Reviewerly/exHarmony huggingface dataset:
Estimating the Number of Cameras Required for a Given Classroom for Face-based Smart Attendance System

Manual attendance systems are time consuming, thus, there is a growing interest in automatic attendance systems. In particular, there has been growing interest in face-based automatic attendance systems recently. However, to our knowledge, one important aspect of face automatic attendance using face has not been addressed. Thus, in this paper, a methodology to estimate the number of cameras with a given resolution required to enable capturing all students' faces in a given classroom with sufficient details to enable automatic attendance using face in a typical classroom environment is presented. The methodology was tested using two types of camera's resolution in two classrooms with different sizes. It was found that the proposed methodology can be used to estimate the number of cameras with a given resolution required to provide coverage of all students' faces with sufficient details that enables faces of students looking at the camera to be detected.
Authentication Scheme for IoT

Internet of Things (IoT) is used in variety of applications such as healthcare and smart cities where there are critical and sensitive data that need to be secured. Authentication is one of the security requirements in IoT to prevent unauthorized user from accessing the data. In this paper we analyzed different authentication methods for IoT and their weakness. Some security requirements and challenges in IoT are discussed. Also, authentication scheme phases for IoT are discussed.
News Background Linking Using Document Similarity Techniques

Ajnadkar, Omkar Jaiswal, Aman Gourav Sharma, P. Shekhar, Chandra Soren, Arun KumarNews readers still look forward to well-established news sources for a more accurate and detailed analysis of the news or articles. In this paper, we present a model that can retrieve other news articles that provide essential context and background information related to the news article that is being looked into. The abstract topics have been recognized using Latent Dirichlet Allocation. A similarity matrix has been created after the extraction of topics using two methods, cosine similarity and Jensen–Shannon divergence. This paper describes the implementation and gives a brief description of both the similarity methods. It also gives a comparative study of both methods.
Automatic Classification of Erythrocytes Using Artificial Neural Networks and Integral Geometry-Based Functions

The red blood cell deformation caused by disorders like sickle cell disease can be assessed by observing blood samples under a microscope. This manual process is cumbersome and prone to errors but can be supported by automated techniques that allow red blood cells to be classified according to the shape they present. There are proposals in the literature that use functions based on integral geometry to obtain a description of the cells' contour before performing classification, reaching 96.16% accuracy with the use of the k-Nearest Neighbor (KNN) classifier. In those approaches, the classification-confusion cases persist mainly in the classes of most significant interest, which are those related to the detection of deformed cells. In this work, we use artificial neural networks-based classifiers, trained with the characteristics obtained from integral geometry-based functions, to classify erythrocytes into normal, sickle, and other deformations classes. Our proposal achieves accuracy of 98.40%. This result is superior to those of previous studies concerning the classes of greatest interest. Also, our approach is computationally more efficient than previous works, making it suitable for supporting medical follow-up diagnosis of sickle cell disease.
Improving Product Placement in Retail with Generalized High-Utility Itemsets

Product placement in retail has a significant impact on the sales revenue of retailers. Hence, research efforts are being made to improve retailer revenue using high-utility pattern mining based product placement approaches. However, none of these existing approaches has explored generalized high-utility itemset mining for determining product placement in retail. The knowledge of generalized high-utility itemsets extracted from user purchase transactional database in conjunction with a product taxonomy can provide new insights about customer purchase behaviour. This work proposes the generalized utility itemset (GUI) index for retrieving generalized high-utility (revenue) itemsets. We also present a framework, which leverages the GUI index towards retail product placement to improve revenue. Our performance study using real datasets shows the effectiveness of our proposed scheme w.r.t. two existing schemes.
Workload Prediction with Regression for Over and Under Provisioning Problems in Multi-agent Dynamic Resource Provisioning Framework

The infrastructure of cloud computing is distributed environment, where resources, such as CPU, storage, and network bandwidth, are shared. The cloud computing system can have the over and under provision problems. Recently, the problems have been handled using the multi-agent dynamic resource provisioning framework, where Machine Learning algorithm is used for workload prediction. We empirically investigated the effectiveness of regression models for the over and under provisioning on cloud system. We found that while linear regression, polynomial regression, and support vector machine (SVM) performed equally well in term of workload prediction. However, polynomial regression was able to distribute the load among the hosts better. This resulting in the lower total energy consumption. All regression model requires little time for parameter tuning. As such, we suggest that the model's parameters should be readjusted as needed.
A Bedsheet for Baby Monitoring at Night: Measurement and Characterization Results

