Reviewerly/exHarmony huggingface dataset:
Assisting Dependent People at Home Through Autonomous Unmanned Aerial Vehicles

This work describes a proposal of autonomous unmanned aerial vehicles (AUAVs) for home assistance of dependent people. AUAVs will monitor and recognize human activities during flight to improve their quality of life. However, before bringing such AUAV assistance to real homes, several challenges must be faced to make them viable and practical. Some challenges are technical and some others are related to human factors. In particular, several technical aspects are described for AUAV assistance: (1) flight control, based on our active disturbance rejection control algorithm, (2) flight planning (navigation in obstacle environments), and, (3) processing signals, acquired both from flight-control and monitoring sensors. From the assisted person’s viewpoint, our research focuses on three cues: (1) the user’s perception about AUAV assistance, (2) the influence on human acceptance of AUAV appearance and behavior at home, and (3) the human-robot interaction between assistant AUAV and assisted person. Finally, virtual reality environments are proposed to carry out preliminary tests and user acceptance evaluations.
All-Weather Deep Outdoor Lighting Estimation

We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LDR panorama, and regress the parameters of the Lalonde-Matthews outdoor illumination model. This model is trained such that it a) reconstructs the appearance of the sky, and b) renders the appearance of objects lit by this illumination. We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network. We demonstrate, via extensive experiments, that both our panorama and single image networks outperform the state of the art, and unlike prior work, are able to handle weather conditions ranging from fully sunny to overcast skies.
Non-uniform Illumination Image Enhancement Based on Retinex and Gamma Correction

Abstract Since ambient light or reflection from the surface of the object, the phenomena of none-uniform illumination exist in images.At present, some problems may appear in the image after the non-uniform illumination image processed by conventional algorithms, such as unnatural, the compressed dynamic range of brightness and halo artifacts in strong edge.To effectively process none-uniform images, the image enhancement based on Retinex theory and gamma correction was proposed.First, the bilateral filter is used to decompose the original image into an illumination and reflectance, to avoid halo artifacts in the reflectance.Second, Gamma correction and are used to equalize the dynamic range of brightness and extended brightness.Finally, the reflectance and the illumination are re-fused to restore the detail and naturalness of the image.The experimental results show that the proposed method effectively enhances the image and maintains the naturalness of the image.The objective evaluation index and computation time are also significantly better than other algorithms.
ADVISER: A Dialog System Framework for Education &amp; Research

In this paper, we present ADVISER 1 -an open source dialog system framework for education and research purposes.This system supports multi-domain task-oriented conversations in two languages.It additionally provides a flexible architecture in which modules can be arbitrarily combined or exchanged -allowing for easy switching between rules-based and neural network based implementations.Furthermore, ADVISER offers a transparent, user-friendly framework designed for interdisciplinary collaboration: from a flexible back end, allowing easy integration of new features, to an intuitive graphical user interface supporting nontechnical users.
Self-attention Based Collaborative Neural Network for Recommendation

With the rapid development of e-commerce, various types of recommendation systems have emerged in an endless stream. Collaborative filtering based recommendation methods are either based on user similarity or item similarity. Neural network as another choice of recommendation method is also based on item similarity. In this paper, we propose a new model named Self Attention based Collaborative Neural Network (SATCoNN) to combine both user similarity and item similarity. SATCoNN is an extension of Recurrent Neural Network (RNN). SATCoNN model uses self-attention mechanism to compute the weight of products in multi aspects from user purchase history which form a user purchase history vector. Borrowing the idea of image style transfer, we model the users’ shopping style by gram matrix. We exploit the max-pooling technique to extract users style as a style vector in gram matrix. The experimental results show that our model has better performance by comparison with other recommendation algorithms.
Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data

