Reviewerly/exHarmony huggingface dataset:
GazeSync: Eye Movement Transfer Using an Optical Eye Tracker and Monochrome Liquid Crystal Displays

Can we see the world through the eyes of somebody else? We present an early work to transfer eye gaze from one person to another. Imagine you can follow the eye gaze of an instructor while explaining a complex work step, or you can experience a painting like an expert would: Your gaze is directed to the important parts and follows the appropriate steps. In this work, we explore the possibility to transmit eye-gaze information in a subtle, unobtrusive fashion between two individuals. We present an early prototype consisting of an optical eye-tracker for the leader (person who shares the eye gaze) and two monochrome see-through displays for the follower (person who follows the eye gaze of the leader). We report the results of an initial user test and discuss future works.
A Boosting-Based Deep Distance Metric Learning Method

By leveraging neural networks, deep distance metric learning has yielded impressive results in computer vision applications. However, the existing approaches mostly focus a single deep distance metric based on pairs or triplets of samples. It is difficult for them to handle heterogeneous data and avoid overfitting. This study proposes a boosting-based learning method of multiple deep distance metrics, which generates the final distance metric through iterative training of multiple weak distance metrics. Firstly, the distance of sample pairs was mapped by a convolution neural network (CNN) and evaluated by a piecewise linear function. Secondly, the evaluation function was added as a weak learner to the boosting algorithm to generate a strong learner. Each weak learner targets the difficult samples different from the samples of previous learners. Next, an alternating optimization method was employed to train the network and loss function. Finally, the effectiveness of our method was demonstrated in contrast to state of the arts on retrieving the images from the CUB-200-2011, Cars-196, and Stanford Online Products (SOP) datasets.
Opinion Mining and Sentiment Analysis on Product Reviews

Opinion mining and sentiment analysis are two popular topics for analyzing and extracting information from diverse sources such as Amazon, Face book, and Twitter. It helps businesses make better decisions during online purchasing by allowing them to work actively on refining their business strategy and obtaining an in-depthknowledge of the buyer&#x0027;s opinion about the goods. It includes a computational analysis of an individual&#x0027;s buying behaviors and then mining their opinions about a company&#x0027;s corporate entity. There were many Machine learning algorithms which is applied to classify the user opinions. In this paper, the proposed work will be done with one of the machine learning algorithms called Na&#x00EF;ve Bayes and Support Vector machine for the best results and accuracy, the datasets are gathered from amazon.com website for laptop product.
Integration of Gesture Generation System Using Gesture Library with DIY Robot Design Kit

Conversational agents are expected to improve the quality of communication by adding gestures to the speech, and are considered to be a promising tool. Recent data-driven methods are capable of attaching gestures to arbitrary speech, but the output is still not in line with human intuition. Therefore, we propose a gesture transformation system that utilizes gesture types as intermediate information, based on the theory of psycholinguistics. We employ the gesture-first principle to create gesture clusters based on gesture similarities among imagistic gestures, one type of gesture to represent image-like motions, which are considered to represent important concepts in conversations. Since this system explicitly takes into account the gesture types recognized by a deep neural network (DNN) and the semantics of the sentence to select gestures, it is expected to output gestures that are more in line with human intuition than existing end-to-end systems that do not place these intermediate states. We prepared a DIY robot kit consisting of cheap parts so that conversational agents at home become available to ordinary users, and implemented the proposed gesture generation system on this robot. In order to evaluate the effectiveness of the conversational agent, we evaluated user impression when using various media for conversation and confirmed the advantage of using our agent.
A privacy enhancing model for Internet of Things using three-way decisions and differential privacy

The recent advancements in Internet of Things (IoT) have brought enormous advantages for businesses. These benefits are achieved by services that collect large volumes of data that is collected for analysis. The data may also contain sensitive information. Privacy of such data is an important research challenge. Differential privacy is a recent technique for data privacy. It works by anonymizing the attributes that may contain sensitive information. An essential step before applying differential privacy is the division of attribute set into three groups called sensitive, non-sensitive and ambiguous. A key issue in existing studies is that the division of attribute set is done manually by a domain expert and is therefore costly. We introduce a three-way approach for differential privacy and a supporting algorithm for this demarcation of attribute sets. Results indicate that the information content and stability of the dataset improves considerably with our approach.
Construction and Application of Comprehensive Nursing Information Service Platform Based on Internet of Things Technology