This paper presents the characterization of a smart bedsheet developed by Studio 1 Labs, which could be used to monitor the movement of an infant at night in order to detect and prevent sleep-related disorders. This smart bedsheet consists of an array of conductive fabrics to be used as pressure sensors to track the baby's movement. Electrical impedance spectroscopy (EIS) has been performed using the Metrohm Autolab potentiostat on a single and two-fabric interface. The results of this study will provide the information required to develop a sensitive and reliable smart bedsheet.
Dense multiview stereo based on image texture enhancement

Abstract In this paper, we propose a novel Multiview Stereo (MVS) method which can effectively estimate geometry in low‐textured regions. Conventional MVS algorithms predict geometry by performing dense correspondence estimation across multiple views under the constraint of epipolar geometry. As low‐textured regions contain less feature information for reliable matching, estimating geometry for low‐textured regions remains hard work for previous MVS methods. To address this issue, we propose an MVS method based on texture enhancement. By enhancing texture information for each input image via our multiscale bilateral decomposition and reconstruction algorithm, our method can estimate reliable geometry for low‐textured regions that are intractable for previous MVS methods. To densify the final output point cloud, we further propose a novel selective joint bilateral propagation filter, which can effectively propagate reliable geometry estimation to neighboring unpredicted regions. We validate the effectiveness of our method on the ETH3D benchmark. Quantitative and qualitative comparisons demonstrate that our method can significantly improve the quality of reconstruction in low‐textured regions.
Design Aspects of a Virtual Reality Learning Environment to Assess Knowledge Transfer in Science

Science educators need assessment tools to assess to what extent learners’ knowledge and skills can be transferred to real-life situations. Virtual reality learning environments (VRLEs) can be used to create authentic virtual spaces where situated learning and assessment can take place. However, there are considerable design and implementation challenges when developing a VRLE. This research explored the design aspects of a virtual reality environment for the assessment of knowledge transfer in science education. A design science research approach was followed, implementing existing guidelines from literature in building a VRLE. Lessons learned from the implementation were formulated, and the theoretical contribution of this study is a set of literature-based, practice evaluated guidelines, synthesising lessons learned. From the study, it is apparent that there are many benefits from using a VRLE for assessment, and we hope that using these guidelines could mitigate some of the usability issues that remain.
Active learning for image preparation of automatic vending machine (AVM) employing transfer learning method

Abstract In this paper, we employed active learning methods to prepare annotated images for our training of automatic vending machine (AVM) system, in order to minimize human annotation cost. Due to the tiny data of our system, transfer learning approach is used by implementing the already trained Yolov3-tiny model for COCO dataset as our training start. Also, we evaluated the effectiveness of 3 annotation strategies: smallest annotation area (SAA), largest annotation area (LAA) and moderate annotation area (MAA), for photos of top views from above. The results show that the idea of employing active learning methods to prepare annotated data is feasible. Also, the annotation strategy of MAA demonstrates the superior performance, for its enough object area and the least background area.
Synthesized Optimal Control by Group Interaction of Quadcopters*

The problem of optimal control for interaction of three robots is considered. To solve the problem, the synthesized optimal control method is used. According to this method, firstly the synthesis problem of control system for each robot is solved. The synthesized control system allows to stabilize robot relatively some point in the state space. On the second stage positions of some stabilization points are found in the state space such that at switching these points from one to the next through a set time interval, robots move a cargo from initial position to terminal one with optimal value of quality criterion. For the solution of the synthesis problem the symbolic regression method is used. All phase constraints describing group interaction and obstacles, have included in quality criterion as a penalty functions. For searching positions of the points the evolutionary algorithm particle swarm optimization is used.
Using QRCode to Enhance Extraction Efficiency of Video Watermark Algorithm

