Reviewerly/exHarmony huggingface dataset:
Assisting Students to Understand Mathematical Graphs Using Virtual Reality Application

Many face difficulties in understanding mathematical equations and their graphs. Implementing virtual reality to plot graphs of the mathematical equation will help to understand the equations better. Virtual reality (VR) is a computer-generated environment in which a client can explore and collaborate with it. Virtual reality system allows a user to view three-dimensional images. VR has a wide range of applications. VR is being utilized in entertainment for gaming or 3D movies, in medicine for simulating the surgical environment, in robotics development and many more. VR has a wide scope of application in the education system though only a few kinds of research have been proposed. In this paper, we have introduced a new approach to making the user understand any mathematical equation better by plotting their graph using virtual reality application. Unity, a real-time engine and C# are being used to develop this novel approach. The proposed method will be compared with current method of learning mathematical equations.
Sparse Weight Activation Training

Neural network training is computationally and memory intensive. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, but it can affect network convergence. In this work, we propose a novel CNN training algorithm Sparse Weight Activation Training (SWAT). SWAT is more computation and memory-efficient than conventional training. SWAT modifies back-propagation based on the empirical insight that convergence during training tends to be robust to the elimination of (i) small magnitude weights during the forward pass and (ii) both small magnitude weights and activations during the backward pass. We evaluate SWAT on recent CNN architectures such as ResNet, VGG, DenseNet and WideResNet using CIFAR-10, CIFAR-100 and ImageNet datasets. For ResNet-50 on ImageNet SWAT reduces total floating-point operations (FLOPS) during training by 80% resulting in a 3.3$\times$ training speedup when run on a simulated sparse learning accelerator representative of emerging platforms while incurring only 1.63% reduction in validation accuracy. Moreover, SWAT reduces memory footprint during the backward pass by 23% to 50% for activations and 50% to 90% for weights.
TASKS ON MATEMATICAL LOGIC IN THE INFORMATICS UNIFIED STATE EXAM

The article presents an overview of the tasks on analysis of logical expressions included in test materials for the Unified State Exam on informatics. Solutions to a number of such tasks are given based on analytical and graph-analytical methods.
Photon statistics of twisted heralded single photons

We study the correlation properties of single photons carrying orbital angular momentum (OAM) in a Hanbury Brown and Twiss (HBT) type experiment. We have characterized single photon sources obtained by pumping a nonlinear crystal with a laser beam carrying different OAM under same experimental conditions. For twisted heralded single photons carrying OAM, we calculate g(2)(0), a measurable parameter characterizing the quality of a single photon source, and observe an increment with the OAM of the single photon. The single photon behaviour of the heralded photons is observed to be reducing with higher orders of their OAM. An OAM-dependent variation of the photon statistics will be relevant to quantum information experiments involving twisted heralded single photons.
Communication-based Evaluation for Natural Language Generation

Natural language generation (NLG) systems are commonly evaluated using n-gram overlap measures (e.g. BLEU, ROUGE). These measures do not directly capture semantics or speaker intentions, and so they often turn out to be misaligned with our true goals for NLG. In this work, we argue instead for communication-based evaluations: assuming the purpose of an NLG system is to convey information to a reader/listener, we can directly evaluate its effectiveness at this task using the Rational Speech Acts model of pragmatic language use. We illustrate with a color reference dataset that contains descriptions in pre-defined quality categories, showing that our method better aligns with these quality categories than do any of the prominent n-gram overlap methods.
A Correlation-Based Sorting Algorithm of Inductive Modeling using Argument Rating

In this paper, a sparse regression algorithm is proposed that corresponds to a linear model with the smallest possible number of regressors without loss of quality of the model based on correlation analysis and using an external criterion for choosing an optimal model based on the division of the input data sample. That is why the proposed method relates to the class of GMDH algorithms. The main stages of the algorithm work are described. The core feature of the algorithm is the use of frequency analysis of regressor ratings. The results of testing its effectiveness on test experiments are shown in comparison with the known algorithm LASSO.
Instance-Wise Dynamic Sensor Selection for Human Activity Recognition

Human Activity Recognition (HAR) is an important application of smart wearable/mobile systems for many human-centric problems such as healthcare. The multi-sensor synchronous measurement has shown better performance for HAR than a single sensor. However, the multi-sensor setting increases the costs of data transmission, computation and energy. Therefore, the efficient sensor selection to balance recognition accuracy and sensor cost is the critical challenge. In this paper, we propose an Instance-wise Dynamic Sensor Selection (IDSS) method for HAR. Firstly, we formalize this problem as minimizing both activity classification loss and sensor number by dynamically selecting a sparse subset for each instance. Then, IDSS solves the above minimization problem via Markov Decision Process whose policy for sensor selection is learned by exploiting the instance-wise states using Imitation Learning. In order to optimize the parameters of the activity classification model and the sensor selection policy, an algorithm named Mutual DAgger is proposed to alternatively enhance their learning process. To evaluate the performance of IDSS, we conduct experiments on three real-world HAR datasets. The experimental results show that IDSS can effectively reduce the overall sensor number without losing accuracy and outperforms the state-of-the-art methods regarding the combined measurement of accuracy and sensor number.
An SVM Based Secural Image Steganography Algorithm for IoT