This study illustrates the construction and application of comprehensive nursing information service platform based on Internet of Things technology. An Internet-based cardiovascular care management platform, convenient before and after platform application, was constructed; 42 patients with cardiovascular disease were selected as the control group and observation group and 33 nurses participated in the same group, and the intervention lasted for 4 months. The control group received routine admission nursing, and the follow-up nursing nurses received traditional face-to-face teaching, training in the form of assessment. The observation group with the help of the cardiovascular specialist nursing management platform, during the whole process of nursing, nurses also manage patients and receive training through the platform. Experiments show the following: As a result, after the application of the platform, the satisfaction rate of nurses and patients on the platform was high, both reaching more than 92%. After the application of the platform, the scores of various nursing indicators were significantly higher than those before application (all P < 0.01). The construction of a cardiovascular specialist nursing management platform based on IoT technology integrates multiple modules on the medical and patient ends, which not only improves the level of nursing management but also improves the satisfaction of nurses and patients and, at the same time, helps to improve the self-value of nurses.
Pilot of a digital contact tracing card in a hospital setting in New Zealand, 2020

Countries are rapidly developing digital contact tracing solutions to augment manual contact tracing. There is limited empirical evidence evaluating these tools. We conducted a feasibility study of a Bluetooth-enabled card with hospital staff in New Zealand (n = 42). We compared the card data against self-report contact surveys and a stronger Bluetooth device. The cards detected substantially more contacts than self-report contact surveys, while the concordance between Bluetooth devices was high, suggesting that the cards detected clinically relevant close contacts. There was high acceptability among participants, suggesting that their integration would be accepted by healthcare staff. As the pandemic shifts, there is a need to rapidly contact trace and conduct informed risk management, particularly in critical settings such as healthcare.
Real-Time Gender Recognition for Juvenile and Adult Faces

Facial gender recognition is a crucial research topic due to its comprehensive use cases, including a demographic gender survey, visitor profile identification, targeted advertisement, access control, security, and surveillance from CCTV. For these real-time applications, the face of a person can be oriented to any angle from the camera axis, and the person can be of any age group, including juveniles. A child's face consists of immature craniofacial feature points in texture and edge compared to an adult face, making it very hard to recognize gender using the child's face. Real-word faces captured in an unconstrained environment make the gender prediction system more complex to identify correctly due to orientation. These factors reduce the accuracy of the existing state-of-the-art models developed so far for real-time facial gender prediction. This paper presents the novelty of facial gender recognition for juveniles, adults, and unconstrained-oriented faces. The progressive calibration network (PCN) detects rotation-invariant faces in the proposed model. Then, a Gabor filter is applied to extract unique edge and texture features from the detected face. The Gabor filter is invariant to illumination and produces texture and edge features with redundant feature coefficients in large dimensions. Gabor has drawbacks such as redundancy and a large dimension resolved by the proposed meanDWT feature optimization method, which optimizes the system's accuracy, the size of the model, and computational timing. The proposed feature engineering model is classified with different classifiers such as Naïve Bayes, Logistic Regression, SVM with linear, and RBF kernel. Its results are compared with the state-of-the-art techniques; detailed experimental analysis is presented and concluded to support the argument. We also present a review of approaches based on conventional and deep learning methods with their pros and cons for facial gender recognition on different datasets available for facial gender recognition.
Semantic Segmentation of PHT Based on Improved DeeplabV3+

This work aimed to address the two shortcomings of the printed and handwritten texts (PHT) classification. The classification accuracy of FCN and U-net, which are used for PHT pixel-level classification, still has room to improve. PHT public datasets have small sample sizes, and the generalization ability of the models is not good. In this paper, first, a pixel-level sample-making method for PHT identification was proposed, and a PHT dataset 2021 (PHTD 2021), containing 3,000 samples, was constructed. Second, because there is a large number of words but the contours are small in documents, the DeeplabV3+ model was improved. The network layer number and pooling times were reduced, and the convolution kernel and dilated rate were increased. In the experiment, the improved DeeplabV3+ model had a classification accuracy of 95.06% on the test samples from the PHTD 2021 dataset. The improved DeeplabV3+ model has a higher recognition accuracy than the FCN and DeeplabV3+ models. Finally, after the classification of PHT, applications of handwritten texts removal and handwritten texts extraction are provided.
Effect of Label Noise on Multi-Class Semantic Segmentation: A Case Study on Bangladesh Marine Region

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Multifunctional Web-system to register and control intellectual products in scientific research

