Reviewerly/exHarmony huggingface dataset:
Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit

We study goodness-of-fit of discrete distributions in the distributed setting, where samples are divided between multiple users who can only release a limited amount of information about their samples due to various information constraints. Recently, a subset of the authors showed that having access to a common random seed (i.e., shared randomness) leads to a significant reduction in the sample complexity of this problem. In this work, we provide a complete understanding of the interplay between the amount of shared randomness available, the stringency of information constraints, and the sample complexity of the testing problem by characterizing a tight trade-off between these three parameters. We provide a general distributed goodness-of-fit protocol that as a function of the amount of shared randomness interpolates smoothly between the private- and public-coin sample complexities. We complement our upper bound with a general framework to prove lower bounds on the sample complexity of this testing problem under limited shared randomness. Finally, we instantiate our bounds for the two archetypal information constraints of communication and local privacy and show that our sample complexity bounds are optimal as a function of all the parameters of the problem, including the amount of shared randomness.\n\nA key component of our upper bounds is a new primitive of domain compression, a tool that allows us to map distributions to a much smaller domain size while preserving their pairwise distances, using a limited amount of randomness.
The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT

In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English.Given the low amount of parallel training data, we resort to using Russian as pivot language, training subword-based statistical translation systems for Russian-Kazakh and Russian-English that were then used to create two synthetic pseudo-parallel corpora for Kazakh-English and English-Kazakh respectively.Finally, a self-attention model based on the decoder part of the Transformer architecture was trained on the two pseudoparallel corpora.
English-Czech Systems in WMT19: Document-Level Transformer

We describe our NMT systems submitted to the WMT19 shared task in English→Czech news translation. Our systems are based on the Transformer model implemented in either Tensor2Tensor (T2T) or Marian framework. We aimed at improving the adequacy and coherence of translated documents by enlarging the context of the source and target. Instead of translating each sentence independently, we split the document into possibly overlapping multi-sentence segments. In case of the T2T implementation, this “document-level”-trained system achieves a +0.6 BLEU improvement (p < 0.05) relative to the same system applied on isolated sentences. To assess the potential effect document-level models might have on lexical coherence, we performed a semi-automatic analysis, which revealed only a few sentences improved in this aspect. Thus, we cannot draw any conclusions from this week evidence.
SmartGrid: Video Retargeting With Spatiotemporal Grid Optimization

We propose SmartGrid, a new video-retargeting method that preserves a shape of the salient object and maintains a temporal coherence for the static background regions in the video. Previous methods mainly focus on preserving the shape of the salient object or maintaining the temporal coherence for each region. However, these methods do not explicitly adjust the sizes of the grids corresponding to the salient objects and the static background regions. Thus, they have difficulty in maintaining the consistency of the salient object shape and the temporal coherence for the static background regions. The basic idea of SmartGrid is to maintain the consistency of the contents in consecutive frames by analyzing the degree of the spatiotemporal consistency. Compared to the best results obtained using eight previous methods, SmartGrid achieved improvements of 1.19× in Bidirectional Similarity Measure, 7.59× in Jittery Metric 1, and 13.16× in Jittery Metric 2, and reduced the average computation time per pixel by 6.14×.
The Study on Human Action Recognition with Depth Video for Intelligent Monitoring

As an important application in computer vision field, human action recognition with depth video sequence comes to a hot research topic in recent years. With the popularity of Kinect devices, which makes the application of action recognition with depth video can be applied to intelligent monitoring more effective. This paper makes full use of the existing Kinect extraction technology and human action recognition technology. Specifically, we extracted human body depth images and skeleton data information from deep data streams obtained by the Kinect SDK in real time. Furthermore, with human action characteristics are extracted, human action recognized can be realized by the way of making comparison for its application in intelligent monitoring. In addition, we verify the proposed method by designing of the intelligent surveillance platform. The experimental results show that the proposed method achieves good performance in the real cheating detection task and has good application value.
Preparing SNACS for Subjects and Objects

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens.Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax.Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participants-including subjects and objectsdirectly, without reference to a frame lexicon.We present new guidelines for English and the results of an interannotator agreement study.
Optimizing the Topology of Bayesian Network Classifiers by Applying Conditional Entropy to Mine Causal Relationships Between Attributes

