Reviewerly/exHarmony huggingface dataset:
Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition

We present the Teacher-Student Generative Adversarial Network (TS-GAN) to generate depth images from single RGB images in order to boost the performance of face recognition systems. For our method to generalize well across unseen datasets, we design two components in the architecture, a teacher and a student. The teacher, which itself consists of a generator and a discriminator, learns a latent mapping between input RGB and paired depth images in a supervised fashion. The student, which consists of two generators (one shared with the teacher) and a discriminator, learns from new RGB data with no available paired depth information, for improved generalization. The fully trained shared generator can then be used in runtime to hallucinate depth from RGB for downstream applications such as face recognition. We perform rigorous experiments to show the superiority of TS-GAN over other methods in generating synthetic depth images. Moreover, face recognition experiments demonstrate that our hallucinated depth along with the input RGB images boost performance across various architectures when compared to a single RGB modality by average values of +1.2%, +2.6%, and +2.6% for IIIT-D, EURECOM, and LFW datasets respectively. We make our implementation public at: https://github.com/hardik-uppal/teacher-student-gan.git.
Skeleton-based visualization of poor body movements in a child's gross-motor assessment using convolutional auto-encoder

This paper deals with human activity recognition (AR), which is the basic technology for understanding human behavior and movement in the field of sensing applications for human support systems. Focusing on children's gross motor (GM) skills as an AR target, a new visualization method to point out children's poor body movements is presented. The visualization is achieved as anomaly detection by an autoencoder (AE) trained with good GM movements with a complete rating score, and poor limb motion in GM is detected as anomal points by the GM-AE which is combined with authors' previous GM-AR and AE. In preparation for the GM-AE, the previous dataset and data augmentation have been improved, and new GM-AR was completely realized with an identification accuracy of 99.3 % to 148 actual assessment patterns out of 200 theoretical assessment combinations of the GM assessment tool TGMD-3. Using appropriate preparations proven by the GM-AR stage, we investigated some deep learning conditions related to GM-AE. Finally, it was confirmed that the presented visualization method can emphasize the points that match the evaluation items of TGMD-3.
A systematic review of research on reading in English on screen and on paper

Abstract The introduction and wide use of devices, especially mobile ones, has changed the way learners read and do research for a variety of reasons, and this trend has attracted a number of studies conducted regarding reading on screen and on paper in addition to those dealing with the students’ behavior in using online resources to print ones. This paper aims at identifying the major findings and trends in reading research by describing the current state of knowledge and practice in the studies comparing reading on screen and on paper and to provide guidance for practitioners by analyzing and summarizing the existing research. The current review adopted a systematic review as the research methodology as well as the article selection and screening process. The articles published between 2009 and 2017 were reviewed, and 37 articles were included in the analysis. The review revealed that the research on onscreen and paper-based reading focused on comparing the learners’ performances in reading activities in both contexts and sharing preliminary findings and students’ views. However, the findings are inconclusive as mixed findings were produced as to the effects of reading on screen and on paper. Moreover, the review also indicates that practitioners are also interested in examining the factors and affordances in reading on screen. As a result, there is still further research needed to establish the factors affecting reading and comprehension while reading on screen and on paper.
COVID-19’s Telemedicine Platform

The fight against COVID-19 has become part of our everyday lives. In this paper, we will introduce a novel telehealth solution we developed to provide powerful help in this fight. The integrated system we have developed provides technology support for special features that have already proven their effectiveness in fighting the virus. Such key functions include social distance control, effective contact detection, continuous monitoring of people's health status, disinfection of affected areas, analysis and optimization of work processes to prevent the spread of the virus. Our health and safety solutions for COVID-19 are based on digitizing and monitoring spaces, time, motion and physiological parameters with smart IoT and artificial intelligence.
A Pre-training Approach for Stance Classification in Online Forums

