Reviewerly/exHarmony huggingface dataset:
Pandemia de Covid-19, Trabalhadores e Saúde Mental: Uma Revisão de Literatura

Objetivo : Analisar a literatura existente acerca da saúde mental de trabalhadores de saúde no contexto pandêmico da covid-19. Métodos: Revisão de literatura na base de pesquisa Scielo. Resultados : Foram encontrados 14 artigos combinando os termos “Pandemia, trabalhadores e saúde mental”, dos quais 11 atenderam aos critérios de inclusão para esta pesquisa. A maioria dos estudos realizados em relação ao tema tem enfoque na perspectiva dos profissionais de saúde, especialmente de enfermagem. A atuação dos trabalhadores frente à pandemia demonstra surgimento de maiores indíces de sofrimento mental, relacionados a fatores como sobrecarga, falta de apoio e condições inadequadas de trabalho. Conclusão : conclui-se que o estado de pandemia intensificou demandas de saúde mental já existentes em relação ao trabalho, além de proporcionar o surgimento de novas; assim, é necessário que sejam realizadas ações para minimizar esses impactos e favorecer a promoção da saúde mental de trabalhadores de saúde. Palavras-chave: Pandemia. Profissionais de Saúde. Enfermagem. Saúde Mental. ABSTRACT Objective: To analyze the existing literature about the mental health of health workers in the context of the covid-19 pandemic. Methods: Literature review in the Scielo research base. Results : 14 articles were found combining the terms “Pandemic, workers and mental health”, of which 11 answered the inclusion criteria for this research. Most studies carried out on the subject focus on the perspective of health professionals, especially nurses. The performance of workers in the face of the pandemic demonstrates the emergence of higher rates of mental suffering, related to factors such as overload, lack of support and inadequate working conditions. Conclusion : it is concluded that the pandemic state intensified existing mental health demands in relation to work, in addition to providing the emergence of new ones; therefore, it is necessary that actions are taken to minimize these impacts and favor the promotion of mental health of health workers. Keywords : Pandemic. Health Professionals. Nursing. Mental Health.
End-to-End Learning of Visual Representations from Uncurated Instructional Videos

Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video models still rely on manually annotated data. With the recent introduction of the HowTo100M dataset, narrated videos now offer the possibility of learning video representations without manual supervision. In this work we propose a new learning approach, MIL-NCE, capable of addressing misalignments inherent to narrated videos. With this approach we are able to learn strong video representations from scratch, without the need for any manual annotation. We evaluate our representations on a wide range of four downstream tasks over eight datasets: action recognition (HMDB-51, UCF-101, Kinetics-700), text-to-video retrieval (YouCook2, MSR-VTT), action localization (YouTube-8M Segments, CrossTask) and action segmentation (COIN). Our method outperforms all published self-supervised approaches for these tasks as well as several fully supervised baselines.
Global alignment for relation extraction in Microbiology

We investigate a method to extract relations from texts based on global alignment and syntactic information. Combined with SVM, this method is shown to have a performance comparable or even better than LSTM on two RE tasks.
Target Entropy Annealing for Discrete Soft Actor-Critic

Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in continuous action space settings. It uses the maximum entropy framework for efficiency and stability, and applies a heuristic temperature Lagrange term to tune the temperature $\alpha$, which determines how "soft" the policy should be. It is counter-intuitive that empirical evidence shows SAC does not perform well in discrete domains. In this paper we investigate the possible explanations for this phenomenon and propose Target Entropy Scheduled SAC (TES-SAC), an annealing method for the target entropy parameter applied on SAC. Target entropy is a constant in the temperature Lagrange term and represents the target policy entropy in discrete SAC. We compare our method on Atari 2600 games with different constant target entropy SAC, and analyze on how our scheduling affects SAC.
TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting

With the outbreak of COVID-19 pandemic, a dire need to effectively identify the individuals who may have come in close-contact to others who have been infected with COVID-19 has risen. This process of identifying individuals, also termed as 'Contact tracing', has significant implications for the containment and control of the spread of this virus. However, manual tracing has proven to be ineffective calling for automated contact tracing approaches. As such, this research presents an automated machine learning system for identifying individuals who may have come in contact with others infected with COVID-19 using sensor data transmitted through handheld devices. This paper describes the different approaches followed in arriving at an optimal solution model that effectually predicts whether a person has been in close proximity to an infected individual using a gradient boosting algorithm and time series feature extraction.
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness

