Reviewerly/exHarmony huggingface dataset:
Complement Objective Training

Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the groundtruth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.
SPEECH RECOGNITION SYSTEMS – A COMPREHENSIVE STUDY OF CONCEPTS AND MECHANISM

Speech Recognition Systems now-a-days use many interdisciplinary technologies ranging from Pattern Recognition, Signal Processing, Natural Language Processing implementing to unified statistical framework.Such systems find a wide area of applications in areas like signal processing problems and many more.The objective of this paper is to present the concepts about Speech Recognition Systems starting from the evolution to the advancements that have now been adapted to the Speech Recognition Systems to make them more robust and accurate.This paper has the detailed study of the mechanism, the challenges and the tools to overcome those challenges with a concluding note that would ensure that with the advancements of the technologies, this world is surely going to experience revolutionary changes in the near future.
Contribution-Based Multi-Stream Feature Distance Fusion Method With &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;${k}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;-Distribution Re-Ranking for Person Re-Identification

Person re-identification (re-ID) is an important and challenging topic in video surveillance and public security. Re-ID aims to retrieve persons from different cameras. Despite the developments in recent years, re-ID still faces many challenges due to different camera views, changeable person posture, complex background, and occlusion. To exploit more discriminative image similarity descriptor, we propose a novel method in this paper. First, we design a body partition extraction network to extract three body regions with efficient alignment. Second, we propose a multi-stream contribution framework to fuse feature distance with different contributions and generate the final image similarity descriptor. In addition, we combine re-ID and semantic segmentation. A mask feature is introduced to the proposed framework and we design a contribution feedback module to generate contribution coefficients dynamically. Third, in order to improve re-ID performance, we propose a fragment learning method to optimize the contribution feedback module. Fourth and last, we propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -distribution re-ranking strategy to further improve performance. Our method achieves competitive results on two popular datasets, CUHK03, and Market1501, with rank-1 accuracy of 93.5% and 85.7%. The proposed re-ranking method achieves 2.3% and 2.8% performance boost. The data demonstrate the effectiveness of the proposed multi-stream contribution framework and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -distribution re-ranking strategy.
Quantum Correlation in Squeezed Generalized Amplitude Damping Channels with Memory

Abstract A squeezed generalized amplitude damping (SGAD) channel is a quantum channel that models a general noise process incorporating the effects of bath squeezing, dissipation, and decoherence. In this paper, we analyze the dynamics of quantum entanglement and discord in the SGAD channel with memory. By obtaining a stochastic map defining this noisy quantum channel, we derive the concurrence and discord of Werner-like mixed states sent by successive uses of the channel. It is shown that these quantum correlations can be preserved or even generated depending on the initial channel input states, channel parameters, and the degree of channel memory. In particular, the squeezing effect does not contribute to the dynamics of quantum correlation for singlet-like states under correlated noise.
Peaches and eggplants or. . . something else? The role of context in emoji interpretations

This paper presents the results of an experiment designed to measure interpretations of two emojis oft-discussed in popular culture, the eggplant and the peach. The experiment asked people to judge how sexual an emoji-containing text message was. The context surrounding these messages was manipulated across experimental conditions, altering both the preceding discourse and the presence of a sentence-final wink emoji. Unsurprisingly, the baseline interpretation of both the eggplant and peach emoji is euphemism. When one of these emojis is used in a context that strongly biases towards the non-euphemistic interpretation, ratings for sexualness decrease and variability increases. This suggests that participants are still able to access non-euphemistic interpretations of these emojis, but it must be under specific circumstances and will nonetheless come with a high degree of variability. Wink emojis added to messages containing non-euphemistic food emojis were also rated as more highly sexual (albeit still low on the rating scale), indicating an affective role for this emoji.
Prediction of the Satisfaction With the Student Life, Based on Teaching Competence and Satisfaction With the School

The aim of this article was to assess how students evaluate the professional personality competence of Physical Education Teachers in high school and its relation to student satisfaction and student satisfaction with life itself. In line with these aims, this study was completed as a cross-sectional study, which was carried out in a total 890 PE. Of the research group, 50.3% were female and 49.7% were male. Age average was 15.49 years old for females (SD 1.79) and 15.00 years old for males (SD 2.00). We used a questionnaire featuring a Physical Education Teacher Competence, Intrinsic Satisfaction Classroom Scale and Satisfaction with Life Scale. The results are presented as descriptive statistics, correlations and structural equation modelling analysis showing that students’ perceived competence, predicted self-determined satisfaction, which in turn corresponded to life satisfaction.
Science Education as a Response to the Needs of the Modern Open “Education for Everyone” System

The aim of this work is to analyze the suitability of Science Education in teaching pupils with different educational needs.In the open education, the most important is to understand that the effectiveness of education has an influence on preparing for the job, finding a place on the labor market in accordance with the possibilities and predispositions, but also taking up professional activity, moving away from the help of others and the state.The article presents information on the inclusive trend in modern education.Attention is paid to the positions of the pupils, in particular, the disabled ones, and the teachers whose attitudes, specifics of work, appropriate methods and ways of passing knowledge, skills and competencies appear to be a real impact on the prospective adult life of foster children.The need to introduce farreaching changes in the modern education system has been presented.
Six Simple Steps to Share Your Data When Publishing Research Articles

