Reviewerly/exHarmony huggingface dataset:
The GraphBLAS in Julia and Python: the PageRank and Triangle Centralities

The GraphBLAS is a standard API for expressing Graphs in the language of linear algebra. The goal is to provide high performance while exploiting the fundamental simplicity of Graph algorithms in terms of a common set of "Basic Linear Algebra Subprograms". A robust parallel implementation of the GraphBLAS C specification is available as the SuiteSparse GraphBLAS library [1]. The simplicity of the GraphBLAS, so apparent "in the math", is diminished when expressed in terms of a low level language such as C. To see the full expressive power of the GraphBLAS, a high level interface is needed so that the elegance of the mathematical underpinnings of the GraphBLAS is clearly apparent in the code. In this paper we introduce the Julia interface to the SuiteSparse:GraphBLAS library and compare it to the Python interface [2]. We implement the PageRank and Triangle Centrality algorithms with remarkably little code and show that no significant performance is sacrificed by moving from C to the more productive Python and Julia interfaces.
Constructing a visualization dashboard to improve educational standards in Arizona legislative districts

The quality of K-12 public education is a perennial issue in Arizona that has heightened in salience over the past several years, with broad public concerns over insufficient funding sparking the Red for Ed movement for higher teacher pay. However, despite the push for educational change, there remain many barriers to K-12 public school education funding, including a lack of visibility for how Arizona public schools are performing at a legislative district level. Such information is released at a school district level by organizations like the Arizona Department of Education, but much of the information is limited and can be difficult for legislators to parse, particularly when school districts lie on the boundary between two legislative districts. Moreover, school outcome data is often limited to raw spreadsheets for the public and may be fragmented between government websites and educational organizations depending on the metric. Ultimately, this hinders the public’s understanding of the current educational standing. As such, a visualization dashboard that clearly identifies schools and their relative performance within each legislative district would be an invaluable tool for legislative bodies and the Arizona public. It is proposed that a dashboard for Arizona at the district level would increase transparency and availability of public information about these districts, allowing legislators to utilize the dashboard as a tool for greater understanding and more effective policymaking. While there are many positive social implications to be afforded by educational dashboards, this article also points to potential risks of this new visibility without end-user training.
Non-local medians filter for joint Gaussian and impulsive image denoising

Image denoising concerns with the development of filters to remove or attenuate random perturbations in the observed data, but at the same time, preserving most of edges and fine details in the scene. One problem with joint additive Gaussian and impulsive noise degradation is that they are spread over all frequencies of the signal. Hence, the most effective filters for this kind of noise are implemented in the spatial domain. In this paper, we proposed a Non-Local Medians filter that combine the medians of every patch of a search window using two distinct similarity measures: the Euclidean distance and the Kullback-Leibler divergence between Gaussian densities estimated from the patches. Computational experiments with 25 images corrupted by joint Gaussian and impulsive noises show that the proposed method is capable of producing, on average, significant higher PSNR and SSIM than the combination of the median filter and the Non-Local Means filter applied independently.
An Edge Computing System for Estimating Road Surface Condition on Winter Expressway

This paper proposes an edge computing system for estimation of road surface condition on winter expressway by utilizing in-vehicle camera images. In the field of transportation engineering, the realization of image processing-based methods for road scene analysis has been expected. Therefore, many images obtained by in-vehicle cameras are accumulated in various organizations of transportation engineering. However, since analyzing these accumulated images requires too much time and huge-sized computer, immediate analysis is difficult. Thus, this paper constructs an edge computing system that realizes immediate analysis of road surface condition by utilizing images obtained from in-vehicle camera. The proposed system is constructed on a small-sized computer. Also, an algorithm installed on this system estimates winter road surface condition via Mobile Nets using in-vehicle camera images.
A Recursive Inclusion Checker for Recursively Defined Subtypes

We present a tableaux procedure that checks logical relations between recursively defined subtypes of recursively defined types and apply this procedure to the problem of resolving ambiguous names in a programming language. This work is part of a project to design a new programming language suitable for efficient implementation of logic. Logical formulas are tree-like structures with many constructors having different arities and argument types. Algorithms that use these structures must perform case analysis on the constructors, and access subtrees whose type and existence depend on the constructor used. In many programming languages, case analysis is handled by matching, but we want to take a different approach, based on recursively defined subtypes. Instead of matching a tree against different constructors, we will classify it by using a set of disjoint subtypes. Subtypes are more general than structural forms based on constructors, we expect that they can be implemented more efficiently, and in addition can be used in static type checking. This makes it possible to use recursively defined subtypes as preconditions or postconditions of functions. We define the types and the subtypes (which we will call adjectives), define their semantics, and give a tableaux-based inclusion checker for adjectives. We show how to use this inclusion checker for resolving ambiguous field references in declarations of adjectives. The same procedure can be used for resolving ambiguous function calls.
Collaborative Spatial-Temporal Interaction for Language-Based Moment Retrieval

