Reviewerly/exHarmony huggingface dataset:
Energy-Efficient Swarming Flight Formation Transitions Using the Improved Fair Hungarian Algorithm

Recently, drone shows have impressed many people through a convergence of technology and art. However, these demonstrations have limited operating hours based on the battery life. Thus, it is important to minimize the unnecessary transition time between scenes without collision to increase operating time. This paper proposes a fast and energy-efficient scene transition algorithm that minimizes the transition times between scenes. This algorithm reduces the maximum drone movement distance to increase the operating time and exploits a multilayer method to avoid collisions between drones. In addition, a swarming flight system including robust communication and position estimation is presented as a concrete experimental system. The proposed algorithm was verified using the swarming flight system at a drone show performed with 100 drones.
Demographic Analysis of Candidates Supporter in Twitter During Indonesian Presidential Election 2019

In today's political world, the use of social media as a medium to convey political views and using it for campaign has become a very common thing. Social media can be used to express one's expression both in supporting a political party and someone who is running for politics. Twitter is one of the social media that is often used for campaign, this is because of Twitter's unformal and chronological feature. But Twitter also has weakness such as the absence of demographic information. Therefore, this study aims to find out the demographic patterns of supporter of each Indonesian Presidential candidate 2019 on Indonesian language Twitter. For searching the demographics, this study using text classification method and dictionary of names and genders. The dictionary comes from KPU permanent voter data. Demographic labels used in this study consist of male, female, generation Z, millennial generation, generation X+, outside Java and Java. To determine the political preference, this study uses the sentiment analysis method that is suitable for short sentences, namely the SentiStrength method. The results of this study indicate that most Twitter users who actively talk related to the politics of 2019 Presidential and Vice-Presidential elections are women with millennial age categories or 25-39 years old.
Robotic didactic aid Ozobot in Czech schools

This paper deals with a qualitative study on the implementation of the robotic teaching aid Ozobot in Czech schools. The authors focus their attention on the reasons for choosing this tool, the ways of use, the problems that teachers have to solve and the benefits that Ozobot brings to students in the educational process. The research involved nine teachers with different lengths of practice and teachers of different subjects. Data were collected through interviews and subsequent transcriptional analysis. According to all respondents in this study, students with the use of Ozobots are enthusiastic and highly motivated. Pupils learn essential skills and knowledge either in algorithmization, programming and working with robots or in other science subjects. The implementation of Ozobots in the educational process combines effective teaching with the enthusiasm and motivation of students, which is an ideal state in any educational endeavour.
Few-Shot Bayesian Optimization with Deep Kernel Surrogates

Hyperparameter optimization (HPO) is a central pillar in the automation of machine learning solutions and is mainly performed via Bayesian optimization, where a parametric surrogate is learned to approximate the black box response function (e.g. validation error). Unfortunately, evaluating the response function is computationally intensive. As a remedy, earlier work emphasizes the need for transfer learning surrogates which learn to optimize hyperparameters for an algorithm from other tasks. In contrast to previous work, we propose to rethink HPO as a few-shot learning problem in which we train a shared deep surrogate model to quickly adapt (with few response evaluations) to the response function of a new task. We propose the use of a deep kernel network for a Gaussian process surrogate that is meta-learned in an end-to-end fashion in order to jointly approximate the response functions of a collection of training data sets. As a result, the novel few-shot optimization of our deep kernel surrogate leads to new state-of-the-art results at HPO compared to several recent methods on diverse metadata sets.
AlwaysSafe: Reinforcement Learning without Safety Constraint Violations during Training

Deploying reinforcement learning (RL) involves major concerns around safety. Engineering a reward signal that allows the agent to maximize its performance while remaining safe is not trivial. Safe RL studies how to mitigate such problems. For instance, we can decouple safety from reward using constrained Markov decision processes (CMDPs), where an independent signal models the safety aspects. In this setting, an RL agent can autonomously find tradeoffs between performance and safety. Unfortunately, most RL agents designed for CMDPs only guarantee safety after the learning phase, which might prevent their direct deployment. In this work, we investigate settings where a concise abstract model of the safety aspects is given, a reasonable assumption since a thorough understanding of safety-related matters is a prerequisite for deploying RL in typical applications. Factored CMDPs provide such compact models when a small subset of features describe the dynamics relevant for the safety constraints. We propose an RL algorithm that uses this abstract model to learn policies for CMDPs safely, that is without violating the constraints. During the training process, this algorithm can seamlessly switch from a conservative policy to a greedy policy without violating the safety constraints. We prove that this algorithm is safe under the given assumptions. Empirically, we show that even if safety and reward signals are contradictory, this algorithm always operates safely and, when they are aligned, this approach also improves the agent's performance.
Prediction of Patients Severity at Emergency Department Using NARX and Ensemble Learning