Named Entity Recognition(NER) is one of the important tasks in Natural Language Processing(NLP) and also is a sub task of Information Extraction.In this paper we present our work on NER in Telugu-English code-mixed social media data.Code-Mixing, a progeny of multilingualism is a way in which multilingual people express themselves on social media by using linguistics units from different languages within a sentence or speech context.Entity Extraction from social media data such as tweets(twitter) 1 is in general difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information.We present a Telugu-English code-mixed corpus with the corresponding named entity tags.The named entities used to tag data are Person('Per'), Organization('Org') and Location('Loc').We experimented with the machine learning models Conditional Random Fields(CRFs), Decision Trees and Bidirectional LSTMs on our corpus which resulted in a F1-score of 0.96, 0.94 and 0.95 respectively.
Defending against Whitebox Adversarial Attacks via Randomized Discretization

Adversarial perturbations dramatically decrease the accuracy of state-of-the-art image classifiers. In this paper, we propose and analyze a simple and computationally efficient defense strategy: inject random Gaussian noise, discretize each pixel, and then feed the result into any pre-trained classifier. Theoretically, we show that our randomized discretization strategy reduces the KL divergence between original and adversarial inputs, leading to a lower bound on the classification accuracy of any classifier against any (potentially whitebox) $\ell_\infty$-bounded adversarial attack. Empirically, we evaluate our defense on adversarial examples generated by a strong iterative PGD attack. On ImageNet, our defense is more robust than adversarially-trained networks and the winning defenses of the NIPS 2017 Adversarial Attacks & Defenses competition.
Argument Mining for Understanding Peer Reviews

Peer-review plays a critical role in the scientific writing and publication ecosystem. To assess the efficiency and efficacy of the reviewing process, one essential element is to understand and evaluate the reviews themselves. In this work, we study the content and structure of peer reviews under the argument mining framework, through automatically detecting (1) argumentative propositions put forward by reviewers, and (2) their types (e.g., evaluating the work or making suggestions for improvement). We first collect 14.2K reviews from major machine learning and natural language processing venues. 400 reviews are annotated with 10,386 propositions and corresponding types of Evaluation, Request, Fact, Reference, or Quote. We then train state-of-the-art proposition segmentation and classification models on the data to evaluate their utilities and identify new challenges for this new domain, motivating future directions for argument mining. Further experiments show that proposition usage varies across venues in amount, type, and topic.
Energy Efficient Cloud Data Center Using Dynamic Virtual Machine Consolidation Algorithm

In Cloud Data centers, virtual machine consolidation on minimizing energy consumed aims at reducing the number of active physical servers. Dynamic consolidation of virtual machines (VMs) and switching idle nodes off allow Cloud providers to optimize resource usage and reduce energy consumption. One aspect of dynamic VM consolidation that directly influences Quality of Service (QoS) delivered by the system is to determine the best moment to reallocate VMs from an overloaded or undeloaded host. In this article we focus on energy-efficiency of Cloud datacenter using Dynamic Virtual Machine Consolidation Algorithms by planetLab workload traces, which consists of a thousand PlanetLab VMs with large-scale simulation environments. Experiments are done in a simulated cloud environment by the CloudSim simulation tool. The obtained results show that consolidation reduces the number of migrations and the power consumption of the servers. Also application performances are improved.
Inference about the Ratio of the Coefficients of Variation of Two Independent Symmetric or Asymmetric Populations

Coefficient of variation (CV) is a simple but useful statistical tool to make comparisons about the independent populations in many research areas. In this study, firstly, we proposed the asymptotic distribution for the ratio of the CVs of two separate symmetric or asymmetric populations. Then, we derived the asymptotic confidence interval and test statistic for hypothesis testing about the ratio of the CVs of these populations. Finally, the performance of the introduced approach was studied through simulation study.
Rough carving of 3D models with spatial augmented reality

Carving is a subtractive process where we get the shape by removing materials. While most people can get roughly the right intended shape, it is usually challenging not to over-cut the model. We propose a method that helps an unskilled user to carve a rough physical replica of a 3D model using the minimum number of cuts while only using manual cutting tools. The method starts by analyzing the input 3D model and generates the minimum set of cutting steps that remove most of the material. Then using a projector, we project the instructions sequentially onto a block of material to guide the user in performing them. We use the projector-camera setup to 3D scan the object after cutting and automatically detect the changes to reflect them on the digital model. We demonstrate a complete system to support this operation and show several examples of manually carved 3D models while using the system.
Expressing Visual Relationships via Language