Language-based moment retrieval attempts to distinguish the most related video moment semantically corresponding to the language query. The core of this task not only includes a mutual comprehension of query semantics and video details, but also requires accurately excavating the location information from both temporal and spatial dimensions. Unfortunately, existing methods fail to consider the fine-grained relationship of intrinsic spatial-temporal information and language query. In this work, we introduce a collaborative spatial-temporal interaction (CSTI) model to explore the complicated alignment patterns between visual and linguistic features. Firstly, we present a video-enhanced query attention block to improve language understanding, which summarizes the frame features to calculate a compact video abstract for every query word utilizing the attention mechanism. Secondly, we develop a cross-modal semantic modulation block, which decomposes the video-enhanced query feature into spatial and temporal-relevant linguistic parts to conduct the mining of context-aware visual location evidence in the specific dimension. Finally, we employ a visual gate on every frame to implement the distinct influences of spatial and temporal-relevant query features. Experimental evaluations on two popular benchmark datasets suggest that our model exceeds the state-of-the-arts by a clear margin.
Inspiring Movement — Physical Activity in a Virtual Sea as a Driver for Ecological Awareness

As environmental pollution continues to expand, new ways for raising awareness for the consequences need to be explored. Virtual reality has emerged as an effective tool for behavioral change. This paper investigates if virtual reality applications controlled through physical activity can support an even stronger effect, because they enhance attention and recall performance by stimulating working memory through motor functions. This was tested in an experimental study ([Formula: see text]) using a virtual reality head-mounted display in combination with the ICAROS fitness device enabling participants to explore either a plastic-polluted or a non-polluted sea. Results indicated that using a regular controller elicits more presence and a more intense Flow experience than the ICAROS condition, which people controlled via their physical activity. Moreover, the plastic-polluted stimulus was more effective in inducing people’s stated tendency to change their attitude than a non-polluted sea.
Environment Recognition from A Spherical Camera Image Based on DeepLab v3+

The number of users of electric wheelchairs has been increasing in recent years because it is easy to operate the electric wheelchair and do not require physical strength. However, the traffic accidents are also increasing because of the large number of wheelchairs. The development of autonomous electric wheelchairs is expected to reduce the risk of accidents and improve the convenience of electric wheelchairs. Environmental recognition is essential for the development of autonomous electric wheelchairs. In this paper, we propose a method for recognizing roads, sidewalks, buildings, electric wheelchair drivers, poles, electric wheelchairs, vegetation, curbs, sky, pedestrians, lanes, cars, steps, and bicycles. For recognizing those objects, we use a panoramic image acquired from a spherical camera. As the machine techniques, we use DeepLab v3+, a semantic segmentation algorithm based on Convolutional Neural Network (CNN). In the proposed method, a new CNN model is constructed by adding deformable convolution, SE-block, and MobileNet v2 to DeepLab v3+ into the original DeepLab v3+. In the experiment, IoU 38.8&#x0025; and Dice of 46.7&#x0025; were obtained.
Differentiative Feature-based Fall Detection System

Elderly people are dealing with falling down on a daily basis. This incident can happen anytime at any place. There is high risk of falling not only the elder but also the caregiver. Although there are numbers of applications and devices in the market for the user, the cutting-edge technology as a machine learning-based algorithm can increase effectiveness of fall detection model into device's effectiveness. The available technology is embedded accelerometer and gyroscope sensor into a smartphone provide benefit dataset. These data can be used for reducing and managing serious injury and caregiver can assist on time. The leverage performance of a Smart Steps application by including the essence of machine learning algorithm and 5-fold cross validation rises accuracy in fall detection. Thus, this paper proposed a novel method of 4 binary classification--Decision Tree, SVM, K-Nearest Neighbors, and Gradient Boosting. The focusing on acceleration magnitude, angular velocity magnitude, and difference between pre-current, current-post values are taken into account in the study. The opened dataset, MobiFall, are split into 2 groups 1) train group 80% and 2) test group 20% for gathering effectiveness result. The model's assessment measures in 4-dimension 1) accuracy, 2) precision, 3) recall and, 4) F1-Score. The results demonstrates increasing values that 95.65% of accuracy, 91.20% precision, 90.86% recall and, 91.03% F1-Score. The fall detection of the study can conclude that the machine learning-based algorithm offers more accuracy and effectively than threshold-based algorithm.
Static Code Analysis Tool for Laravel Framework Based Web Application