Stance detection is the task of automatically determining whether the author of a piece of text is in favor of, against, or neutral towards a target such as a topic, entity, or claim. In this paper, we propose a method based on RoBERTa to classify stances by capturing the context of the discussion through the examination of pairs of stances and relational structures of debates specific to each topic within the defined window of each forum participant's interventions. Furthermore, we examine the degree of disagreement and neutrality in various debate topics to measure divergence of opinion in the course of the debate and estimate the emotional state manifested in different debate topics. We conduct extensive experiments using two publicly available datasets and demonstrate that our method considers more stance classes, provides better results and yields statistical improvements over existing techniques. Our quantitative analysis of model performance yields F-1 scores of over 0.745. Interestingly, we obtained the highest F-1 score, 0.814, on a stance class which was not taken into consideration in prior work. We report that none of the metrics utilized to measure divergence of opinion yield values exceeding 50 % and the correlations between the same topics over 10-fold cross-validation are statistically significant for the majority of them (p <; 0.005). Several future research avenues are proposed.
Development of the concept of managing stakeholders in the Russian educational agglomeration based on the Agile methodology

Abstract The paper substantiates the relevance of developing the concept of managing stakeholders in the educational agglomeration based on the flexible methodology in an unstable environment. The aim of the paper is to conceptualize the management of stakeholders of educational agglomeration based on the Agile methodology. The main research methods were dialectical method, critical analysis method, descriptive statistics methods, communicative and pragmatic approach, stakeholder approach, and conceptualization. The concept of managing stakeholders in the Russian educational agglomeration based on the Agile methodology was developed. This conceptualization implements the empirically-based and design-based orientation of the flexible methodology and takes into account the practical benefits of stakeholder effects from participation in the educational agglomeration. The main elements of the stakeholder management system are: the person, object and subject of management, management principles, management cycle and management of the terms of cooperation. The conclusion about managing the stakeholders of the educational agglomeration based on the Agile methodology is substantiated.
The Tacit Knowledge in Games: From Validation to Debriefing

Game sessions consist of three phases: briefing, gameplay, and debriefing, with the latter being considered the most important feature of games. Nevertheless, given that games are considered by many to be more of an artistic form rather than a scientific artifact, a question that rises is: Can game sessions in general and debriefing in particular be analyzed and performed in a rigorous scientific way? In other words, can they be consistently structured, given the different characteristics of games, and can clear criteria on what would constitute a successful game session and debriefing be defined? The answer to these questions is yes. Yet, it remains a challenge to extract the knowledge of experts, which resides to a large extent in the tacit knowledge spectrum. Hence, the aim of this paper is to shed some light in this tacit knowledge possessed by experts and to gain understanding on why certain practices are more prone to success than others as well as bring into the surface other practices that have remained well hidden. In order to accomplish this goal, three rounds of interviews were conducted.
Unsupervised Image Restoration Using Partially Linear Denoisers

Deep neural network based methods are the state of the art in various image restoration problems. Standard supervised learning frameworks require a set of noisy measurement and clean image pairs for which a distance between the output of the restoration model and the ground truth, clean images is minimized. The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications. We circumvent this problem by proposing a class of structured denoisers that can be decomposed as the sum of a nonlinear image-dependent mapping, a linear noise-dependent term and a small residual term. We show that these denoisers can be trained with only noisy images under the condition that the noise has zero mean and known variance. The exact distribution of the noise, however, is not assumed to be known. We show the superiority of our approach for image denoising, and demonstrate its extension to solving other restoration problems such as image deblurring where the ground truth is not available. Our method outperforms some recent unsupervised and self-supervised deep denoising models that do not require clean images for their training. For deblurring problems, the method, using only one noisy and blurry observation per image, reaches a quality not far away from its fully supervised counterparts on a benchmark dataset.
Explicit Recursive Track-to-Track Fusion Rules for Nonlinear Multi-Sensor Systems

This letter presents explicit sub-optimal track-to-track fusion algorithms for Multi-Sensor Systems (MSS) estimating nonlinear processes. The individual tracks in an MSS are correlated due to the presence of a common process noise in the track estimation errors. Herein, we propose recursive formulae for consistent correlation estimation in mildly and highly nonlinear systems that respectively use Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF) for track estimation. In a mildly nonlinear system, the linearized model employed in the EKF-based MSS architecture offers a correlation propagation formula whose coupling with the optimal track fusion rule generates a sub-optimal fused estimate. On the other hand in highly nonlinear systems, the UKF-based architectures are proven effective for track estimation. The UKF works based on the unscented transform of deterministic sigma points, which is equivalent to the Statistical Linearization Regression (SLR) process. For UKF-based MSS architectures, we propose a consistent correlation propagation recursion according to the SLR technique that will be coupled with the optimal track fusion rule to generate a sub-optimal fused estimate. The performance of the developed fusion algorithms is demonstrated through conducting a statistical test and an average root mean square error analysis.
A Study on Movie Recommendations using Collaborative Filtering