With the fast development of IoT network, there are more and more images generated by sensors and other devices, which increases the transmission expenses. By adopting image steganography, the images can deliver more information than they could. Therefore, the transmission expenses could be significantly reduced. However, the safety and quality of steganographic algorithms is not promising nowadays. To improve this situation, we propose an SVM-based steganography algorithm. The algorithm takes advantage of four features, including the variance of the image, the overall difference, the shape context matching and the smoothness. The analysis and experimental results show that the information hiding algorithm can effectively optimize the information steganography and anti-steganography analysis, which could be used in IoT.
Full-field vibration profilometry using time-averaged interference microscopy aided by variational analysis

Full-field vibration testing is indispensable in characterization of micro-electro-mechanical components. Time-averaged interference (TAI) microscopy is a very capable and accurate vibration profilometry technique. It employs natural all-optical multiplexing of required information, i.e., recorded interferogram is amplitude-modulated by the Bessel pattern, which in turn encodes spatial distribution of vibration amplitude in its underlying phase function. We propose a complete end-to-end numerical scheme for efficient and robust vibration amplitude map demodulation based on the variational data-analysis paradigm. First, interferogram is variationally pre-filtered and complex analytic-interferogram is generated, exploiting the Hilbert spiral transform. The amplitude term of analytic-interferogram is accessed for Besselogram, i.e., TAI amplitude modulation distribution. Next, the Besselogram is variationally pre-filtered and complex analytic-Besselogram is calculated applying the Hilbert spiral transform. Finally, the phase term of the analytic-Besselogram is determined, unwrapped and post-filtered to achieve spatial distribution of vibration amplitude. Proposed approach is verified using simulated interferograms and corroborated upon experimental vibration testing. Reported method compares favorably with the reference Hilbert-Huang transform-based method. The improvement was gained by adding two new steps to the calculation path: (1) additional removal of the interferogram’s residual background and noise and (2) variational based vibration amplitude map error correction method.
Content-Aware Cubemap Projection for Panoramic Image via Deep Q-Learning

Cubemap projection (CMP) becomes a potential panoramic data format for its efficiency. However, default CMP coordinate system with fixed viewpoint may cause distortion, especially around the boundaries of each projection plane. To promote quality of panoramic images in CMP, we propose a content-awared CMP optimization method via deep Q-learning. The key of this method is to predict an angle for rotating the image in Equirectangular projection (ERP), which attempts to keep foreground objects away from the edge of each projection plane after the image is re-projected with CMP. Firstly, the panoramic image in ERP is preprocessed for obtaining a foreground pixel map. Secondly, we feed the foreground map into the proposed deep convolutional network (ConvNet) to obtain the predicted rotation angle. The model parameters are training through the deep Q-learning scheme. Experimental results show our method keep more foreground pixels in center of each projection plane than the baseline.
Component Elimination Strategies to Fit Mixtures of Multiple Scale Distributions

We address the issue of selecting automatically the number of components in mixture models with non-Gaussian components. As a more efficient alternative to the traditional comparison of several model scores in a range, we consider procedures based on a single run of the inference scheme. Starting from an overfitting mixture in a Bayesian setting, we investigate two strategies to eliminate superfluous components. We implement these strategies for mixtures of multiple scale distributions which exhibit a variety of shapes not necessarily elliptical while remaining analytical and tractable in multiple dimensions. A Bayesian formulation and a tractable inference procedure based on variational approximation are proposed. Preliminary results on simulated and real data show promising performance in terms of model selection and computational time.
Towards Robust ML-Algorithms for the Condition Monitoring of Switchgear

In this paper, we describe our work-in-progress regarding the use of artificially generated data for the training of classifiers in an industrial context. In particular, our goal is to classify faulty/healthy switchgear by using infrared images. The paper describes the use of Generative Adversarial Networks (GANs) for the generation of new infrared images in order to bolster the meagre, repetitive and unbalanced data available to us. The paper describes the obstacles encountered, potential solutions, and the results of multiple experiments to test the impact of synthetic data on training.
Making Your First Progressive Web App

