Reviewerly/exHarmony huggingface dataset:
Backlit Image Enhancement Based on Illumination-Reflection Imaging Model

In order to address the problem of brightness reduction and detail information loss of backlit image, this paper proposes a backlit image restoration algorithm based on Illumination-reflection imaging model. Firstly, the backlit image is converted from RGB color space to HSV color space. Secondly, the illumination component and the reflection component of the image are produced by using the illumination-reflection imaging model. When the illumination component is corrected, an adaptive correction function is constructed based on the illumination distribution characteristics to correct the illumination component, which can not only compensate the brightness in the low-brightness region, but also maintain the brightness in the general medium brightness region and high-brightness region. Finally, the modified illumination component is combined with the original reflection component. The experimental results demonstrate that the proposed algorithm can compensate the brightness of the low-brightness of the backlit image and obtain more detailed information about the low-brightness region.
Evolutionary Clustering Algorithm Using Supervised Classifiers

Cluster analysis is a data mining tool for searching patterns automatically on different types of data. However, it is not always clear which clustering criterion would be the most accurate as this decision is domain-dependent. This paper focuses on the design of a single-objective evolutionary clustering algorithm that generates solutions that are less biased towards one cluster structure. This work's motivation starts from the idea that a good partition induces a well-trained classifier. The resulting evolutionary clustering algorithm using classifiers aims to enhance the generalization capability of the resulting partition. The proposed objective function trains a set of classifiers using an individual's chromosome as class labels. The obtained average area under the curve of the classifiers is used as the cluster quality index for measuring fitness. The experimental phase shows a similarity increase between the generated solutions and the reference partitions of the datasets.
Least Square based System Bias Registration in Elevation for Radar when Tracking an Electrically Large Object

In this study, we focus on the problem of radar system bias registration for elevation angle in an air-ground integration task where the radar tracks an electrically large object like a stratospheric airship. And we present a least square (LS) based system bias estimation method with the measuring parts inconsistency in consideration. In the case that the radar beam scattering centers are inconsistent with the position of GNSS equipment on airship, an approximate bias estimation model incorporating radar tracking measurements and cooperative-available GPS information is established according to the characteristics of both airship flight in floating phase and the capsule structure. Then the recursive least square (RLS) method is utilized to estimate radar system bias along with height error simultaneously. A case simulation is carried out to demonstrate the effectiveness of the proposed method.
E-Approximate Coded Matrix Multiplication is Nearly Twice as Efficient as Exact Multiplication

We study coded distributed matrix multiplication from an approximate recovery viewpoint. We consider a system of <tex>$P$</tex> computation nodes where each node stores 1/m of each multiplicand via linear encoding. Our main result shows that the matrix product can be recovered with &#x220A; relative error from any <tex>$m$</tex> of the <tex>$P$</tex> nodes for any &#x220A; &#x003E;0. We obtain this result through a careful specialization of MatDot codes-a class of matrix multiplication code previously developed in the context of exact recovery (&#x220A; &#x003D; 0). Since previous results showed that the MatDot code is tight for a class of linear coding schemes for exact recovery, our result shows that allowing for mild approximations leads to a system that is nearly twice as efficient as exact reconstruction. Moreover, we develop an optimization framework based on alternating minimization that enables the discovery of new codes for approximate matrix multiplication.
An Improved Teaching-Learning-Based Optimization for Multitask Optimization Problems

In recent years, multitask optimization (MTO) plays an important role in real life, covering various fields such as engineering, finance, and agriculture. MTO can solve multiple optimization problems in the meantime and heighten the performance of solving each task. The teaching-learning-based optimization (TLBO) algorithm and its improved variants focus on solving single problem. In this article, a novel multitask teaching-learning-based optimization (MTTLBO) algorithm is proposed to handle multitask optimization (MTO) problems. Firstly, MTTLBO makes full use of knowledge transfer between different optimization problems to improve performance and efficiency on solving MTO problems. The concept of opposition-based learning (OBL) is introduced into the teaching stage which allow students to receive a wide range of knowledge and find individuals with great differences. MTTLBO is compared with some advanced evolutionary multitask algorithms. The experimental results state clearly that the MTTLBO algorithm owns excellent performance on nine sets of single-objective problems.
Person Detection, Tracking and Following in a Differential Drive Mobile Robot

