Reviewerly/exHarmony huggingface dataset:
Image Colorization Algorithm Based on Graph Signal Processing Using Two-Steps Image Segmentation

An image colorization algorithm based on a graph signal processing has been proposed, and it achieves a high colorization performance. In order to model the chrominance image, this colorization algorithm constructs the graph based on the image segmentation from the given grayscale image. However, there is room for improvement respect to the image segmentation technique since the size of each object on the image have not been considered. This paper proposes the image colorization algorithm using a spatial region-based image segmentation algorithm based both on QuadTree with nested Multi-type Tree (QTMT) segmentation and Simple Linear Iterative Clustering (SLIC). Numerical experiments, this paper demonstrates the effectiveness of the proposed algorithm by comparing it with previous studies using eight standard images.
A user-centered approach to evolutionary algorithms and their use in industry

The key contribution of this article to the domain of engineering optimisation is establishing best practices with respect to the development of evolutionary algorithms in the context of design engineering. Despite their various uses, the uptake of evolutionary algorithms in industry remains limited. In order to understand why uptake is low a survey of engineers was undertaken, the results of which are presented here. A total of 23 participants (N = 23) took part in the 3-section mixed methods survey. Reflexive thematic analysis was conducted on the open-ended questions. A common thread throughout participants responses is that there is a question of trust towards evolutionary algorithms within industry. Perhaps surprising is that the key to gaining this trust is not producing good results, but creating algorithms which explain the process they take in reaching a result. Based on this, recommendations have been made to increase their use in industry.
Search for a Flavor Suited to Beverage by Interactive Genetic Algorithm

Interactive evolutionary computation (IEC) is a method to optimize media contents suited to user’s subjective feelings and preferences. Previous IECs employed various evolutionary algorithms, and most of them employed Genetic Algorithm (GA). The interactive type of GA is called IGA, and the IGA is applied to create user’s media content such as computer graphics, music, and sound. As a special application of the IGA, the creation of the scent was already proposed. In the IGA related to the scent, the intensity of the source aromas was treated as variable of GA individuals. In this study, the IGA is applied to create the flavor suited to beverage. The soda water with no sugar is treated as the beverage. It is popular among health-minded people, and some of the soda waters wear a flavor. In other words, the purpose of this study is to create a good flavor suited to soda water by reflecting each user’s feeling using IGA. In the use of the IGA, the user looks at the soda water with no sugar and smells the mixed flavor. In the mixture of the source aromas, Aromageur is used in the system. To investigate the fundamental efficiency of the IGA, a smelling experiment was conducted. The target of creation was “delicious” flavor for soda water, and six aroma oils were used as the source oils. The significant increase in fitness value was observed.
Mass-Based Similarity Weighted k-Neighbor for Class Imbalance

Conventional distance-based or density-based classifiers like k-Nearest Neighbor algorithm face difficulty for class imbalance problem because they treat all neighbors equally though most of these instances belong to the majority class. Additionally, a varied density of data points existing in an imbalanced dataset poses another significant challenge for the distance-based classifiers. These two main challenges motivated us to investigate further a new mass-based similarity weighted k-neighbor approach for the imbalanced classification task. In our framework, the mass-based dissimilarity measurement is normed to obtain the similarity degree between two instances. Then the confidence of a neighbor instance is derived as the posterior probability while knowing the prior of the class label in training space and the likelihood in Bayes' theorem. We aim to add more importance to the neighbors, which have higher mass-based similarity weighted confidence than the others. Then, the weighted sum operator is used to combine the weighted confidences for predicting the class label. The experiments conducted on 60 imbalanced datasets show that the proposed approach outperforms the other 11 existing competitive methods in terms of precision-recall curves. We use the F1 score in our comparisons where we tested 12 models. We also use the Wilcoxon signed ranks test as a non-parametric statistical analysis to validate the experimental results.
Acionamento de um Sistema de Rastreamento Solar Real em Malha Fechada Utilizando FPGA

The present work aims to propose the development of a real solar tracking system using an FPGA (Field Programmable Gate Array) for control, and together with the development of all interfaces and drivers connected to the electronics involved for the correct functioning of the components. The module in question is a set of 6 solar panels in series, supported on a single axis support. The movement of the plates will be carried out by a DC motor that will be attached to the structure that it supports. Thus, the aim is to develop the control system for the motor activation in closed loop using MATLAB/Simulink associated with the FPGA as an auxiliary platform. Finally, it is verified the closed loop operation, simulating the behavior of the DC motor by an RL load, performing the activation of the system in the positive and negative direction, obtaining in the end a similar response between the simulation and the real system.
Data pre-processing of website browsing record: An initial step for web page classification