This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings. In our work, we replace the conventional ReLU-based nonlinearities with blocks comprising locally and stochastically competing linear units. The output of each network layer now yields a sparse output, depending on the outcome of winner sampling in each block. We rely on the Variational Bayesian framework for training and inference; we incorporate conventional PGD-based adversarial training arguments to increase the overall adversarial robustness. As we experimentally show, the arising networks yield state-of-the-art robustness against powerful adversarial attacks while retaining very high classification rate in the benign case.
FastWARC: Optimizing Large-Scale Web Archive Analytics

Web search and other large-scale web data analytics rely on processing archives of web pages stored in a standardized and efficient format. Since its introduction in 2008, the IIPC's Web ARCive (WARC) format has become the standard format for this purpose. As a list of individually compressed records of HTTP requests and responses, it allows for constant-time random access to all kinds of web data via off-the-shelf open source parsers in many programming languages, such as WARCIO, the de-facto standard for Python. When processing web archives at the terabyte or petabyte scale, however, even small inefficiencies in these tools add up quickly, resulting in hours, days, or even weeks of wasted compute time. Reviewing the basic components of WARCIO and analyzing its bottlenecks, we proceed to build FastWARC, a new high-performance WARC processing library for Python, written in C++/Cython, which yields performance improvements by factors of 1.6-8x.
LTT-GAN: Looking Through Turbulence by Inverting GANs

In many applications of long-range imaging, we are faced with a scenario where a person appearing in the captured imagery is often degraded by atmospheric turbulence. However, restoring such degraded images for face verification is difficult since the degradation causes images to be geometrically distorted and blurry. To mitigate the turbulence effect, in this paper, we propose the first turbulence mitigation method that makes use of visual priors encapsulated by a well-trained GAN. Based on the visual priors, we propose to learn to preserve the identity of restored images on a spatial periodic contextual distance. Such a distance can keep the realism of restored images from the GAN while considering the identity difference at the network learning. In addition, hierarchical pseudo connections are proposed for facilitating the identity-preserving learning by introducing more appearance variance without identity changing. Extensive experiments show that our method significantly outperforms prior art in both the visual quality and face verification accuracy of restored results.
Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain

We present a reward-predictive, model-based deep learning method featuring trajectory-constrained visual attention for local planning in visual navigation tasks. Our method learns to place visual attention at locations in latent image space which follow trajectories caused by vehicle control actions to enhance predictive accuracy during planning. The attention model is jointly optimized by the task-specific loss and an additional trajectory-constraint loss, allowing adaptability yet encouraging a regularized structure for improved generalization and reliability. Importantly, visual attention is applied in latent feature map space instead of raw image space to promote efficient planning. We validated our model in visual navigation tasks of planning low turbulence, collision-free trajectories in off-road settings and hill climbing with locking differentials in the presence of slippery terrain. Experiments involved randomized procedural generated simulation and real-world environments. We found our method improved generalization and learning efficiency when compared to no-attention and self-attention alternatives.
Fine-grained Image Captioning with CLIP Reward

Modern image captioning models are usually trained with text similarity objectives. However, since reference captions in public datasets often describe the most salient common objects, models trained with text similarity objectives tend to ignore specific and detailed aspects of an image that distinguish it from others. Toward more descriptive and distinctive caption generation, we propose using CLIP, a multimodal encoder trained on huge image-text pairs from web, to calculate multimodal similarity and use it as a reward function. We also propose a simple finetuning strategy of the CLIP text encoder to improve grammar that does not require extra text annotation. This completely eliminates the need for reference captions during the reward computation. To comprehensively evaluate descriptive captions, we introduce FineCapEval, a new dataset for caption evaluation with fine-grained criteria: overall, background, object, relations. In our experiments on text-to-image retrieval and FineCapEval, the proposed CLIP-guided model generates more distinctive captions than the CIDEr-optimized model. We also show that our unsupervised grammar finetuning of the CLIP text encoder alleviates the degeneration problem of the naive CLIP reward. Lastly, we show human analysis where the annotators strongly prefer the CLIP reward to the CIDEr and MLE objectives according to various criteria. Code and Data: https://github.com/j-min/CLIP-Caption-Reward
Hample filter based Short term Solar Forecasting using Facebook Prophet Library in Energy Harvested Wireless Sensor Network (EHWSN)

