Reviewerly/exHarmony huggingface dataset:
Neural Machine Translation with Attention Based on a New Syntactic Branch Distance

Attention mechanism has been proved to be able to improve the quality of neural machine translation by selectively focusing on partial words of a source sentence during translation process. Attention mechanism usually focuses on local attention by using solely the linear index distance of words while ignores syntax structures of sentences. In this paper, we extend local attention through syntax distance constraint, and propose an attention mechanism based on a new syntactic branch distance, which simultaneously pays attention to words with similar linear index distances and syntax-related words. Based on the English-to-German translation task, experiment results showed that our model outperforms a recent baseline method with an improvement of 1.61 BLEU points, demonstrating the effectiveness of the proposed model.
Mitigation of Runway Incursions by Using a Convolutional Neural Network to Detect and Identify Airport Signs and Markings

Runway incursions have resulted in incidents, confusions, and delays in airport operation.With the aim of reducing the risk of runway incursions, in this work, we investigate the use of a machine learning (ML) approach to detect and identify airport signs and markings to enhance operational safety especially in a low-visibility scenario.An artificial intelligence (AI) sensor for detecting the pixels developed and modeled using a convolutional neural network (CNN) is developed.In this design, the neural network outputs the feature vector model after the convolution operation.A filter is used to detect the pixels of the background image of the airport environment.The weight of the feature object is then added with a maximum pool layer after a convolution layer to find the feature map.The CNN is trained to demonstrate its capability in performing object detection and identification.It is expected that the proposed approach can be used to enhance airport operational safety and mitigate the risk of runway incursion.
Quantile-based clustering

A new cluster analysis method, $K$-quantiles clustering, is introduced. $K$-quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd’s algorithm for $K$-means. It can be applied to large and high-dimensional datasets. It allows for within-cluster skewness and internal variable scaling based on within-cluster variation. Different versions allow for different levels of parsimony and computational efficiency. Although $K$-quantiles clustering is conceived as nonparametric, it can be connected to a fixed partition model of generalized asymmetric Laplace-distributions. The consistency of $K$-quantiles clustering is proved, and it is shown that $K$-quantiles clusters correspond to well separated mixture components in a nonparametric mixture. In a simulation, $K$-quantiles clustering is compared with a number of popular clustering methods with good results. A high-dimensional microarray dataset is clustered by $K$-quantiles.
Generative Ratio Matching Networks

Deep generative models can learn to generate realistic-looking images, but many of the most effective methods are adversarial and involve a saddlepoint optimization, which require careful balancing of training between a generator network and a critic network. Maximum mean discrepancy networks (MMD-nets) avoid this issue by using kernel as a fixed adversary, but unfortunately they have not on their own been able to match the generative quality of adversarial training. In this work, we take their insight of using kernels as fixed adversaries further and present a novel method for training deep generative models that does not involve saddlepoint optimization. We call our method generative ratio matching or GRAM for short. In GRAM, the generator and the critic networks do not play a zero-sum game against each other, instead they do so against a fixed kernel. Thus GRAM networks are not only stable to train like MMD-nets but they also match and beat the generative quality of adversarially trained generative networks.
Punctuation Prediction for Vietnamese Texts Using Conditional Random Fields

We investigate the punctuation prediction for the Vietnamese language. This problem is crucial as it can be used to add suitable punctuation marks to machine-transcribed speeches, which usually do not have such information. Similar to previous works for English and Chinese languages, we formulate this task as a sequence labeling problem. After that, we apply the conditional random field model for solving the problem and propose a set of appropriate features that are useful for prediction. Moreover, we build two corpora from Vietnamese online news and movie subtitles and perform extensive experiments on these data. Finally, we ask four volunteers to insert punctuations into a small sample of our dataset. The experimental results show that this problem is challenging, even for a human, and our model can achieve near performance in comparison to a human.
Patch-Based Identification of Lexical Semantic Relations

The identification of lexical semantic relations is of the utmost importance to enhance reasoning capacities of Natural Language Processing and Information Retrieval systems. Within this context, successful results have been achieved based on the distributional hypothesis and/or the paradigmatic assumption. However, both strategies solely rely on the input words to predict the lexical semantic relation. In this paper, we make the hypothesis that the decision process should not only rely on the input words but also on their K closest neighbors in some semantic space. For that purpose, we present different binary and multi-task classification strategies that include two distinct attention mechanisms based on PageRank. Evaluation results over four gold-standard datasets show that average improvements of 10.6% for binary and 8% for multi-task classification can be achieved over baseline approaches in terms of F $$_1$$ . The code and the datasets are available upon demand.
An OCR system for the Unified Northern Alphabet