To increase and maintain web application security, developers could use some different methods, one of them is static code analysis. This method could find security vulnerabilities inside a source code without the need of running the program. It could also be automated by using tools, which considered more efficient than manual reviews. One specific method which is commonly used in static code analysis is taint analysis. Taint analysis usually utilizes source code modeling to prepare the code for analysis process to detect any untrusted data flows into security sensitives computations. While this kind of analysis could be very helpful, static code analysis tool for Laravel-based web application is still quite rare, despite its popularity. Therefore, in this research, we want to know how static code (taint) analysis could be utilized to detect security vulnerabilities and how the projects (Laravel-based) should be modeled in order to facilitate this analysis. We then developed a static analysis tool, which models the application’s source code using AST and dictionary to be used as the base of the taint analysis. The tool first parsed the route file of Laravel project to get a list of controller files. Each file in that list would be parsed in order to build the source code representation, before actually being analyzed using taint analysis method. The experiments was done using this tool shows that the tools (with taint analysis) could detect 13 security vulnerabilities from 6 Laravel-based projects with one False Negative. An ineffective sanitizer was the suspected cause of this False Negative. This also shows that proposed modeling technique could be helpful in facilitating taint analysis in Laravel-based projects. For future development and studies, this tool should be tested with more Laravel and even other framework based web application with a wider range of security vulnerabilities.
Comparison of Invertible Architectures for High Speed Channel Design

In this paper, we perform a comparison of state-of-the-art inverse design strategies for a high-speed link. We take into account three machine learning architectures: (1) traditional fully-connected neural networks, (2) conditional generative adversarial networks and (3) invertible neural networks. The metrics for evaluation include quantitative mean-squared-errors on the test set as well as qualitative posterior distributions’ similarity to the actual posterior distributions. We find that, on average, invertible neural networks have minimum mean-squared error for the input design tuple and their posterior shapes are in accordance with the actual distributions.
Study and work in times of pandemic, how do university students solve technological-digital situations?

After the declaration of a health contingency by the WHO and the Mexican government, the university community took refuge in their homes, waiting to return to face-to-face classes. As the confinement dates lengthen, online educational interactions are hampered because students’ manifest problems related to the internet and technological-digital resources, since now they must be shared with relatives who are also in confinement; In addition, some students work online, which complicates the situation in which they live, study and work. Although learning is involved in these situations, it is not addressed in the present study; The results provide elements that lead to lines of research where, from the student perspective, the role of the teaching-learning process, academic performance and learning are reviewed, as well as the relationship between education and online work. In this context, the proposal of the study is exploratory, quantitative, and longitudinal; The sample is for convenience and 31 (August 2020) and subsequently 28 students (March 2021) voluntarily participated. The objective is to explore the conditions in which undergraduate students solve socio-educational-labor situations with the use of technological-digital resources during the COVID-19 pandemic.
Experimental investigations on robotic linear stage traverse error using draw wire sensor

Measuring linear displacement and rotation in robotic joints is vital for end effector positioning and producing the desired trajectory. The output pulse produced by driver system may not be completely converted into rotary motion of the stepper motor due to elevated loading conditions. This necessitates the gauging of stage traverse taking into consideration the effects of minor plays, guide screw pitch non uniformity, ill responses to pulses provided by the driver etc. The current paper presents a technique of measuring the errors occurring in displacement of a ball screw driven linear stage using a draw wire sensor (DW sensor) mounted on a stationary platform. The procedure involves proper design of mounting platform, ensuring orthogonal orientation of the sensor body, wire straightness check in both perpendicular planes and fabrication of wire fixtures. The output pulses from the DW sensor are acquired through NI Labview software using NI USB 6225 Data acquisition system. A mechanical dial gauge with 1 µm resolution and a laser displacement sensor is used to cross verify the obtained data. The results are then used to study the positioning accuracy of the developed 4 axis robotic arm.
Organizing Individual Work of Students on Drawing in Higher School

