Reviewerly/exHarmony huggingface dataset:
Analysis of the Impact of COVID-19 on Education Based on Geotagged Twitter

More than 150 colleges have reported hundreds of COVID-19 confirmed cases over all the states as the campuses have reopened and the schools have resumed in-person classes, after switching overnight to online teaching in the spring. We conduct a large scale study on education by using a geotagged Twitter dataset, which spans the whole U.S. during parts of the spring, summer, and fall terms of 2020. We analyze the temporal and spatial patterns of COVID-19 cases. Then, we conduct content and sentiment analysis to discover which topics and which thoughts people located at U.S. colleges and universities are communicating.
Making Sense of CNNs: Interpreting Deep Representations and Their Invariances with INNs

To tackle increasingly complex tasks, it has become an essential ability of neural networks to learn abstract representations. These task-specific representations and, particularly, the invariances they capture turn neural networks into black box models that lack interpretability. To open such a black box, it is, therefore, crucial to uncover the different semantic concepts a model has learned as well as those that it has learned to be invariant to. We present an approach based on INNs that (i) recovers the task-specific, learned invariances by disentangling the remaining factor of variation in the data and that (ii) invertibly transforms these recovered invariances combined with the model representation into an equally expressive one with accessible semantic concepts. As a consequence, neural network representations become understandable by providing the means to (i) expose their semantic meaning, (ii) semantically modify a representation, and (iii) visualize individual learned semantic concepts and invariances. Our invertible approach significantly extends the abilities to understand black box models by enabling post-hoc interpretations of state-of-the-art networks without compromising their performance.
Look Here! A Parametric Learning Based Approach to Redirect Visual Attention

Across photography, marketing, and website design, being able to direct the viewer's attention is a powerful tool. Motivated by professional workflows, we introduce an automatic method to make an image region more attention-capturing via subtle image edits that maintain realism and fidelity to the original. From an input image and a user-provided mask, our GazeShiftNet model predicts a distinct set of global parametric transformations to be applied to the foreground and background image regions separately. We present the results of quantitative and qualitative experiments that demonstrate improvements over prior state-of-the-art. In contrast to existing attention shifting algorithms, our global parametric approach better preserves image semantics and avoids typical generative artifacts. Our edits enable inference at interactive rates on any image size, and easily generalize to videos. Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region. Furthermore, users can customize the edited images by dialing the edits up or down via interpolations in parameter space. This paper presents a practical tool that can simplify future image editing pipelines.
Using cognitive mapping to understand conservation planning

Abstract We considered a common research tool for understanding the mental models behind conservation decisions: cognitive mapping. Developed by cognitive psychologists, the elicitation of mental models with cognitive mapping has been used to understand soil management in Spain, invasive grass management in Australia, community forest management in the Bolivian Amazon, and small‐scale fisheries access in Belize, among others. A generalized cognitive mapping process considers specific factors associated with the design, data‐collection, data‐analyses, and interpretation phases of research. We applied this tool in a study about the integration of social data in shoreline master plans of Washington State. Fourteen policy makers and managers (approximately 85% of the region's potential sample) were asked to identify the factors they considered when making their plans. Researchers coded these factors into mental‐model objects and summarized mental‐object frequency and co‐occurrence trends. Although managers prioritized the perceived needs of social groups in their mental model of shoreline master plans, they focused specifically on tribal and private property rights, even though existing social data identified a diversity of interests around timber harvesting, tourism, and agriculture. Understanding their mental models allowed us to more effectively present this social data so that it could fit within their existing thoughts around planning. Although our case study provides a description of the cognition of a particular policy process, cognitive mapping can be used to understand cognitive processes that influence any conservation planning context.
Implementing an embedded system to identify possible COVID-19 suspects using thermovision cameras

The main goal of this paper is to prove that by combining thermal vision cameras and image processing with many deep learning classification algorithms we developed an effective embedded system with high applicability in this critical period caused by COVID-19 pandemic disease.Using fixed and mobile thermal cameras we envisioned and developed a real time temperature screening capable of sending alarm signals over network or by SMS to local authorities along with multiple detection metrics such as the age, the gender, the facial emotion, the GPS location where the alarm went off, the temperature reading from the human face and also if the subject is wearing or not a medical face mask.
A Model for Medical Students’ Behavioral Intention to Use Mobile Learning

