Reviewerly/exHarmony huggingface dataset:
Educational and research competencies in the training of the course “Discrete mathematics” for training bachelors in computer engineering

In this paper we consider the control problem of ensemble of trajectories for one In recent years, the importance of discrete mathematics has increased dramatically. This is not the name of a branch of mathematics such as number theory, algebra, calculus. This is a separate branch of knowledge, which is called “discrete” (individual). However, there are also processes of continuous mathematics, that is, it becomes a continuous whole. Discrete data can only take integer values, while continuous data can take any value. Discrete mathematics was created several decades ago and is the mathematical language of computer science. Scientists have found that the mathematical disciplines taught in universities are insufficient for learning a programming language. Therefore, they combine additional maths topics into one course, now called discrete mathematics. Discrete mathematics is the foundation of computer science and theoretical computer science. Without learning discrete mathematics, we miss the essence of the logic of computer science. Due to the multiplicity of objects within discrete mathematics, rational clarity in solving programming issues is possible. Every field in computer science is associated with discrete objects, be it databases, network components, computer organization, compilers, network programming, and so on. Essentially, discrete mathematics studies the concepts of counting, mathematical structures, logic, recursion, computational modeling, graph theory and algorithms, and many other disciplines. Evidence, a basic concept in discrete mathematics that includes deductive thinking, simplification, pattern identification, mathematical notation, working within constraints and conditions. This does not mean that a formal course in discrete mathematics is necessary or should be required for programmers. However, many of the concepts discussed in discrete mathematics are applied using this science. In this article, we will look at the basic postulates of discrete mathematics that are necessary to learn the basics of programming.
Study of data deduplication for file chunking approaches

Data deduplication is a mechanism or method for handling problems of data duplication especially in the storage elements. The duplicate chunks of data are eliminated such that it reduces the storage problems related to the original and its duplicates and the storage utilization is optimized. The duplicate data elements are assigned a single pointer element associated with the data by using a hash function. These pointer elements or any referencing elements are used to point to the unique data elements for maintaining the data integrity over all the data chunks. There are various chunking approaches, such as fixed and the variable size chunking approaches. Modifications in data chunk size lead to the boundary shift problem as the window slides and there are measures such as content defined control to handle the variable chunk size, in order to optimize the storage, throughput, and efficiency utilization.
iDeBlurInfo: An Assessment and Evaluation Study of Image Deblurring Techniques

Quality of images used in a variety of systems contributes to the success of discovering beneficial details used in different fields of computing. The quality of the image plays a vital role in increasing the success rate of image recognition and related fields. Image quality can be degraded due to distortion, imperfection during image capture, inappropriate light balancing, and exposure to name a few. Various deblurring techniques were introduced to address blurred issues, transforming an image to lesser imperfections, successfully reading and/or identifying available details on it. This study aims to identify, define, and evaluate deblurring techniques in a way that demonstrates comprehension of the tradeoffs involved in the design. Significantly, the study compares various techniques that can be used to recover hidden details in the presence of image noise. The MA TLAB was used as a tool for testing and evaluating the deblurring techniques. PSNR, MSE, and computational time were used to compare each identified deblurring techniques. Based on the comparative performance results of each technique, in terms of accuracy measure, Lucy-Richardson got the highest PSNR ratio of 25.8312 that leads the best result while wiener filter got the lowest PSNR ratio of 21.3012. While, based on the MSE results, Wiener Filter got a higher rate of 0.0074; while blind deconvolution and Lucy-Richardson got the lowest value of 0.0026. And in terms of execution time, Wiener Filter performs the fastest computational time.
Controlling Population Diversity of Harris Hawks Optimization Algorithm Using Self-adaptive Clustering Approach

Harris Hawks optimization (HHO) algorithm is a new adaptive search method that can be employed as approximation heuristic for complex optimization problems. HHO algorithm processes a population of search space with two operations: Soft besiege and Hard besiege. One of main problems in the use of population-based algorithms is premature convergence. A premature stagnation of the search creates a shortage of diversity, which affects the relationship between exploration and exploitation processes. Here, we propose a self-adaptive clustering approach based on k-means and density for all solutions in the population pool. The k-means clustering method will examine the distribution (i.e., locations) of the solutions over the search space. Once most of solutions (more than \(90\%\)) are related to one cluster, an exploitation process will gain a higher priority compared to exploration one. Redistributing the solutions over several clusters by a redistribution process will keep the ratio between exploration and exploitation stable over all iterations. The proposed approach will be tested over several mathematical test functions. The obtained results show that the proposed modification can enhance the performance of HHO algorithm and prevent premature convergence.
The Undyeing Swatch