Video watermarking can effectively protect the copyright of video contents, but how to improve efficiency of watermarking algorithms is an urgent problem to be solved. In this paper, QRCode is embedded in the scene change frames of videos based on the advantage of QRCode's strong fault tolerance. Combined with the characteristic of high decoding reliability of QRCode, a strategy to terminate the extraction process in advance is proposed to improve the extraction efficiency of the watermark algorithm. Experimental results show that the proposed algorithm has higher extraction efficiency than the algorithm that directly uses character string as watermark.
Fuzzy Control Systems with Reduced Parametric Sensitivity Design Based on Hybrid Grey Wolf Optimizer–Particle Swarm Optimization

This paper proposes an optimal tuning method for Takagi-Sugeno-Kang Proportional-Integral fuzzy controllers (TSK PI-FCs) based on a novel hybridization of Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms referred to as hybrid GWO-PSO algorithm. The optimization problem defined for servo system processes controlled by TSK PI-FCs is solved by applying the hybrid form of GWO-PSO in the minimization of an objective function that depends on the output sensitivity function of the sensitivity model. The sensitivity analysis applied to these fuzzy control systems produces sensitivity models regarding the parametric variations of the dynamic processes subjected to control (i.e., the servo system). Solving the optimization problem implies the minimization of the objective function by means of the hybrid GWO-PSO algorithm. This hybrid variation of the two nature-inspired algorithms allows an increased control over the exploitation phase by inserting PSO search process features along with the exploration capabilities of GWO. The design method is validated using an experimental setup based on a nonlinear servo system.
Age verification using random forests on facial 3D landmarks

Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and verification. This paper proposes an approach using random forests, a machine learning method, to develop and test models for classification of legal age thresholds (15 years and 18 years) using 3D facial landmarks. Our approach was developed on a set of 3D facial scans from 394 Czech individuals (194 males and 200 females) aged between 10 and 25 years. The dataset was retrieved from a sizable database of Central European faces – The FIDENTIS 3D Face Database. Three main types of input variables were processed using random forests: I) shape (size-invariant) coordinates of 3D landmarks, II) size and shape coordinates of 3D landmarks, and III) inter-landmark distances, angles and indices. The performance rates for the combinations of variables and age threshold were expressed in terms of sensitivity and specificity. The overall accuracy rates varied from 71.4%–91.5% (when the male and female samples were pooled). In general, higher accuracy was achieved for the age limit of 18 years than for 15 years. Whereas size-variant variables showed a better performance rate for the age limit of 15 years, the size-invariant variables (i.e., shape variables) were better for classifying individuals under 18 years. The verification models grounded on traditional variables (distances, angles, indices) yielded consistently higher performance rates on females than on males, whereas the inverse trend was observed for the models built on 3D coordinates. The results indicate that age verification based on 3D facial data with processing by the random forests method has high potential for further forensic or biometric applications.
CoVR: Co-located Virtual Reality Experience Sharing for Facilitating Joint Attention via Projected View of HMD Users

Sharing virtual reality (VR) experiences between users wearing head-mounted displays (HMD users) and users not wearing HMDs (Non-HMD users) is a promising approach that can help bridge the gap between these users' experiences. In previous studies, the role of these users and the differences in their attention targets were not considered, causing a lack of joint attention in user communication. Also, previous systems required cumbersome installation in the spaces in which VR was being experienced. Therefore, this paper proposes "CoVR," a co-located VR sharing system comprising an HMD with a focus-free projector and projecting the perspective of HMD users. Further, we introduce a design methodology for controlling the perspective of the images displayed to the HMD and Non-HMD users. We also discuss three application scenarios where additional information provided are different for each user.
Self-management resource in increasing professional vitality and developing the managerial culture of teachers

The article discusses issues related to the problems of professional viability of an education worker in an electronic information and educational environment. Self-management and time management are considered as tools for the professional and personal development of teachers. The theses are confirmed with the research study results of the faculty of the Department of Educational Systems Management named after T. I. Shamova of MPGU.
Challenges in Developing Software in Today's Scenario