Abstract In this article, I provide six simple steps for authors to make their data available and meet the most common data publishing requirements for both open‐access and non‐open–access journals. These steps are simple and can easily be integrated into authors' workflows when conducting research and writing research articles. The steps include: deciding authorship of the dataset vs. authorship of the research article; exporting the data needed for all article analyses into archivable (preferably plain text) files using simple table formats; writing the metadata; depositing the data and metadata in a data repository; writing a data availability statement in the article; and citing the dataset in the methods and literature cited sections. Authors who implement these guidelines will reap many benefits for their science while also following best practices for open, robust, and reproducible science.
Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems

Given the glut of information on the web, it is crucially important to have a system, which will parse the information appropriately and recommend users with relevant information, this class of systems is known as Recommendation Systems (RS)-it is one of the most extensively used systems on the web today. Recently, Deep Learning (DL) models are being used to generate recommendations, as it has shown state-of-the-art (SoTA) results in the field of Speech Recognition and Computer Vision in the last decade. However, the RS is a much harder problem, as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their interest. These user-item interactions cannot be fully represented in the Euclidean-Space, as it will trivialize the interaction and undermine the implicit interactions patterns. So to preserve the implicit as well as explicit interactions of user and items, we propose a new graph based recommendation framework. The fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the users. In this paper, we propose the first step, a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as AutoEncoders. To generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.
Improving Dialogue State Tracking by Discerning the Relevant Context

Sanuj Sharma, Prafulla Kumar Choubey, Ruihong Huang. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Learning Class Disjointness Axioms Using Grammatical Evolution

Today, with the development of the Semantic Web, Linked Open Data (LOD), expressed using the Resource Description Framework (RDF), has reached the status of “big data” and can be considered as a giant data resource from which knowledge can be discovered. The process of learning knowledge defined in terms of OWL 2 axioms from the RDF datasets can be viewed as a special case of knowledge discovery from data or “data mining”, which can be called“RDF mining”. The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization. Based on the insight that discovering new knowledge is essentially an evolutionary process, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose the use of Grammatical Evolution, one type of evolutionary algorithm, for mining disjointness OWL 2 axioms from an RDF data repository such as DBpedia. For the evaluation of candidate axioms against the DBpedia dataset, we adopt an approach based on possibility theory.
An LGMD Based Competitive Collision Avoidance Strategy for UAV

Building a reliable and efficient collision avoidance system for unmanned aerial vehicles (UAVs) is still a challenging problem. This research takes inspiration from locusts, which can fly in dense swarms for hundreds of miles without collision. In the locust's brain, a visual pathway of LGMD-DCMD (lobula giant movement detector and descending contra-lateral motion detector) has been identified as collision perception system guiding fast collision avoidance for locusts, which is ideal for designing artificial vision systems. However, there is very few works investigating its potential in real-world UAV applications. In this paper, we present an LGMD based competitive collision avoidance method for UAV indoor navigation. Compared to previous works, we divided the UAV's field of view into four subfields each handled by an LGMD neuron. Therefore, four individual competitive LGMDs (C-LGMD) compete for guiding the directional collision avoidance of UAV. With more degrees of freedom compared to ground robots and vehicles, the UAV can escape from collision along four cardinal directions (e.g. the object approaching from the left-side triggers a rightward shifting of the UAV). Our proposed method has been validated by both simulations and real-time quadcopter arena experiments.
Real-time three-dimensional shape measurement based on color binary fringe projection

A real-time three-dimensional (3-D) shape measurement based on single-shot color binary fringe (CBF) projection is proposed. In the traditional 3-D shape measurement methods based on binary fringe projection, the duty cycle of the binary fringe is always set to 1/2, so as to approximate the sinusoidal fringe by defocusing projection. In the proposed method, the binary fringe with a duty cycle of 1/3 is introduced. It is found that although the duty cycle is not 1/2, a nearly unbroken sinusoidal fringe pattern can be extracted from the captured fringe pattern by a filtering operation in the spatial frequency domain. In order to realize real-time 3-D shape measurement, a composite CBF was designed, in which three monochromatic binary fringes share the same duty cycle of 1/3 but misaligned 1/3 periods one by one are encoded in red (R), green (G), and blue (B) channels. When this composite CBF is projected onto the measured object, only one color-deformed pattern (CDP) needs to be captured and three monochromatic sinusoidal deformed patterns with a phase-shifting of 2π / 3 one another can be extracted from the single-shot captured CDP. So the 3-D shape of the measured object can be reconstructed with three-step phase measuring profilometry. The experimental results show the feasibility and validity of the proposed method. It can either effectively avoid the color overlapping in traditional color sinusoidal fringe or avoid the grayscale nonlinearity of sinusoidal fringe caused by the effect of gamma.
Still ‘few, slow and low’? On the female dimension of technology, labour markets and economic activity: Evidence for the period of 1990-2017

