Reviewerly/exHarmony huggingface dataset:
Citizen Science – Science as a Vocation

The author analyzes contemporary technoscience. He claims that for the functioning of technoscience it is necessary to train a large number of specialists with higher education. Not all of them are employed in scientific and research organizations. Some of the people who have a higher education but are not professionally engaged in science, participate in the implementation of various citizen science projects. As a result, two groups of scientists can be distinguished: professional scientists and amateur scientists. For the former, scientific activity is a profession, whereas for the citizen scientists it can rather be described in terms of satisfying their curiosity.
Empirical Game-Theoretic Methods for Adaptive Cyber-Defense

Game-theoretic applications in cyber-security are often restricted by the need to simplify complex domains to render them amenable to analysis. In the empirical game-theoretic analysis approach, games are modeled by simulation, thus significantly increasing the level of complexity that can be addressed. We survey applications of this approach to scenarios of adaptive cyber-defense, illustrating how the method operates, and assessing its strengths and limitations .
Alchemy: Techniques for Rectification Based Irregular Scene Text Recognition

Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc.. The perspective distortion and non-linear spatial arrangement of characters make it further difficult. While rectification based method is intuitively grounded and has pushed the envelope by far, its potential is far from being well exploited. In this paper, we present a bag of tricks that prove to significantly improve the performance of rectification based method. On curved text dataset, our method achieves an accuracy of 89.6% on CUTE-80 and 76.3% on Total-Text, an improvement over previous state-of-the-art by 6.3% and 14.7% respectively. Furthermore, our combination of tricks helps us win the ICDAR 2019 Arbitrary-Shaped Text Challenge (Latin script), achieving an accuracy of 74.3% on the held-out test set. We release our code as well as data samples for further exploration at this https URL
Competence models for special libraries’ professionals: Foreign experience

New technologies and communication models question the value of libraries and librarians in the digital epoch. The libraries of all types and departmental subordination are challenged. National and foreign studies are to enable to comprehend the new role of the libraries and librarians. The author examines conceptualizing the role of special libraries and their professionals. She analyzes studies, reports and publications made by several professional associations of special libraries that have been intensively investigating into solutions for the libraries to preserve their value for the future. Thus, the Special Libraries Association (USA) and American Association of Law Libraries have accomplished their rebranding. The foreign librarians argue that professional competences make efficient instrument that would enable the librarians to develop their career and to demonstrate the value of the libraries. The author examines the competence models for special libraries professionals as suggested by the mentioned professional associations. Management competences, leadership and communication skills are particularly important for small libraries’ librarians.
The Data Complexity of Ontology-Mediated Queries with Closed Predicates

In the context of ontology-mediated querying with description logics (DLs), we study the data complexity of queries in which selected predicates can be closed (OMQCs). We provide a non-uniform analysis, aiming at a classification of the complexity into tractable and non-tractable for ontologies in the lightweight DLs DL-Lite and EL, and the expressive DL ALCHI. At the level of ontologies, we prove a dichotomy between FO-rewritable and coNP-complete for DL-Lite and between PTime and coNP-complete for EL. The meta problem of deciding tractability is proved to be in PTime. At the level of OMQCs, we show that there is no dichotomy (unless NP equals PTime) if both concept and role names can be closed. If only concept names can be closed, we tightly link the complexity of query evaluation to the complexity of surjective CSPs. We also identify a class of OMQCs based on ontologies formulated in DL-Lite that are guaranteed to be tractable and even FO-rewritable.
JU-Saarland Submission to the WMT2019 English–Gujarati Translation Shared Task

In this paper we describe our joint submission (JU-Saarland) from Jadavpur University and Saarland University in the WMT 2019 news translation shared task for English-Gujarati language pair within the translation task subtrack.Our baseline and primary submissions are built using a Recurrent neural network (RNN) based neural machine translation (NMT) system which follows attention mechanism followed by fine-tuning using in-domain data.Given the fact that the two languages belong to different language families and there is not enough parallel data for this language pair, building a high quality NMT system for this language pair is a difficult task.We produced synthetic data through back-translation from available monolingual data.We report the automatic evaluation scores of our English-Gujarati and Gujarati-English NMT systems trained at word, byte-pair and character encoding levels where RNN at word level is considered as the baseline and used for comparison purpose.Our English-Gujarati system ranked in the second position in the shared task.
Optimization of Log Gabor Filters Using Genetic Algorithm for Query by Image Content Systems