Abstract The Recommendation system plays a major role nowadays, which is used for many applications. We know that the online content and service providers have a huge amount of content so the problem which arises is which data is required for whom so the problem of providing apposite content frequently. This paper represents the overview and approaches of techniques generated in a recommendation system. One of them is Collaborative Filtering which we will discuss about.
Expert camouflage-breakers can accurately localize search targets

Camouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report whether or not a given camouflage scene contains a target. But it remains unclear whether such expert subjects can actually detect the target in this task, or just vaguely sense that the two classes of images are somehow different, without being able to find the target per se. Here, we show that when subjects break camouflage, they can also localize the camouflaged target accurately, even though they had received no specific training in localizing the target. The localization was significantly accurate when the subjects viewed the scene as briefly as 50 ms, but more so when the subjects were able to freely view the scenes. The accuracy and precision of target localization by expert subjects in the camouflage-breaking task were statistically indistinguishable from the accuracy and precision of target localization by naive subjects during a conventional visual search where the target 'pops out', i.e., is readily visible to the untrained eye. Together, these results indicate that when expert camouflage-breakers detect a camouflaged target, they can also localize it accurately.
Breast Cancer Histological Images Nuclei Segmentation using Mask Regional Convolutional Neural Network

the breast cancer cases have increased globally, in the last two decades. Analysis of histological images acquired through biopsy of the breast tissues is thought to be the most reliable way to assess if any cells show signs of malignancy. With the advent of deep neural networks, we are now able to diagnose the disease with near perfect accuracy instead of conventional techniques of mammography for breast cancer detection; however, work done in this area considers nuclei segmentation. This work is focussed on extending the applicability of deep learning technology where network is trained to perform segmentation and classify data as one of the four classes: normal, benign, in-situ and invasive. The classification accuracy achieved is 98.16% and the segmentation accuracy calculated using overlap coefficient from mean intersection over union is 86.39% on the ICIAR2018 Breast Cancer dataset.
Mobile app design for sustainable agriculture in Mali-West Africa

Abstract In Mali, agricultural activities such as seeding, harvesting, and irrigation play a significant role in productivity. These activities must be carried out appropriately and above all, at an appropriate time to achieve excellent performance. Unfortunately, most farmers are unaware of the impact of these activities on the yield of their crops. This study aims to help farmers and youth people wishing to gather information needed in the field of agriculture entrepreneurship through a mobile application (mobile app). The app designed is mainly used to disseminate information to farmers on how to perform a wide range of agricultural activities such as detailed information about the type of crops, fertilizers, pesticides, selling and buying a product of agriculture, etc. This research was conducted for the adoption of the mobile application in the agricultural field in Mali. The design process followed a User-Centered Design approach to meet the users’ requirements. The evaluation of the design showed that 89, 66% of the participants agreed with the designed application prototype. This app helps farmers and youth people improve their agricultural productivity and not to harm the environment. Therefore, the app helps them for sustainable agriculture to make better land management decisions.
To See or Not to See

People visit public places with different intentions and motivations. While some explore it carefully, others may just want to pass or are otherwise engaged. We investigate how to exploit the inattentional blindness (IB) of indirect users in the design of public interfaces to apply to such diverse needs. Beginning with a structured literature study in the ACM Digital Library on IB, we analyzed 135 publications to derive design strategies that benefit from IB or avoid IB. Using these findings, we selected three existing interfaces for information presentation on a large public square and created two additional interfaces ourselves. We then compared users' perceptions through a self-reported photography study (N = 40). Participants followed one of four scripted profiles to imitate different user intentions, two for direct and two for indirect users. We hypothesized that direct users would recognize the interfaces, while indirect users would experience IB and ignore them. Our results show that direct users reported up to 68% of our interfaces, whereas indirect users noticed only 16%. Thus, IB can be exploited to hide interfaces from indirect users while keeping them noticeable to direct users.
Online project-based learning on surface tension during Covid-19 pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Andriani Noviyanti, Maria Paristiowati, Moersilah; Online project-based learning on surface tension during Covid-19 pandemic. AIP Conf. Proc. 2 April 2021; 2331 (1): 040037. https://doi.org/10.1063/5.0041802 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A Novel Pandemic Tracking Map: From Theory to Implementation