The Undyeing Swatch utilizes a combination of visible Light Emitting Diodes (LEDs) and photocatalytic nanoparticles to diminish the color of organically dyed textiles. As such, this swatch explores 'undyeing' as a design process that utilizes light and dye as materials for controlled interaction. The swatch itself consists of a knitted cotton I-cord coated with silver doped titanium dioxide nanoparticles (TiO2/Ag) and dyed with hibiscus. The I-cord is used to encase a strand of LEDs and was then continuously woven into a swatch. When the LEDs are turned on, the light activates the nanoparticles which in turn break down organic matter (the dye). This swatch provides a proof of concept for the undyeing process, which I believe could be an interesting area of future exploration for HCI researchers and artists alike.
A Framework for Social Media Data Mining and Analysis to Product and Service Development- Case of The Zimbabwean National Art Gallery

Social networks have become a vital component in personal life. People are addicted to social network features, updating their profile page and collaborating virtually with other members have become daily routines. Web data mining is a new trend in current research studies. This study sought to develop a framework for social media data mining and analysis for the betterment and advancement of products (art effects) and services (exhibitions) with in the contemporary art industry of Zimbabwe through a case study of the Zimbabwean National Art Gallery. The information for this paper was gathered through the use of in-depth interviews, questionnaires, key-word search and API from the organization’s social media twitter handle were used to get the data for analysis. Focus group participants were chosen from the National art gallery and a matching number was selected from the artist who makes the artworks which will be soles or exhibited through the national art gallery. The findings suggested that, yes it is possible to inform the next batch of products with information mined from analyzing the sentiments or reviews of the previous set of artworks. Hence with this in mind the researchers managed to develop a framework which can be used to implement social media mining in the art sector of Zimbabwe. The proposed framework tried to handle the major limitations in current web mining frameworks by handling challenges such as special symbols, slang use, Data Validity analysis, time frame, and methodologies.
Ethics and Statistics

chapter Share on Ethics and Statistics Author: David McMenemy Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 101–103https://doi.org/10.1145/3447404.3447411Online:23 February 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
P‐15.4: Virtual reality gesture recognition based on depth information

With the rapid development of computer technology, the information revolution is constantly affecting and changing our lives. Today's virtual reality technology is no longer just a virtual model for viewing. Experiencers are more willing to personally interact with the virtual environment in a virtual scene to increase their sense of gaming experience. Today's virtual reality interaction methods mainly interact through the handles that come with VR helmets. Previously, some scholars have researched gesture interaction in virtual environments. They use data gloves to obtain information about hand movements. This method makes users have to wear heavy data gloves, which limits the natural movement of their hands. To overcome this shortcoming, the need for low‐cost vision‐based virtual reality gesture interaction is becoming more and more urgent. Traditional gesture recognition technologies based on artificial modeling include template‐based matching, data‐based gloves, and machine learning. As machine learning gesture classifiers, Hidden Markov Model (HMM) and Support Vector Machine (SVM) [1] have been widely used. However, in the hand motion detection of ordinary cameras, the image is prone to speckles, lighting changes, and other factors, which seriously affects the performance of the classifier, making it difficult to accurately and efficiently detect the first image when the background is complex and the lighting is affected. In recent years, deep learning‐based object detection methods have achieved great breakthroughs [2]. In the past decade, many deep learning‐based dynamic gesture recognition algorithms have been introduced. In the case of changing lighting conditions, in order to improve the classification accuracy of gesture recognition [3]. Therefore, this article chooses a depth camera combined with HTC VIVE device for gesture recognition in virtual reality. First obtain the depth image of the hand through the depth camera, then label the depth image, and then learn through the deep learning network hand movement training [4], preprocess the detected human hand image, and recognize it by hand detection The position of the hand in the image is obtained, and the movement of the hand is recognized as a result of recognition. The algorithm in this paper uses the advanced deep learning network YOLOv3, and changes the network structure and multiscale structure based on the original network. This algorithm is compared with other deep learning algorithms for gesture recognition, and the recognition effect is better than other algorithms in the same category.
PENGARUH PENGGUNAAN MODEL KOOPERATIF TIPE STAD TERHADAP KETERAMPILAN MENULIS TEKS DESKRIPSI SISWA KELAS VII SMP NEGERI 12 TANJUNG JABUNG TIMUR

