Reviewerly/exHarmony huggingface dataset:
Enhanced Amharic-Arabic Cross-Language Information Retrieval System using Part of Speech Tagging

In this paper we extended our first experiment on Neural Machine Translation (NMT) based query translation for Amharic-Arabic Cross Language Information Retrieval (CLIR) task to retrieve relevant documents from Amharic and Arabic text collections in response to a query expressed in the Amharic language by modifying the ranking algorithm with Parts of speech Tags (POS). We used a pre-trained NMT model, to map a query in the source language into an equivalent query in the language of the target document collection. The relevant documents are then retrieved using a Language Modeling (LM) based retrieval algorithm by substituting lambda with POS based LM. The experimental result is compared with four conventional IR models, namely Uni-gram and Bi-gram LM, Probabilistic model and Vector Space Model (VSM). The proposed POS based LM ranking algorithm outperform all others for both Amharic and Arabic language document collections.
Alibaba, Amazon, and Counterfeiting in the Age of the Internet

The advent of e-commerce marketplaces such as Alibaba and Amazon in the new millennium has led to the proliferation of the sale of counterfeit goods around the world through the Internet. Brand owners find that Internet counterfeiters operating in the digital world present even more challenges than those using only brick and mortar operations. Internet counterfeiters have unprecedented access to consumers, use false identities and addresses, and vanish into cyberspace at the first sign of trouble. Brand owners seeking help from Alibaba and Amazon to remove listings of counterfeits have become frustrated by their convoluted and labyrinthine notice and take-down procedures. Even when these procedures are used successfully, brand owners find that the process can take months only to have the counterfeiter reappear in short order using a new false identity. Many brand owners find that dealing with Alibaba and Amazon only adds to their misery and believe that both tolerate counterfeits as they earn revenue from all sales, including sales of counterfeit goods.This Article sets forth for the first time how brand owners can use a set of currently available information technology tools to help create an effective deterrent to counterfeits on the Internet. Using these tools, brand owners can force counterfeiters to abandon the subterfuge and disguise that they rely on so that brand owners can – without the assistance of e-commerce sites – directly pursue counterfeiters in civil and criminal actions in China where most of the counterfeiters are located and in the United States. The proposed approach should help deter counterfeiters who always work in secrecy and disguise by exposing them to what they fear and loathe the most: transparency and accountability for their illegal actions.
Tracking Transactions in Crypto Currencies Using the Graph Theory

this article is devoted to the study of blockchain wallets transactions related to trafficking in illicit substances in Russia. A program has been written in Python that allows to find arbitrarily distant connections of any wallet, as well as visualize these connections in the form of a graph. All wallets found are added to the database, which can subsequently expand significantly. From the wallet of any person it will be possible to judge whether user belongs to any criminal organization or to find out if several people are accomplices.
Automatic answer ranking based on sememe vector in KBQA

This paper proposes an answer ranking method used in Knowledge Base Question Answering (KBQA) system. This method first extracts the features of predicate sequence similarity based on sememe vector, predicates' edit distances, predicates' word co-occurrences and classification. Then the above features are used as inputs of the ranking learning algorithm Ranking SVM to rank the candidate answers. In this paper, the experimental results on the data set of KBQA system evaluation task in the 2016 Natural Language Processing & Chinese Computing (NLPCC 2016) show that, the method of word similarity calculation based on sememe vector has better results than the method based on word2vec. Its accuracy, recall rate and average F1 value respectively are 73.88%, 82.29% and 75.88%. The above results show that the word representation with knowledge has import effect on natural language processing.
Speed sign recognition in complex scenarios based on deep cascade networks

Speed sign is one of the most important instant indications for drivers to adjust the speed of their cars. In the literature, almost all of the existing methods for speed sign recognition are based on static pictures with clean images. When dealing with the traffic signs in complex environments, these existing approaches often have inaccurate detected areas, which lead to the difficulty of recognition. The authors propose a deep cascade network to improve the recognition of the speed signs with a structure of cascade subnetworks. The proposed network is composed of a localisation subnetwork and a classification subnetwork. The difficult issue in complex scenarios is the detection of the speed sign due to its small resolution, occlusion, colour fading etc. The proposed localisation subnetwork can improve the localisation accuracy by borrowing the idea of locating the targets from coarse to fine. Ultimately, the classification sub-network extracts more effective features for speed sign recognition. The experimental results illustrate that the proposed method outperforms the YOLOv2 or YOLOv3 model in identifying the speed sign in complex scenarios with at least 6% higher in terms of area under curve, and this will promote the improvement of recognition significantly.
Graph Representation Methods for the Discrete Mathematics Instructions Set Computer