The use of mobile devices that have high technical capabilities has increased in the last years. These devices are appropriate instructional tools reflecting the trends in modern education by providing instant access to information that is used with mobile learning purposes. As is in many areas of education, m-learning has been becoming widespread in medical education. Therefore, medical students’ readiness for m-learning is highly important. This study aims to investigate how medical students’ beliefs influence their behavioral intention to use mobile devices for learning purposes. The 376 medical students (222 juniors, 154 sophomores; aged between 18 and 24 years; 214 males, 162 females) participated in this study. All participants had mobile devices. Data were collected through a survey. Structural equation modeling was used to analyze the findings. The proposed model, which is created based on the theory of planned behavior, was tested in the study. Based on the findings, the medical students’ perceived ease of use, perceived usefulness, learning autonomy, intention to use, perceived self-efficacy toward mobile devices, and m-learning are found to be high level. However, according to medical students, instructors’ readiness to apply m-learning has been found to be low level. The findings showed that the proposed model explains medical students’ behavioral intention to use m-learning reasonably well. The behavioral intention is explained with a variance of 76% in the model. Subjective norm is the main indicator of behavioral intention, followed by perceived behavioral control and attitude. The proposed model in the study could be useful to design m-learning applications, environments, and implementation plans effectively in medical education.
App Review Sentiment Analysis Shopee Application In Google Play Store Using Naive Bayes Algorithm

Abstract An online marketplace site is a shopping place that is currently popular with the community because it offers a variety of convenience and one of the marketplace apps is Shopee. Some people are satisfied with the service provided by the Shopee app. But unisex some people who give complaints about this application. User-provided response to Shopee app in the Comments field of Shopee Google Play Store can be analyzed for negative and positive sentiments. This research aims to assist Shopee’s management of the positive or negative opinions of application users and can provide empirical evidence for related theories so that it can be used as a donation of thought for the development of theories Next. With the number of reviews shown, you need an analysis that can classify these reviews into positive or negative classes. The method used for the sentiment analysis of Shopee app reviews is the Naive Bayes algorithm obtaining an accuracy yield of 96,667%
Governance, Inequality and Inclusive Education in Sub-Saharan Africa

The study provides thresholds of income inequality that if exceeded will nullify the positive effect of governance dynamics on gender-inclusive education in 42 countries in sub-Saharan Africa for the period 2004–2014. The Generalised Method of Moments is used as an estimation strategy. The following findings are established. First, the unconditional effects of governance dynamics on inclusive education are consistently positive whereas the corresponding conditional effects from the interaction between inequality and governance dynamics are consistently negative. Second, the levels of inequality that completely crowd-out the positive incidence of governance on inclusive 'primary and secondary education' are: 0.587 for the rule of law and 0.565 for corruption-control. Third, the levels of inequality that completely dampen the positive incidence of governance on inclusive 'secondary education' are: 0.601 for 'voice & accountability' and 0.700 for regulation quality. Fourth, for tertiary education, inequality thresholds are respectively 0.568 for political stability and 0.562 for corruption-control. The main policy implication is that for governance dynamics to promote inclusive education in the sampled countries, income inequality levels should be kept within the established thresholds. Other implications are discussed in the light of Sustainable Development Goals.
Continual improvement of nasopharyngeal carcinoma segmentation with less labeling effort

PurposeConvolutional neural networks (CNNs) offer a promising approach to automated segmentation. However, labeling contours on a large scale is laborious. Here we propose a method to improve segmentation continually with less labeling effort.MethodsThe cohort included 600 patients with nasopharyngeal carcinoma. The proposed method was comprised of four steps. First, an initial CNN model was trained from scratch to perform segmentation of the clinical target volume. Second, a binary classifier was trained using a secondary CNN to identify samples for which the initial model gave a dice similarity coefficient (DSC) < 0.85. Third, the classifier was used to select such samples from the new coming data. Forth, the final model was fine-tuned from the initial model, using only selected samples.ResultsThe classifier can detect poor segmentation of the model with an accuracy of 92%. The proposed segmentation method improved the DSC from 0.82 to 0.86 while reducing the labeling effort by 45%.ConclusionsThe proposed method reduces the amount of labeled training data and improves segmentation by continually acquiring, fine-tuning, and transferring knowledge over long time spans.
DigiCraft: A Pedagogical Innovative Proposal for the Development of the Digital Competence in Vulnerable Children

