Reviewerly/exHarmony huggingface dataset:
A doubly-inflated Poisson regression for correlated count data

Count data have emerged in many applied research areas. In recent years, there has been a considerable interest in models for count data. In modelling such data, it is common to face a large frequency of zeroes. The data are regarded as zero-inflated when the frequency of observed zeroes is larger than what is expected from a theoretical distribution such as Poisson distribution, as a standard model for analysing count data. Data analysis, using the simple Poisson model, may lead to over-dispersion. Several classes of different mixture models were proposed for handling zero-inflated data. But they do not apply to cases when inflated counts happen at some other points, in addition to zero. In these cases, a doubly-inflated Poisson model has been suggested which only be used for cross-sectional data and cannot consider correlations between observations. However, correlated count data have a large application, especially in the health and medical fields. The present study aims to introduce a Doubly-Inflated Poisson models with random effect for correlated doubly-inflated data. Then, the best performance of the proposed method is shown via different simulation scenarios. Finally, the proposed model is applied to a dental study.
An Improved Gas Efficient Library for Securing IoT Smart Contracts Against Arithmetic Vulnerabilities

Public blockchains targeting Internet of Things (IoT) are gaining more traction every day with majority of them being built on top of the Ethereum infrastructure. However, a growing number of these blockchains introduces security issues. There are 525 entries already in the Common Vulnerabilities and Exposure database related to Ethereum smart contracts. 479 of them are related to arithmetic errors, which include integer overflow or underflow. This paper, thus, concentrates on analyzing arithmetic vulnerabilities found in existing public blockchains targeted at IoT applications. Furthermore, the performance in terms of security and gas cost of smart contracts is analyzed with and without SafeMath library. In addition, an improved SafeMath library is proposed that has better arithmetic coverage and requires lower gas consumption. Four security tools are used to analyze the arithmetic protection of the improved SafeMath library. The results show that the improved SafeMath library is able to cover 4 more arithmetic operations compared to the original one by using only two common conditions checks and is capable of saving 26 units of gas, which is a significant amount in the long run.
Developing Infographic Competence as the Integration Model of Engineering and Linguistic Education

The article considers the problem of the infographic competence formation, which is an important and universal component of professional culture and intellectual creativity of a modern specialist. On the one hand, the authors aim to define the competence as willingness and ability of students to perceive and interpret information from both graphical and textual images. On the other hand, it is considered to be the ability to independently create an informational and graphical image based on the key aspects of a text. Thus, university students are mastering the basics of text mediation both in the oral and written forms by means of infographic images that reflect the essence of the presented data in a laconic and concise form. This requires synthesizing the linguistic and engineering experience, and can only be possible under the conditions of professionally oriented foreign languages training.The authors analyze the most preferable methods and principles for creating a visual representation of information (infographics) by university students. They demonstrate the results of a comparative study of the teaching methods for the infographic competence development in the framework of the Russian and American higher education systems. At the same time special attention is given to modeling of the authors’ own system of methods for the competence formation. As far as the methodological basis is concerned, it is the integrative approach that provides an opportunity to implement the spatiotemporal interactions between the target, content and methodological components in the process of the infographic competence development.
Understanding usability evaluation setup for VR products in industry

VR development practices have a diverse set of practices compared to traditional software development. Tasks like scene design, acoustic design, vergence manipulation, image depth, etc. are specific to VR apps and hence require evaluation processes that may be different from the traditional means. Usability Evaluation is one such process which is being executed in an unconventional way by Industrial Practitioners today. In this paper, the researchers detail a Systematic Literature Review of the Usability Evaluation Methods practised by Industrial researchers while building VR Products. The researchers found that VR Product teams follow unique methods to improve usability in their products. Further, the researchers consolidate these methods and provide insights into choosing the best to build a real-world VR Product based on the defined product constraints
Semantic segmentation with hybrid pyramid pooling and stacked pyramid structure