Early detection of adverse events at hospitals could be useful in terms of reducing costs, morbidity, and mortality. Therefore, in this paper, we present a personalized real-time hybrid model based on Nonlinear Autoregressive Exogenous (NARX) model and Ensemble Learning (EL) to predict patients' severity during hospitalization at Emergency Departments (ED). This model utilizes vital signs of patients, including Pulse Rate (PR), Respiratory Rate (RR), Arterial Blood Oxygen Saturation (SpO2) and Systolic Blood Pressure (SBP), which are collected automatically during the treatment to predict the illness severity of hospitalized patients at ED in the next hour based on their vital signs of the previous two hours. Two EL algorithms, including Random Forest (RF) and Adaptive Boosting (AdaBoost) are considered to build hybrid models. The performance of NARX-EL models is compared with Auto Regressive Integrated Moving Average (ARIMA), combination of NARX and Linear Regression (LR), Support Vector Regression (SVR) and K-Nearest Neighbors Regression (KNN). The results show that our proposed hybrid models can predict patients' severity with significantly higher accuracy. It is also found that NARX-RF has the best performance in the prediction of sudden changes and unexpected adverse events in patients' vital signs (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score =0.978, NRMSE =6.16%).
Robot navigation based on pseudo-binocular stereo vision and linear fitting

In this paper, we propose a pseudo-binocular stereo vision and linear fitting to achieve robot navigation. Pseudo-binocular stereo vision is realized by moving the robot body to the left or right with a certain distance. Then, a disparity map is calculated with the semi-global matching algorithm. This method can overcome the interference of complex color and texture for objects that are at the same distance. After edge detection and binarizing of the disparity map, road lines are extracted by the Hough transform algorithm. To extract the road lines from the noisy edges, the least square method for linear fitting is utilized for high-accuracy navigation.
Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications

Sentic computing relies on well-defined affective models of different complexity-polarity to distinguish positive and negative sentiment, for example, or more nuanced models to capture expressions of human emotions. When used to measure communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation's strategic positioning goals. Such goals often deviate from the assumptions of standardised affective models. While certain emotions such as Joy and Trust typically represent desirable brand associations, specific communication goals formulated by marketing professionals often go beyond such standard dimensions. For instance, the brand manager of a television show may consider fear or sadness to be desired emotions for its audience. This article introduces expansion techniques for affective models, combining common and commonsense knowledge available in knowledge graphs with language models and affective reasoning, improving coverage and consistency as well as supporting domain-specific interpretations of emotions. An extensive evaluation compares the performance of different expansion techniques: (i) a quantitative evaluation based on the revisited Hourglass of Emotions model to assess performance on complex models that cover multiple affective categories, using manually compiled gold standard data, and (ii) a qualitative evaluation of a domain-specific affective model for television programme brands. The results of these evaluations demonstrate that the introduced techniques support a variety of embeddings and pre-trained models. The paper concludes with a discussion on applying this approach to other scenarios where affective model resources are scarce.
Taming GANs with Lookahead-Minmax

Generative Adversarial Networks are notoriously challenging to train. The underlying minmax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field. To tackle these challenges, we propose the Lookahead algorithm for minmax optimization, originally developed for single objective minimization only. The backtracking step of our Lookahead–minmax naturally handles the rotational game dynamics, a property which was identified to be key for enabling gradient ascent descent methods to converge on challenging examples often analyzed in the literature. Moreover, it implicitly handles high variance without using large mini-batches, known to be essential for reaching state of the art performance. Experimental results on MNIST, SVHN, CIFAR-10, and ImageNet demonstrate a clear advantage of combining Lookahead–minmax with Adam or extragradient, in terms of performance and improved stability, for negligible memory and computational cost. Using 30-fold fewer parameters and 16-fold smaller minibatches we outperform the reported performance of the class-dependent BigGAN on CIFAR-10 by obtaining FID of 12.19 without using the class labels, bringing state-of-the-art GAN training within reach of common computational resources.
Enfermagem e a pandemia da Covid-19: uma conjugação entre liderança e vulnerabilidade profissional

