underlying machine learning, like deep neural networks, still lack scalable automatic verification support. Due to size, non-linearity, and non-convexity, neural network verification is a challenge to state-of-art Mixed Integer linear programming (MILP) solvers and satisfiability modulo theories (SMT) solvers [2], [3]. In this research, we focus on artificial neural network with activation functions beyond the Rectified Linear Unit (ReLU). We are thus leaving the area of piecewise linear function supported by the majority of SMT solvers and specialized solvers for Artificial Neural Networks (ANNs), the successful like Reluplex solver [1]. A major part of this research is using the SMT solver iSAT [4] which aims at solving complex Boolean combinations of linear and non-linear constraint formulas (including transcendental functions), and therefore is suitable to verify the safety properties of a specific kind of neural network known as Multi-Layer Perceptron (MLP) which contain non-linear activation functions.
A Unified Framework for Improving Misclassifications in Modern Deep Neural Networks for Sentiment Analysis

Deep Neural Networks (DNNs) have achieved high accuracy in multiple Natural Language Processing (NLP) applications. The great success lies in the test data is drawn from the same distribution of the training samples. However, researches have found that the current models classify out-of-distribution, adversarial, and erroneous samples incorrectly with high confidence. Researchers also find the problem comes from the softmax layer of DNN. In this paper, we address this issue and propose a method that ignores the softmax layer in the DNN architecture. Specifically, we estimate the training samples' parameters of the output of the pre-softmax layer of DNN using the Dirichlet Process Gaussian Mixture Model (DPGMM). Then, we compute the distance between a test sample and the distribution of the training samples using Mahalanobis distance to get the classification results. We evaluate our method on a classic NLP task, sentiment analysis, by conducting extensive experiments on different models across several real-world datasets. The results demonstrate that our method assigns correct labels to the samples that are misclassified by current DNNs with softmax layer. Our method can be generalized to any pre-trained DNN without the need to re-train the models and it also does not need supervision learning.
Copy-Move Forgery Detection Based On Key Point Clustering And Similar Neighborhood Search Algorithm

Copy-move is one of the most commonly used methods of tampering with digital images. Keypoint-based detection is recognized as effective in copy-move forgery detection (CMFD).Hence, this paper introduces a new robust algorithm to detect copy-move forgery based on Speeded Up Robust Feature (SURF) descriptor, high pass filtering as a feature matching, Nearest Neighbor (NN) used as a clustering algorithm to divide the whole image into superpixel blocks. The doubted regions are determined by replacing the matched feature points with corresponding superpixel blocks then the neighboring blocks have been merged based on similar Local Color Features (LCF). Finally, the morphological close operation was applied to elicit the doubted forged regions. We experimented on image forgery data set including kinds of tampering means to compare and verify the effectiveness and robustness of the proposed method. The experimental results show that the proposed method is superior to existing state-of-art methods in terms of matching time complexity, detection reliability, and forgery location accuracy.
The Universal Theory Tool Building Toolkit Is Substructural

Consider the set of inferences that are acceptable to use in all our theory building endeavors. Call this set of inferences the universal theory building toolkit, or just ’the toolkit’ for short. It is clear that the toolkit is tightly connected to logic in a variety of ways. Beall, for example, has argued that logic just is the toolkit. This paper avoids making a stand on that issue and instead investigates reasons for thinking that, logic or not, the toolkit is substructural. It is presented as a dialogue for the simple reason that it summarizes a range of dialogues on this subject that the author has had with various folks over the past few years. The method I use to investigate the toolkit is inspired in both philosophical and technical details by Alasdair Urquhart’s work on semantics for relevance logics from the early 1970s.
Single Image Super-Resolution Based on Capsule Network

Super-resolution reconstruction of a single image refers to the process of reconstructing a low-resolution image into a high-resolution image. Although image super-resolution has made good progress, the existing methods still rarely consider the semantic prior information of the image. In the field of deep learning, the more a prior information is used, the better the effect will be achieved. Based on the powerful object semantic information representation ability of capsule network, we propose a capsule neural network based on semantic information super-resolution. The mapping network is used to map the feature distribution of low-resolution image to the feature distribution of high-resolution image, and then input the decoder trained by high-resolution image to obtain the corresponding high-resolution image. The super-resolution task on data sets such as MNIST and set5 is visual display, which proves that our proposed method is effective.
NETWORKING WITH STUDENTS AS A FACTOR IN THE IMPLEMENTATION OF STUDENT SELF-GOVERNMENT

The topic of the article is devoted to the study of the role of network interaction in student self-government. The article substantiates the expediency of organizing student self-government with the use of virtual communication means. The types of student self-government are designated, the organization of which is possible as a synthesis of online and offline communication using software tools. The authors of the article revise the functions of student self-government in modern informational conditions. The practical significance of the work lies in the study of the experience of implementing the main tasks of student self-government at the university using virtual means of communication, which contributes to the integrated management of the student collective, the successful organization of independent activity, the activation of cognitive and creative activity in the virtual space of student self-government.
An Improved Non-negative Latent Factor Model via Momentum-Based Additive Gradient Descent Method

Nonnegative latent factor (NLF) model has a great ability of acquiring useful knowledge from symmetric, high-dimensional, sparse (SHiDS) matrices. However, the tradition NLF model in terms of the double factorization (DF) technique is somewhat conservative due to the inadequate consideration of difference cases of NLFs. In order to address this issue, we propose an improved DF-SNLF (IDF-SNLF) model to extract NLF matrices in various scenarios. Meanwhile, single NLF-dependent and multiplicative update (SNL-MU) learning method is employed to build an NLF model on SHiDS matrices, yet it suffers from a fairly low convergence rate. So, for the purpose of accelerating the convergence rate, a so-called momentum-based additive gradient descent (MAGD) method is adopted to train the model. Empirical studies on two SHiDS matrices demonstrate that our proposed IDF-SNLF model with MAGD can obtain a desirable performance on both prediction accuracy for missing data and the algorithm convergence rate.