Due to the excellent classification performance and expressivity, the study of Bayesian network classifiers (BNCs) has attracted great attention ever since the success of Naive Bayes (NB).Information theory has established mathematical basis for the rapid development of BNC.In this paper we propose the definition of entropy function H B (D), which corresponds to the optimal number of bits encoded in the network structure B and can roughly measure the amount of information implicated in training data D. Each factor in H B (D) explicitly represents statements about causal relationships.An efficient heuristic search strategy is introduced to minimize H B (D) and explore the optimal topology of BNC.Our extensive experimental evaluation on 40 datasets reveals that this out-of-core algorithm achieves competitive classification performance compared to state-of-the-art learners such as tree augmented Naive Bayes, k-dependence Bayesian classifier, support vector machine, logistic regression and neural network.
Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems

In this paper, a kinematic motion planning algorithm for cooperative spatial payload manipulation is presented. A hierarchical approach is introduced to compute realtime collision-free motion plans for a formation of mobile manipulator robots. Initially, collision-free configurations of a deformable 2-D virtual bounding box are identified, over a planning horizon, to determine a convex workspace for the entire system. Then, 3-D payload configurations whose projections lie within the convex workspace are computed. Finally, a convex decentralized model-predictive controller is formulated to plan collision-free trajectories for the formation of mobile manipulators. Our work facilitates real-time motion planning for the system and is scalable in the number of robots. The algorithm is validated in simulated dynamic environments.
Isolation-based feature Selection for Unsupervised Outlier Detection

For high-dimensional datasets, bad features and complex interactions between features can cause high computational costs and make outlier detection algorithms inefficient. Most feature selection methods are designed for supervised classification and regression, and limited works are specifically for unsupervised outlier detection. This paper proposes a novel isolation-based feature selection (IBFS) method for unsupervised outlier detection. It is based on the training process of isolation forest. When a point of a feature is used to split the data, the imbalanced distribution of split data is measured and used to quantify how strong this feature can detect outliers. We also compare the proposed method with variance, Laplacian score and kurtosis. These methods are benchmarked on simulated data to show their characteristics. Then we evaluate the performance using one-class support vector machine, isolation forest and local outlier factor on several real-word datasets. The evaluation results show that the proposed method can improve the performance of isolation forest, and its results are similar to and sometimes better than another useful outlier indicator: kurtosis, which demonstrate the effectiveness of the proposed method. We also notice that sometimes variance and Laplacian score has similar performance on the datasets.
Structured Light-Based Visual Servoing for Robotic Pipe Welding Pose Optimization

In order to optimize the pose of welding torch preplanned by offline programming, a structured light-based visual servoing method is proposed.First of all, a series of phase shifting patterns are projected to acquire the so-called phase map.Afterwards, unlike usual feature extraction methods, which were based on 3-D cloud, a cylinder axis is extracted directly from the phase map to represent the connecting pipes' cylindrical surface.Then, a visual servoing control law based on the axis combined with the seam center in phase map is proposed to optimize the pose of the welding torch.Moreover, global asymptotic stability of this method is proved.Finally, simulations and real experiments are performed to demonstrate the effectiveness and robustness of this method.Results show this method can improve the mean error of deviated distance and angle of offline programming by 73.5% and 82.5%, respectively.
Knowledge Discovery in the Hadith According to the Reliability and Memory of the Reporters Using Machine Learning Techniques

Muslims suffer from not knowing the validity of the Hadiths or verification of its degree, which represented the second source of legislation in Islam after the Quran. Although many sites allow users in general and Muslims, in particular, the possibility of verifying the authenticity of the hadith, but it is through an information system which is connected to the database of the Hadith. So, there is no intelligent system that can distinguish the hadith automatically, therefore, in this study, we propose a model that can recognize and categorize the hadith automatically and conclusion the essential features through Hadith classification into Sahih, Hasan, Da’if, and Maudu, based on machine learning techniques. This study is primarily concerned with classifying the hadith according to the memory and reliability of the Hadith’s narrators. This classification does not depend only on the text of the hadith, as in the rest of the other Arab documents, but depend on also the Sanad of Hadith. Therefore, this study was conducted on three methodologies; these methodologies help us to obtain an accuracy that is more reliable for hadith classification compared to previous researches in this area. In addition to building a model using the Decision tree technique based on the Sanad of hadith for helping us deciding to judge the validity of the hadith, the accuracy of this classifier reached up to 92.59%. Several Learning algorithms have been used in this study, but we reported the best three classifiers (LinearSVC, SGDClassifier, and Logistic regression), which achieved higher accuracy reached up to 93.69%, 93.51, and 92.27% respectively.
Effects of the Dynamic and Energy Based Feature Extraction on Hindi Speech Recognition

