Reviewerly/exHarmony huggingface dataset:
Visualization pipeline of autonomous driving scenes based on FCCR-3D reconstruction

Visual sensor-based human–computer interaction can provide environmental perception data of traffic road scenes for mechatronic intelligent vehicles. The existing three-dimensional (3D) reconstruction visualization schemes are mainly based on structured scene reconstruction with multiview geometry and are offline operations. It is difficult for large-scale autonomous driving scenes with wide baselines to ensure consistent reconstruction results in the wide field-of-view area and the center of the light-of-sight area. It is also difficult to achieve uniform reconstruction results for regions with drastic depth changes and inconsistent texture fineness. Therefore, we propose a 3D reconstruction-based visualization pipeline for autonomous driving scenes of wide baseline. First, the stereo geometric model is initialized. The parameters of distortion correction and polar calibration are calibrated. Second, the proposed feature chain code resampling (FCCR) is used to uniformly extract the features for regions with inconsistent texture fineness and depth variation. The filtered 3D data are obtained based on the stereo geometric model. Finally, the texture mapping of the mesh skeleton of 3D data is carried out by the bilinear combination of surrounding multilevel gradual textures. The visualization results of scene rendering and scene roaming are realized. The pipeline has been effectively verified on the simulated experimental platform, real vehicle test, and public dataset. It can meet the real-time 3D reconstruction visualization of wide-baseline scenes. The reconstruction texture is uniform, and the detail response is rich. We can present the intuitive, 3D, real-time, graphical physical quantities of scene data for drivers, intelligent vehicles, AR-HUD, and control systems. The method we present has research value and application significance for the practical engineering of electronic images.
OmniLantern: Design and Implementations of a Portable and Coaxial Omnidirectional Projector-Camera System

Projector-camera (ProCam) systems have been widely used to develop the interactive spatial augmented reality (SAR) environment. However, since the projection angle of the existing ProCams is narrow, it is necessary to deploy multiple ProCams to make an omnidirectional SAR environment. These ProCams need to be calibrated and cannot be moved after calibration. To solve this limitation, we introduce OmniProCam, which is a portable coaxial ProCam combining with an ultra-fisheye lens. The ultra-fisheye lens enables omnidirectional projection and interaction, and the coaxial ProCam enables calibration-free. In order to prove the availability of the OmniProcam system, we develop a prototype named OmniLantern, a portable and omnidirectional device in the shape of Lantern. In this paper, we describe the implementation details of the OmniLantern prototype that bases on the retroreflective markers, distortion correction, gesture recognition, and touch detection. We also introduce some applications which show the advantages of OmniLantern. Finally, we summarize the issue of the OmniLantern and the future potential of the OmniProCam system.
A Study for the Development of a Photogrammetry Based AR and GR Projection System and Examining its Further Application

With the ever-growing tech industry, an incredible amount of strides are being made everyday to make our interactions with these technologies absolutely seamless. The technologies mentioned in this paper can help developing a system that will bring new possibilities in the fields of Healthcare, Education, Defense and various others. This paper will be discussing a system via the aid of Augmented Reality along with Gesture Recognition that will act as the medium of interaction between the user and the system. This paper examines existing literature on photogrammetry, augmented reality and gesture recognition respectively. An important part of the study will be investigating further implications of how the system will be embedded within other already existing stale applications. Fields like Healthcare and Defense still have some unexplored and untapped sectors that require state of the art technology. There will be some research-based analysis for different methodologies accessible to aid making these technologies more accessible to the general public.
Solar GHI Ensemble Prediction Based on a Meteorological Model and Method Kalman Filter

The intensity of light emanating from sun is determined by using a meteorological version and is altered with the numerical version, and the forecast accuracy is improved in advance by using Kalman Filter. As the accuracy of the version output related to its specific position is often questionable, group prediction constituting three members is suggested and agreed upon measurement. Also, this ensemble prediction provides an estimation of the solar global horizontal irradiance uncertainty (i.e., coverage rate of the prediction interval), which can be useful to provide flexible energy production forecasts. This article displays how the method Kalman filter could be used as an error correction way to alter the predicted irradiance value. The Kalman filter ameliorates the prediction of solar global horizontal irradiance as well as its interval. As the empirical coverage rate increases and closes to the nominal coverage rate, the interval size reduces.
Force-Feedback Haptic Device for Representation of Tugs in Virtual Reality