One primary application of robotics is the application of the robots as a personal assistant. Thus this would require the robot to perceive the environment and perform actions required by the person. One such application is a person following. Other applications are following a soldier on a battlefield and a worker in a factory carrying tools. We will take the relatively simple problem of carrying luggage. To do this, we addressed three sub-problems Detection of a Human, Tracking a human, Following a Human. And the additional requirement is that it must be done in real-time and use the robot’s on-board compute capabilities. We chose hardware and were able to achieve the required outcome in both simulation and real life.
Virtual Piano System Based on Monocular Camera

This paper studies the realization of the virtual piano system based on a monocular inverted camera. Compared to the traditional physical piano with the high price and large size, the virtual piano based on computer vision is economical and portable for novices to learn playing piano. However, while reducing learning cost, the virtual piano has to make compromises in terms of user experience and interactivity. On one hand, the software virtual piano only uses a computer keyboard, which cannot meet the players' cognition of the positions of physical piano keys. On the other hand, the virtual piano system which uses external binocular cameras is still at the theoretical stage. In practice, it is greatly limited by the problems of mutual coverage by left and right hands, large depth detection error, poor real-time performance, and so on. Therefore, in order to simulate the real experience of physical piano playing, this paper proposes the realization method of the virtual piano system based on a monocular inverted camera and a transparent plate with piano keys pattern. In detail, we use the offline modeling method to get the position of the piano keys and use the color threshold segmentation algorithm to realize the fingertips detection. Then, we detect the keys' position and depth according to fingertips pressing the keys, in turn, realize the virtual piano functions and complete the human-computer interaction. The experiments verify the practicality of the virtual piano system.
Web-Based Model for Apparel Classification

Clothing classification has its application in e-commerce and advertising. With the advancement of fashion industry, it becomes necessary to introduce a new method for online shopping. This paper describes how a convolutional neural network (CNN) can be used to classify garments from a given dataset. The final aim of the project is to identify apparels which may or may not be worn by an individual and to give a list of similar apparels. For this purpose, a CNN model is trained on the relevant dataset with the help of tensorflow. Later, a web application is created using tensorflow.js wherein a user can upload an image and the model will identify the image using the labels in the dataset and display it on the webpage. Further, this web application will be converted into an android application which is optimized for smaller screens for the ease of accessibility.
VMAF and variants: towards a unified VQA

Video quality analysis (VQA) is a critical processing task for all major video services. We take streaming services as a key example. Among many possible video encodings of the source material, it is vital to distinguish which encoding would produce the most favorable ratings in terms of quality by the viewers. VQA techniques aim to predict that human quality rating by objective methods. Among such methods, the VMAF algorithm, designed by Netflix, has come into prominence in recent years. We examine VMAF as well as several variants that we have developed, and assess their ability to predict human quality scoring.
Door Access System Using Face Recognition

Abstract This is a security system to restrict access to rooms using Raspberry Pi as main controller and implementation of Histogram of Oriented Gradients (HOG) feature extraction method. Upon face detection, the system is able to do face recognition for person identification by matching face embedding of the person face with one that already stored in database. This system also presents a random head-pose challenge to identify a real person from an image and then activate a solenoid to unlock the door. In this experiment, if the person is not wearing any face attributes, this system is tested to have 100% successful detection and recognition rate at 60 cm distance. If the person is wearing mask attribute, this system is tested to have 70% successful rate for detection and 45% successful rate for recognition at 60 cm distance.
H-Infinity Controller Design of Two-Wheeled Mobile Robot Under Disturbance

This study investigates a controller design on a two-wheeled robot model under disturbance. The concept of a two-wheeled robot is based on the principle of an inverted pendulum model. The control is investigated on the state-space-based controller design. The controller is designed to satisfy a two-wheeled self-balancing robot’s stability, capable of maintaining the robot in a steady vertical position and robust to disturbance. The main focus of this study is developing the H-infinity controllers for the two-wheeled robot model. The controller is simulated through trial and error and compared to distinguish the best performing controller between other Linear Quadratics Controller (LQR) methods to achieve a solid validation. This investigation shows that the H-infinity controller performs a better performance in the two-wheeled robot model under disturbance.
Detection of Pointing Position by Omnidirectional Camera