This purposes of this research are (1) to identify the effect of the cooperative model STAD type on students’ writing skill of seventh grade students of SMP Negeri 12 Tanjung Jabung Timur academic year 2019/2020, and (2) to describe the steps of writing descriptive text through the cooperative model STAD type of class VII students of SMP Negeri 12 Tanjung Jabung Timur academic year 2019/2020. This research is Quasi-Experimental. The subjects of the research was the students of class VII SMP Negeri 12 Tanjung Jabung Timur in which 30 students in control class and 30 students in experimental class. The data was taken by written test techniques. The data analysis was done by calculating the total score of the control class and the experimental class of each student which was then compared with their skill categories. The indicator of success in this research is the effect of students’ ability in writing descriptive text in class VII of SMP Negeri 12 Tanjung Jabung Timur through the use of cooperative learning model STAD type. This research is said to be successful if the percentage of the students in the experimental class is 82.63 higher than the control class which falls into 70.20. The results showed: (1) the use of cooperative learning model type STAD has an effect in writing descriptive text. (2) The steps of the cooperative learning model STAD type in writing descriptive text are: 1) conveying goals and motivating students, 2) presenting information, 3) organizing students in cooperative groups, 4) guiding groups to work and study, 5) evaluating, and 6) giving awards. Thus the cooperative learning model STAD type significantly influence students’ skill in writing descriptive text.
A Benchmark for the Bayesian Inversion of Coefficients in Partial Differential Equations

Bayesian methods have been widely used in the last two decades to infer statistical properties of spatially variable coefficients in partial differential equations from measurements of the solutions of these equations. Yet, in many cases the number of variables used to parameterize these coefficients is large, and oobtaining meaningful statistics of their probability distributions is difficult using simple sampling methods such as the basic Metropolis--Hastings algorithm---in particular, if the inverse problem is ill-conditioned or ill-posed. As a consequence, many advanced sampling methods have been described in the literature that converge faster than Metropolis--Hastings, for example, by exploiting hierarchies of statistical models or hierarchies of discretizations of the underlying differential equation. At the same time, it remains difficult for the reader of the literature to quantify the advantages of these algorithms because there is no commonly used benchmark. This paper presents a benchmark Bayesian inverse problem---namely, the determination of a spatially variable coefficient, discretized by 64 values, in a Poisson equation, based on point measurements of the solution---that fills the gap between widely used simple test cases (such as superpositions of Gaussians) and real applications that are difficult to replicate for developers of sampling algorithms. We provide a complete description of the test case and provide an open-source implementation that can serve as the basis for further experiments. We have also computed $2\times 10^{11}$ samples, at a cost of some 30 CPU years, of the posterior probability distribution from which we have generated detailed and accurate statistics against which other sampling algorithms can be tested.
Multi agent-based framework for interactive learning system with conversational user interface for visually and speech impaired

Abstract The purpose of this research article is to illustrate the role of Conversational User Interface to foster inclusive education among the Speech Impaired People (SIP) and the Visually Impaired People (VIP). The multiagent framework on “Conversational User Interface for interactive learning” will throw light on the role of the host and the learner in communicating and facilitating knowledge with each other. The article has been built after the in-depth exploration of various sources, which were handpicked for the study. This multiagent model can be put into implementation in the education industry. The scope of the study extends only to SIP’s and VIP’s because of their growing numbers across the globe, and yet they are deprived of formal education. The Conversational User interface can be used to bridge the gap, especially with the advancement of technologies such as VUI, MUI, WUI, BOTs, etc., for the VIPs and SIPs.
Understanding Event Boundaries for Egocentric Activity Recognition from Photo-Streams

The recognition of human activities captured by a wearable photo-camera is especially suited for understanding the behavior of a person. However, it has received comparatively little attention with respect to activity recognition from fixed cameras. In this work, we propose to use segmented events from photo-streams as temporal boundaries to improve the performance of activity recognition. Furthermore, we robustly measure its effectiveness when images of the evaluated person have been seen during training, and when the person is completely unknown during testing. Experimental results show that leveraging temporal boundary information on pictures of seen people improves all classification metrics, particularly it improves the classification accuracy up to 85.73%.
P-Tracer: Proximity Detection for Contact Tracing