In this study, a haptic device is proposed for virtual reality (VR) applications. The proposed haptic device is linked to the bundle controller of a VR device. The device proposed in this study is a force-feedback device that focuses on the sensation felt by the hand rather than the feeling of the tool colliding with the object. The device provides sensations of centrifugal or inertial forces due to the weight of the tool when the user swings the tool and the sensation of a reaction force when the user stabs or pokes an object with the tool. The force generated by a haptic device of the linked type was measured and examined to determine whether a sensation of actually using a tool in the virtual environment was provided. In addition, we conducted two types of experiments to evaluate the proposed haptic device. First, VR content was created for the user experience, and a survey was conducted with 30 experimenters to compare the bundle controllers and a haptic device, called HapTug, in terms of realism, immersion, and enjoyment. The survey results demonstrated that the realism, immersion, and enjoyment increased compared to using the bundle controller alone. A total of 41 experimenters were allowed to play freely in a virtual environment for six sessions of 30 s each. They pressed the reset session button when they felt 30 s had passed, and the device they were wearing turned on and off randomly every session. During the experience, their playback time, number of interactions, and range were measured. As a result, when the device was activated, experimenters felt the time was shorter and showed widespread movement. Thus, it was proven that the proposed linked-type haptic device effectively delivered the feeling of wielding a tool, allowing the user to feel the virtual object coming in and going out of the hand when interacting with it. Moreover, three conclusions were drawn from the results of the experiments: previous VR experience times have little to do with satisfaction related to haptic device use, users have low expectations for haptic realism, and users interact more actively and exaggeratedly with a haptic device.
Product review opinion based on sentiment analysis

Sentiment analysis is a natural language processing (NLP) technique for determining emotional tone in a body of text. Using product reviews in sentiment analysis and opinion mining various methods have been developed previously. Although, existing product review analyzing techniques could not accurately detect the product aspect and non-aspect. Hence a novel Detach Frequency Assort is proposed to detect the product aspect term using TF-ISF (Term frequency-inverse sentence frequency) with Part of Speech (POS) tags for sentence segmentation and additionally using Feedback Neural Network to combine product aspect feedback loop. Furthermore, decision-making problem occurs during classification of sentiments. Hence, to solve this problem a novel technique named, Systemize Polarity Shift is proposed in which flow search based Support Vector Machine (SVM) with Bag of Words model classifies pre-trained review comments as positive, negative, and neutral sentiments. Moreover, the identification of specific products is not focused in sentiment analysis. Hence, a novel Revival Extraction is proposed in which a specific product is extracted based on thematic analysis method to obtain accurate data. Thus, the proposed Product Review Opinion framework gives effective optimized results in sentiment analysis with high accuracy, specificity, recall, sensitivity, F1-Score, and precision.
A Term Weight Measure based Approach for Author Profiling

Author Profiling (AP) is a task of determining the demographic features like Age, Gender, Nativity language, location, Personality traits etc. about the author of a document. AP is used in different applications such as marketing linguistic profile, security and forensic science. The researchers proposed different types of solutions to author profiling based on stylistic features, content based features and deep learning techniques. The content based features proved their significance to improve the performance of author profiles prediction. Several approaches faced a problem of high dimensionality of features when experimented with content based features. The researchers used feature selection algorithms to identify the important features for experimentation. In this work, a Term Weight Measure (TWM) based approach is proposed for author profiling problem. In this approach, the important features are identified by using Feature Selection (FS) algorithm. After features are identified, the next important task is representation of document with identified features. The documents are represented as vectors and computation of each feature value in the vector representation is another important research task. TWMs are used to determine the importance of a feature in the vector representation. In the proposed approach, we proposed a new TWM based on the way the terms are distributed in corpus of documents. The proposed term weight measure performance is compared with different existing TWMs. The PAN competition 2014 reviews dataset is used for age and gender prediction of the author. Two Machine Learning (ML) algorithms such as Random Forest (RF) and Support Vector Machine (SVM) are used to evaluate the proposed term weight measure based approach. The experimental results attained in this work for age and gender prediction are good when compared with several popular solutions to author profiling.
Modeling Human Visual Search in Natural Scenes: A Combined Bayesian Searcher and Saliency Map Approach