The authors present the web-system providing services of acquiring and accessing multiaspect information on science and research products (publications and papers delivered at scientific events) generated by researchers at an organization or a group of organizations. The system is designed at the Interdepartmental Supercomputer Center of the Russian Academy of Sciences. The System is targeted at both end users interested in obtaining specific data and at administrators and librarians who are to report to their parent organizations. The System’s information base comprises linked data on the following categories: personalia (authors of publications and papers); organizations with their divisions; publications at analytical, monographic and union levels; inventor certificates; scientific events (conferences, symposia, seminars); papers. The system is structured in two modules, the administrative (for data loading and editing) and user module – retrieval system for information search, visualization, navigation through linked resources and data exporting. The System specific feature is that it introduces the concept of “equivalent” objects, i. e. the objects represented by different metadata though related to the same physical entity. In compliance with current reporting standards, the entries also comprise funding resources of the publications, as well as the authors’ affiliation as specified in the articles. While accentuating the friendliness of the system user interface, the authors recommend to use the System in research and academic libraries as an instrument of publication activity assessment.
Semantic Segmentation Using Pixel-Wise Adaptive Label Smoothing via Self-Knowledge Distillation for Limited Labeling Data

To achieve high performance, most deep convolutional neural networks (DCNNs) require a significant amount of training data with ground truth labels. However, creating ground-truth labels for semantic segmentation requires more time, human effort, and cost compared with other tasks such as classification and object detection, because the ground-truth label of every pixel in an image is required. Hence, it is practically demanding to train DCNNs using a limited amount of training data for semantic segmentation. Generally, training DCNNs using a limited amount of data is problematic as it easily results in a decrease in the accuracy of the networks because of overfitting to the training data. Here, we propose a new regularization method called pixel-wise adaptive label smoothing (PALS) via self-knowledge distillation to stably train semantic segmentation networks in a practical situation, in which only a limited amount of training data is available. To mitigate the problem caused by limited training data, our method fully utilizes the internal statistics of pixels within an input image. Consequently, the proposed method generates a pixel-wise aggregated probability distribution using a similarity matrix that encodes the affinities between all pairs of pixels. To further increase the accuracy, we add one-hot encoded distributions with ground-truth labels to these aggregated distributions, and obtain our final soft labels. We demonstrate the effectiveness of our method for the Cityscapes dataset and the Pascal VOC2012 dataset using limited amounts of training data, such as 10%, 30%, 50%, and 100%. Based on various quantitative and qualitative comparisons, our method demonstrates more accurate results compared with previous methods. Specifically, for the Cityscapes test set, our method achieved mIoU improvements of 0.076%, 1.848%, 1.137%, and 1.063% for 10%, 30%, 50%, and 100% training data, respectively, compared with the method of the cross-entropy loss using one-hot encoding with ground truth labels.
Ordinary people, criminals, addicts and recreational users: Swedish court of law descriptions of persons sentenced for online drug purchases

Background: The aim of this study was to analyze how Swedish courts describe persons sentenced for purchasing illicit drugs online. Methods: Qualitative analysis of naturally occurring data through 201 sentences that included 248 individuals sentenced for having purchased drugs online between January 1 2010 and January 1 2020. Results: The analysis resulted in the construction of four ideal types regarding the described characteristics of the sentenced persons; the ordinary person, the recreational user, the addict and the criminal. The courts operate with a notable dichotomy between traditional drug markets and online drug markets, that can be understood in relation to descriptions of Bourdieusian capital forms, specifically street capital and digital capital. Conclusion: Descriptions relating to street capital were of larger interest to the courts compared to digital capital, although there were examples of when the courts argued that uses of digital capital should be viewed as an aggravating circumstance. The courts largely held a dichotomous view of online and offline drug markets that focus on street-based criminality, which may have implications for how emerging digital drug markets are responded to by drug law enforcement and judicial systems.
Sentiment Analysis of Tweets in Twitter Using CNN

Sentimental investigation is the computational investigation of individuals&#x0027; perspectives, mentalities, and feelings toward elements, individuals, issues, occasions, or subjects. A ton of examination has been done to work on the exactness of feeling investigation, fluctuating from straightforward more direct models to intricate Deep Neural Network models. As of late, Deep Learning has shown extraordinary accomplishment the opinion examination and considered the best in class model. Twitter information forces many difficulties, because of its intricate construction, different vernaculars, notwithstanding the absence of its assets. Albeit the new profound learning model has worked on the precision of the Twitter feeling examination, there is even more opportunity for development. This urged us to investigate distinctive models that poor people been applied to twitter information, to further develop the Twitter opinion examination precision. The fundamental point of the venture is to work on the precision of opinion examination of twitter information by utilizing CNN procedures and furthermore look at the exhibition of the proposed technique with existing models.
A Telepresence-based Remote Learning System