This paper presents experiments done in order to build a functional OCR model for the Unified Northern Alphabet.This writing system was used between 1931 and 1937 for 16 (Uralic and non-Uralic) minority languages spoken in the Soviet Union.The character accuracy of the developed model reaches more than 98% and clearly shows cross-linguistic applicability.The tests described here therefore also include general guidelines for the amount of training data needed to bootstrap an OCR system under similar conditions.
The embedded research librarian: a project partner

This paper presents new services developed by the Lille University Library for European and National research project coordinators. This is a specific audience that libraries are not used to target, with a widely recognised institutional status and academic background. Supporting them in their coordination activities is an opportunity to gain a new role for libraries, which starts from the design of research at the submission stage and lasts several years after, during the project lifetime. These services help coordinators to meet their funders’ expectations on open access and research data management. It is also a way to develop new collaborations with research units and some university services, such as the Grant Office. The Lille University Library has already supported the writing of forty grant proposals since 2017, including about thirty since early 2019. The Library currently follows twelve projects on open access, research data management or both. This second figure is likely to increase in 2020 due to the number of projects supported at submission stage since the beginning of 2019. The paper describes our set of services and the lessons we learned from our approach.
Information technology of Blockchain: database, smart contracts, architecture

The article deals with information technology blockchain. The possibility of databases creating using blockchain is described. The simple writing data procedure to the blockchain is realized. The main domains for the technology applying are given. The smart contract in blockchain are shown. The potential of this technology is described.
Human Action Recognition Using Modified Bag of Visual Word based on Spectral Perception

Human action recognition has a very vast application such as security, patient care, etc. Background cluttering, appearance change due to variation in viewpoint and occlusion are the prominent hurdles that can reduce the recognition rate significantly.Methodologies based on Bag-of-visual-words are very popular because they do not require accurate background subtraction.But the main disadvantage with these methods is that they do not retain the geometrical structural information of the clusters that they form.As a result, they show intra-class mismatching.Furthermore, these methods are very sensitive to noise.Addition of noise in the cluster also results in the misclassification of the action.To overcome these problems we proposed a new approach based on modified Bag-of-visual-word.Proposed methodology retains the geometrical structural information of the cluster based on the calculation of contextual distance among the points of the cluster.Normally contextual distance based on Euclidean measure cannot deal with the noise but in the proposed methodology contextual distance is calculated on the basis of a difference between the contributions of cluster points to maintain its geometrical structure.Later directed graphs of all clusters are formed and these directed graphs are described by the Laplacian.Then the feature vectors representing Laplacian are fed to the Radial Basis Function based Support Vector Machine (RBF-SVM) classifier.
Prediction-Error Value Ordering for High-Fidelity Reversible Data Hiding

Prediction-error expansion (PEE) is the most widely utilized reversible data hiding (RDH) technique. However, the performance of PEE is far from optimal since the correlations among prediction-errors are not fully exploited. Then, to enhance the embedding performance of PEE, a new RDH method named prediction-error value ordering (PEVO) is proposed in this paper. The main idea of PEVO is to exploit the inter-correlations of prediction-errors by combining PEE with the recent RDH technique of pixel value ordering. Specifically, the prediction-errors within an image block are first sorted, and then the maximum and minimum prediction-errors of the block are predicted and modified for data embedding. By the proposed approach, the image redundancy is better exploited and promising embedding performance is achieved. Experimental results demonstrate that the proposed PEVO embedding method is better than the PEE-based ones and some other state-of-the-art works.
An Online Dictionary and Thesaurus