Query By Image Content (QBIC) system permits the user to extract similar images from the large database by specifying the query image. In this paper, a new hybrid QBIC system is proposed, which effectively utilizes the color, texture, and shape features by sequential process of three pipe stages which gives good retrieval performance and also reduces computational complexity. In first pipe stage, color features are extracted with histograms based on HSV color space. In second pipe stage, texture information is obtained by optimizing log Gabor filters using genetic algorithm. Finally, in third pipe stage, shape features are extracted with signature functions using polygonal algorithm. Simulation results show that the proposed hybrid sequential pipe stage combination gives good performance metrics in terms of mean recall and mean precision and also reduces the number of filters compared to the existing algorithms.
Apertium-fin-eng–Rule-based Shallow Machine Translation for WMT 2019 Shared Task

In this paper I describe a rule-based, bidirectional machine translation system for the Finnish-English language pair.The original system is based on the existing data of FinnWordNet, omorfi and apertium-eng.I have built the disambiguation, lexical selection and translation rules by hand.The dictionaries and rules have been developed based on the shared task data.I describe in this article the use of the shared task data as a kind of a test-driven development workflow in RBMT development and show that it suits perfectly to a modern software engineering continuous integration workflow of RBMT and yields big increases to BLEU scores with minimal effort.The system described in the article is mainly developed during shared tasks.
SHOAL

E-commerce taxonomy plays an essential role in online retail business. Existing taxonomy of e-commerce platforms organizes items into an ontology structure. However, the ontology-driven approach is subject to costly manual maintenance and often does not capture user's search intention, particularly when user searches by her personalized needs rather than a universal definition of the items. Observing that search queries can effectively express user's intention, we present a novel large-Scale Hierarchical taxOnomy via grAph based query coaLition ( SHOAL ) to bridge the gap between item taxonomy and user search intention. SHOAL organizes hundreds of millions of items into a hierarchical topic structure . Each topic that consists of a cluster of items denotes a conceptual shopping scenario, and is tagged with easy-to-interpret descriptions extracted from search queries. Furthermore, SHOAL establishes correlation between categories of ontology-driven taxonomy, and offers opportunities for explainable recommendation. The feedback from domain experts shows that SHOAL achieves a precision of 98% in terms of placing items into the right topics, and the result of an online A/B test demonstrates that SHOAL boosts the Click Through Rate (CTR) by 5%. SHOAL has been deployed in Alibaba and supports millions of searches for online shopping per day.
A Neural Graph-based Approach to Verbal MWE Identification

We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach.Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings.This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training.Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-ofthe-art (Al Saied et al., 2018).
FFBNet : Lightweight Backbone for Object Detection Based Feature Fusion Block

Currently, most advanced object detectors equip with deep convolutional neural network (CNN) backbones(e.g., ResNet-101) for capturing strong feature representation, but which leads to suffer heavy computational burden. Inversely, some detectors based on lightweight backbones implement real-time processing while accuracy is often not satisfactory. In this paper, we explore an approach to construct a light yet powerful detector by using efficient lightweight backbone (e.g., MobileNet) with our proposed Feature Fusion Block (FFB), composed of Feature Aggregation Block (FAB) and Dense Feature Pyramid (DFP). The extensive experiments confirmed that our proposed Feature Fusion Block Network (FFBNet) is able to improve the accuracy significantly of MobileNet-SSD as well as maintain a close processing speed. Specifically, on Pascal VOC datasets, FFBNet achieves 73.54 mAP at speed of 185 FPS and surpasses MobileNet-SSD five points. Moreover, we apply VGG16 as backbone to further indicate the effectiveness of FFB, which reaches 80.2 mAP on Pascal VOC benchmark. Code is available at https://github.com/fanbinqi/FFBNet.
The CLiGS Textbox: Building and Using Collections of Literary Texts in Romance Languages Encoded in TEI XML

