Reviewerly/exHarmony huggingface dataset:
Challenges and enablers of augmented reality technology for in situ walkthrough applications

The successful implementation of situational-awareness enhancement using next-generation augmented reality in construction project walkthroughs has significant associated challenges. In this research, an augmented reality (AR) approach was developed to evaluate and showcase the trade-offs between capacity, latency, and reliability for the technology. The approach, named “i-Tracker”, overlays visualizations of designs by creating virtual content to be superimposed into physical contexts. i-Tracker uses the latest generation of mobile computing technology to effectively locate design information from existing parametric engineering designs (architectural, structural, and mechanical objects) and create a fully animated scene in situ. The i-Tracker technology uses a combination of depth-sensing cameras and inertial measurement unit (IMU) sensors. The evaluation of this technology demonstrates the requirements and limitations applicable to the implementation of this technology in job sites. In the example use case, the device’s relative position and orientation with respect to the user are estimated. Performance features such as motion tracking, localization, error dispersion with respect to luminance, system processing speed, and ambiguity in the feature tracking are evaluated. i-Tracker contributes to the body of literature and current work on the use of positioning and tracking systems in real construction sites within the AR context. This project advances understanding of rapid implementations and the use of AR visualizations on job sites, utilizing significant progress in mobile and ubiquitous computing with faster central processing units (CPUs) and graphical processing units (GPUs).
On-Line Visual Tracking with Occlusion Handling

One of the core challenges in visual multi-target tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of déjà vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled Multi-Bernoulli (GLMB) filter, originally designed for tracking point-sized targets, to be used in visual multi-target tracking. The proposed algorithm includes a novel false alarm detection/removal and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the state-of-the-art methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the state-of-the-art methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.
A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

Aiming at the problems of feature point calibration method of 3D light detection and ranging (LiDAR) and camera calibration that are calibration boards in various forms, incomplete information extraction methods and large calibration errors, a novel calibration board with local gradient depth information and main plane square corner information (BWDC) was designed. In addition, the "three-step fitting interpolation method" was proposed to select feature points and obtain the corresponding coordinates of feature points in the LiDAR coordinate system and camera pixel coordinate system based on BWDC. Finally, calibration experiments were carried out, and the calibration results were verified by methods such as incremental verification and reprojection error comparison. The calibration results show that using BWDC and the "three-step fitting interpolation method" can solve quite accurate coordinate transformation matrix and intrinsic and external parameters of sensors, which dynamically change within 0.2% in the repeatable experiments. The difference between the experimental value and the actual value in the incremental verification experiment is about 0.5%. The average reprojection error is 1.8312 pixels, and the value changes at different distances do not exceed 0.1 pixels, which also show that the calibration method is accurate and stable.
Auto Removal of Bright Spot from Images Captured Against Flashing Light Source

An image captured using a single lens camera with the sun or flashing source of light in the background frequently exhibits a bright spot in it. Usually, we observe bright spots in images caught during sunrise or sunset. The color of the bright spot varies depending on the overall background of the scene and mainly relies upon the light reflected from the lens. In several instances, bright spot, haze, or flare can arise anywhere in the image. The phenomenon occurs due to the light reflected or scattered by the imperfection of the lens and improper angle of incidence of light. One can avoid by taking the picture at a proper angle such that the spot is positioned directly on the source of light which necessitates expertise. Manual intervention is exacted for images bearing a bright spot; however, it demands the understanding of extra software tools. Moreover, manual editing becomes a time-consuming process for an extensive quantity of images. In the proposed work, we address this issue by determining the bright spot automatically and its removal by filling the gap. We experiment using available feature detection approaches for spot detection and well known in-painting methods to fill the spot area. As a promising method, we select speeded up robust features to identify the bright spot, and exemplar-based in-painting method to fill the gap in that location. We test the proposed method on more than 30 images captured against the flashing source of light. Experimental results on bright spot images reveal that the proposed method is efficient and corrected images appear natural without any artifacts.
Multi-Features Refinement and Aggregation for Medical Brain Segmentation