Abstract Semantic similarity and definition of words are very crucial part of the data; therefore various online dictionary and thesaurus had been studied in the literature and it was found that wordnet is the most famous and better one. WordNet design is inspired by current psycholinguistic theories of human lexical memory. It is an ontology-based system which organizes English nouns, verbs, and adjectives into synonym sets, each representing one underlying lexical concept. Although wordnet is a more efficient dictionary and thesaurus then also there are lots of problems in it which make it not so good. Therefore in this paper we will discuss all the challenges of existing online dictionaries and thesauruses and then we will conclude that why WordNet is good but not the best system. This state of the art paper clarifies the difference between all existing online dictionaries and thesauruses by using an effective parameter. Keywords: WordNet, ontology, dictionary, thesaurus Cite this Article Dikshit Kumar, Agam Kumar, Man Singh, Archana Patel, Sarika Jain . An Online Dictionary and Thesaurus. Journal of Artificial Intelligence Research & Advances . 2019; 6(1): 32–38p.
Interactive visualization of model dependencies in a transdisciplinary environment

Innovation processes are influenced by cyclical factors and involve many stakeholders from different disciplines within a company. Working together in transdisciplinary teams can save time and therefore improve the innovation process. However, the stakeholders have a completely different understanding of the processes based on their domain specific knowledge and experience and the requirements of their individual task. To avoid conflicts between the disciplines, this paper presents an interactive two-level graph-based approach to visualize cross-discipline model dependencies. This visualization can help to support the transdisciplinary communication and comprehension.
An Modified Video Stream Classification Method Which Fuses Three-Dimensional Convolutional Neural Network

The three-dimensional mathematiccal model can well represent the multi-dimensional information of continuous video stream data, and is an important means in video classification research. In order to improve the accuracy of video classification, based on three-dimensional convolutional network, this paper proposes an improved video stream classification method by using video features of different time scales. Firstly, the video frame sequences of different time scales are inputting into the 3D network for feature extraction, and secondly, the features of different time scales are weighted and fused. Then, after merging multiple sequential models, the fused three-dimensional convolutional neural network model is constructed by fully connected, and the influence of optical flow input on video classification results is weakened. By classifying the 101 action behaviors of the UCF-101 video stream dataset, the results show that the improved network model obtains 90.6% classification accuracy on the UCF-101 dataset, and the classification accuracy compared with the 3DConv_ Ensemble model [7] is improved 2.9%.
A Multi-Channel Neural Graphical Event Model with Negative Evidence

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains. Existing work for modeling events using conditional intensities rely on either using some underlying parametric form to capture historical dependencies, or on non-parametric models that focus primarily on tasks such as prediction. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions. We use a novel multi-channel RNN that optimally reinforces the negative evidence of no observable events with the introduction of fake event epochs within each consecutive inter-event interval. We evaluate our method against state-of-the-art baselines on model fitting tasks as gauged by log-likelihood. Through experiments on both synthetic and real-world datasets, we find that our proposed approach outperforms existing baselines on most of the datasets studied.
MAJOR AREAS OF FOREIGN LANGUAGE SPEECH ACTIVITY OF UNIVERSITY STUDENTS IMPROVEMENT

Purposes: Increased international activity in Russia leads to a steady claim of foreign language knowledge. The list of professional characteristics of a university graduate includes the ability to adapt to new information realities in the professional community, where foreign language speech activities become highly demanded, when not only language skills, but also social and cultural arrangements of the country. Thus, innovative approaches to foreign language teaching are required. The article explored the following aspects of foreign-language speech activity of university students. Methodology: methodology of teaching foreign-language speech activity; principles of development of foreign language speech activity of students; realization of educational resources for this activity development; model of pedagogical technology of formation of foreign language speech activity, etc. Result: The results of the pilot work showed the validity of using the principles of the development of foreign language speech activities in the organization of the educational process. Implications/Applications: This article will help the student to promote the ability to give a speech. Novelty/Originality: At the same time, being a type of speech activity, foreign language communication in the process of training solves, in turn, educational and development tasks by means of a foreign language sign system, where the whole set of speech actions of the individual is actually present.
Ensuring Genuineness for Selectively Disclosed Confidential Data Using Distributed Ledgers: Applications to Rail Wayside Monitoring

In railway incidents, data from sensors installed on railway tracks can help finding the cause of the incident and identifying the responsible parties. Since the data collected may contain business-relevant information, it is usually treated as confidential by the companies collecting it. However, this data can only be considered as evidence if it can be proven that the data is genuine and unaltered, even if it is only accessible for involved companies in the first place. In this paper, we present an approach to ensure the genuineness of confidential railway measurement data using distributed ledgers and describe an approach for selectively sharing parts of the data without compromising confidentiality or the verifiability of genuineness. We discuss the specific characteristics of rail wayside measurement data, existing approaches to ensure data genuineness and the necessary modifications to apply them to rail wayside measurement data. We also discuss how our approach can be generalized beyond the railway domain to show how distributed ledger-based approaches can be used to ensure the genuineness of confidential and selectively shared data.
Implementation of Mastery-Based Testing in Calculus I