Welcome to this journey of building your first progressive web app (PWA). In this chapter, we build a Vue note app, which is a simple notebook app, and then convert it to a PWA, all while discovering some critical concepts along the way.
Line Segmentation of Handwritten Kannada Documents

Line Segmentation of handwritten documents is the process of detecting/segmenting lines from an image. It plays an important role in Optical Character Recognition (OCR) and Word Spotting. This paper focuses on the segmentation of handwritten Kannada documents. The importance of the research arises from the need for a paperless environment, making documents searchable and the difficulty in storing and preserving old books and documents. In this paper, we have proposed two novel algorithms for line segmentation of the handwritten Kannada documents - Vertical Strips Method and Connected Components Method. A dataset comprising of around 200 images was used to evaluate the algorithms. The dataset is divided into three categories based on the skew in the lines and the spacing between lines - Easy, Medium and Complex. The algorithm is evaluated based on these three categories and the results are compared for both the methods respectively.
An Image Encryption Scheme Based on Hybrid Fresnel Phase Mask and Singular Value Decomposition

Background: An asymmetric cryptanalysis is suggested in Affine and Fresnel transform using Hybrid Fresnel phase Mask (HFM), Hybrid Mask (HM) and Singular Value Decomposition (SVD) to deliver additional security to the scheme. The usage of Affine Transform (AT) provides randomness in the input plane which benefits in enlarging the key space and SVD gives the nonlinearity in the process. Objective: In the FrT domain, usage of hybrid masks and AT in an asymmetric cryptosystem with SVD is to make encoded procedure difficult. Methods: On the plain image we firstly apply affine transform and then convoluted it with HFM, in FrT domain with propagation distance Z1 and the obtained part is convoluted with HM in FrT with propagation distance Z2 and then lastly on the encoded image SVD is applied. Results: Validity of the suggested scheme has been confirmed by using MATLAB R2018a (9.4.0.813654). The capability of the recommended scheme has been tested by statistical simulations such as histogram, entropy and correlation coefficient. Noise attack analysis has also done so that the system becomes robust against attacks. Conclusion: Asymmetric cryptosystem is recommended using pixel scrambling technique i.e. affine transform which shuffles the pixels hence helps for security of the system. Usage of SVD in the algorithm is to make the system robust. Performance and strength analysis are carried out for scrutiny of the forte and feasibility of the algorithm.
Oracle RAC performance analysis on VMware Virtual SAN

A brand-new Oracle RAC system on VMware Virtual SAN was built with serval rack server. We use this new hardware platform to run the one of the busiest campus information database system on campus. This system takes full advantage of the features of VMware Virtual SAN such as high reliability, flexible expansion, simple backup and recovery, and low maintenance costs. We also tested the performance of the whole system including IO, TPM, query speed, throughput, latency and more. After testing the overall performance of the test achieved the expected results, which can fully meet the actual use.
Mobile-Based Geographic Information System For Culinary Tour Mapping In Indonesia

Indonesia is the largest archipelagic country in the world consisting of 17,499 islands from Sabang to Merauke. With the vastness of the area, Indonesia has so many and scattered tourist attractions. Indonesia is also a target of tourism by local and foreign tourists. Problems that occur are many tourist attractions that can not be known by tourists so that they experience a severe problem in getting information related to the culinary. One of the problems of information about its existence, which is spread in several locations and knowledge about the area that is not good, especially for tourists from outside the city. Of course, if this is ignored continuously will have a negative impact on culinary tourism, such as the lonely tourists who come to visit in the country of Indonesia. This study provides a solution that is seen from the existing problems; it is necessary to answer the need for fast and more efficient information about culinary attractions in Indonesia, namely by developing an Android-based GIS-based application that provides an easy search for information and culinary tourism locations. User required. Software development uses the waterfall method and tests on users with questionnaires to users. The results of this study, culinary tourism mobile applications that are based on the results of tests carried out obtain a percentage score of 81-100, which can be interpreted that this application is very feasible to use and apply to provide culinary information.
Learning Weighted Model Integration Distributions

Weighted model integration (WMI) is a framework for probabilistic inference over distributions with discrete and continuous variables and structured supports. Despite the growing popularity of WMI, existing density estimators ignore the problem of learning a structured support, and thus fail to handle unfeasible configurations and piecewise-linear relations between continuous variables. We propose lariat, a novel method to tackle this challenging problem. In a first step, our approach induces an SMT(ℒℛA) formula representing the support of the structured distribution. Next, it combines the latter with a density learned using a state-of-the-art estimation method. The overall model automatically accounts for the discontinuous nature of the underlying structured distribution. Our experimental results with synthetic and real-world data highlight the promise of the approach.
A Study on Tiredness Measurement using Computer Vision