Due to the complexity of the anatomical structure for human organs, medical image segmentation is always a challenging computer vision task. The Convolutional Neural Network (CNN) requires a rich feature representation, which not only needs the convolutional layers from shallow to deep,but also requires the resolution from small to large. Although CNN can be used to fuse mid-level features that are employed short-cutting, this just is a simple “shallow” connection. Thus, how to obtain useful features and how to utilize these features to improve the segmentation processes are still the key issues. In this paper, Multi-features Refinement and Aggregation (MRA) makes full use of hierarchical features by using the features fusion on several levels, and reveal the importance of refinement and aggregation of features in the medical image segmentation process. The network get low-level, high-level and even mid-level features by sampling. After aggregation and re-extraction, these features are more effectively combined. Experiment results show that our method can significantly improve segmentation accuracy compared to existing feature fusion schemes. And our approach is generalized to different backbone networks with consistent accuracy gain in brain segmentation, and it sets a new state-of-the-art in the Brat-2015 benchmarks.
Capture, Reconstruction, and Representation of the Visual Real World for Virtual Reality

We provide an overview of the concerns, current practice, and limitations for capturing, reconstructing, and representing the real world visually within virtual reality. Given that our goals are to capture, transmit, and depict complex real-world phenomena to humans, these challenges cover the opto-electro-mechanical, computational, informational, and perceptual fields. Practically producing a system for real-world VR capture requires navigating a complex design space and pushing the state of the art in each of these areas. As such, we outline several promising directions for future work to improve the quality and flexibility of real-world VR capture systems.
Explaining away results in accurate and tolerant template matching

Recognising and locating image patches or sets of image features is an important task underlying much work in computer vision. Traditionally this has been accomplished using template matching. However, template matching is notoriously brittle in the face of changes in appearance caused by, for example, variations in viewpoint, partial occlusion, and non-rigid deformations. This article tests a method of template matching that is more tolerant to such changes in appearance and that can, therefore, more accurately identify image patches. In traditional template matching the comparison between a template and the image is independent of the other templates. In contrast, the method advocated here takes into account the evidence provided by the image for the template at each location and the full range of alternative explanations represented by the same template at other locations and by other templates. Specifically, the proposed method of template matching is performed using a form of probabilistic inference known as “explaining away”. The algorithm used to implement explaining away has previously been used to simulate several neurobiological mechanisms, and been applied to image contour detection and pattern recognition tasks. Here it is applied for the first time to image patch matching, and is shown to produce superior results in comparison to the current state-of-the-art methods.
The impact of strategic human resources planning on the organizational performance of public shareholding companies in Jordan

Performance management (PM) is a common practice used by organizations to assess and manage employees’ work. Much of PM research is closely related to management practices. Corporations in the public and nonprofit sector continuously develop PM programs to ensure the sustainability of their organizations. The study aims to analyze the impact of strategic human resources planning on the organizational performance of Jordanian public shareholding companies for senior management and functional unit managers (human resources, marketing, finance, and accounting). The researchers surveyed all the public shareholding companies registered with the Jordan Securities Commission (JSC) in 2019, wherein they found that only 60 companies applied strategic planning and human resources planning (HRP) together. Two hundred and twenty questionnaires were distributed in 52 companies surveyed, and 203 were adopted for statistical analysis. Several statistical methods were used, most notably the multiple regression analysis. The researchers found out a statistically significant impact of the strategic human resources planning (integration of HRP and strategic planning; strategic participation) on organizational performance. The results showed that adopting the strategic HRP dimensions leads to an increase in an organization’s overall productivity, employee satisfaction and reputation, as well as reduced operating costs. HR managers must understand the effectiveness of strategically designed HR practices across functions.
Diversity Teams in Soccer League Competition Algorithm for Wireless Sensor Network Deployment Problem