COVID-19 pandemic has impacted millions of people worldwide. To tackle the spread of disease, governments all over the world had to impose sudden lockdowns with strict restrictions and has resulted in stressing most of the enterprises on the financial fronts. As the governments all over the world are now relaxing the restrictions, businesses are re-opening gradually and employees are returning back to work. There is huge responsibility on the enterprises to stay vigilant when an employee tests positive for COVID-19. To target the issue, we have come up with a contact tracing solution, P-Tracer application which uses users wifi association data from Arista Networks' Cloud-Vision Wifi. This paper shortly presents the P-Tracer application working principle. We claim that the application will be useful in tracing the users association journey and detection of new hot zones and possible new cases.
Spam Detection Using Threshold Method on Whatsapp Image Data

We receive many wishing images during the festive season which actually spam our devices or fill up the necessary space of storage. In this paper, we are discussing a method to find out those redundant images using a threshold method. The proposed algorithm discuss in this paper will remove the redundant images in a targeted folder and keep them aside.
Simultaneous Detection of Regular Patterns in Ancient Manuscripts Using GAN-Based Deep Unsupervised Segmentation

Document Information Retrieval has attracted researchers’ attention when discovering secrets behind ancient manuscripts. To understand such documents, analyzing their layouts and segmenting their relevant features are fundamental tasks. Recent efforts represent unsupervised document segmentation, and its importance in ancient manuscripts has provided a unique opportunity to study the said problem. This paper proposes a novel collaborative deep learning architecture in an unsupervised mode that can generate synthetic data to avoid uncertainties regarding their degradations. Moreover, this approach utilizes the generated distribution to assign labels that are associated with superpixels. The unsupervised trained model is used to segment the page, ornaments, and characters simultaneously. Promising accuracies in the segmentation task were noted. Experiments with data from degraded documents show that the proposed method can synthesize noise-free documents and enhance associations better than the state-of-the-art methods. We also investigate the usage of overall generated samples, and their effectiveness in different unlabelled historical documents tasks.
Enhancing Ant Colony Optimization by Adaptive Gradient Descent

Ant Colony Optimization (ACO) is one of the widely used metaheuristic algorithms applicable to various optimization problems. The ACO design has been inspired by the foraging behavior of ant colonies. The performance of the algorithm, however, is not satisfactory since the update stage of pheromones in the algorithm is static and less intelligent, leading to early maturity and slow convergence. To improve the algorithm, in this research, a new ACO algorithm with an innovative adaptive gradient descent strategy (ADACO) is designed, and the algorithm is applied to the Travelling Salesman Problems (TSP) problem for validation. In the chapter, first, the ACO algorithm for TSP is modeled in the framework of stochastic gradient descent (SGD). A new loss function aiming at minimizing the expectation of error ratio and its gradient is defined. Then, an adaptive gradient descent strategy, which can exploit the update history of per-dimensional pheromones to achieve intelligent convergence, is integrated into the ACO algorithm as ADACO. A parallel computation process is also implemented in the algorithm. Finally, ADACO was trialed on various sizes of TSP instances and benchmarked with the Max–Min Ant System (MMAS) algorithm, the Ant Colony System (ACS) algorithm, and the Iterated Local Search (ILS) algorithm. Results show that ADACO outperformed those comparative algorithms in terms of accuracy, stability and adaptability. Furthermore, the results also elucidate that ADACO maintained high-performance computational efficiency owing to its parallel implementation.
Distance Estimation for Endoscopy Based on Depth Detection in the Small Intestine Using Image Processing techniques

In recent years, three-dimensional measurement techniques have been widely used in medical sciences, and thus, depth detection in an image plays an important role in computer vision applications. In this paper, we discuss the estimation of the distance between the head of an endoscope and the small intestine septum and its problems. The main objective is to detect the depth of the small intestine to estimate distance. Images were collected through video sampling, and then the data are preprocessed. Morphological reconstruction, bounding box, Convex Hull, and Euclidean distance are employed to update the mentioned distance. At the end of this process, the outputs are simulated, and we are given the output distance in centimeters. This method will assist the endoscope to move inside the small intestine without injuries.