The integration of citizens in the society of information and knowledge is a pillar in social cohesiveness. The digital gap, a direct consequence of socioeconomic inequality among citizens enables e-exclusion, which is a new way of social exclusion that excludes people from different economic, social and economic backgrounds out of the society of information and knowledge. The aim of this paper is to present an educational programme lead by the Foundation Vodafone Spain with the assessment of educators and experts at university. The objective is focused on the development of the digital competence centred on vulnerable childhood. This program uses a method based on learning-by-doing and the “maker” movement. The majority of educators who have participated have underlined that children have improved their digital competence and also some transversal competencies and have defined it as an innovative and creative programme. DigiCraft contributes to the acquisition and development of digital competence from a social perspective. The proposal avoids discrimination of children depending on their sociable heterogeneous factors in their education.
Structural Deep Metric Learning for Room Layout Estimation

In this paper, we propose a structural deep metric learning (SDML) method for room layout estimation, which aims to recover the 3D spatial layout of a cluttered indoor scene from a monocular RGB image. Different from existing room layout estimation methods that solve a regression or per-pixel classification problem, we formulate the room layout estimation problem from a metric learning perspective where we explicitly model the structural relations across different images. We propose to learn a latent embedding space where the Euclidean distance can characterize the actual structural difference between the layouts of two rooms. We then minimize the discrepancy between an image and its ground-truth layout in the learned embedding space. We employ a metric model and a layout encoder to map the RGB images and the ground-truth layouts to the embedding space, respectively, and a layout decoder to map the embeddings to the corresponding layouts, where the whole framework is trained in an end-to-end manner. We perform experiments on the widely used Hedau and LSUN datasets and achieve state-of-the-art performance.
Trace-Based Dynamic Gas Estimation of Loops in Smart Contracts

Smart contracts on Ethereum can be used to encode business logic and have been applied to many different areas, such as token exchanges and games. Unlike general programs, the computations of contracts on Ethereum are restricted by the gas limit. If a transaction runs out of the gas limit before an execution finishes, the Ethereum virtual machine throws an out-of-gas exception, and the entire transaction fails, which reverts to the state before the transaction started, although the transaction fee is still deducted. It is therefore, essential to conduct a gas estimation before sending a transaction. Existing studies have mostly failed in estimating the gas for a loop function because the number of iterations of the loops cannot be statically determined. However, we found that a quarter of all contracts have loop functions, and the gas cost for the loops is higher than for the other functions. Therefore, it is necessary to apply a gas estimation for the loop functions. In this study, we propose a gas estimation approach based on the transaction trace to dynamically estimate the gas for the loop functions. Our belief is that we can learn the relationship between the historical transaction traces and their gas costs to estimate the gas for new transactions. We considered three different abstractions of the original transaction trace and fed them to different machine learning models. The results show that our approach is effective in gas estimation and that a random forest can achieve the most accurate estimation.
Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning

Peer-review of Target Volume (TV) and Organ at Risk (OAR) contours in radiotherapy planning are typically conducted visually; this can be time consuming and subject to interobserver variation. This study investigated automatic evaluation of contouring using conformity indices and supervised machine learning.A total of 393 contours from 253 Stereotactic Ablative Body Radiotherapy (SABR) benchmark cases (adrenal gland, liver, pelvic lymph node and spine), delineated by 132 clinicians from 25 centres, were visually evaluated for conformity against gold standard contours. Contours were scored as "pass" or "fail" on visual peer review and six Conformity Indices (CIs) were applied. CI values were mapped to pass/fail scores for each contour and used to train supervised machine learning models. A 5-fold cross validation method was employed to determine the predictive accuracies of each model.The stomach structure produced models with the highest predictive accuracy overall (96% using Support Vector Machine and Ensemble models), whilst the liver GTV produced models with the lowest predictive accuracy (76% using Logistic Regression). Predictive accuracies across all models ranged from 68-96% (68-87% for TV and 71-96% for OARs).Although a final visual review by an experienced clinician is still required, the automatic contour evaluation method could reduce the time for benchmark case reviews by identifying gross contouring errors. This method could be successfully implemented to support departmental training and the continuous assessment of outlining for clinical staff in the peer-review process, to reduce interobserver variability in contouring and improve interpretation of radiological anatomy.
Unsupervised Selection of Optimal Operating Parameters for Visual Place Recognition Algorithms Using Gaussian Mixture Models