Energy harvesting prolongs the life of wireless sensor networks. Among all ambient sources of energy, solar energy is believed to be the most efficient in terms of energy density but fluctuates with time and location. The main objective of this study is to forecast solar energy for Energy Harvested Wireless Sensor Network (EHWSN) using the Facebook Prophet Library with and without a filter and to assess the model efficacy. The present study examined the models using National Renewal Energy Laboratory (NREL) one-minute sample data for June 2010 to 2016. The outliers were eliminated using a Hample filter with a standard deviation of 3 and a window size of 3. The forecast error was measured using the cross-validation feature of the Prophet Library. The Mean squared Error ( MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE ) values for the Prophet model were 292.81, 14.56, 19.37, and 17.11, respectively, while they were 92.56, 14.86, 14.86, and 17.10, respectively, for the Prophet model based on the Hample Filter. MAPE was reduced by 18.60 %, while the rest of the metrics changed significantly. The Prophet and Hample–Prophet give the best result regarding RMSE and accuracy compared with previous work based on the Ensemble Approach.
Progressive Dehazing and Depth Estimation from a Single Hazy Image

We propose a progressive dehazing and depth estimation (PDDE) method with optimal estimation of the attenuation coefficient. We investigated the characteristics of dehazing operations related to the depth and attenuation coefficient for PDDE. Entropy was used as a non-reference image quality metric for optimality assessment of dehazed images. The proposed PDDE method provides an optimal dehazed image and depth estimation from a single hazy image. Experimental results show that the proposed method provides clearer and subjectively better results for single image dehazing than previous image dehazing methods. It also results in a significant improvement in the accuracy of depth estimation from a single hazy image compared with previous depth estimation methods.
Polygon-free: Unconstrained Scene Text Detection with Box Annotations

Although a polygon is a more accurate representation than an upright bounding box for text detection, the annotations of polygons are extremely expensive and challenging. Unlike existing works that employ fully-supervised training with polygon annotations, this study proposes an unconstrained text detection system termed Polygon-free (PF), in which most existing polygon-based text detectors (e.g., PSENet [33],DB [16]) are trained with only upright bounding box annotations. Our core idea is to transfer knowledge from synthetic data to real data to enhance the supervision information of upright bounding boxes. This is made possible with a simple segmentation network, namely Skeleton Attention Segmentation Network (SASN), that includes three vital components (i.e., channel attention, spatial attention and skeleton attention map) and one soft cross-entropy loss. Experiments demonstrate that the proposed Polygonfree system can combine general detectors (e.g., EAST, PSENet, DB) to yield surprisingly high-quality pixel-level results with only upright bounding box annotations on a variety of datasets (e.g., ICDAR2019-Art, TotalText, ICDAR2015). For example, without using polygon annotations, PSENet achieves an 80.5% F-score on TotalText [3] (vs. 80.9% of fully supervised counterpart), 31.1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs. We hope that PF can provide a new perspective for text detection to reduce the labeling costs. The code can be found at https://github.com/weijiawu/Unconstrained-Text-Detection-with-Box-Supervisionand-Dynamic-Self-Training.
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach

Pre-trained models (PTMs) have lead to great improvements in natural language generation (NLG). However, it is still unclear how much commonsense knowledge they possess. With the goal of evaluating commonsense knowledge of NLG models, recent work has proposed the problem of generative commonsense reasoning, e.g., to compose a logical sentence given a set of unordered concepts. Existing approaches to this problem hypothesize that PTMs lack sufficient parametric knowledge for this task, which can be overcome by introducing external knowledge or task-specific pre-training objectives. Different from this trend, we argue that PTM's inherent ability for generative commonsense reasoning is underestimated due to the order-agnostic property of its input. In particular, we hypothesize that the order of the input concepts can affect the PTM's ability to utilize its commonsense knowledge. To this end, we propose a pre-ordering approach to elaborately manipulate the order of the given concepts before generation. Experiments show that our approach can outperform the more sophisticated models that have access to a lot of external data and resources.
Evaluation of Heuristics to Manage a Data Center Under Power Constraints

In recent years, academics and industry have increased their efforts to find solutions to reduce greenhouse gas (GHG) due to its impact on climate change. Two approaches to reducing these emissions are decreasing energy consumption and/or increasing the use of clean energy. Data centers are one of the most expensive energy actors in Information and Communications Technology (ICT). One way to provide clean energy to Data Centers is by using power from renewable sources, such as solar and wind. However, renewable energy introduces several uncertainties due to its intermittence. Dealing with these uncertainties demands different approaches at different levels of management. This work is part of the Datazero2 Project which introduces a clean-by-design data center architecture using only renewable energy. Due to no connection to the grid, the data center manager must handle power envelope constraints. This article investigates some scheduling and power capping online heuristics in an attempt to identify the best algorithms to handle fluctuating power profiles without hindering job execution. Then, it details experiments comparing the results of the heuristics. The results show that our heuristic provides a well-balanced solution considering power and Quality of Service (QoS).
Semantically Supervised Appearance Decomposition for Virtual Staging from a Single Panorama