The Internet utilization has resulted in an increase in the number of web pages on the World Wide Web. The classification of web pages is required to organize the growing number of web pages. A web page classification system is proposed to be constructed using a deep learning algorithm. The initial step for web page classification is data pre-processing. The website browsing record is used as a dataset in this study. The raw dataset needs to be pre-processing to fetch the cleaned data by removing missing value data, redundant data, and error data. There are many steps in data pre-processing which include data cleaning and web content pre-processing. The main contribution of this paper is to investigate how to do data pre-processing on website browsing records that focusing on the Game and Online Video web pages that will be utilized as the dataset to construct the web page classification model. After doing the data pre-processing, the number of datasets will be reduced. This shows many datasets have been removed because it is inactive and not suitable to be used in this study as the dataset of Game and Online Video web pages.
Diagnosis of Breast Cancer by Malignant Changes in Buccal Epithelium Using Artificial Intelligence, Internet of Things, and Cloud Storage

The aim of the paper is to describe a project of novel highly accurate AI system for screening of breast cancer based on investigation of fractal properties of chromatin in Feulgen-stained nuclei of buccal epithelium. The work of the system consists in data collection in cloud storage, morphological pre-processing and classification on the cloud platform and traditional computers, and communication with patients using IoT technology. We studied data on 130 patients: 68 patients with breast cancer, 33 patients with fibroadenomatosis, and 29 healthy individuals containing in average data on 52 nuclei of buccal epithelium. The data set consists of 20,256 images of interphase nuclei of buccal epithelium (6752 nuclei scanned without filter, through a yellow filter, and through a violet filter). Each image consists of three channels: red, green, blue, as well as gray halftones. The combination of cloud storage of data, classification on cloud platform and using the IoT technologies for communication with patients provides high accuracy of diagnosis and comfort for patients.
Discriminative Appearance Model for Robust Online Multiple Target Tracking

Multiple target tracking algorithm faces challenges of occlusion, halt, merge and split of the moving objects. The change in appearance of the moving targets complicates the tracker. Hence the discriminative appearance model is needed for the robust multiple target tracking. This paper incorporates tracking-by-detection approach along with Kalman filter based motion model. The appearance of the target proposed in this paper is modeled based on object's texture features. Phase congruency derived by gray level co-occurrence matrix (GLCM) constitutes the appearance model of the moving object. Thus the proposed tracker is invariant to image illumination and contrast variation. Confidence based data association helps for track management in this paper. The proposed tracker is evaluated on the standard benchmark datasets namely CAVIAR, PETS2009 and ETH. The experimental results of the proposed tracker demonstrate zero error in identity matching when tested on ETH dataset.
Compressive Sensing Image Steganography via Directional Lifting Wavelet Transform

This paper proposes a deep-based compressive sensing (CS) image steganography scheme that inserts the plain image into the directional lifting wavelet transform (DLWT) domain of the carrier image. For the data hiding processing, we exploit two logistic maps to create the measurement matrix and simulated noise respectively, which are used to randomly under-sample and diffuse the plain image to obtain the secret image. Then, we employ singular value decomposition (SVD) to embed the secret image into the DLWT domain of the carrier image, which generates the meaningful stego image. For the data extraction, we introduce the deep-based CS reconstruction algorithm to enhance the quality of the reconstructed image. Experimental results show that the proposed approach maintains security while achieving a high quality of the stego image and the reconstructed image.
Development and Pilot Testing of GoTrace: A Web Application for Contact Tracing Using Quick Response (QR) Codes

It is known that the COVID-19 spread mainly from having close contact with people who have histories of exposure to the novel Corona virus (2019-nCoV or SARS-CoV-2). Though some countries have succeeded in developing the vaccine, its arrival remains a challenge in most countries like the Philippines. For this purpose, strict health protocols and contact tracing remain to be the principal public health interventions and preventive measures to limit the spread of COVID-19. With the number of available contact tracing applications that use Bluetooth and GPS technologies, this study focused on the development and pilot testing of GoTrace, a web-based contact tracing application that uses Quick Response (QR) codes. GoTrace integrates the collection of daily health surveys and location tracking data of the end-users on a digital and web platform to generate real-time, accurate, and reliable contact tracing reports. With the positive feedback of the participants during the pilot testing, GoTrace will offer a promising mobile contact tracing technology that can be adopted in various environments through its dynamic, flexible, and user-friendly architecture.
Pengembangan Modul Pembelajaran Ilmu Pengetahuan Sosial Berbasis Inkuiri