Convolution filters play important roles in improving the abstraction ability of segmentation networks due to its method of fusing features from all input channels. Such a method pays little attention to protecting the channel-wise features which is important in preserving the detail features in the final result. Though a few works has discussed this problem, the function of channel-wise features is still not sufficiently exploited. For investigating the beneficial effect of channel-wise feature on improving details representation ability, we design a Hybrid Pyramid Pooling (HPP) structure to explicitly exploit the channel-wise features. Through utilizing different scales of depthwise dilated convolution, segmentation networks can fully utilize the superiority of channel-wise features to improve the details representation ability. For deeply developing the advantages of the channel-wisely calculated features by HPP, we design another Stacked Pyramid Structure (SPS), which contains stacked pyramid pooling structures. The SPS distributes sufficient sampling points in each finely divided sampling area, and fuses features from a large range of receptive fields to produce rich feature representation. Finally, the combination of HPP and SPS can not only maintains both the advantages of features calculated from a particular channel and all input channels, but also provides sufficiently enriched receptive fields for depicting details in the final result.
Classification of Arabic Text Using Singular Value Decomposition and Fuzzy C-Means Algorithms

This paper proposes the use of singular value decomposition and fuzzy c-means algorithms for Arabic text classification. Al Jazeera Arabic news and CNN Arabic news datasets are used to measure the effectiveness of the proposed approach in classifying Arabic texts. The experimental results are compared with four supervised classification methods that have been used in the previous work on the same datasets we used in this research. These include: support vector machine, naive Bayes, decision tree, and polynomial networks. The results proved the effectiveness of the proposed approach compared to recent works in Arabic text classification.
An Explainable Method of Phishing Emails Generation and Its Application in Machine Learning

The fact that phishing emails cannot be released because they contain private information greatly hinders researchers from obtaining large-scale samples of real phishing emails. At the same time, the legal email data set is often collected from a certain field, which is quite different from the phishing samples, and it is easy to cause overfitting or spatial bias during the process of building the model. This paper proposes a method for generating phishing emails based on data insertion, which can increase the number of phishing samples without changing the malicious attributes, solve the problem of spatial bias during model training, and can reduce the difference in statistical characteristics between benign and malicious samples to a certain extent. Based on the differences in the email HTML content of the Phishing dataset and the Enron dataset, this paper implements six resource generators and a communication relationship selector. It controls the generation of new samples by implementing control-quantity sequence pairs, and proposes quantitative evaluation methods and indicators of the classifier's generalization ability, and verified that the newly generated samples can be used to train a classifier with stronger generalization ability. The main contribution of this paper is to propose a method to provide the model with higher quality data.
Pit Defect Detection on Steel Shell End Face based on Machine Vision

An algorithm based on ellipse fitting and distance threshold is proposed to detect the pit defect of steel shell. According to the obvious change of the gray value of the pit defect, the method uses the gradient algorithm to highlight the pit defect, and extracts the non-edge defect and the ring affected by the gray value of the edge defect by the OTSU method. Then, ellipse fitting is performed on the extracted inner circle curve, and the nearest distance from the point on the inner circle curve to the fitting ellipse is calculated. Finally, based on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\sigma$</tex> principle, the upper and lower distance threshold is set to detect the defect. Experimental results show that this method has high accuracy and detection efficiency for pit defects. Compared with other testing methods, it can better adapt to the actual production requirements.
A Comparative Study of Machine Learning Methods for Detection of Fake Online Consumer Reviews

Online product reviews provide valuable information for consumer decision making. Customers increasingly rely on the reviews and consider them a trusted source of information. For businesses, it is therefore tempting to purchase fake reviews because competitive advantage can be easily achieved by producing positive or negative fake reviews. Machine learning methods have become a critical tool to automatically identify fake reviews. Recently, deep neural networks have shown promising detection accuracy. However, there have been no studies which compare the performance of state-of-the-art deep learning approaches with traditional machine learning methods, such as Naïve Bayes, support vector machines or decision trees. The aim of this study is to examine the performance of several machine learning methods used for the detection of positive and negative fake consumer reviews. Here we show that deep neural networks, including convolutional neural networks and long short term memory, significantly outperform the traditional machine learning methods in terms of accuracy while preserving desirable time performance.
A Visual Comfort Assessment Approach of Stereoscopic Images based on Random Forest Regressor

Visual comfort is an important component for stereoscopic image quality and viewing experience. In recent years, it has been widely investigated through feature analysis and different kinds of subjective experiments. In this paper, according to the existing works for objective evaluation model, we have screened and extracted thirteen disparity-based and content-based features through the stereoscopic image. To find the relationship between these features and the given mean opinion score(MOS) of each image, the method of Random Forest (RF) Regressor was used for simulation on the NBU S3D-VCA and IVY database. Compared with other state-of-the-art methods, the experimental results of two benchmarks data have confirmed the superior performance of our proposed approach.
Secure and Decentralized Academic Transcript System Based on Blockchain Technology