Graph operations and manipulation techniques are commonly used to solve many real-world issues, from effective networking to financial analysis. The Discrete mathematics Instruction Set Computer (DISC) for large dimension graphs processing was developed at Bauman University. So-call Structure Processing Unit (SPU), as a core of such a computer, implements a set of high-level discrete mathematics commands over key-value datasets. SPU's presence in the computer system frees CPU from managing RAM addresses for organizing complex data structures, such as trees, graphs, etc. In this paper, we introduce a special API for the DISC system for high-level graph operations. API allows designing data structures with a linear fields-based sorting. We consider a problem of the graph mapping onto SPU's memory as a template data structure and show some practical examples.
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks

Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, "snapshot" measurements that sample the output of the system are often available. In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is trained using samples of current and next-state pairs, and thus does not require longitudinal measurements. We show the advantage of DyMoN over shallow models such as Kalman filters and hidden Markov models, and other deep models such as recurrent neural networks in its ability to embody the dynamics (which can be studied via perturbation of the neural network) and generate longitudinal hypothetical trajectories. We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model.
Intellectual Methods of Estimation of Intending Primary School Teachers’ Foreign Language Communicative Competence

This article deals with using the Bayesian approach algorithms for the classification processing and selection of the best option from the set of possible. The considered methods were used to assess the foreign languages communicative competence of primary school teachers. The hierarchy method and Bayesian classification were offered and used to evaluate the level of students' (intending primary school teachers') foreign language competence and their communication skills. The hierarchy consists of three levels. At the first level, a pairwise comparing the students' skills in terms of foreign language communicative competence was carried out. Student's rating was determined by each criterion. At the second level, ratings were determined in accordance with criteria based on their pairwise comparison. At the third level, students' ratings were determined based on pairwise comparison of criteria. Bayesian decision-making procedures are used to select one of two or four possible options.
Filtering the Continuous Wavelet Transform Using Hyperbolic Triangulations

We propose a methodology that detects signal components of a signal in white noise based on a hyperbolic triangulation of its wavelet transform (WT). The theoretical background is a connection between analyticity inducing wavelets and Gaussian analytic functions. This relation allows us to obtain some useful details on the random distribution of the zeros of the wavelet transformed signal. We apply our method to some acoustic signals and observe that many signal components are found but as predicted by the theory there is no guarantee to find all signal components.
An Effective Coverage Approach for Attention-based Neural Machine Translation

Neural Machine Translation recently has become the state-of-the-art approach in Machine Translation. One of the more advanced techniques concerning this approach, the attention model, tends to not use alignments from past translation steps and selects the context word purely using the devised attention score. Unfortunately, this sometimes leads to repetition and omission of important words in translations. To solve this problem, we propose a simple approach using coverage techniques that can be used in conjunction with a diverse number of attention models. Our experiments show that our improved technique increases the quality of translation on both English -Vietnamese and Japanese - Vietnamese language pairings.
Framework for Project Management in Agile Projects: A Quantitative Study

Recent studies have confirmed the efficacy of agile methodologies in project success, but can projects skip several project management tasks and still deliver the expected results? How are traditional project managers engaged in agile projects? Who executes what project management tasks in projects applying agile methodologies? The aim of this study was to define a framework for project management tasks in agile projects. The results quantify subjective and theoretical speculation on who performs the project management tasks in agile projects. Project managers are engaged in agile projects, and the team, the product owner, and the project sponsor are significantly involved in project management tasks. The agile coach is not a substitute for the project manager. This study identifies areas where agile methodologies should be updated to clarify team responsibilities for project management activities.
Phenomenology Experience in the Context of the Existential Approach to Psychological Counseling

The author of the article substantiates relevance of the stated problem, taking into account the analysis of modern literature; the emphasis is made on growing interest from researchers and practical psychologists in the existential tradition. It is proved necessary to turn to the origins of the existential approach formation for a more complete understanding of the methods and techniques that are the basis of psychological counseling today. The influence of the phenomenology of E. Husserl on the existential approach formation in psychology is considered. Phenomenology is presented as the main methodological principle, on the basis of which the foundations of the existential approach are formed, as a tool with the help of which the dualism between subject and object is overcome. The role of eidetic reduction in the formation of the existential approach principles is shown; the origins of the reflection mechanism, which occupies a key place in the practice of existentially oriented counseling are defined. Special attention is paid to the categorial system of criteria borrowed by the existential approach from phenomenology.
Predicting Personal Attitudes Using Contextual Microblog Activity Logs