This article aimed to provide an overview of the development of inquiry-based Social Science learning modules with a material of business models in economic activities for the fifth grade of elementary school students. This study also aimed to increase students' learning activities through that module. The goal derived from the low level of students' learning activities and the less varied teaching materials used. The development model for this module followed Bord &amp; Gall. The developed modules were researched and assessed by a team of experts, which consisted of Social Studies and Inquiry Learning experts (87.03%), Materials experts (90.89%), Language experts (87.5%), and Media experts (79.33 %). Overall, the assessment reached 86.18% which was categorized as very good. At the same time, students' responses to the developed modules have an overall percentage of 90.13% which was also very good. In addition, students' learning activities before and after using the module obtained t-count (18.272), while the t-table was 2.045. So, it indicated that t-count ≥ t-table, meaning that accepted Ha and rejected Ho. Therefore, students' learning activities have increased after using the developed inquiry-based social studies learning module.
Visualising Lattices with Tabular Diagrams

Euler and Hasse diagrams are well-known visualisations of sets. This paper introduces a novel type of visualisation, Tabular diagrams, which is essentially a type of Euler diagram where lines have been omitted or a 2-dimensional Linear diagram. Tabular diagrams are utilised to visualise lattices in comparison to Euler and Hasse diagrams. For that purpose, lattice terminology is applied to all three types of diagrams.
Human Behavior Prediction for Risk Analysis

The Conflicting Incentives Risk Analysis (CIRA) method makes predictions about human decisions to characterize risks within the domain of information security. Since traditional behavior prediction approaches utilizing personal features achieve low prediction accuracies in general, there is a need for improving predictive capabilities. Therefore, the primary objective of this study is to propose and test a psychological approach for behavior prediction, which utilizes features of situations to achieve improved predictive accuracy. An online questionnaire was used for collecting behavioral and trait data to enable a comparison of approaches. Results show that the proposed behavior prediction approach outperforms the traditional approach across a range of decisions. Additionally, interrater reliabilities are analyzed to estimate the extent of objectivity in situation evaluations, providing an indication about the potential performance of the approach when a risk analyst needs to rely on unobtrusive assessment of action-desirability.
Efficient Septuple Formula for Elliptic curve and Efficient Scalar Multiplication Using a Triple-Base Chain Representation

Because of shorter key and higher security, elliptic curve cryptosystem has attracted people’s attention and it is widely used in various fields, such as wireless sensor networks. Scalar multiplication is one of the most basic and critical links in the realization of elliptic curve public key systems, and its operation efficiency directly affects the implementation efficiency of the entire cryptographic system. To improve speed up the efficiency of the scalar multiplication algorithm, we express k for scalar multiplication kP by using triple-base chain representation of the scalar using {2,3,7} as basis of the triple-base chain in this paper. However, the efficiency of scalar multiplication is not only related to the length of representation but also the numbers and costs of doubling, tripling, septupling and addition. Therefore, we improve septuple formula of the elliptic curve by using Co_Z operation, which costs decreased by 8.3%. Due to the high redundancy of the triple-base chain representation, the algorithm can resist side channel attacks. The experimental results show that the proposed algorithm compared with that of other scalar multiplication algorithms, it requires less cost.
Real-Time Implementation of a Friction Drum Inspired Instrument Using Finite Difference Schemes

Physical modelling sound synthesis is a powerful method for constructing virtual instruments aiming to mimic the sound of real-world counterparts, while allowing for the possibility of engaging with these instruments in ways which may be impossible in person. Such a case is explored in this paper: particularly the simulation of a friction drum inspired instrument. It is an instrument played by causing the membrane of a drum head to vibrate via friction. This involves rubbing the membrane via a stick or a cord attached to its center, with the induced vibrations being transferred to the air inside a sound box. This paper describes the development of a real-time audio application which models such an instrument as a bowed membrane connected to an acoustic tube. This is done by means of a numerical simulation using finite-difference time-domain (FDTD) methods in which the excitation, whose position is free to change in real-time, is modelled by a highly non-linear elasto-plastic friction model. Additionally, the virtual instrument allows for dynamically modifying physical parameters of the model, thereby allowing the user to generate new and interesting sounds that go beyond a real-world friction drum.
Domain-Aware Multiagent Reinforcement Learning in Navigation

Multiagent reinforcement learning has shown success in guiding the agents' behaviour in systems that have realworld significance. In these frameworks, agents learn how to interact with the environment and other agents while satisfying their objectives. Unfortunately, the level of complexity of realworld problems requires a significant investment of computational resources before multiagent reinforcement learning methods are able to deliver results. However, by incorporating a priori domain knowledge, more computationally-efficient algorithms can be developed. In this paper, for the first time, we present a Domain-Aware Multiagent Actor-Critic (DAMAC) algorithm, which integrates domain knowledge with the centralised learning and decentralised execution multiagent reinforcement learning approach using domain-specific solvers. Our experiments show that our algorithm achieves substantial high reward and reduces the training time by two orders of magnitude as compared to other multiagent reinforcement learning algorithms. This enables the adoption of this powerful framework in more resource-constrained scenarios.
Looking Back and Forward: Enhancing Image Captioning with Global Semantic Guidance