A transcript is a document that provides information about student's academic records such as courses taken by the student, grades and/or honors received in different courses by the student, and degree(s) conferred to the student. An authorized person of the academic institute, in which the student is/was affiliated, provides a transcript to the student. In order to generate, verify, and distribute the transcript, the institute requires sufficient manpower and time. The major drawback of the manual procedure is that it is relatively easy for an adversary to tamper the transcripts while it is difficult to detect such unauthorized modifications. In this paper, we propose an academic transcript system based on blockchain technology. We implement the proposed academic transcript system using a smart contract and a distributed application running on Ethereum blockchain. In the proposed system, the process of uploading a transcript in the blockchain costs around 0.01 ETH (approx. 2.45 US dollars). The proposed academic transcript system based on blockchain technology not only ensures security and transparency of records, but it also reduces the communication delay and transaction cost.
A Deeper Look at Bongard Problems

Machine learning, especially deep learning, has been successfully applied to a wide array of computer vision classification tasks in recent years. Infamous for requiring massive amounts of data to perform well at image classification problems, deep learning has so far been unable to solve Bongard problems (BPs), a set of abstract visual reasoning tasks invented in the 1960s. Each BP can be seen as a supervised learning task, with few training samples (6 for positive and 6 for negative), and often requiring highly abstract features to learn well. Automatically solving Bongard problems directly from images remains an ambitious goal, with very little machine learning literature on the topic. In this paper, we discuss several special properties of BPs as well as what it means to solve a BP. Making use of an expanded set of BP-like tasks to allow for a more careful evaluation of automated solvers, we develop and benchmark a deep learning based approach to solve these problems. To encourage work on this interesting problem, we also make freely available a dataset of over 200 BPs (https://github.com/XinyuYun/bongard-problems).
Complete Scene Parsing for Autonomous Navigation in Unstructured Environments

Recent developments in Machine Learning and Computer Vision have enabled progress in autonomous navigation. However, most of the existing research focuses on European driving situations, with not much progress made in the Indian context. Our paper aims to achieve complete spatial understanding for the Indian context. Our focus is directed towards Semantic Segmentation and Instance Segmentation. We aim to develop an autonomous navigation pipeline by a combination of both approaches. Using DeepLabv3+ for Semantic Segmentation we achieve an mIOU of 68.58%, and using Hybrid Task Cascade we achieve an Average Recall of 56.5%.
Handwritten Bengali Number Detection using Region Proposal Network

As a seventh most spoken native language, Bengali needs a robust and accurate optical character recognition (OCR) especially for number detection. As there is no publicly available well-organized dataset for Bangla number OCR, a synthesized dataset was generated to fill the lack of available data. The recent advancement in artificial intelligence using deep neural networks easily outperforms prior hand selected feature-based machine learning approaches. As the region proposal networks (RPN) in deep neural networks perform very well in detecting objects, it can be used for digit detection in an image. So, in this work a very robust Bengali handwritten number detection system is presented where with the help of deep neural networks and a very well-organized, unbiased generated dataset we achieved state of the art result in handwritten Bangla number detection. This system beats any related prior works by a large margin while considering a real world dataset for benchmarks. The overall detection accuracy was 97.8%. The processing can be done real-time with about 35 images per second using a GPU. Also, while implementing the solution is completely based on python, the framework used for deep learning is Google's Tensorflow and the dependencies, all of which are publicly available.
Activity-selection Behavior of Users in StackExchange Websites

In this work, we examine the disparity in contribution behavior of users with respect to the activities that they choose to perform on Q&A websites. We collect the data of 156 websites of StackExchange and analyze the contribution made by over 5.3 million users. We find that most of the users tend to contribute predominantly to one of the primary activities. Such a behavior yields a high-level distribution of users across the activities, which provides useful insights into the user base composition of these websites. In this work, we show how this distribution varies for StackExchange websites and discuss how it can be a valuable maintenance parameter for these websites.
A Comparison Study of Available Sofware Security Ontologies

A rising number of software and services malfunctioning due to security flaws has increased the importance of software security and resulted in numerous knowledge sources of the domain. Building secure software systems require the understanding and extraction of the available knowledge, and a standard knowledge management platform is needed. Ontologies form an integral part of knowledge management platforms as they capture and structure the given knowledge. Various software security ontologies have been proposed previously, either stand-alone or as part of some bigger ontology like a computer or information security. However, these ontologies do not cover the entire domain and cannot be used as a standard ontology for software security in its current form. In this paper, we have identified and evaluated the existing ontologies that specifically capture software security knowledge, both qualitatively and quantitatively with the help of ontology evaluation tools, in order to select the best ontology that can be extended to prepare the standard ontology for the software security domain.
Concurrent Nearest-Neighbor Searching for Parallel Sampling-Based Motion Planning in SO(3), SE(3), and Euclidean Spaces