In this paper, we studied tiredness measurement based on several different detection methods in real time. We know that the driver tiredness is one of the major causes of traffic accidents. So tiredness detection can play a vital role for preventing road accidents. By developing an automatic solution for alerting drivers of tiredness before an accident occurs, this could reduce the number of traffic accidents. The Haar-cascade classifier is exploited based on Haar-like features to find the eyes. The main purpose of the Haar-cascade classifier is to classify closed or open state of the eyes. If we can notice that the eyes are closed for a predefined span of time, we consider the state of the eyes can be closed. Based on this closed-state of the eye, a notification (like alarm) is initiated to alert. We have detected only right eye for saving processing load on the system. The reason is that when a person closes his eyes he usually does not close one eye, but both eyes at the same time. Several steps are taken into account for this system; we first capture the frame from the webcam. Then we need to detect face as well as eye. To detect blinks, we process ROI (region of image) of pupil area. Our result is found to be satisfactory.
Comparison of Nikto and Uniscan for measuring URL vulnerability

Vulnerability scanning tools are essential to measure the security risk of a system and its impact on the whole organization. A number of vulnerability testing tools are reported and used widely based on the security requirements of a firm. In this paper, we compare and analyze the vulnerability test results of two tools, Nikto and Uniscan using test URLs from the security blogs and literature. It is observed that Nikto is quick and suitable for surface level scan, however the Uniscan take longer time to perform detailed testing which includes the database test such as SQl Injection and is recommended for detailed vulnerability analysis.
Class Prior Estimation with Biased Positives and Unlabeled Examples

Positive-unlabeled learning is often studied under the assumption that the labeled positive sample is drawn randomly from the true distribution of positives. In many application domains, however, certain regions in the support of the positive class-conditional distribution are over-represented while others are under-represented in the positive sample. Although this introduces problems in all aspects of positive-unlabeled learning, we begin to address this challenge by focusing on the estimation of class priors, quantities central to the estimation of posterior probabilities and the recovery of true classification performance. We start by making a set of assumptions to model the sampling bias. We then extend the identifiability theory of class priors from the unbiased to the biased setting. Finally, we derive an algorithm for estimating the class priors that relies on clustering to decompose the original problem into subproblems of unbiased positive-unlabeled learning. Our empirical investigation suggests feasibility of the correction strategy and overall good performance.
An study of the type of interorganizational defamation risk based on Grounded Theory——an analysis from the Chinese scenario

Image spoiling occurs when an organization uses words or other symbols to attack another organization's image. The most common form of image destruction is defamation. With the intensification of market competition, the defamation behavior between organizations has become a means of unfair competition. Based on Grounded Theory, from the perspective of organizational impression management, this study clarifies the types of inter-organizational defamation risk through 1307 defamation cases published by China Referee Instrument Network between 2013 and 2018. The research results show that, first, inter-organizational defamation is divided into three categories: vilification, product disparagement and out-casting. Second, different types of defamation have differences motivations. Third, defamation between organizations has difference by regions and industries. The conclusion of this paper is helpful to understand the unfair competition situation of defamation between organizations, and enriches the research of impression management after different types of negative events under the same situation.
Fuzzy particle swarm optimization with nearest-better neighborhood for multimodal optimization

In the last decades, many efforts have been made to solve multimodal optimization problems using Particle Swarm Optimization (PSO). To produce good results, these PSO algorithms need to specify some niching parameters to define the local neighborhood. In this paper, our motivation is to propose the novel neighborhood structures that remove undesirable niching parameters without sacrificing performance. Hence, this paper has two main contributions. First, two novel parameter-free neighborhood structures named Topological Nearest-Better (TNB) neighborhood and Distance-based Nearest-Better (DNB) neighborhood are proposed in the topological space and decision space, respectively. Second, two proposed neighborhoods are combined with Fuzzy PSO (FPSO) and two novel niching algorithms, called TNB-FPSO and DNB-FPSO, are proposed for solving multimodal optimization problems. It should be noted that we use a zero-order fuzzy system to balance between exploration and exploitation in the proposed algorithms. To evaluate the performance of proposed algorithms, we performed a detailed empirical evaluation on the several standard multimodal benchmark functions. Our results show that DNB-FPSO statistically outperforms the other compared multimodal optimization algorithms.
Controlled cyclic quantum teleportation of an arbitrary qubit state from 4 participants via a nine-qubit entangled

We proposed a scheme of teleported arbitrary four-qubit states from four participants cyclically. In this scheme, Alice sends an arbitrary qubit state to Bob. Bob sends an arbitrary qubit to Charli. At the same time, Charli sends his arbitrary qubit state to David while David sends his arbitrary qubit state to Alice. The teleportation scheme is carried out cyclically via a nine-qubit entangled channel with Eve’s assistance as a controller and Bell’s measurements.