We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically generated semantic maps (with floor, wall, ceiling, lamp, window and door labels) that have shown success on perspective views and are trained for panoramas using transfer learning without any further annotations. A GAN-based approach supervised by coarse information obtained from the semantic map extracts specular reflection and direct sunlight regions on the floor and walls. These lighting effects are removed via a similar GAN-based approach and a semantic-aware inpainting step. The appearance decomposition enables multiple applications including sun direction estimation, virtual furniture insertion, floor material replacement, and sun direction change, providing an effective tool for virtual home staging. We demonstrate the effectiveness of our approach on a large and recently released dataset of panoramas of empty homes.
‘What Would Denis Have Done?’ A Critical Reflection Upon 2020’s Rapid and Obligatory Transition to Remote Delivery

In 2020, the COVID-19 pandemic forced law schools to rapidly transition to remote delivery of their programs and to place a greater emphasis upon technology-enhanced learning. Those legal academics who were unfamiliar with this method of delivery were obliged to very quickly develop their digital skills to facilitate this transition. The outcomes of this transition were mixed: while most law schools managed to continue to deliver their programs during the pandemic, student feedback about the quality of the remote delivery was not always positive. Nevertheless, the emphasis upon remote delivery and technology-enhanced learning is likely to continue and even increase in the coming years. In this paper I interrogate the assumption that the rapid and obligatory transition to remote delivery that took place because of the pandemic will form a stable basis for further development of digital skills by legal academics. Drawing upon the notion of academic resistance as well as the well-known distinctions between surface and deep approaches to learning and intrinsic and extrinsic student motivation, I argue that the impact of the rapid and obligatory transition to remote delivery upon academic motivation, morale, and freedom exposes law schools to the risk that, without mindful intervention, the quality of technology-enhanced learning in law schools will be lower than optimal.
Descartes’ Intelligent Agent

Neither the natural science explanation about the agent nor the modern and postmodern philosophical interpretation of the agent (subject) have sounded the death knell of the metaphysical subject in the era of information revolution. The artificial intelligence technology represented by Microsoft Avatar Framework has spread to the creative fields such as music and literature, which are unique to human beings, showing a broad application prospect and arousing academic reflection on human subject. Despite the numerous advantages posed intelligent agents, it is neither desirable nor possible to pull out the subject’s power supply and give up AI or give up human social intelligence. Rethinking Descartes' theory of mind and body will lead to in-depth development of AI.
Peran Pengajaran Bahasa Berbasis Drama dalam Meningkatkan Kemampuan Presentasi Peserta Didik

This study aimed to study the effect of drama-based language teaching on students' presentation skills in college. This study used a quasi-experimental method by dividing two groups of participants, namely experimental and control. The samples involved in this study were 100 students from semester 5, divided into 2 groups with the composition of each group of 50 students. The experimental group received a drama-based teaching intervention and was assigned to perform live drama at the end of the semester, while the control group did not. The research results show that drama-based language teaching contributes significantly to students' presentation skills. The improvement in presentation skills is seen in the presentation aspect, namely organization, adaptability, and presentation content. It can be concluded that the intervention of language teaching through drama can improve students' oral language skills or presentation skills. This study implies that in supporting academic activities, students' speaking skills can use drama as an alternative, especially for students who take language studies programs.
Pengembangan Metode Menulis Puisi Menggunakan Teknik “Atafora” untuk Meningkatkan Kepekaan Siswa terhadap Lingkungan

Writing poetry is a person's creative activity in expressing ideas, feelings, and thoughts in written form with a figurative meaning. Poetry writing activities are included in the competence of learning Indonesian. However, a few SMA Negeri 3 Malang students are interested in writing poetry, especially in class X. In addition, the method of writing poetry taught by the teacher is less innovative, so students are less interested. Based on these problems, this study aims to develop a method of writing poetry "Atafora" technique or "Aku Metafora", by combining students' sensory experiences with metaphorical figures of speech. The method used to study is Research &amp; Development (R&amp;D), with validation carried out by experts, practitioners, and limited users. The research was conducted at SMA Negeri 3 Malang. The study's results prove that the "Atafora" technique of writing poetry is considered valid, with a validation score of 80% by poetry experts, 95% by practitioners, and 90% by users. The overall average percentage is 88.33%. Through the development of this method of writing poetry, it is hoped that the interest and ability to write poetry of the students of SMA Negeri 3 Malang will increase.