Reviewerly/exHarmony huggingface dataset:
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling

Joint intent detection and slot filling has recently achieved tremendous success in advancing the performance of utterance understanding.However, many joint models still suffer from the robustness problem, especially on noisy inputs or rare/unseen events.To address this issue, we propose a Joint Adversarial Training (JAT) model to improve the robustness of joint intent detection and slot filling, which consists of two parts: (1) automatically generating joint adversarial examples to attack the joint model, and (2) training the model to defend against the joint adversarial examples so as to robustify the model on small perturbations.As the generated joint adversarial examples have different impacts on the intent detection and slot filling loss, we further propose a Balanced Joint Adversarial Training (BJAT) model that applies a balance factor as a regularization term to the final loss function, which yields a stable training procedure.Extensive experiments and analyses on the lightweight models show that our proposed methods achieve significantly higher scores and substantially improve the robustness of both intent detection and slot filling.In addition, the combination of our BJAT with BERT-large achieves state-of-the-art results on two datasets.
Peacock Exploration: A Lightweight Exploration for UAV Using Control-Efficient Trajectory

Unmanned Aerial Vehicles have received much attention in recent years due to its wide range of applications, such as exploration of an unknown environment to acquire a 3D map without prior knowledge of it. Existing exploration methods have been largely challenged by computationally heavy probabilistic path planning. Similarly, kinodynamic constraints or proper sensors considering the payload for UAVs were not considered. In this paper, to solve those issues and to consider the limited payload and computational resource of UAVs, we propose “Peacock Exploration”: A lightweight exploration method for UAVs using precomputed minimum snap trajectories which look like a peacock’s tail. Using the widely known, control efficient minimum snap trajectories and OctoMap, the UAV equipped with a RGB-D camera can explore unknown 3D environments without any prior knowledge or human-guidance with only O(logN) computational complexity. It also adopts the receding horizon approach and simple, heuristic scoring criteria. The proposed algorithm's performance is demonstrated by exploring a challenging 3D maze environment and compared with a state-of-the-art algorithm.
The Impact of Computer and Internet at Home on Academic Results of the Saber 11 National Exam in Colombia

The use of a computer and internet connection allows high school and university students to access more relevant information. However, there is no consensus on the effects of the use of these tools on academic results. This work evaluates the impact of computers and the internet at home on the results of the Saber 11 test, a national exam taken by all students finishing their secondary education in Colombia, between 2017 and 2019. This impact was estimated from a pooled two-stage least squares (pooled 2SLS) model, applied to data from 1,578,460 Colombian high school students. We found that computers and the internet at home had a positive impact on English language performance in the Saber 11 test in Colombia. This work allows a better understanding of the technological effects on educational achievement and provides information for the design of public policies for education in developing countries.
The ADAPT's Submissions to the WMT20 Biomedical Translation Task.

This paper describes the ADAPT Centre’s submissions to the WMT20 Biomedical Translation Shared Task for English-to-Basque. We present the machine translation (MT) systems that were built to translate scientific abstracts and terms from biomedical terminologies, and using the state-of-the-art neural MT (NMT) model: Transformer. In order to improve our baseline NMT system, we employ a number of methods, e.g. “pseudo” parallel data selection, monolingual data selection for synthetic corpus creation, mining monolingual sentences for adapting our NMT systems to this task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that systematic addition of the aforementioned techniques to the baseline yields an excellent performance in the English-to-Basque translation task.
Proactive Assistance System for Visually Challenged Persons Using Internet of Things Based on Machine Learning

In this Paper, mainly focusing the visually challenged persons are struggling to move from one place to another place, identification of location, non-visibility of persons and reading newspapers. Due to lack of non visual information in their nearby environment, In order to reduce the burden of the visually challenged persons problems, the proposed system is consist of Wearable device with well equipped earphone and camera, location identification, face recognition, text recognition by use of Android mobile device. The deep learning approach is proposed to predict secure and consistent walk able commands. The Android Mobile Application is used to voice recognition, identifying the location, face recognition and text recognition. The proposed low cost prototype is effective way the visually challenged person to interact with their surrounding environment.
Containing All Permutations