In this paper, we present a corpus model to show how personal attitudes can be predicted from social media or microblog activities for a specific domain of events such as natural disasters. More specifically, given a user tweet and an event, the model is used to predict whether the user will be willing to help or show a positive attitude towards that event or similar events in the future. We present a new dataset related to a specific natural disaster event, i.e., Hurricane Harvey, that distinguishes user tweets into positive and non-positive attitudes. We build Term Embeddings for Tweet (TEmT) to generate features to model personal attitudes for arbitrary user tweets. Finally, we evaluate the effectiveness of our method by employing multiple classification techniques on the newly created dataset.
Extraction of Sound Signal from Tiny Vibrations in Motion Magnified Video Using Optical Flow

Sound is a wave of pressure and displacement through the air or a different medium. When sound waves hit an object, they cause tiny vibrations which are impossible to see with the naked eye on the objects' surface. A few techniques exist in the literature that can extract sound partially from the high-speed video. In this paper, it is shown that how using the only normal mobile camera, those vibrations can detect and partially recover the sound that produced them. Through this technique, any kind of lighter object like a glass of water, a box of tissues, or a bag of chips is turned into a visual microphone which allows to convert the subtle motions into a sound signal. This technique includes computing local motion signal which is based on the computation of optical flow velocity components for determining vibration signal and then computing global motion signal by squared average values of the local motion signals.
UDAT: Compound quantitative analysis of text using machine learning

Abstract Computing machines allow quantitative analysis of large databases of text, providing knowledge that is difficult to obtain without using automation. This article describes Universal Data Analysis of Text (UDAT) —a text analysis method that extracts a large set of numerical text content descriptors from text files and performs various pattern recognition tasks such as classification, similarity between classes, correlation between text and numerical values, and query by example. Unlike several previously proposed methods, UDAT is not based on frequency of words and links between certain key words and topics. The method is implemented as an open-source software tool that can provide detailed reports about the quantitative analysis of sets of text files, as well as exporting the numerical text content descriptors in the form of comma-separated values files to allow statistical or pattern recognition analysis with external tools. It also allows the identification of specific text descriptors that differentiate between classes or correlate with numerical values and can be applied to problems related to knowledge discovery in domains such as literature and social media. UDAT is implemented as a command-line tool that runs in Windows, and the open source is available and can be compiled in Linux systems. UDAT can be downloaded from http://people.cs.ksu.edu/∼lshamir/downloads/udat.
Exploring Paraphrasing Techniques on Formal Language for Generating Semantics Preserving Source Code Transformations

Automatically identifying and generating equivalent semantic content to a word, phrase, or sentence is an important part of natural language processing (NLP). The research done so far in paraphrases in NLP has been focused exclusively on textual data, but has significant potential if it is applied to formal languages like source code. In this paper, we present a novel technique for generating source code transformations via the use of paraphrases. We explore how to extract and validate source code paraphrases. The transformations can be used for stylometry tasks and processes like refactoring. A machine learning method of identifying valid transformations has the advantage of avoiding the generation of transformations by hand and is more likely to have more valid transformations. Our dataset is comprised by 27,300 C++ source code files, consisting of 273topics each with 10 parallel files. This generates approximately152,000 paraphrases. Of these paraphrases, 11% yield valid code transformations. We then train a random forest classifier that can identify valid transformations with 83% accuracy. In this paper we also discuss some of the observed relationships betweenlinked paraphrase transformations. We depict the relationshipsthat emerge between alternative equivalent code transformationsin a graph formalism.
An OpenMP Parallelization of the K-means Algorithm Accelerated Using KD-trees

In the paper a KD-tree based filtering algorithm for K-means clustering is considered. A parallel version of the algorithm for shared memory systems, which uses OpenMP tasks both for KD-tree construction and filtering in the assignment step of K-means, is proposed. In our approach, an OpenMP task is created for a recursive call performed by tree construction and filtering procedures. A data partitioning step during the tree construction is also parallelized by OpenMP tasks. In computational experiments we measured runtimes of the parallel and serial version of the filtering algorithm and a parallel version of classical Lloyd’s algorithm for six datasets sampled from two distributions. The results of experiments, performed on a 24-core system indicate that our version filtering algorithm has very good parallel efficiency. Its runtime is up to four orders of magnitude shorter than the runtime of parallel Lloyd’s algorithm.
Iterative Document Retrieval via Deep Learning Approaches for Biomedical Question Answering