Visual place recognition (VPR) algorithms are a key part of many autonomous systems, but typically consist of many parameters which require non-trivial optimization for a given deployment environment. Being able to automatically select the optimal operating point for parameters within a VPR algorithm would greatly improve the deployability of autonomous systems in real world scenarios. For example, in an aerial context, platform altitude and camera field of view play a critical role in how much of the environment a downward facing camera can perceive. The sensor coverage and its subsequent processing also has significant computational implications. In this letter, we develop an unsupervised system that can predict the performance of a VPR algorithm, using only a limited number of analogous training images. At the core of our approach is the estimation of a recall proxy using Gaussian mixture models and domain-valid assumptions. We develop a robust, intuitive selection criteria to choose the optimal operating point for a deployment environment to show how our system can facilitate automatic parameter selection. Finally, we show how our system can continuously estimate the performance of a VPR system “on-the-fly”. We evaluate our method's effectiveness and generality on both aerial and ground-based real-world datasets. We believe these results will assist in the streamlined deployment of visual localization algorithms in real-world situations.
Budget-Bounded Incentives for Federated Learning

We consider federated learning settings with independent, self-interested participants. As all contributions are made privately, participants may be tempted to free-ride and provide redundant or low-quality data while still enjoying the benefits of the FL model. In Federated Learning, this is especially harmful as low-quality data can degrade the quality of the FL model. Free-riding can be countered by giving incentives to participants to provide truthful data. While there are game-theoretic schemes for rewarding truthful data, they do not take into account redundancy of data with previous contributions. This creates arbitrage opportunities where participants can gain rewards for redundant data, and the federation may be forced to pay out more incentives than justified by the value of the FL model. We show how a scheme based on influence can both guarantee that the incentive budget is bounded in proportion to the value of the FL model, and that truthfully reporting data is the dominant strategy of the participants. We show that under reasonable conditions, this result holds even when the testing data is provided by participants.
On the use of the analytic hierarchy process in the evaluation of domain-specific modeling languages for multi-agent systems

Software agents and Multi-agent Systems (MAS) composed by these agents are used in the development of the complex intelligent systems. In order to facilitate MAS software development, various domain-specific modeling languages (DSMLs) exist. Unfortunately, the usability evaluation of these languages are mostly not considered or only a few assessments which cover one single MAS DSML are made. A comparative evaluation, which is missing in the existing studies, may help agent software developers to choose the MAS DSML which fits well into the system development requirements. Hence, in this paper, we introduce a comparative MAS DSML evaluation methodology based on the Analytical Hierarchy Process (AHP). A categorized set of MAS DSML criteria which can be used for the multi-criteria decision making is defined. These criteria can be prioritized by the developers according to their modeling language expectations and the application of the methodology allows the evaluation of DSML alternatives based on this prioritization. As the result of the automatic calculation of the importance distributions, the most appropriate DSML is determined. With the voluntarily participation of a group of agent software developers, the proposed methodology was applied for the comparative evaluation of four well-known MAS DSMLs. The conducted evaluation showed that the agent developers prioritized appropriateness, completeness and shortening the development time as the most significant criteria for the MAS DSML assessment while the attractiveness of the notations had a minimum effect on preferring a language. Favorite DSML for each comparison category and criteria was determined within this evaluation.
Recommendation system for human physical activities using smartphones

Important information can be obtained from smartphone users data such as profile modeling, behavior recognition, geolocalization, etc. Human activity recognition (HAR) from sensor smartphone data is a field which has garnered a lot of attention due to its high application in various domains such as the user health. In this paper, we will consider data from accelerometer to recognize the kind of user movements that we will classify to six kinds using machine and deep learning algorithms. Then, based on these results, we will make a recommendation system to inform the users of smartphone about their healthy behavior related to their physical activities.
An Improved Grey-Wolf Optimization Algorithm Based on Circle Map

Abstract To overcome the shortcomings of grey wolf optimization algorithm (GWO) such as being easy to fall into the local optimum, and the slow convergence rate in the later stage, an adaptive weighted grey-wolf optimization algorithm based on the Circle map is proposed. Firstly, in this algorithm, Circle chaotic map, which enhances the diversity of the initialization population, is introduced into the initialization of population, therefore, the search space can be searched more thoroughly; Secondly, trigonometric function and the beta distribution are introduced in the convergence factor ‘a’ and the population position update formula, which improve the convergence speed in the later period of the algorithm; Finally, the simulation experiments on the four common test functions on CEC2017 show that under the same experimental conditions, the improved grey wolf optimization algorithm improves the solution accuracy and convergence speed significantly, and its performance is obviously better than other smart optimization algorithms and other improved GWO algorithms.