Numerous versions of the question "what is the shortest object containing all permutations of a given length?" have been asked over the past fifty years: by Karp (via Knuth) in 1972; by Chung, Diaconis, and Graham in 1992; by Ashlock and Tillotson in 1993; and by Arratia in 1999. The large variety of questions of this form, which have previously been considered in isolation, stands in stark contrast to the dearth of answers. We survey and synthesize these questions and their partial answers, introduce infinitely more related questions, and then establish an improved upper bound for one of these questions.
Regional Attention Network (RAN) for Head Pose and Fine-Grained Gesture Recognition

Affect is often expressed via non-verbal body language such as actions/gestures, which are vital indicators for human behaviors. Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions and variations in local appearance. The results show that this is a brittle approach since it relies on accurate body parts/objects detection. In this work, we argue that there exist local discriminative semantic regions, whose "informativeness" can be evaluated by the attention mechanism for inferring fine-grained gestures/actions. To this end, we propose a novel end-to-end \textbf{Regional Attention Network (RAN)}, which is a fully Convolutional Neural Network (CNN) to combine multiple contextual regions through attention mechanism, focusing on parts of the images that are most relevant to a given task. Our regions consist of one or more consecutive cells and are adapted from the strategies used in computing HOG (Histogram of Oriented Gradient) descriptor. The model is extensively evaluated on ten datasets belonging to 3 different scenarios: 1) head pose recognition, 2) drivers state recognition, and 3) human action and facial expression recognition. The proposed approach outperforms the state-of-the-art by a considerable margin in different metrics.
Teachers’ attitudes towards e-teaching during COVID-19

The purpose of the current study is to examine whether “learning by Zoom” or e-learning was a short-term temporary solution or whether there is a real chance that distance teaching and learning will become a teaching technique in the post-COVID era.. What is teachers’ personal preference for e-learning with regard to interpersonal interaction with the students, the benefits of e-learning for students, as well as interest, order, organization, and clarity in teaching. Moreover, the effectiveness of e-teaching was examined from a multicultural perspective – at schools in Israel, the hi-tech state, and in Belarus, where the culture of the book is a major symbol. We explored the perceptions of teachers in Israel and in Belarus on four measures: personal preferences, advantages and disadvantages for teaching and learning, measures of good teaching, and interpersonal interactions between the teacher, the students, and the faculty. E-teaching is not an alternative for traditional education but does generate a new reality that facilitates great improvements and advantages but produces new challenges for the educational system.
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning

Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited. However, in many practical applications, the situation is reversed: an agent may have access to large amounts of undirected offline experience data, while access to the online environment is severely limited. In this work, we focus on this offline setting. Our main insight is that, when presented with offline data composed of a variety of behaviors, an effective way to leverage this data is to extract a continuous space of recurring and temporally extended primitive behaviors before using these primitives for downstream task learning. Primitives extracted in this way serve two purposes: they delineate the behaviors that are supported by the data from those that are not, making them useful for avoiding distributional shift in offline RL; and they provide a degree of temporal abstraction, which reduces the effective horizon yielding better learning in theory, and improved offline RL in practice. In addition to benefiting offline policy optimization, we show that performing offline primitive learning in this way can also be leveraged for improving few-shot imitation learning as well as exploration and transfer in online RL on a variety of benchmark domains. Visualizations are available at https://sites.google.com/view/opal-iclr
Asymmetric Adaptive Fusion in a Two-Stream Network for RGB-D Human Detection

In recent years, human detection in indoor scenes has been widely applied in smart buildings and smart security, but many related challenges can still be difficult to address, such as frequent occlusion, low illumination and multiple poses. This paper proposes an asymmetric adaptive fusion two-stream network (AAFTS-net) for RGB-D human detection. This network can fully extract person-specific depth features and RGB features while reducing the typical complexity of a two-stream network. A depth feature pyramid is constructed by combining contextual information, with the motivation of combining multiscale depth features to improve the adaptability for targets of different sizes. An adaptive channel weighting (ACW) module weights the RGB-D feature channels to achieve efficient feature selection and information complementation. This paper also introduces a novel RGB-D dataset for human detection called RGBD-human, on which we verify the performance of the proposed algorithm. The experimental results show that AAFTS-net outperforms existing state-of-the-art methods and can maintain stable performance under conditions of frequent occlusion, low illumination and multiple poses.
STAC: Spatial-Temporal Attention on Compensation Information for Activity Recognition in FPV