The ever expanding nature of the scientific literature makes finding answers in them increasingly more challenging for researchers. With the goal to ease this challenge, we have developed a biomedical question answering system called Bio-AnswerFinder which uses a greedy iterative document retrieval approach to find candidate documents in which the answer supporting sentences are searched. To improve the performance of the baseline retrieval approach, neural network based keyword selection and importance ranking approaches are introduced. Together with two ensemble approaches and a non-iterative word embedding based nearest neighbor approach, seven retrieval approaches are evaluated using Bio-AnswerFinder on hundred test questions with manual inspection. The test results revealed that the iterative keyword ranking approach more than doubled MRR@10 score over the baseline having the best Precision@1 score and a close second MRR@10 score to the ensemble of keyword selection and ranking iterative retrieval approaches.
Texture based Image Species Classification with Deep Convolutional Neural Network

Our goal of study, texture based butterfly image classification with considering two standard deep learning networks and their shallower versions. Butterfly images involve only low and mid level texture information, which is typically learnt in the lower and mid-layers of the networks. We explore, whether smaller versions of these networks are robust enough to maintain a reasonable performance, as compared to the complete network, on the butterfly classification task. To identify the homogeneous texture region with texture class it belongs. We also demonstrate the effect of masking the background under the above scenario. Our approach yields high quality best results for the butterfly classification task, and we draw some interesting observations from our experimental analysis.
Analytics and Visualization of Trends in News Articles

Nowadays, analysis of news after its publication is being done on a huge scale to draw useful conclusions that can benefit several sectors like advertising, journalism, education, businesses, product manufacturing etc. The analysis results are more comprehensive and easy to interpret when plotted graphically. The purpose of this paper is to demonstrate a simple technique for representing the news data in the form of graphs and diagrams which can be used to quickly grasp trends in news data. Also, this paper demonstrates the use of a simple, user-friendly News API provided by Event Registry, and explains how this API simplifies the collection and preprocessing of data. Using this API, we selected the English news articles of the last one month, from the top 10 percentile sources only. Further, we cleaned the data by eliminating duplicated entries and filling in the missing values of certain attributes using Event Registry News API. Additionally, we reduced the data by excluding attributes that are not useful for analysis and attributes that have almost same values for all tuples. Finally, we converted the cleaned and reduced data into visual representations like simple and stacked bar graph, line graph (or time series diagram), doughnut chart, word cloud and Sunburst diagram, using Python libraries like NumPy, Pandas and Matplotlib, and illustrated what conclusions can be drawn from these diagrams. The technique used in this paper is simple, fast and extremely convenient when quick and approximate results are expected by the analyst.
Study on the Integrated Model of Modern Agricultural Variety Breeding in the Internet of Things Environment

In order to meet the requirements of informationization and intelligence construction in modern agriculture, the paper makes a proposal to develop a smart agricultural data cloud warehouse under the Hadoop cheap PC clusters and the Internet of Things(IoT), using advanced intelligent greenhouse technology to collect information through various types of sensing devices, to realize the timely, reliable and efficient storage, access as well as analysis of the data. Cloud storage, data management, reasoning and interpretation mechanism, artificial intelligence and other technologies are applied to develop a complete set of new crop breeding process, thus forming an integrated model of new varieties breeding technology to meet the needs of agricultural research applications.
Time-Aware Dummy-Based Privacy Protection for Continuous LBSs

The flourishing of intelligent mobile devices, significantly prompts various location-Based Services (LBSs), especially continuous LBSs. However, continuous LBSs can suffer from location association attacks and trajectory attacks. The existing dummy-based schemes aren't protect users' privacy effectively since ignoring the reasonability of dummies. Therefore, in this paper we consider the Time-Sensitive Side Information(TSSI), spatiotemporal correlations and movement patterns of previous locations simultaneously to present a Dummy Filtering Algorithm(DFA). First we consider the TSSI to generate dummies. Then we propose two constraints time reachability and out-degree/indegree to cut off spatiotemporal correlations. Next, we utilize area overlapping and trend similarity to protect users' trajectory privacy. Security analysis indicates that our scheme can assure dummies' rationality and availability. Furthermore, experiments show that our scheme not only defends location association attacks and trajectory attacks effectively, but increases the ratio of available dummies from 42% up to 66.7%.