Image captioning requires models to translate an image into natural language description. Recent captioning methods are mostly based on the popular encoder-decoder framework, which uses the encoder to encode an image into a set of vectors and a decoder to produce the caption word-by-word. However, when generating the next word, the decoder only looks back at the preceding words but fails to foresee the posterior ones that are supposed to be produced, lacking awareness of the global semantic information of the image. Moreover, the existing generated caption provides global semantic information of a given image but sometimes contains inconsistent content that needs to be fixed. This paper proposes a novel Temporal-Free Semantic-Guided attention mechanism (TFSG) to utilize the raw caption pre-generated by a primary decoder as the extra input to provide global semantic guidance during generation, deepening visual understanding by balancing the semantic and visual information. Specifically, we design two attention-based structures for TFSG to selectively take the essence from the semantic and visual information to produce refined descriptions. We apply TFSG to two popular captioning models and conduct extensive experiments on the public MS COCO benchmark. Results show that our proposed model achieves superior performance over many state-of-the-art models.
Fast Detection of Ground Plane in Stereo Vision Systems Based on Iso-Disparity Strip Pattern

In this paper, first a closed-form formulation is analytically derived for the pattern of iso-disparity strip widths associated with the ground plane in disparity map images. This formulation calculates the iso-disparity strip widths based on the parameters of the stereo camera setup ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , height, tilt angle, and baseline of the cameras) used to capture the images. The proposed formulation was then statistically validated using the results achieved in the case of a bank of stereo image pairs with different stereo camera setup parameters. Validation of the proposed formulation was in the presence of the strip width imperfections that appear in reality, and were shown to be in the form of additive Gaussian noise. Using the formulation proposed for iso-disparity strip widths, the paper then introduces a novel approach for ground plane detection based on the concept of iso-disparity strips in disparity map images. To add to the robustness of the proposed procedure against strip width noise, Cumulative Moving Average (CMA) and a dynamic thresholding technique are used to find the width of the strips corresponding to the ground plane. According to experimental results for synthesized and captured datasets, while exhibiting sufficiently low false positive rates (1.38% and 5.83%), the proposed method detects the ground plane with average true positive rates of as high as 80% and 54% in 65 ms and 224 ms, respectively.
Optimizing Communication Data Streams in Edge Computing Systems Using Bayesian Algorithms

Due to companies’ awareness of the real value of data, the amount of data they handle has increased significantly in recent years []. In order to obtain value from the data collected in each company, Big Data and Data Analytics techniques have to be applied. In carrying out this analysis in order to obtain valuable information for the company, several issues related to the computing power of the machines often emerge due to the high volume of data collected. In this paper we emphasis the importance of processing data effectively through data compression and an approach that can help to achieve this. In particular, we have used Bayesian networks to perform data compression without missing useful information.
Features of Using Information and Communicative Technologies in Preschool and Primary Education

Besides the problems, related to the Teaching of Information Technologies at an early age, there is often expressed one point of view about the problem: Should we use computer in junior and elementary schools? We think, that computer could be used with kids of mentioned ages, but only by the specific methods of teaching, taking into consideration their health care and protection. We have indicated how to explain to the kids the ideas of “famous people” on the plain language and various aspects of computer teaching. We have also discussed the essential three reasons for the integrated course of informatics in junior and elementary schools. Finely, we underlined the fact, that during the teaching of elements of Information Technologies in aforementioned children, the circumstances, teaching and upbringing methods may promote and accelerate the logical development of mentality with the help of computer, or vice versa, hinder this process with prolonged negative results.
Action Recognition Based on Hierarchical Graph Recurrent Networks with 2D Skeleton

Graph convolutional neural networks are recently studied for action recognition. We develop a hierarchical graph recurrent networks for action recognition based on 2D skeleton. Hierarchical graph structure is designed for human pose features extraction and representation of movement including spatial-temporal information. Action labels are online updated by frame graph composed of key frames, which are dynamical weighted by consecutive frames with soft self-attention. Human skeleton graph emphasizes keypoints features learning with position and orientation between adjacent joints. The qualitative and quantitative experiments demonstrate that hierarchical graph recurrent networks (HGRNs) effectively increase accuracy and robustness of action recognition and predict.