Egocentric activity recognition in first-person video (FPV) requires fine-grained matching of the camera wearer’s action and the objects being operated. The traditional method used for third-person action recognition does not suffice because of (1) the background ego-noise introduced by the unstructured movement of the wearable devices caused by body movement; (2) the small-sized and fine-grained objects with single scale in FPV. Size compensation is performed to augment the data. It generates a multi-scale set of regions, including multi-size objects, leading to superior performance. We compensate for the optical flow to eliminate the camera noise in motion. We developed a novel two-stream convolutional neural network-recurrent attention neural network (CNN-RAN) architecture: spatial temporal attention on compensation information (STAC), able to generate generic descriptors under weak supervision and focus on the locations of activated objects and the capture of effective motion. We encode the RGB features using a spatial location-aware attention mechanism to guide the representation of visual features. Similar location-aware channel attention is applied to the temporal stream in the form of stacked optical flow to implicitly select the relevant frames and pay attention to where the action occurs. The two streams are complementary since one is object-centric and the other focuses on the motion. We conducted extensive ablation analysis to validate the complementarity and effectiveness of our STAC model qualitatively and quantitatively. It achieved state-of-the-art performance on two egocentric datasets.
Dynamics of Fourier Modes in Torus Generative Adversarial Networks

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable, and typically, it is necessary to implement several accessory heuristics to the networks to reach acceptable convergence of the model. In this paper, we introduce a novel method to analyze the convergence and stability in the training of generative adversarial networks. For this purpose, we propose to decompose the objective function of the adversary min–max game defining a periodic GAN into its Fourier series. By studying the dynamics of the truncated Fourier series for the continuous alternating gradient descend algorithm, we are able to approximate the real flow and to identify the main features of the convergence of GAN. This approach is confirmed empirically by studying the training flow in a 2-parametric GAN, aiming to generate an unknown exponential distribution. As a by-product, we show that convergent orbits in GANs are small perturbations of periodic orbits so the Nash equillibria are spiral attractors. This theoretically justifies the slow and unstable training observed in GANs.
LocalDrop: A Hybrid Regularization for Deep Neural Networks

In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularization function for both fully-connected networks (FCNs) and convolutional neural networks (CNNs), including drop rates and weight matrices, has been developed based on the proposed upper bound of the local Rademacher complexity by the strict mathematical deduction. The analyses of dropout in FCNs and DropBlock in CNNs with keep rate matrices in different layers are also included in the complexity analyses. With the new regularization function, we establish a two-stage procedure to obtain the optimal keep rate matrix and weight matrix to realize the whole training model. Extensive experiments have been conducted to demonstrate the effectiveness of LocalDrop in different models by comparing it with several algorithms and the effects of different hyperparameters on the final performances.
Two‐stage visible watermark removal architecture based on deep learning

With the rapid development of the Internet, watermarks are widely used in images to protect copyright. This implies that the robustness of watermark is very important. In recent years, there have been some studies to evaluate watermark performance by removing the watermark. Among them, some methods need to mark the watermark position in advance, and some require multiple images with the same watermark. Moreover, when the colour of thewatermark is similar to that of the background, the existing methods can hardly remove the watermark from the watermarked image. In the proposed work, the authors presented a watermark removal structure consisting of watermark extraction and image inpainting to address the aforementioned issues. In particular, the extraction network is used to extract the watermark in the watermarked image, and the inpainting network is used to inpainting image for a better watermark removal image, respectively. Finally, the authors train and test the developed network architecture by constructing two data sets, i.e. white watermarked image data set (WW-data set) and colour watermarked image data set (CW-data set). The proposed method not only has better performance on the WW-data set than the current latest methods (on the CW-data set, other methods have almost failed) but also effectively removes the watermarks.
Social Justice, Participation and School during the COVID-19—The International Project Gira por la Infancia