The CLiGS textbox is published by the Computational Literary Genre Stylistics (CLiGS) group. The textbox is the group's publication channel for several collections of literary texts. We describe the rationale for the manner in which the collections of literary texts included in the textbox have been compiled, annotated, and published. Furthermore, we suggest several ways in which the text collections can be used for research in literary studies. We aim to document some of the work of the CLiGS group, to showcase the unique TEI XML-based collections of French, Spanish, Spanish-American, and Portuguese novels and French drama we make available, and to encourage reuse of these text collections by others. We argue that agreement on common formats and procedures for text preparation, encoding, and publication fosters the accessibility, analysis, and reuse potential of literary text collections.
Margin-Based Generalization Lower Bounds for Boosted Classifiers

Boosting is one of the most successful ideas in machine learning. The most well-accepted explanations for the low generalization error of boosting algorithms such as AdaBoost stem from margin theory. The study of margins in the context of boosting algorithms was initiated by Schapire, Freund, Bartlett and Lee (1998), and has inspired numerous boosting algorithms and generalization bounds. To date, the strongest known generalization (upper bound) is the $k$th margin bound of Gao and Zhou (2013). Despite the numerous generalization upper bounds that have been proved over the last two decades, nothing is known about the tightness of these bounds. In this paper, we give the first margin-based lower bounds on the generalization error of boosted classifiers. Our lower bounds nearly match the $k$th margin bound and thus almost settle the generalization performance of boosted classifiers in terms of margins.
Optimization and Tradeoffs in Secure Floating-Point Computation

A broad range of privacy preserving collaborative applications require efficient and accurate secure computation with real numbers (e.g., statistical analysis, data mining, and optimizations). Secure fixed-point arithmetic offers efficient solutions for certain tasks, but many applications require the dynamic range and accuracy provided by floating-point arithmetic. We focus in this paper on secure multi-operand multiplication and related tasks. For these tasks, secure floating-point arithmetic can offer better tradeoffs between performance and accuracy than fixed-point arithmetic. We present optimized protocols for evaluating products, powers, and polynomials, that offer important performance gains with respect to generic constructions. These protocols are part of a more comprehensive framework for secure multiparty computation with real numbers, constructed using a small collection of building blocks based on Shamir secret sharing. With these additional protocols, the framework offers a better foundation for secure evaluation of mathematical functions (e.g., by polynomial approximation).
Multi-rover Exploration Strategies: Coverage Path Planning with Myopic Sensing

The past few decades have seen tremendous progress in planetary exploration rovers and the most groundbreaking missions in space exploration history. Although the main focus has been toward single rover deployment, there has also been substantial research into swarm intelligence and multi-rover systems. This paper presents single and multi-rover path planning strategies for future planetary exploration missions. The main objective is to develop real-time Coverage Path Planning (CPP) solutions in unknown environments. The algorithms presented here are based on simple building blocks that use solely myopic sensing information to iteratively compute a motion cost function and decide their next move. Several single rover exploration methods are introduced and compared through simulation. The results show that complete coverage is not always possible in planetary exploration scenarios. Also, by slightly reducing the required percent of terrain to explore, the efficiency can be improved. In addition to this, two variants of multi-rover path planning algorithms are defined. A first method is a fully collaborative method, where the rovers explore a region together and a second where the terrain is segmented and the rover operate separately from each other.
Combination of Caesar Cipher Algorithm and Rivest Shamir Adleman Algorithm for Securing Document Files and Text Messages

Abstract At this time, the advancement technology of computer science and telecommunications are developed highly and progressing rapidly. Data security is very important to maintain the important data content of the parties that can harm by destroying important data from the data owner. By improving data security, using a combination of algorithms, it can keep data security more secure than attacks that can harm the content of stored data, especially data in the form of document files and text messaging. The combination of algorithms used for data security used is caesare cipher algorithm and RSA algorithm. So by using a combination of caesar cipher algotitm and RSA algorithm, the security level of document files and text messages can be more awake the authenticity of the data.
A Self-Attentive Convolutional Neural Networks for Emotion Classification on User-Generated Contents