In recent years, voice recognition devices such as smart speakers have been in the limelight, but intuitive communication such as instructing the target with ambiguous expressions is difficult with voice alone. In order to realize such communication, it is effective to combine gestures such as pointing with voice. Therefore, we propose a method to detect the pointing position from the image of the omnidirectional camera using a convolutional neural network. Although many methods have been proposed to detect the pointing position using a normal camera, the standing position of the person who gives instructions is limited since the observation area is small. We solve this problem by using an omnidirectional camera. First, the proposed method converts a hemisphere image taken from an omnidirectional camera to a panoramic image. Next, the bounding box surrounding the person with pointing gesture is detected in the panoramic image by the object detection network. Finally, the pointing position estimation network estimates the pointing position in the panoramic image from the image in the bounding box and its location. Since it is difficult to prepare a large number of pointing gesture images, CG images created by Unity are used for pre-training. Experiments using real images of pointing gesture shows that the proposed method is effective for pointing position detection.
Retrospective special issue—Information behavior

Journal of the Association for Information Science and TechnologyEarly View GUEST EDITORIAL Retrospective special issue—Information behavior Rebekah Willson, Corresponding Author Rebekah Willson rebekah.willson@mcgill.ca orcid.org/0000-0001-8430-8827 School of Information Studies, McGill University, Montreal, Canada Correspondence Rebekah Willson, School of Information Studies, McGill University, Montreal, Canada. Email: rebekah.willson@mcgill.caSearch for more papers by this authorHeidi Julien, Heidi Julien Department of Information Science, University at Buffalo, Buffalo, New York, USASearch for more papers by this authorDavid Allen, David Allen Leeds University Business School, Leeds University, Leeds, UKSearch for more papers by this author Rebekah Willson, Corresponding Author Rebekah Willson rebekah.willson@mcgill.ca orcid.org/0000-0001-8430-8827 School of Information Studies, McGill University, Montreal, Canada Correspondence Rebekah Willson, School of Information Studies, McGill University, Montreal, Canada. Email: rebekah.willson@mcgill.caSearch for more papers by this authorHeidi Julien, Heidi Julien Department of Information Science, University at Buffalo, Buffalo, New York, USASearch for more papers by this authorDavid Allen, David Allen Leeds University Business School, Leeds University, Leeds, UKSearch for more papers by this author First published: 12 August 2021 https://doi.org/10.1002/asi.24557Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Design of Intelligent Garbage Classification System in Shanghai

A design scheme of intelligent garbage classification system in Shanghai was presented which was applied to front-end processing of garbage classification in Shanghai. The system used speech technology to recognize garbage words automatically. At first, the system established a speech corpus of garbage vocabulary of five age groups which contain children, teenagers, youths, middle aged people and old people, including 15 men and 15 women per age group. Then characteristic parameters pattern base was set up by extracting Mel Frequency Cepstral Coefficients (MFCC) of speech samples via the use of MATLAB programming. Finally, based on Dynamic Time Warping (DTW), it can recognize categories of garbage automatically, including recyclable waste, hazardous waste, household food waste, residual waste and specially disposed waste, such as pet corpses and pet feces. Based on the results, the accuracy of the speech recognition rate for specific person and non-specific person reaches 100% and 98.78%.
Comparison of Digital Image Correlation (DIC) Technique with Nanomaterial-Based Sensor for the Analysis of Strain Measurements

Digital Image Correlation (DIC) techniques can be used to visually map and measure strain in materials such as metals and metallic alloys. The strain induced in an American Society for Testing and Materials (ASTMs) standard specimen can be measured using a DIC technique. Image patterns indicating the localized strain variations as a function of time for the constant load applied were also obtained. Results obtained using the DIC technique were more accurate compared to conventional strain sensors. DIC results were also compared with nanomaterial-based strain sensor output. Localized strain induced in the material can be visualized and quantified analytically using DIC.
Recognition of trivial humanoid group event using clustering and higher order local auto-correlation techniques

A video surveillance system is explicitly established to manage a small human group, which is the designated subject of this paper. Predominantly, the described methodology concentrated on the trivial humanoid groups remained in the identical location for some time and characterizing the group activity. The defined methodology has widespread applications in numerous areas such as video reconnaissance system, cluster interface, and activities classification. The video surveillance system primarily covenants with the action recognition and classifying the cluster activity by considering violence activities such as fighting. The steps in action recognition includes generation of frames, segmentation using fuzzy c-mean clustering, feature extraction by completed local binary pattern (CLBP), and high order local auto-correlation (HLAC), classification by Recurrent Neural Network (RNN). Spatial Gray Level Difference Method (SGLDM) extracts the statistical features while Bag-of-words (BOW) technique creates vocabulary features set which is derived from Local Group Activity (LGA) descriptors. Convolution Neural Network (CNN) classifier is employed to classify the human activities.
Personalized query recommendation using semantic factor model