Software engineering emphasises to adopt a well-defined and structured approach to develop any software. Nonetheless, serious challenges exist in the software development process and these challenges are faced in developing software in today's scenario. This is majorly to satisfy the need for developing good quality product which has the capability to meet the “volatile user requirements” in organizations. As new products are developed to tend to the current technological needs, new challenges arise inevitably. So, until a new challenge is encountered, solutions to these unknown and newly arising challenges cannot be devised. Hence, a better software development strategy would be to realise all the previously encountered, more frequent or obvious challenges, and to design an efficient solution to these known challenges beforehand so that no more extra resources and time need to be diverted during the software development cycle in order to overcome the challenges. Therefore, recognizing and addressing the challenges in software development at each and every developmental stage is extremely necessary in order for organizations to succeed. In this study, the authors have attempted to structurally and systematically reviewing the literature to arrive at the software development challenges and provided ways of addressing those challenges. The study also provided for possible solutions and recommendations and scope for future research in this area.
The Influence of Text Length on Text Classification Model

In this paper, we detect the mental health status of the posters based on social media. Due to the wide range of textual data length in social media, we prove that the length of text input has an effect on the main deep learning architectures such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) used in text classification. We argue through three distinct indicators in the English standard dataset and Reddit mental illness dataset, including accuracy, model parameter complexity and time complexity, and set the maximum length of text input on the text classification models by changing the proportion of training/test dataset. As text grows, the accuracy of text classification algorithms also tends to increase, as does the time complexity and model parameter complexity overhead. The shorter maximum length of text results in lower accuracy, while the longer maximum text length leads to higher training costs of algorithms. It allows us to find that the common text classification algorithm models have shown significant influence on the standard English dataset and Reddit mental illness dataset. The length of text or a string, especially for controlling the maximum length of text input on the models separately, puts a strict limit on the text classification algorithm.
Analysis of Formats of Young People’s Communicative Behavior in Social Network

The research is devoted to the analysis and classification of young people's communicative behavior in social network. We define this behavior as information provided within the user's profiles in the social network. To classify young people's communicative behavior we use the scales of Ego-structure test (by G. Ammon). The neural networks are used for classification. The paper describes the feature extraction process for neural network training. The features are based on the information provided within the user's profiles. The experimental results for different classifiers are given. The analysis of the used classification is conducted and directions of future work are described.
A New Adaptive Filter for Eliminating Salt and Pepper Noise

Abstract Salt-and-pepper noise can substantially degrade the appearance of images and make further processing difficult. This paper develops a new filter for eliminating different levels of salt-and-pepper noise efficiently without degrading important image details. The filter uses a variable window size. After identifying distorted pixels, if there is at least one or more undistorted pixels are encountered in the processing window, the updated value of the distorted pixel is calculated by the weighted mean of undistorted pixels when a window size is 3×3 and replaced by the mean value of the undistorted pixels with the highest frequency distribution when a window size is larger. This filter is applied twice in order to sufficiently remove high noise levels. The comparison results with other denoising filters indicate that the developed filter has superior or comparable denoising capability in terms of visual appearance and objective measures.
Visualizing dynamic graphs with heat triangles

In this paper an overview-based interactive visualization for temporally long dynamic graph sequences is described. To reach this goal, each graph can be mapped to a certain value based on a given property. Among others, a property can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total). To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire graph sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data.
Good codes from metacyclic groups II

In this paper, we consider semisimple group algebras [Formula: see text] of split metacyclic groups over finite fields. We construct left codes in [Formula: see text] in the case when the order [Formula: see text] is [Formula: see text], where [Formula: see text] and [Formula: see text] are different primes such that [Formula: see text] extend the construction described in a previous paper, determine their dual codes and find some good codes.
Fringe analysis with a two-step phase shifting method based on local Lissajous ellipse fitting

This work aims to report an algorithm for performing fringe analysis with two phase-stepped fringe patterns. By performing a particular Least Squares Fitting (LSF) it is intended to overcome the restrictions to fringe patterns of having constant background or being high-pass filtered when using Lissajous ellipse fitting. With the new method, the phase step and the fringes background are represented by single coefficients that depend on image position, avoiding the utilization of additional arithmetic operations for its calculation, as done with previous methods, increasing its accuracy and range of applicability. Experimental and simulations are presented to elucidate our proposal.