Finding objects is essential for almost any daily-life visual task. Saliency models have been useful to predict fixation locations in natural images during a free-exploring task. However, it is still challenging to predict the sequence of fixations during visual search. Bayesian observer models are particularly suited for this task because they represent visual search as an active sampling process. Nevertheless, how they adapt to natural images remains largely unexplored. Here, we propose a unified Bayesian model for visual search guided by saliency maps as prior information. We validated our model with a visual search experiment in natural scenes. We showed that, although state-of-the-art saliency models performed well in predicting the first two fixations in a visual search task ( 90% of the performance achieved by humans), their performance degraded to chance afterward. Therefore, saliency maps alone could model bottom-up first impressions but they were not enough to explain scanpaths when top-down task information was critical. In contrast, our model led to human-like performance and scanpaths as revealed by: first, the agreement between targets found by the model and the humans on a trial-by-trial basis; and second, the scanpath similarity between the model and the humans, that makes the behavior of the model indistinguishable from that of humans. Altogether, the combination of deep neural networks based saliency models for image processing and a Bayesian framework for scanpath integration probes to be a powerful and flexible approach to model human behavior in natural scenarios.
SepNet: A neural network for directionally correlated data

Multi-dimensional tensor data appear in diverse settings, including multichannel signals, spectrograms, and hyperspectral data from remote sensing. In many cases, these data are directionally correlated, i.e. the correlation between variables from different dimensions is significantly weaker than the correlation between variables from the same dimension. Convolutional neural networks are readily applicable to directionally correlated data but are often inefficient, as they impose many unnecessary connections between neurons. Here we propose a novel architecture, SepNet, specifically for directionally correlated datasets. SepNet uses directional operators to extract directional features from each dimension separately, followed by a linear operator along the depth to generate higher-level features from the directional features. Experiments on two representative directionally correlated datasets showed that SepNet improved network efficiency up to 100-fold while maintaining high accuracy comparable with state-of-the-art convolutional neural network models. Furthermore, SepNet can be flexibly constructed with minimal restriction on the output shape of each layer. These results reveal the potential of data-specific architecting of neural networks.
Revenue Prediction for Malaysian Federal Government Using Machine Learning Technique

Every country has its own federal government. Each federal government will have its own financial account which consist of revenue and expenditure. Focusing on the revenue, it has many sources that includes three main categories. They are tax revenue, non-tax revenue and non-revenue receipts. The revenue will then be used for operational and development purposes. Currently in Malaysia, the federal government revenue is only using forecasting. This can cause large forecasting error. Though it can be overcome using predictive analytics. Since there are many machine learning methods available, the appropriate methods can be identified to do the prediction. Based on previous research, feed forward neural network (FFNN), random forest and linear regression seems to be the most suitable. After conducting several experiments, it is found that FFNN achieved highest accuracy, followed by random forest. As for linear regression, it does not achieve good accuracy, thus it is considered as a not suitable method to be used on the federal government revenue dataset.
Interactive Volumetric Stories Across Immersive Displays

We describe three interactive augmented reality stories for children that we showed at the Cannes Film Festival “Marche du Film” in July 2021. The stories were developed using a novel technique: 3D modeling and animation from within the Virtual Reality. The audience at Cannes viewed and interacted with these stories using mixed reality glasses, a prototype of the Sony Spatial Display, and an AR-enabled tablet. We report on the technical development process and the feedback from the Cannes audience.
Steering-by-example for Progressive Visual Analytics

Progressive visual analytics allows users to interact with early, partial results of long-running computations on large datasets. In this context, computational steering is often brought up as a means to prioritize the progressive computation. This is meant to focus computational resources on data subspaces of interest so as to ensure their computation is completed before all others. Yet, current approaches to select a region of the view space and then to prioritize its corresponding data subspace either require a one-to-one mapping between view and data space, or they need to establish and maintain computationally costly index structures to trace complex mappings between view and data space. We present steering-by-example, a novel interactive steering approach for progressive visual analytics, which allows prioritizing data subspaces for the progression by generating a relaxed query from a set of selected data items. Our approach works independently of the particular visualization technique and without additional index structures. First benchmark results show that steering-by-example considerably improves Precision and Recall for prioritizing unprocessed data for a selected view region, clearly outperforming random uniform sampling.
Platform Governance