This paper presents a fast exact nearest neighbor searching data structure and method that is designed to operate under highly-concurrent parallel operation on modern multi-core processors. Based on a kd-tree, the proposed method is fast, supports metric spaces common to robot motion planning, and supports nearest, k-nearest, and radius-based queries. But unlike traditional approaches using kd-trees, our approach supports simultaneous queries and insertions under concurrency, supports wait-free queries, and provides asymptotically diminishing expected wait-times for random concurrent inserts. We provide proofs of correctness under concurrency, and we demonstrate the proposed method’s performance in a parallelized asymptotically-optimal sampling-based motion planner.
Improving Random Projections With Extra Vectors to Approximate Inner Products

This research concerns itself with increasing the accuracy of random projections used to quickly approximate the inner products of data vectors from a given dataset by adding additional information, namely, adding and storing more extra known vectors to the given dataset and associated information. We show how the variance of estimated inner products is reduced as more vectors are added, how variance reduction is related to the geometry of the dataset and moreover, the asymptotic behaviour of the variance as the number of extra vectors added goes to infinity. We provide the formulae governing the estimate of inner products for adding arbitrarily many extra vectors. Lastly, we demonstrate how to efficiently implement the computations of the estimates by showing we can use pre-computed and stored values for most of the computations. Numerical simulations are conducted to support the analytical results.
Research on Image Scrambling Method Based on Combination of Arnold Transform and Exclusive-or Operation

Aiming at the encryption problem in digital image information hiding process, an image scrambling algorithm based on combination of Arnold transform and exclusive-or operation is proposed. The algorithm first uses the Arnold transform to change the pixel position of the original image to obtain the initial scrambled image A, and then performs a bitwise exclusive-or operation on each pixel in the image A with its prior pixel. Finally, the gray value of the image is changed again by the high and low bit transposition operation to improve the image scrambling effect, and the final scrambled image S is obtained. The experimental results show that compared with Arnold's existing scrambling methods, the scrambled image is not only more difficult to recognize subjectively, but also has a significant change in its histogram, which is more suitable for the encryption of digital images and subsequent experiments.
Robust Visual Tracking Using Kernel Sparse Coding on Multiple Covariance Descriptors

In this article, we aim to improve the performance of visual tracking by combing different features of multiple modalities. The core idea is to use covariance matrices as feature descriptors and then use sparse coding to encode different features. The notion of sparsity has been successfully used in visual tracking. In this context, sparsity is used along appearance models often obtained from intensity/color information. In this work, we step outside this trend and propose to model the target appearance by local covariance descriptors (CovDs) in a pyramid structure. The proposed pyramid structure not only enables us to encode local and spatial information of the target appearance but also inherits useful properties of CovDs such as invariance to affine transforms. Since CovDs lie on a Riemannian manifold, we further propose to perform tracking through sparse coding by embedding the Riemannian manifold into an infinite-dimensional Hilbert space. Embedding the manifold into a Hilbert space allows us to perform sparse coding efficiently using the kernel trick. Our empirical study shows that the proposed tracking framework outperforms the existing state-of-the-art methods in challenging scenarios.
Quantifying the thickness of each color material in multilayer transparent specimen based on transmission image

Combing the color transmission image and the Beer–Lambert law shows a great application prospect in quantifying each material in multilayer specimen. Here, a novel, low-cost, and efficient optical algorithm is proposed to predict the thickness of each color material in a multilayer specimen from the color transmission image based on the Beer–Lambert Law. In this work, a normal scanner is employed to achieve the color transmission image of the monochrome transparent films. RGB values represent the transmitted intensity. A linear relationship between the optical depth and physical thickness is observed under different monochromatic lights. It is supposed that for a multilayer transparent film which consisted of different monochrome transparent films, the optical depth is related to the physical thickness of each monochrome transparent component. Therefore, an estimating equation is proposed to predict the thickness of each color material in the multilayer specimen. According to the result, the standard deviation of predicted thickness and practical thickness of each color film in the multilayer specimen is 0.93%. Fairly good agreement and high accuracy are obtained between the practical and predicted values, and the validity of this method is confirmed.