Describing images with text is a fundamental problem in vision-language research. Current studies in this domain mostly focus on single image captioning. However, in various real applications (e.g., image editing, difference interpretation, and retrieval), generating relational captions for two images, can also be very useful. This important problem has not been explored mostly due to lack of datasets and effective models. To push forward the research in this direction, we first introduce a new language-guided image editing dataset that contains a large number of real image pairs with corresponding editing instructions. We then propose a new relational speaker model based on an encoder-decoder architecture with static relational attention and sequential multi-head attention. We also extend the model with dynamic relational attention, which calculates visual alignment while decoding. Our models are evaluated on our newly collected and two public datasets consisting of image pairs annotated with relationship sentences. Experimental results, based on both automatic and human evaluation, demonstrate that our model outperforms all baselines and existing methods on all the datasets.
The Computer Course Correlation between Learning Satisfaction and Learning Effectiveness of Vocational College in Taiwan

In this paper, we surveyed the influence of learn effectiveness in a computer course under the factors of learning attitude and learning problems for students in senior-high school. We followed the formula for a regression line as R = A + BX +ε and simulated on SPSS platform with symmetry to obtained the results as follows: (1) In learning attitude, both the cognitive-level and behavior-level, are positively correlated with satisfaction. This means the students have cognitive-level and behavior-level more positively correlated with satisfaction in computer subjects and have a high degree of self-learning effectiveness. (2) In learning problems, the female students had higher learning effectiveness than male students, and the students who practiced on the computer on their own initiative long-term each week had higher learning effectiveness.
Transformation of the System of Training Specialists during the Transition to FSES 3++

Currently, the universities in the Russian Federation are engaged in the updating of the educational programs being implemented, in accordance with the requirements of the Federal State Educational Standard 3 ++. The article provides information on the conceptual changes in the State Educational Standard, Federal State Educational Standard, and Federal State Educational Standard 3 ++ , as well as on the new opportunities universities have in connection with the introduction of the “two plus” generation standard. By an example of the project “NetworkElectronicUniversityof theRepublicofBashkortostan” the article shows the network interaction on issues of the formation of universal competences. New opportunities for universities during the transition to FSES 3 ++ are considered in the context of globalization conditions. The universities are suggested to be considered not so much as a “talent foundry”, but rather as a presence territory development driver, a space for constant positive changes (4P) due to the comprehensive development of scientific, innovative, socio-cultural activities with concurrent integration into the educational environment. The implementation of such a model within the transition to FSES 3 ++ is proposed to be done by individualizing mass education, building flexible educational trajectories that most meet the needs of the parties involved. At the same time, the so-called soft skills should become an indispensable superstructure for the formation of professional competencies, as without them it is extremely difficult to train specialists able to adapt as quickly as possible to changes in the external environment, and, in conditions of uncertainty of occupational functions in the future, they become more important than hard skills.
FBK-HUPBA Submission to the EPIC-Kitchens 2019 Action Recognition Challenge

In this report we describe the technical details of our submission to the EPIC-Kitchens 2019 action recognition challenge. To participate in the challenge we have developed a number of CNN-LSTA [3] and HF-TSN [2] variants, and submitted predictions from an ensemble compiled out of these two model families. Our submission, visible on the public leaderboard with team name FBK-HUPBA, achieved a top-1 action recognition accuracy of 35.54% on S1 setting, and 20.25% on S2 setting.
3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.
Which Is Which? Evaluation of Local Descriptors for Image Matching in Real-World Scenarios

Matching with local image descriptors is a fundamental task in many computer vision applications. This paper describes the WISW contest held within the framework of the CAIP 2019 conference, aimed at benchmarking recent descriptors in challenging planar and non-planar real image matching scenarios. According to the contest results, the descriptors submitted to the competition, most of which based on deep learning, perform significantly better than the current state-of-the-art in image matching. Nonetheless, there is still room for improvement, especially in the case of non-planar scenes.
Embedding Imputation with Grounded Language Information

Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical problem in language processing.Embedding imputation involves learning representations for rare or unseen words during the training of an embedding model, often in a post-hoc manner.In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.This is in contrast to existing approaches which typically make use of vector space properties or subword information.We propose an online method to construct a graph from grounded information and design an algorithm to map from the resulting graphical structure to the space of the pre-trained embeddings.Finally, we evaluate our approach on a range of rare and unseen word tasks across various domains and show that our model can learn better representations.For example, on the Card-660 task our method improves Pearson's and Spearman's correlation coefficients upon the stateof-the-art by 11% and 17.8% respectively using GloVe embeddings.
Robust estimation of local affine maps and its applications to image matching

The classic approach to image matching consists in the detection, description and matching of keypoints. This defines a zero-order approximation of the mapping between two images, determined by corresponding point coordinates. But the patches around keypoints typically contain more information, which may be exploited to obtain a first-order approximation of the mapping, incorporating local affine maps between corresponding keypoints. In this work, we propose a LOCal Affine Transform Estimator (LOCATE) method based on neural networks. We show that LOCATE drastically improves the accuracy of local geometry estimation by tracking inverse maps. A second contribution on guided matching and refinement is also presented. The novelty here consists in the use of LOCATE to propose new SIFT-keypoint correspondences with precise locations, orientations and scales. Our experiments show that the precision gain provided by LOCATE does play an important role in applications such as guided matching. The third contribution of this paper consists in a modification to the RANSAC algorithm, that uses LOCATE to improve the homography estimation between a pair of images. These approaches outperform RANSAC for different choices of image descriptors and image datasets, and permit to increase the probability of success in identifying image pairs in challenging matching databases. The source codes are available at: https://rdguez-mariano.github.io/ pages/locate .
Informative Image Captioning with External Sources of Information

An image caption should fluently present the essential information in a given image, including informative, fine-grained entity mentions and the manner in which these entities interact. However, current captioning models are usually trained to generate captions that only contain common object names, thus falling short on an important "informativeness" dimension. We present a mechanism for integrating image information together with fine-grained labels (assumed to be generated by some upstream models) into a caption that describes the image in a fluent and informative manner. We introduce a multimodal, multi-encoder model based on Transformer that ingests both image features and multiple sources of entity labels. We demonstrate that we can learn to control the appearance of these entity labels in the output, resulting in captions that are both fluent and informative.
Counterpoint by Convolution

Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.
A Discrete Duality Between Nonmonotonic Consequence Relations and Convex Geometries

In this paper we present a duality between nonmonotonic consequence relations and well-founded convex geometries. On one side of the duality we consider nonmonotonic consequence relations satisfying the axioms of an infinitary variant of System P, which is one of the most studied axiomatic systems for nonmonotonic reasoning, conditional logic and belief revision. On the other side of the duality we consider well-founded convex geometries, which are infinite convex geometries that generalize well-founded posets. Since there is a close correspondence between nonmonotonic consequence relations and path independent choice functions one can view our duality as an extension of an existing duality between path independent choice functions and convex geometries that has been developed independently by Koshevoy and by Johnson and Dean.
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs

We study the problem of knowledge graph (KG) embedding. A widely-established assumption to this problem is that similar entities are likely to have similar relational roles. However, existing related methods derive KG embeddings mainly based on triple-level learning, which lack the capability of capturing long-term relational dependencies of entities. Moreover, triple-level learning is insufficient for the propagation of semantic information among entities, especially for the case of cross-KG embedding. In this paper, we propose recurrent skipping networks (RSNs), which employ a skipping mechanism to bridge the gaps between entities. RSNs integrate recurrent neural networks (RNNs) with residual learning to efficiently capture the long-term relational dependencies within and between KGs. We design an end-to-end framework to support RSNs on different tasks. Our experimental results showed that RSNs outperformed state-of-the-art embedding-based methods for entity alignment and achieved competitive performance for KG completion.