In the field of query recommendation, the current techniques for semantic analysis technology can't meet the demands of users. In order to meet diverse needs, we improved the LDA model and designed a new query recommendation model based on collaborative filtering-Semantic Factor Model (SFM), which combines text information, user interest information and web source. First, we improved the LDA model from bag-of-word to bag-of-phrase to understand the topics expressed by users' frequently used sentences. The phrase bag model treats phrases as a whole and can capture more accurate query intent. Second, we use collaborative filtering to build an evaluation matrix between user interests and personalized expressions. Third, we designed a new scoring function that can recommend the top n resources to users. Finally, we conduct experiments on the AOL data set. The experimental results show that compared with other latest query recommendation techniques, SFM has higher recommendation quality.
Multi-Robot Information Gathering Subject to Resource Constraints

This paper addresses the multi-robot planning problem to generate trajectories for maximum information gathering from an area of interest. To solve this problem, the proposed framework first leverages a Gaussian Mixture Model (GMM) as prior knowledge for modeling informative regions in the target area. Taking samples from the GMM, informative robot paths are then computed that optimize the travel costs subject to energy budgets. Decomposing these paths into waypoints, robot trajectories are planned while respecting kinematic constraints and subsequently replanned online to avoid collisions among themselves. The GMM model is incrementally updated by incorporating the gathered information. We demonstrate that our framework can achieve a significant amount of information gain with the optimal travel distance. We also provide a realistic simulation with a team of mobile robots in a port infrastructure monitoring setting.
Multi-View Image Color Correction Using 3D Point Set Registration

In this paper, a novel color correction method for multi-view images is proposed to improve color consistency successfully. Contrast to the conventional methods trying to find a transfer function per color channel, the proposed method deforms the color distribution of the image non-rigidly into that of the reference image by exploiting a 3D point set registration algorithm. In order to alleviate the computational complexity of the 3D registration process, we approximate the image color distribution consisting of all the image pixels by extracting a much smaller number of representative color points to be used for 3D registration. Extensive experiments confirm that the proposed method achieves excellent performance in color consistency enhancement both subjectively and objectively, and the proposed representative color point set approximation enhances the computational efficiency successfully.
Application of Partial Differential Equation Method in Fingerprint Image Enhancement

In the forensic science, fingerprint features play an important role in providing some clues about the criminal cases and identifying the suspect's identity. However, the latent fingerprint images from criminal scenes are often interfered by Gaussian noise, which leads to the blurring of the latent fingerprint images and affects the subsequent fingerprint identification. In recent years, the partial differential equation (PDE) method has made some progress in the field of image processing. In this paper, the application of the PDE method based on total variation is studied in latent fingerprint image enhancement processing, especially in the process of processing Gaussian noise in fingerprint images. In order to evaluate the performance of the PDE method in removing Gaussian noise in fingerprint images, Gaussian noise with different intensity is added to two different latent fingerprint images in the experiment in this paper. The experimental results of the PDE method are compared with the results of the neighborhood average method by peak signal-to-noise ratio and structural similarity, respectively. By comparison, it is discovered that when the intensity of Gaussian noise is low, the effect of the PDE method is almost the same as the effect of the neighborhood average method in processing Gaussian noise in fingerprint images. But with the increase of Gaussian noise intensity, the PDE method is better than the neighborhood average method in processing Gaussian noise of fingerprint images. The weight in the PDE method has a great influence on the effect of processing Gaussian noise in fingerprint images. The optimal weight is not fixed, which depends on the intensity of Gaussian noise and the fingerprint image itself.
Design of E-Voting Information System for Website-Based Village Head Elections (Case Study : Cemandi Village, Sedati, Sidoarjo, East Java)

Voting in a democratic country is an important part of the means of choosing leaders. The village head election process in Indonesia still uses conventional voting methods, namely using ballot paper media in the election process. Voting that is carried out conventionally has several obstacles, including the lack of guaranteeing the authenticity of voters' votes, so that people think the results of voting results are often manipulated. In addition conventional selection is deemed inaccurate and time-consuming and costly. In this study the aim of this research is to design an information system e-voting that can be used for the Election of the Village Head of Cemandi, Sedati, Sidoarjo, East Java, where by using this system the election process becomes easier by ensuring the accuracy of the vote count. This system development method uses the model of software engineering waterfall. The test results blackbox show that the functions of the features in the system are running well. Based on the UAT test, the system received an average percentage rate of 86%.