Background: Speech Recognition is the most effective and suitable way of communication. Extracted features play an important role in speech recognition. Previous research works for Hindi speech recognition lack detailed comparative analysis of the feature extraction methods using dynamic and energy parameters. Objective: The research work presents experimental work done to explore the effects of integrating dynamic coefficients and energy parameters with different feature extraction techniques on Connected word Hindi Speech recognition. As extracted features play a significant role in speech recognition, a comparative analysis is presented to show the effects of integration of dynamic and energy parameters to basic extracted features. Method: Speaker dependent system was proposed with monophones based five states Hidden Markov Model (HMM) using HTK Tool kit. Speech data set of connected words in Hindi was created. The feature extraction techniques such as Linear Predictive Coding Cepstral coefficients (LPCCs), Mel Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Prediction (PLPs) coefficients were applied integrating delta, delta2, and energy parameters to evaluate the performance of the proposed methodology for speaker dependent recognition. Results: Experimental results show that the system achieved the highest recognition word accuracy of 89.97% using PLP coefficients. The PLP coefficients achieved 4% increment in word accuracy than original MFCCs and a 16% increment in word accuracy than LPCCs. Adding energy parameters to original MFCCs increased word accuracy by 1.5% only while adding dynamic coefficients delta and double delta had no significant effect on speech recognition accuracy. Conclusion: Research findings reveal that PLP coefficients outperformed. Explorations reveal that the integration of energy parameters are better than original MFCCs. Investgations also reveal that adding energy parametres improved recognition score while adding delta and delta2 coefficients to basic features did not improve the recognition scores. Research findings could be used to enhance the performance of a speech recognition system by using a suitable feature extraction technique and combining the different feature extraction techniques. Further, investigations can be used to develop language resources for refining speech recognition. The work can be extended to develop a continuous Hindi speech recognition system.
Fringe pattern denoising using spatial oriented gaussian filters

In this paper, it is proposed a gaussian convolution-based fringe pattern denoising method. As will be shown, this method is robust enough compared with some of the most outstanding methods in the literature. Additionally, the proposed method overcomes the problem of underperformance in low-frequency fringes that is common in most oriented filtering methods, while keeping the great advantages of convolution-based filters. The advantages of the proposed denoising method will be demonstrated with experiments realized over synthetic and real fringe patterns, and comparing the performance with four representative methods, already reported.
Aerodynamics of MAV rotors in ground and corner effect

The work presented in this paper is part of a project called ARChEaN (Aerodynamic of Rotors in Confined ENvironment) whose objective is to study the interactions of a micro drone rotor with its surroundings in the case of flight in enclosed environments such as those encountered, for example, in archeological exploration of caves. To do so the influence of the environment (walls, ground, ceiling, etc) on the rotor’s aerodynamic performance as well as on the flow field between the rotor and the surroundings is studied. This paper focuses on two different configurations, flight near the ground and flight near a corner (wall and ground), and the results are analyzed and compared to a general free flight case (i.e. far away from any obstacle). In order to carry out this analysis both numerical and experimental approaches are conducted. The objective is to validate the numerical model with the results obtained experimentally and to benefit from the advantages of both approaches in terms of flow analysis. This research work will provide knowledge on how to operate these systems as to minimize the possible negative environment disturbances, reduce power consumption and predict the micro drone’s behaviour during enclosed flights.
Automation of the Process of Electrical Substations Design through the Development and Application of CAD when Choosing Electrical Equipment

The work is devoted to the automation of the design of electrical substations. In particular, this article addresses the issue of automated selection and verification of electrical equipment of primary circuits of switchgears. The algorithm proposed in the work of automated selection and verification of electrical devices can significantly reduce the time spent by the designer on this operation. The algorithm developed by the authors is implemented in CAD of electrical substations. The work is carried out with the financial support of a grant from the President of the Russian Federation for young scientists - candidates of sciences MK-939.2019.8.
Space-filling forest for multi-goal path planning