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Comparative Analysis of Transformer based Language Models

Natural language processing (NLP) has witnessed many substantial advancements in the past three years. With the introduction of the Transformer and self-attention mechanism, language models are now able to learn better representations of the natural language. These attentionbased models have achieved exceptional state-of-the-art results on various NLP benchmarks. One of the contributing factors is the growing use of transfer learning. Models are pre-trained on unsupervised objectives using rich datasets that develop fundamental natural language abilities that are fine-tuned further on supervised data for downstream tasks. Surprisingly, current researches have led to a novel era of powerful models that no longer require finetuning. The objective of this paper is to present a comparative analysis of some of the most influential language models. The benchmarks of the study are problem-solving methodologies, model architecture, compute power, standard NLP benchmark accuracies and shortcomings.
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning

Online interactions with the environment to collect data samples for training a Reinforcement Learning agent is not always feasible due to economic and safety concerns. The goal of Offline Reinforcement Learning (RL) is to address this problem by learning effective policies using previously collected datasets. Standard off-policy RL algorithms are prone to overestimations of the values of out-of-distribution (less explored) actions and are hence unsuitable for Offline RL. Behavior regularization, which constraints the learned policy within the support set of the dataset, has been proposed to tackle the limitations of standard off-policy algorithms. In this paper, we improve the behavior regularized offline reinforcement learning and propose \emph{BRAC+}. We use an analytical upper bound on KL divergence as the behavior regularizor to reduce variance associated with sample based estimations. Additionally, we employ state-dependent Lagrange multipliers for the regularization term to avoid distributing KL divergence penalty across all states of the sampled batch. The proposed Lagrange multipliers allow more freedom of deviation to high probability (more explored) states leading to better rewards while simultaneously restricting low probability (less explored) states to prevent out-of-distribution actions. \textcolor{blue}{To prevent catastrophic performance degradation due to rare out-of-distribution actions, we add a gradient penalty term to the policy evaluation objective to penalize the gradient of the Q value w.r.t the out-of-distribution actions. By doing so, the Q values evaluated at the out-of-distribution actions are bounded.} On challenging offline RL benchmarks, BRAC+ outperforms the state-of-the-art model-free and model-based approaches.
Security Analysis of Mobile Authenticator Applications

Deploying Two-Factor Authentication (2FA) is one of the highly-recommended security mechanism against account hijacking attacks. One of the common methods for 2FA is to bring something you know and something you have factors together. For the latter we have options including USB sticks, smart cards, SMS verification, and one-time password values generated by mobile applications (soft OTP). Due to the cost and convenience reasons, deploying 2FA via soft OTPs is more common. However, unlike smart cards which have tamper resistance property, attackers can access smartphones remotely or physically so that they can fetch shared secret seed value - an important security risk for mobile authenticators. For this reason, it is critical to analyze mobile authenticator applications in this context. In this paper, we report our findings after analyzing eleven different Android authenticator applications. We report that we have fetched cleartext shared secret seed value from storage in five applications and from memory in seven applications using standard reverse engineering techniques and open-source tools.
Label super resolution for 3D magnetic resonance images using deformable U-net

Robust and accurate segmentation results from high resolution (HR) 3D Magnetic Resonance (MR) images are desirable in many clinical applications. State-of-the-art deep learning methods for image segmentation require external HR atlas image and label pairs for training. However, the availability of such HR labels is limited due to the annotation accuracy and the time required to manually label. In this paper, we propose a 3D label super resolution (LSR) method which does not use an external image or label from a HR atlas data and can reconstruct HR annotation labels only reliant on a LR image and corresponding label pairs. In our method, we present a Deformable U-net, which uses synthetic data with multiple deformation for training and an iterative topology check during testing, to learn a label slice evolving process. This network requires no external HR data because a deformed version of the input label slice acquired from the LR data itself is used for training. The trained Deformable U-net is then applied to through-plane slices to estimate HR label slices. The estimated HR label slices are further combined by label a fusion method to obtain the 3D HR label. Our results show significant improvement compared to competing methods, in both 2D and 3D scenarios with real data.
Lost Person Search Area Prediction Based on Regression and Transfer Learning Models