It is challenging to detect emotions on user-generated contents (UGC) because it tends to have sparse emotional semantics, multiple emotions in the same text, and a fast update of emotional expression. Word embedding can extract high-level features in words to enrich their semantics. The convolutional neural networks (CNNs) can make model training more efficient to adapt to fast-changing emotional expressions, but its original pooling operation can not simultaneously filter out multiple emotional features that are beneficial to classification results, which is not suitable for UGC. In this paper, we propose a self-attentive convolutional neural networks (SACNNs) trained on top of pre-trained word vectors for emotion detection on UGC, which reserves different kinds of emotion information, avoids the loss of emotional aspects in the pooling process of CNNs structure, and increases the interpretability of the model by visualizing the extraction process of features. Meanwhile, the convergence speed of the model training is accelerated, and the model is updated in real time to detect emotions with rich and novel expressions. The proposed model combines the CNNs and the self-attention mechanism, the self-attention mechanism can select key emotional features after convolution. We evaluate the proposed model with two datasets from NLPCC 2014 and SemEval 2018 Task 1. The experimental results show that our model obtain significant performance than baselines in multi-label classification on UGC. In addition, the experimental results and the rationality of the self-attention mechanism are analyzed in detail, and the influential convolutional filter windows are visualized based on attention weights.
Bayesian nonparametric modelling of the link function in the single-index model using a Bernstein–Dirichlet process prior

This paper proposes the use of the Bernstein–Dirichlet process prior for a new nonparametric approach to estimating the link function in the single-index model (SIM). The Bernstein–Dirichlet process prior has so far mainly been used for nonparametric density estimation. Here we modify this approach to allow for an approximation of the unknown link function. Instead of the usual Gaussian distribution, the error term is assumed to be asymmetric Laplace distributed which increases the flexibility and robustness of the SIM. To automatically identify truly active predictors, spike-and-slab priors are used for Bayesian variable selection. Posterior computations are performed via a Metropolis-Hastings-within-Gibbs sampler using a truncation-based algorithm for stick-breaking priors. We compare the efficiency of the proposed approach with well-established techniques in an extensive simulation study and illustrate its practical performance by an application to nonparametric modelling of the power consumption in a sewage treatment plant.
Personalized Mobile Video Recommendation Based on User Preference Modeling by Deep Features and Social Tags

With the explosive growth of mobile videos, helping users quickly and effectively find mobile videos of interest and further provide personalized recommendation services are the developing trends of mobile video applications. Mobile videos are characterized by their wide variety, single content, and short duration, and thus traditional personalized video recommendation methods cannot produce effective recommendation performance. Therefore, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags. The main contribution of our work is three-fold: (1) deep features of mobile videos are extracted by an improved exponential linear units-3D convolutional neural network (ELU-3DCNN) for representing video content; (2) user preference is modeled by combining user preference for deep features with user preference for social tags that are respectively modeled by maximum likelihood estimation and exponential moving average method; (3) a personalized mobile video recommendation system based on user preference modeling is built after detecting key frames with a differential evolution optimization algorithm. Experiments on YouTube-8M dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized mobile video recommendation.
Learning to Explain: Answering Why-Questions via Rephrasing

Providing plausible responses to why questions is a challenging but critical goal for language based human-machine interaction.Explanations are challenging in that they require many different forms of abstract knowledge and reasoning.Previous work has either relied on human-curated structured knowledge bases or detailed domain representation to generate satisfactory explanations.They are also often limited to ranking pre-existing explanation choices.In our work, we contribute to the under-explored area of generating natural language explanations for general phenomena.We automatically collect large datasets of explanation-phenomenon pairs which allow us to train sequence-to-sequence models to generate natural language explanations.We compare different training strategies and evaluate their performance using both automatic scores and human ratings.We demonstrate that our strategy is sufficient to generate highly plausible explanations for general open-domain phenomena compared to other models trained on different datasets.
A Detail Survey on Automatic Text Summarization

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Imbalanced Sentiment Classification Enhanced with Discourse Marker

Imbalanced data commonly exists in real world, especially in sentiment-related corpus, making it difficult to train a classifier to distinguish latent sentiment in text data. We observe that humans often express transitional emotion between two adjacent discourses with discourse markers like “but”, “though”, “while”, etc., and the head discourse and the tail discourse usually indicate opposite emotional tendencies. Based on this observation, we propose a novel plug-and-play method, which first samples discourses according to transitional discourse markers and then validates sentimental polarities with the help of a pre-trained attention-based model. Our method increases sample diversity in the first place, obtaining a expanded dataset with relatively low imbalanced-ratio, can serve as a upstream preprocessing part in data augmentation. We conduct experiments on three public sentiment datasets, with several frequently used algorithms. Results show that our method is found to be consistently effective, even in highly imbalanced scenario, and easily be integrated with oversampling method to boost the performance on imbalanced sentiment classification.