The drawback of several metaheuristic algorithms is the dropped local optimal trap in the solution to complicated problems. The diversity team is one of the promising ways to enhance the exploration of searching solutions in algorithm to avoid the local optimum trap. This paper proposes a diversity-team soccer league competition algorithm (DSLC) based on updating team member strategies for global optimization and its applied optimization of Wireless sensor network (WSN) deployment. The updating team consists of trading, drafting, and combining strategies. The trading strategy considers player transactions between groups after the ending season. The drafting strategy takes advantage of draft principles in real leagues to bring new players to the association. The combining strategy is a hybrid policy of trading and drafting one. Twenty-one benchmark functions of CEC2017 are used to test the performance of the proposed algorithm. The experimental results of the proposed algorithm compared with the other algorithms in the literature show that the proposed algorithm outperforms the competitors in terms of having an excellent ability to achieve global optimization. Moreover, the proposed DSLC algorithm is applied to solve the problem of WSN deployment and achieved excellent results.
Self-Supervised Learning for Domain Adaptation on Point-Clouds

Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data. It has been applied effectively to domain adaptation (DA) on images and videos. It is still unknown if and how it can be leveraged for domain adaptation in 3D perception problems. Here we describe the first study of SSL for DA on point clouds. We introduce a new family of pretext tasks, Deformation Reconstruction, inspired by the deformations encountered in sim-to-real transformations. In addition, we propose a novel training procedure for labeled point cloud data motivated by the MixUp method called Point cloud Mixup (PCM). Evaluations on domain adaptations datasets for classification and segmentation, demonstrate a large improvement over existing and baseline methods.
Texture Recognition Using Gabor Filter for Extracting Feature Vectors With the Regression Mining Algorithm

This article considered only natural types of texture and then applying the Gabor filter for better classifications. The concept used is to discard the stochastic features to avoid any mixing of feature vector while it is extracting from the image dataset. The proposed approach has considered the Gabor filter for texture recognition primarily but with the combined method of spatial width and orientation to get the optimal alignment, this optical alignment mine the maximum feature vector by applying the REP algorithm over the data mined from the texture. This will result in better accuracy in the results. Initially, the frequency response over the surface due to applying Gabor filter has been calculated and then the work proceeded in a manner that first natural images are loaded into the MATLAB tool then it is preprocessed, and then final classifications are performed for final results. The primarily concentrated over texture information of image datasets rather than the multispectral information along with REP regression algorithm to do actual mining of feature vectors. Unlike the conventional approach of the Gabor filter, this article focuses on the variance and spatial relationship between two or more than two pixels. The deviation calculated is used for normalizing the feature vectors, and the accuracy can be hence increase using the proposed commuted technique.
MNIST-MIX: a multi-language handwritten digit recognition dataset

Abstract In this note, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples. With the same data format with MNIST, MNIST-MIX can be seamlessly applied in existing studies for handwritten digit recognition. By introducing digits from 10 different languages, MNIST-MIX becomes a more challenging dataset and its imbalanced classification requires a better design of models. We also present the results of applying a LeNet model which is pre-trained on MNIST as the baseline.
Evolutionary Latent Space Exploration of Generative Adversarial Networks

Generative Adversarial Networkss (GANs) have gained popularity over the years, presenting state-of-the-art results in the generation of samples that follow the distribution of the input training dataset. While research is being done to make GANs more reliable and able to generate better samples, the exploration of its latent space is not given as much attention. The latent space is unique for each model and is, ultimately, what determines the output from the generator. Usually, a random sample vector is taken from the latent space without regard to which output it produces through the generator. In this paper, we move towards an approach for the generation of latent vectors and traversing the latent space with pre-determined criteria, using different approaches. We focus on the generation of sets of diverse examples by searching in the latent space using Genetic Algorithms and Map Elites. A set of experiments are performed and analysed, comparing the implemented approaches with the traditional approach.
Types of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is one of the most important technologies used in recent times, it allows users (individuals and organizations) to access computing resources (software, hardware, and platform) as services remotely through the Internet.Cloud computing is distinguished from traditional computing paradigms by its scalability, adjustable costs, accessibility, reliability, and ondemand pay-as-you-go services.As cloud computing is serving millions of users simultaneously, it must have the ability to meet all users requests with high performance and guarantee of quality of service (QoS).Therefore, we need to implement an appropriate task scheduling algorithm to fairly and efficiently meet these requests.Task scheduling problem is the one of the most critical issues in cloud computing environment because cloud performance depends mainly on it.There are various types of scheduling algorithms; some of them are static scheduling algorithms that are considered suitable for small or medium scale cloud computing; and dynamic scheduling algorithms that are considered suitable for large scale cloud computing environments.In this research, we attempt to show the most popular three static task scheduling algorithms performance there are: first come first service (FCFS), short job first scheduling (SJF), MAX-MIN.The CloudSim simulator has been used to measure their impact on algorithm complexity, resource availability, total execution time (TET), total waiting time (TWT), and total finish time (TFT).
Personal choice: strategic life decision-making and conscience