In this paper, we propose a methodology and algorithms for search and rescue mission planning. These algorithms construct optimal areas for lost person search having in mind the initial point of planning and features of the surrounding area. The algorithms are trained on previous search and rescue missions data collected from three stations of the Croatian Mountain Rescue Service. The training was performed in two training phases and having two data sets. The first phase was the construction of a regression model of the speed of walking. This model predicts the speed of walking of a rescuer who is considered a well-trained and motivated person since the model is fitted on a dataset made of GPS tracking data collected from Mountain Rescue Service rescuers. The second phase is the calibration of the model for lost person speed of walking prediction with transfer learning on lost person data. The model is used in the simulation of walking in all directions to predict the maximum area where a person can be located. The performance of the algorithms was analysed with respect to a small dataset of archive data of real search and rescue missions that was available and results are discussed.
Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

&lt;p&gt;In the computer vision, background extraction is a promising technique. It is characterized by being applied in many different real time applications in diverse environments and with variety of challenges. Background extraction is the most popular technique employed in the domain of detecting moving foreground objects taken by stationary surveillance cameras. Achieving high performance is required with many perspectives and demands. Choosing the suitable background extraction model plays the major role in affecting the performance matrices of time, memory, and accuracy.&lt;/p&gt;&lt;p&gt;In this article we present an extensive review on background extraction in which we attempt to cover all the related topics. We list the four process stages of background extraction and we consider several well-known models starting with the conventional models and ending up with the state-of-the art models. This review also focuses on the model environments whether it is human activities, Nature or sport environments and illuminates on some of the real time applications where background extraction method is adopted. Many challenges are addressed in respect to environment, camera, foreground objects, background, and computation time. &lt;/p&gt;&lt;p&gt;In addition, this article provides handy tables containing different common datasets and libraries used in the field of background extraction experiments. Eventually, we illustrate the performance evaluation with a table of the set performance metrics to measure the robustness of the background extraction model against other models in terms of time, accurate performance and required memory.&lt;/p&gt;
For self-supervised learning, Rationality implies generalization, provably

We prove a new upper bound on the generalization gap of classifiers that are obtained by first using self-supervision to learn a representation r of the training~data, then fitting a simple (e.g., linear) classifier g to the labels. Specifically, we show that (under the assumptions described below) the generalization gap of such classifiers tends to zero if C(g)≪n, where C(g) is an appropriately-defined measure of the complexity of the simple classifier g, and n is the number of training samples. We stress that our bound is independent of the complexity of the representation r. We do not make any structural or conditional-independence assumptions on the representation-learning task, which can use the same training dataset that is later used for classification. Rather, we assume that the training procedure satisfies certain natural noise-robustness (adding small amount of label noise causes small degradation in performance) and rationality (getting the wrong label is not better than getting no label at all) conditions that widely (and sometimes provably) hold across many standard architectures. We show that our bound is non-vacuous for many popular representation-learning based classifiers on CIFAR-10 and ImageNet, including SimCLR, AMDIM and BigBiGAN.
Reversible Data Hiding Based on Improved Rhombus Predictor and Prediction Error Expansion

Rhombus predictor is an effective technique to achieve prediction error expansion based reversible data hiding. Considering the correlation of adjacent pixels, it achieves high performance prediction of the central pixel with the help of its surrounding four pixels in a rhombus cell. However, for cells with large fluctuation, such correlation is rather weak, leading to poor accuracy of prediction. In this paper, we propose a reversible data hiding scheme based on improved rhombus predictor, which takes the lead to consider consistencies along horizontal, vertical and diagonal directions of the rhombus cell simultaneously so that pixels with higher consistency are employed together to make up the predictor. To reduce the prediction error once watermark bits are not fully embedded, we further present a corresponding fluctuation based sorting strategy. The experimental results show that, with the same amount of watermark bits embedded, the proposed scheme is able to achieve better performance comparing with the classic scheme and the state-of-the art.