The paper aims to give reasons for the possibility of development of students’ self-education skills based on the individualization of learning through the organization of individual work on drawing. The analysis of the scientific and methodological works of psychologists, pedagogists, teaching artists of the past and the present has allowed actualizing in the article the problem of the organization of students’ individual work on drawing at the current stage of education development. The role of drawing in the professional education of an artist, teaching artist, designer and the importance of individual work on this subject in the condition of the need for daily exercises for mastering the visual art literacy have been identified. Motivating factors for self-education, self-development, including the teachers’ ways to stimulate and support students’ individual work, have been considered. The scientific originality of the research lies in the fact that it supports the methodological recommendations for the organization of students’ individual work on drawing in accordance with the modern paradigm of requirements for the specialists which mainly consist in their ability to gain knowledge independently - in the skills of self-development (in the scientific and creative ways), self-education, search for innovations. As a result of the analysis of teaching materials and personal experience, methodological guidelines for the support of teaching drawing to students through the individual form of work have been formulated.
Construction of an Effective Preconditioner for the Even-odd Splitting of Cubic Spline Wavelets

In this study, the method for decomposing splines of degree m and smoothness C^m-1 into a series of wavelets with zero moments is investigated. The system of linear algebraic equations connecting the coefficients of the spline expansion on the initial scale with the spline coefficients and wavelet coefficients on the embedded scale is obtained. The originality consists in the application of some preconditioner that reduces the system to a simpler band system of equations. Examples of applying the method to the cases of first-degree spline wavelets with two first zero moments and cubic spline wavelets with six first zero moments are presented. For the cubic case after splitting the system into even and odd rows, the resulting matrix acquires a seven-diagonals form with strict diagonal dominance, which makes it possible to apply an effective sweep method to its solution
Moving Objects Segmentation in Infrared Scene Videos

Nowadays, developing an intelligent system for segmenting the moving object from the background is essential task for video surveillance applications. Recently, a deep learning segmentation algorithm composed of encoder CNN, a Feature Pooling Module and a decoder CNN called FgSegNET_S has been proposed. It is capable to train the model using few training examples. FgSegNET_S is relying only on the spatial information while it is fundamental to include temporal information to distinguish if an object is moving or not. In this paper, an improved version known as (T_FgSegNET_S) is proposed by using the subtracted images from the initial background as input. The proposed approach is trained and evaluated using two publicly available infrared datasets: remote scene infrared videos captured by medium-wave infrared (MWIR) sensors and the Grayscale Thermal Foreground Detection (GTFD) dataset. The performance of network is evaluated using precision, recall, and F-measure metrics. The experiments show improved results, especially when compared to other state-of-the-art methods.
TST-GAN: A Legal Document Generation Model Based on Text Style Transfer

Legal documents need to be written according to certain rules and grammar. With the development of deep learning in the field of natural language processing, the human cost of legal personnel in writing legal documents has been greatly reduced. Text generation has always been a research topic in the field of natural language processing. In recent years, the emergence of generation confrontation network provides new ideas and achievements for text generation. The traditional generative countermeasure network model is to generate text sequence by inputting keywords into the generator, and then judge the difference between the generated text and the real text by discriminator. However, it takes a certain amount of human resources to mark the keywords in each article, and the training of the generator is difficult to achieve good results due to the lack of good parallel corpus. Based on the generation of confrontation model, this paper proposes a text style transfer model tst-gan (general adverse networks based on text style transfer) for legal documents. First, the discriminator is used to train the real text, and then the generator generates the original text into the format of the target text by the way of text style transfer. The experimental results show that the model can reduce the workload and generate ideal legal texts.
CSPO-DCNN: Competitive Swarm Political Optimisation for Breast Cancer Classification using Histopathological Image

Breast cancer is one of the most familiar diseases, and it is the second main reason with several risk stratification and subtypes in the woman. Furthermore, breast cancer is the second most important cause of women’s death. In this paper, the Competitive Swarm Political Optimisation (CSPO)-based Deep Convolutional Neural Network (DCNN) is devised to classify breast cancer. The Type 2 Fuzzy and Cuckoo Search-based (T2FCS) filter is applied for the effective pre-processing process. In addition, the blood cell segmentation process is carried out using the colour-based thresholding model. The most important features, such as area, solidity, eccentricity, perimeter, entropy, kurtosis and Empirical Mode Decomposition (EMD), are extracted for breast cancer classification. Furthermore, the DCNN classifier is employed for the breast cancer classification process. The developed CSPO technique is utilised for training the DCNN classifier for effective classification. However, the CSPO approach is newly devised by combining Political Optimiser (PO) and Competitive Swarm Optimiser (CSO). In this method, the benign and malignant types of cancers are classified. Moreover, the benign type is classified as adenosis, fibroadenoma, phyllodes and tubular adenoma, whereas the malignant type is classified as carcinoma, lobular carcinoma, mucinous carcinoma and papillary carcinoma. Additionally, the developed cancer classification approach achieved improved performance based on several parameters, namely, accuracy, sensitivity and specificity, with 95.94%, 97% and 94.25%.