The article highlights the problem of subjective choice in making strategic life decisions and its correlation with conscience. The study describes the nature of strategic life decision-making, determines types of strategic life decisions, discloses the essence of human experiences associated with strategic life decision-making, and provides results of empirical research. Decision-making, as viewed in scientific psychological practice, is an intense cognitive process for which a human actor should be prepared to perform, drawing upon a full range of knowledge, abilities, skills, and personal qualities. Decisions, though varied by type and degree of difficulty and complexity, are united by choice – that is the individual’s choice as such. Crucial moments of decision-making may affect people in myriad ways. This article analyses the experiences of such human actors while making strategic life decisions, defined by scientists to mean outstanding, fateful, associated with great responsibility, with their leading role in human living space, and formation of a person’s way of living. Emotions and emotional responses occupy a space of paramount importance in this type of decision-making. As strategic life decisions are cognizantly and predominantly made mainly in late adolescence, the authors present summarized results of the empirical research that was carried out for several years among students enrolled in a variety of higher educational institutions within Ukraine.
The Quantum Approximate Algorithm for Solving Traveling Salesman Problem

The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems. It consists of interleaved unitary transformations induced by two operators labelled the mixing and problem Hamiltonians. To fit this framework, one needs to transform the original problem into a suitable form and embed it into these two Hamiltonians. In this paper, for the well-known NP-hard Traveling Salesman Problem (TSP), we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem Hamiltonian. Moreover, we map edges (routes) connecting each pair of cities to qubits, which decreases the search space significantly in comparison to other approaches. As a result, our method can achieve a higher probability for the shortest round-trip route with only half the number of qubits consumed compared to IBM Q’s approach. We argue the formalization approach presented in this paper would lead to a generalized framework for finding, in the context of QAOA, high-quality approximate solutions to NP optimization problems.
Analysis of Movie Recommendation Systems; with and without considering the low rated movies

Movie recommendation system is one of the top research areas, currently. Due to the impact of high internet speeds, multimedia has become one of the best entertainments. Recommendation system has its applications like movie recommendations, course recommendations, e-commerce etc.. Movie recommendation system scope is not limited to entertainment, but also in information sharing. Movie recommendation systems suffer from problems like Cold-start problem, Sparsity, Long-tail problem, Grey sheep problem etc.. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with and without considering the movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering” and the similarity measure used is the “Pearson correlation coefficient”. Dataset considered is Movie-Lens-100k. This experiment result shows that low rated movies are not significant in finding the movie predictions. So it's suggestable to ignore them while calculating movie predictions.
Analysis of lightning flashes over Bangladesh

Lightning related disasters are considered to be global phenomena, as they may occur at any place, at any instant in the world. These lightning flashes possess great potentiality to produce serious damages to human lives and property. Bangladesh region is vulnerable to severe convective systems during the hot period of pre-monsoon season. In this paper, we tried to analyze the lightning flashes using LIS TRMM satellite data (1995-2014). Later, we calculated stability parameters such as Lifted Index(LI), K Index (KI) and Total Totals Index (TTI), Total Perceptible Water (TPW) and Convective Available Potential Energy (CAPE) associated with lightning flashes over study region were investigated using ERA-Interim ECMWF satellite data (1995-2014). The threshold values of the indices we have taken for the study are satisfied with threshold values considered for the study.