This paper gives a blueprint for reacting to the various issues that an instructor will face in their day-to-day life as the manager of a Mastery-Based Testing (MBT) classroom. For example, I created a rubric to compare traditional testing with MBT, adapted my office hours to accommodate more retake opportunities, and required effort between in-class testing periods. I have also experimented with allowing students to choose their testing method. The rationale and implementation of the adaptations are fully discussed, and student feedback and average scores are examined.
Single Image Super-Resolution Algorithm Possessing Edge and Contrast Preservation

In real time surveillance video applications, it is often required to identify a region of interest in a degraded low resolution (LR) image. State-of-the-art super-resolution (SR) techniques produce images with poor illumination and degraded high frequency details. In this paper, we present a different approach for SISR by correcting the dual-tree complex wavelet transform (DT-CWT) subbands using the multi-stage cascaded joint bilateral filter (MSCJBF) and singular value decomposition (SVD). The proposed method exploits geometric regularity for implementing the covariance-based interpolation in the spatial domain. We decompose the interpolated LR image into different image and wavelet coefficients by employing DT-CWT. To preserve edges, we alter the wavelet sub-bands with the high frequency details obtained from the MSCJBF. Simultaneously, we retain uniform illumination by improving the image coefficients using SVD. In addition, the wavelet sub-bands undergo lanczos interpolation prior to the subband refinement. Experimental results demonstrate the effectiveness of our method.
Explaining Propagators for String Edit Distance Constraints

The computation of string similarity measures has been thoroughly studied in the scientific literature and has applications in a wide variety of different areas. One of the most widely used measures is the so called string edit distance which captures the number of required edit operations to transform a string into another given string. Although polynomial time algorithms are known for calculating the edit distance between two strings, there also exist NP-hard problems from practical applications like scheduling or computational biology that constrain the minimum edit distance between arrays of decision variables. In this work, we propose a novel global constraint to formulate restrictions on the minimum edit distance for such problems. Furthermore, we describe a propagation algorithm and investigate an explanation strategy for an edit distance constraint propagator that can be incorporated into state of the art lazy clause generation solvers. Experimental results show that the proposed propagator is able to significantly improve the performance of existing exact methods regarding solution quality and computation speed for benchmark problems from the literature.
A Computer Vision Based Fall Detection Technique for Home Surveillance

In this modern era where the population and life expectancy are continuously increasing, the demand for an advanced healthcare system is increasing at an unprecedented rate. This paper presents a novel and cost-effective fall detection system for home surveillance which uses a surveillance video to detect the fall. The advantage of the proposed system is that it doesn’t need the person to carry or wear a device. The proposed system uses background subtraction to detect the moving object and marks it with a bounding box. Furthermore, few rules are based on the measures extracted from the bounding box and contours around the moving object. These rules are used with the transitions of a finite state machine (FSM) to detect the fall. It is done using the posture and shape analysis with two measures viz height and speed of falling. An alarm is sent when the fall is confirmed. The proposed approach is tested on three datasets URD, FDD and multicam. The obtained results show that proposed system works with an average accuracy of 97.16% and excels the previous approaches.
Human action recognition using descriptor based on selective finite element analysis

Abstract This paper proposes a novel local descriptor evaluated from the Finite Element Analysis for human action recognition. This local descriptor represents the distinctive human poses in the form of the stiffness matrix. This stiffness matrix gives the information of motion as well as shape change of the human body while performing an action. Initially, the human body is represented in the silhouette form. Most prominent points of the silhouette are then selected. This silhouette is discretized into several finite small triangle faces (elements) where the prominent points of the boundaries are the vertices of the triangles. The stiffness matrix of each triangle is then calculated. The feature vector representing the action video frame is constructed by combining all stiffness matrices of all possible triangles. These feature vectors are given to the Radial Basis Function-Support Vector Machine (RBF-SVM) classifier. The proposed method shows its superiority over other existing state-of-the-art methods on the challenging datasets Weizmann, KTH, Ballet, and IXMAS.