The purpose of this article is to present the data which have been obtained in a research project on education during coronavirus disease 2019 (COVID-19) in the framework of the international project Gira por la Infancia 2020. The aim was to construct a critical, active, and participatory citizenship. The method used has been the pedagogical hermeneutic in which the qualitative analysis of data was preceded by a process of child participation which allowed the analysis of the opinions of 6867 children and adolescents from 22 countries and 3 continents. The results present the children’s thinking about the closure of schools, the alternative teaching carried out, the role of teachers and families, and on the return of the "new school" with the health protocols. This work provides the children’s view on the measures of social distancing and massive school closures. However, without doubt, it also shows children’s and adolescents’ potential to empower themselves in complex situations which put the world’s schools in an unprecedented crisis. In addition, it shows the opportunity to take into consideration a joint construction of a society from the germ of its citizenship: children.
Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism-based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving.
Validity, consonant plausibility measures, and conformal prediction

Prediction of future observations is an important and challenging problem. The two mainstream approaches for quantifying prediction uncertainty use prediction regions and predictive distributions, respectively, with the latter believed to be more informative because it can perform other prediction-related tasks. The standard notion of validity, what we refer to here as Type-1 validity, focuses on coverage probability of prediction regions, while a notion of validity relevant to the other prediction-related tasks performed by predictive distributions is lacking. Here we present a new notion, called Type-2 validity, relevant to these other prediction tasks. We establish connections between Type-2 validity and coherence properties, and show that imprecise probability considerations are required in order to achieve it. We go on to show that both types of prediction validity can be achieved by interpreting the conformal prediction output as the contour function of a consonant plausibility measure. We also offer an alternative characterization of conformal prediction, based on a new nonparametric inferential model construction, wherein the appearance of consonance is natural, and prove its validity.
<i>Hannibal at the gates</i>: Cyberwarfare &amp; the Solarwinds sunburst hack

The connected world economy, compounded by the COVID-19 pandemic, has forced countries, companies, and organizations to pivot to digitally transforming their operations. Sophisticated, state-sponsored perpetrators have seized this forced pivot to lay the groundwork for Cyberwarfare with Advanced Persistent Threats. Using the 2020 Solarwinds Orion Sunburst hack campaign, this research uses a grounded case study approach to highlight the facets of the Sunburst cyberwarfare campaign. Findings suggest that cyberwarfare underscores the need for revisiting organizational processes, culture, and paradigms that are capitalized and leveraged by state-sponsored perpetrators. The Sunburst hack prompted organizations to immediately assume technical solutions. Among them, organizations were quick to isolate infected assets and patch infected systems. However, this research argues that such a reactive technical fix is a vicious trend based on a “wait, watch, react” ideology. Instead, there is prima facie need for process reengineering organizational operations and cultures to build and maintain preventive readiness.
Learning ultrasound rendering from cross-sectional model slices for simulated training

Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality. With ray-tracing based simulations, realistic ultrasound images can be generated. However, due to computational constraints for interactivity, image quality typically needs to be compromised.We propose herein to bypass any rendering and simulation process at interactive time, by conducting such simulations during a non-time-critical offline stage and then learning image translation from cross-sectional model slices to such simulated frames. We use a generative adversarial framework with a dedicated generator architecture and input feeding scheme, which both substantially improve image quality without increase in network parameters. Integral attenuation maps derived from cross-sectional model slices, texture-friendly strided convolutions, providing stochastic noise and input maps to intermediate layers in order to preserve locality are all shown herein to greatly facilitate such translation task.Given several quality metrics, the proposed method with only tissue maps as input is shown to provide comparable or superior results to a state-of-the-art that uses additional images of low-quality ultrasound renderings. An extensive ablation study shows the need and benefits from the individual contributions utilized in this work, based on qualitative examples and quantitative ultrasound similarity metrics. To that end, a local histogram statistics based error metric is proposed and demonstrated for visualization of local dissimilarities between ultrasound images.A deep-learning based direct transformation from interactive tissue slices to likeness of high quality renderings allow to obviate any complex rendering process in real-time, which could enable extremely realistic ultrasound simulations on consumer-hardware by moving the time-intensive processes to a one-time, offline, preprocessing data preparation stage that can be performed on dedicated high-end hardware.