In this paper, we develop a new telepresence-based system for real-time remote learning. By combining low-latency 360 video/audio communication and Virtual Reality, the proposed system allow students feel like as if they are present in the classroom, regardless of their true locations. Especially, the system allows students to perform in-class activities such as note taking by providing a local view in addition to a remote 360-degree view. The proposed system is expected to improve user engagement and study efficiency.
Deep Learning for Intra-Hour Solar Forecasting with Fusion of Features Extracted from Infrared Sky Images

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V2T-GAN: Three-Level Refined Light-Weight GAN with Cascaded Guidance for Visible-to-Thermal Translation

Infrared image simulation is challenging because it is complex to model. To estimate the corresponding infrared image directly from the visible light image, we propose a three-level refined light-weight generative adversarial network with cascaded guidance (V2T-GAN), which can improve the accuracy of the infrared simulation image. V2T-GAN is guided by cascading auxiliary tasks and auxiliary information: the first-level adversarial network uses semantic segmentation as an auxiliary task, focusing on the structural information of the infrared image; the second-level adversarial network uses the grayscale inverted visible image as the auxiliary task to supplement the texture details of the infrared image; the third-level network obtains a sharp and accurate edge by adding auxiliary information of the edge image and a displacement network. Experiments on the public dataset Multispectral Pedestrian Dataset demonstrate that the structure and texture features of the infrared simulation image obtained by V2T-GAN are correct, and outperform the state-of-the-art methods in objective metrics and subjective visualization effects.
COVID-19 Patient Mortality Risk Classification Using Linear Regression and Exponential Smoothing Methods

The dominant explanation for this phenomenon is there is no action to prevent a spike in coronavirus. Seeing this problem, we do some literature and research on artificial intelligence and machine learning to support the development of coronavirus forecasting in Indonesia. This research focuses on analyzing future coronavirus case possibilities using machine learning methodology. We use the official Indonesia Covid website data to collect data from the provinces with the largest cases in Indonesia from January until March. In this research, we used supervised machine learning methods of Linear Regression (LR) and Exponential Smoothing (ES) to make it easier for people to understand the data structure adapted to a particular model. The result of our research showed that each province in Indonesia is expected to decrease in new confirmed recovered cases and death cases from April to May while using the Exponential Smoothing method. In the other case, the Linear Regression method showed that cases would decrease in almost all cases in each province, and some cases will increase, such as Recovered Cases and Death Cases in West Java.
A dataset of eye gaze images for calibration-free eye tracking augmented reality headset

Abstract Eye tracking is a widely used technique. To enhance eye gaze estimation in different contexts, many eye tracking datasets have been proposed. However, these datasets depend on calibrations in data capture and its applications. We seek to construct a dataset that enables the design of a calibration-free eye tracking device irrespective of users and scenes. To reach this goal, we present ARGaze, a dataset with 1,321,968 pairs of eye gaze images at 32 × 32 pixel resolution and 50 corresponding videos of world views based on a replicable augmented reality headset. The dataset was captured from 25 participants who completed eye gaze tasks for 30 min in both real-world and augmented reality scenes. To validate the dataset, we compared it against state-of-the-art eye gaze datasets in terms of effectiveness and accuracy and report that the ARGaze dataset achieved record low gaze estimation error by 3.70 degrees on average and 1.56 degrees on specific participants without calibrations to the two scenes. Implications for generalising the use of the dataset are discussed.
Multi-objective neural network model selection with a graph-based large margin approach

This work presents a new decision-making strategy for multi-objective learning problem of artificial neural networks (ANN). The proposed decision-maker searches for the solution that minimizes a margin-based validation error amongst Pareto set solutions. The proposal is based on a geometric approximation to find the large margin (distance) of separation among the classes. Several benchmarks commonly available in the literature were used for testing. The obtained results showed that the proposal is more efficient in controlling the generalization capacity of neural models than other learning machines. It yields smooth (noise robustness) and well-fitted models straightforwardly, i.e., without the necessity of parameter set definition in advance or validation data use, as often required by learning machines.