Platform - benign as it seems, defining the term succinctly is easier said than done [6]. It may range from an offline shopping mall to a tech-driven e-commerce platform (e.g., Amazon, Walmart, Alibaba); from a forum of public speech to an online social network (e.g., Facebook, Instagram, Twitter); from a classroom to an online encyclopedia (e.g., Wikipedia). The definition of platform varies across contexts and over time. For the purpose of this article, platforms may be understood as the online sites and services that host, organize and circulate contents and goods by facilitating social interactions among its stakeholders [5]. These interactions can range from as simple as chats, sharing and liking contents to as complicated as business transactions between stakeholders.
Feature Fusion with Non-local for Pedestrian Attribute Recognition

In the Pedestrian Attribute Recognition (PAR) research topic, how to extract comprehensive features to represent pedestrian attributes is still an open problem due to its multi-label nature. In order to tackle this problem, we proposed a new end-to-end PAR network based on Feature Fusion with a Non-local operation(FFNL), named FFNL-net. Compared with existing feature fusion approaches that only mechanically pays attention to feature maps from multiple levels, it also exploits the strongest semantic but still higher resolution feature maps. Firstly, the pedestrian image is extracted from a backbone network. Then, three feature maps from different levels and scales are obtained, and the Non-local operation on the above multiple feature maps are applied respectively to further extract spatial information. Finally, a novel feature fusion strategy is performed on them to fetch a fused feature map. Our experimental results on existing popular pedestrian attribute datasets of PETA, PA-100K, and RAP prove that our proposed approach achieves state-of-the-art results.
Image Model and Algorithm of Human Resource Optimal Configuration Based on FPGA and Microsystem Analysis

Human resources are the most dynamic factor among all productivity factors. The goal of human resource management is to achieve effective and optimal allocation of resources, while the goal of optimal allocation is to promote the realization of corporate goals. At present, the overall domestic human resource management is relatively backward, and most of it is nonstandardized management, and the level of informatization and technology is not high. Therefore, this paper proposes the image model and algorithm research of human resource optimization configuration based on FPGA and microsystem analysis. First, through the data analysis method, in-depth study of traditional resource allocation algorithms: genetic algorithm and particle swarm optimization algorithm, combined with machine learning algorithms, established a human resource optimization configuration model based on FPGA and microsystem analysis, and then optimized the configuration of human resources for the impact The standard deviation and variance measurement of 18 factors of the company, as well as the analysis of the matching degree between employees and positions, and required capabilities. Finally, it is concluded that the sample average is higher and the top three are employees’ positive spirit A12, employees’ executive ability A15, and the relationship between salary and performance A11. The sample averages are 4.615, 4.522, and 4.479, respectively; standard deviation of the three factors with the smallest values are good values A8, strong employee cooperation ability A17, and perfect reward and punishment system A16. This shows that the above considerations should be considered in the optimization of enterprise human resources. Moreover, the algorithm in this paper is easier to obtain the optimal solution, and it is easier to obtain the optimal allocation result of human resources.
An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

In cloud data centers, the consolidation of workload is one of the phases during which the available hosts are allocated tasks.This phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement (SLA).To consolidate the workloads, the hosts are segregated into three categories: normal hosts, under-loaded hosts, and overloaded hosts based on their utilization.It is to be noted that the identification of an extensively used host or underloaded host is challenging to accomplish.Threshold values were proposed in the literature to detect this scenario.The current study aims to improve the existing methods that choose the underloaded hosts, get rid of Virtual Machines (VMs) from them, and finally place them in some other hosts.The researcher proposes a Host Resource Utilization Aware (HRUAA) Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud environment.The mechanism presented in this study is contrasted with existing mechanisms empirically.The results attained from the study establish that numerous hosts can be shut down, while at the same time, the user's workload requirement can also be met.The proposed method is energy-efficient in workload consolidation, saves cost and time, and leverages active hosts.
Weak supervision using cell tracking annotation and image registration improves cell segmentation

Learning-based cell segmentation methods have proved to be very effective in cell tracking. The main difficulty of using machine learning is the lack of expert annotation of biomedical data. We propose a weakly-supervised approach that extends the amount of segmentation training data for image sequences where only a couple of frames are annotated. The approach uses the tracking annotations as weak labels and image registration to extend the segmentation annotation to the neighbouring frames. This technique was applied to cell segmentation step in the cell tracking problem. An experimental comparison of the baseline segmentation network trained on the data with pure GT annotation and the same segmentation network trained on the GT data and additional annotations generated with the proposed approach has been performed. The proposed weakly-supervised approach increased the IoU and SEG metrics on the data from the Cell Tracking Challenge.