In multi-goal path planning, the task is to find a sequence to visit a set of target locations in an environment. The combinatorial part of the problem (finding the sequence) can be solved as an instance of Traveling Salesman Problem, which requires knowledge about collision-free paths (and distances) between the individual targets. Finding the collision-free paths between the targets is essential in this task. Sampling-based planners like Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Tree (RRT) can be used to find these paths. However, PRM can be computationally demanding, as it attempts to connect each node in the roadmap to its neighbors, regardless of their later usage in the solution. Contrary, RRT is a tree-based planner, and one run can only provide paths starting in the root of the tree (a single target). In this paper, we propose a novel planner for multi-goal path planning. Multiple trees (forest) are constructed simultaneously from the targets and expanded by collision-free configurations until they touch each other or obstacles. Each tree, therefore, does not explore the whole configuration space (as in the case of RRT), and its construction is faster than PRM, as it uses lower number of edges. The efficiency of this new planning approach is demonstrated in the multi-goal path planning in 2D environment with tens of targets and with narrow passages.
On the robustness of JPEG post-compression to resampling factor estimation

The research on image resampling detection is urgently called for digital image forensics recently. In this paper, the forensics of resampling operation with JPEG compression as post-process is addressed, namely post-JPEG resampling. We first discover the coupling effect between resampling and post-JPEG compression, which is shown as features of frequency mixing in spectrum and named as octant symmetric aliasing peaks(OSAP). The frequency location of OSAP depends on the resampling factor. We verify that the feature of OSAP is prone to be more prominent than resampling when quality factor (QF) of post-JPEG is low enough. By addressing the frequency response of post-JPEG compression, an approximate model of OSAP is derived. The prior knowledge of OSAP can help to enhance the resampling feature, based on which a new method of resampling factor estimation is proposed. In the proposed method, the location of OSAP is estimated from local maximum of spectrum with strong symmetry, and a candidate set of resampling factor is derived from OSAP. The optimal estimation among candidates is chosen in a heuristic way, which weakens the feature of OSAP appropriately. Experiments implemented on natural image set show the effectiveness of proposed method.
FERRO – UM ELEMENTO QUÍMICO ESTRATÉGICO QUE PERMEIA HISTÓRIA, ECONOMIA E SOCIEDADE

ECONOMY AND SOCIETY.Iron is the 4th most abundant element on the earth crust and it has an important role in several geochemical and biological processes.The importance of the steel for the industrial development of a country makes the iron ore extraction a strategic input responsible for part of the world commerce.Brazil is one of the largest iron ore producers in the world: actually, it is a primary commodity and one of the most important items of its exports.In the present paper, the main sources of iron, how it is available in the nature, its occurrence, its processing and the steel production will be presented.A critical analysis of its importance for the economical and social development of the country will be briefly discussed.
<i>embComp</i>: Visual Interactive Comparison of Vector Embeddings

This article introduces embComp, a novel approach for comparing two embeddings that capture the similarity between objects, such as word and document embeddings. We survey scenarios where comparing these embedding spaces is useful. From those scenarios, we derive common tasks, introduce visual analysis methods that support these tasks, and combine them into a comprehensive system. One of embComp's central features are overview visualizations that are based on metrics for measuring differences in the local structure around objects. Summarizing these local metrics over the embeddings provides global overviews of similarities and differences. Detail views allow comparison of the local structure around selected objects and relating this local information to the global views. Integrating and connecting all of these components, embComp supports a range of analysis workflows that help understand similarities and differences between embedding spaces. We assess our approach by applying it in several use cases, including understanding corpora differences via word vector embeddings, and understanding algorithmic differences in generating embeddings.
Improving Hybrid CTC/Attention Architecture with Time-Restricted Self-Attention CTC for End-to-End Speech Recognition

As demonstrated in hybrid connectionist temporal classification (CTC)/Attention architecture, joint training with a CTC objective is very effective to solve the misalignment problem existing in the attention-based end-to-end automatic speech recognition (ASR) framework. However, the CTC output relies only on the current input, which leads to the hard alignment issue. To address this problem, this paper proposes the time-restricted attention CTC/Attention architecture, which integrates an attention mechanism with the CTC branch. “Time-restricted” means that the attention mechanism is conducted on a limited window of frames to the left and right. In this study, we first explore time-restricted location-aware attention CTC/Attention, establishing the proper time-restricted attention window size. Inspired by the success of self-attention in machine translation, we further introduce the time-restricted self-attention CTC/Attention that can better model the long-range dependencies among the frames. Experiments with wall street journal (WSJ), augmented multiparty interaction (AMI), and switchboard (SWBD) tasks demonstrate the effectiveness of the proposed time-restricted self-attention CTC/Attention. Finally, to explore the robustness of this method to noise and reverberation, we join a train neural beamformer frontend with the time-restricted attention CTC/Attention ASR backend in the CHIME-4 dataset. The reduction of word error rate (WER) and the increase of perceptual evaluation of speech quality (PESQ) approve the effectiveness of this framework.