The wide spread of the novel COVID-19 virus all over the world has caused major economical and social damages combined with the death of more than two million people so far around the globe. Therefore, the design of a model that can predict the persons that are most likely to be infected is a necessity to control the spread of this infectious disease as well as any other future novel pandemic. In this paper, an Internet of Things (IoT) sensing network is designed to anonymously track the movement of individuals in crowded zones through collecting the beacons of WiFi and Bluetooth devices from mobile phones to triangulate and estimate the locations of individuals inside buildings without violating their privacy. A mathematical model is presented to compute the expected time of exposure between users. Furthermore, a virus spread mathematical model as well as iterative spread tracking algorithms are proposed to predict the probability of individuals being infected even with limited data.
Class knowledge overlay to visual feature learning for zero-shot image classification

New categories can be discovered by transforming semantic features into synthesized visual features without corresponding training samples in zero-shot image classification. Although significant progress has been made in generating high-quality synthesized visual features using generative adversarial networks, guaranteeing semantic consistency between the semantic features and visual features remains very challenging. In this paper, we propose a novel zero-shot learning approach, GAN-CST, based on class knowledge to visual feature learning to tackle the problem. The approach consists of three parts, class knowledge overlay, semi-supervised learning and triplet loss. It applies class knowledge overlay (CKO) to obtain knowledge not only from the corresponding class but also from other classes that have the knowledge overlay. It ensures that the knowledge-to-visual learning process has adequate information to generate synthesized visual features. The approach also applies a semi-supervised learning process to re-train knowledge-to-visual model. It contributes to reinforcing synthesized visual features generation as well as new category prediction. We tabulate results on a number of benchmark datasets demonstrating that the proposed model delivers superior performance over state-of-the-art approaches.
Structural Performance Characteristics of Nanomaterials and Its Application in Traditional Architectural Cultural Design and Landscape Planning

In recent years, with the rapid development of industrial technology, industrial materials have also begun to develop in more and more refined directions. Nanotechnology is one of the most developed core technologies in industrial technology, and these characteristics of nanomaterials make them shine in many fields, especially in the field of construction. Nowadays, conventional materials still have the problems of heavy weight and high thermal conductivity as thermal insulation materials for external walls. At the same time, it has outstanding performance that conventional materials do not have. Nanomaterials are widely used in various fields with rapid growth due to their many excellent properties and have produced huge economic benefits and social significance. This article describes the basic characteristics of nanomaterials, including surface effects, quantum size effects, small size effects, and macroscopic quantum tunneling effects. As nanomaterials and nanotechnology become more and more mature, the research on architectural coatings in our country has been slightly strengthened. Nanomaterials have a huge impact on future economic development and have become the most promising objects in the current material field. The experimental results prove that the foaming ability of nanomaterials is much better than that of ordinary materials, and the fault tolerance rate of the cement paste is also the highest. This can also positively show that the relationship between nanotechnology and construction engineering is getting closer and closer, and the combination of nanomaterials and construction materials shows people the broad prospects of construction materials for the development of ecological construction materials that improve the living environment and improve the quality of life.
Analysis of DNA Sequence Pattern Matching: A Brief Survey

In this review paper, we have given different methods which are used to match the patterns and also sequences of DNA structures. From the thorough literature survey, we also addressed different challenges involved in pattern analysis and also sequence matching in DNA structures.
A Camera PTZ Control Algorithm For Autonomous Mobile Inspection Robot

Traditional inspection robots usually carry fixed visual inspection equipment. Due to the limited monitoring field of view, the target cannot be tracked and photographed. Therefore, an intelligent pan-tilt tracking and snapping system is designed in this paper. The PTZ camera is combined with the kernel correlation filter tracking algorithm. The position information and scale information of the target are calculated by the tracking algorithm, and then the rotation and magnification of the PTZ camera are adjusted in real time through the control program, so that the target is always in the center of the monitoring field. The PTZ control algorithm is divided into three parts: initialization part, pan / tile control part and zoom control part. Based on the traditional kernel correlation filter tracking algorithm, a multi-scale tracking method is added, which solves the adaptive problem of target scale change in the original algorithm. It can be well applied to the tracking scene where the target scale changes or the pan tilt camera rotates and zooms.