The known in empirical economics question 'Why so Few?Why so Slow?Why so Low?' refers here to the persistently small number of women involved in innovative activities, the slowness of change in the inequalities between women and men in these fields, and women's continuing lower rank in business and academic positions.In developing countries, women`s labour and entrepreneurial activity remains an 'untapped resource' for economic growth.In recent years, the rising proportion of women participating in the labour market has drawn the attention of many scholars.This positive change towards mobilising previously unused human resources is perceived as one of the positive externalities enhanced by the seemingly boundless flow of information and communication technology.This research examines, from a macroperspective, the association between economic deployment of ICT, women`s labour market participation, and economic growth in 64 developing countries between 1990 and 2017.We rely on the macrodata extracted from the World Bank Development Indicators (2018), the World Bank Enterprise Survey, the World Development Reports and the World Telecommunication/ICT Indicators Database (2018).Our methodological framework, in addition to standard descriptive statistics, combines time trends, graphical non-parametric analysis and panel vector-autoregressive models.
A Unified Framework for Bug Report Assignment

It is typically a manual, time-consuming, and tedious task of assigning bug reports to individual developers. Although some machine learning techniques are adopted to alleviate this dilemma, they are mainly focused on the open source projects, which use traditional repositories such as Bugzilla to manage their bug reports. With the boom of the mobile Internet, some new requirements and methods of software testing are emerging, especially the crowdsourced testing. Unlike the traditional channels, whose bug reports are often heavyweight, which means their bug reports are standardized with detailed attribute localization, bug reports tend to be lightweight in the context of crowdsourced testing. To exploit the differences of the bug reports assignment in the new settings, a unified bug reports assignment framework is proposed in this paper. This framework is capable of handling both the traditional heavyweight bug reports and the lightweight ones by (i) first preprocessing the bug reports and feature selections, (ii) then tuning the parameters that indicate the ratios of choosing different methods to vectorize bug reports, (iii) and finally applying classification algorithms to assign bug reports. Extensive experiments are conducted on three datasets to evaluate the proposed framework. The results indicate the applicability of the proposed framework, and also reveal the differences of bug report assignment between traditional repositories and crowdsourced ones.
PAI-WSIT: An AI Service Platform With Support for Storing and Sharing Whole-Slide Images With Metadata and Annotations

Advances in whole-slide images (WSI) technology can promote the pathology environment based on digital imaging and make a very compelling case as part of the clinical workflow.Furthermore, the applications of artificial intelligence (AI) for morphometric image analysis are accelerating the transition of pathology into a more quantitative science.However, several challenges, such as storing, sharing, visualizing, and analyzing large-size WSI files and the acquisition of annotations from pathologists for WSIs still exist in working with WSIs in AI researches.We, therefore, developed a web-based platform PAI-WSIT: Pathologists Annotated and AI-empowered WSI Database for Tumors, which provides storing, sharing, and visualizing services for WSIs with annotations.In addition, PAI-WSIT also supports the management of annotations from different pathologists and integrates an AI service for detecting malignant regions in colorectal WSIs.The platform will provide collaborations between the computer scientists and research pathologists that are of utmost importance for the development of relevant algorithms and computer-aided prognosis.PAI-WSIT is freely accessible from http://www.paiwsit.com.
PENGEMBANGAN MEDIA PEMBELAJARAN POP-UP BOOK BERBASIS AUDIO PADA MATERI BANGUN DATAR SEGIEMPAT DI SMP

This study aimed to develop the learning media and investigate d the effectiveness of audio-based Pop-Up Book media on the two-dimensional rectangular shape . This media was tested twice in a small group of 6 students and in a large group of 25 students . Respondents were the 7 th grade students . The result of expert validation showed the average percentage of 88,16 % and was considered valid, thus the learning media was allowed to be used and tested. The analysis of effectiveness in small and large groups in a row revealed that students ’ respond was very good, with the percentage of 88.1% and 88.87%. In addition, the activities of students in the class were very good, showed by the percentage of 92% and 86.8% . Finally, the classical completeness of the learning outcomes obtained the percentage of 100% from 6 students and 92% from 23 students who completed the test . Therefore, it can be concluded that the application of audio-based Pop-Up Book on the two-dimensional rectangular shape was effective for Junior High School students.
Deep learning-based three-dimensional segmentation of the prostate on computed tomography images

Segmentation of the prostate in computed tomography (CT) is used for planning and guidance of prostate treatment procedures. However, due to the low soft-tissue contrast of the images, manual delineation of the prostate on CT is a time-consuming task with high interobserver variability. We developed an automatic, three-dimensional (3-D) prostate segmentation algorithm based on a customized U-Net architecture. Our dataset contained 92 3-D abdominal CT scans from 92 patients, of which 69 images were used for training and validation and the remaining for testing the convolutional neural network model. Compared to manual segmentation by an expert radiologist, our method achieved 83 % ± 6 % for Dice similarity coefficient (DSC), 2.3 ± 0.6 mm for mean absolute distance (MAD), and 1.9 ± 4.0 cm3 for signed volume difference (ΔV). The average recorded interexpert difference measured on the same test dataset was 92% (DSC), 1.1 mm (MAD), and 2.1 cm3 (ΔV). The proposed algorithm is fast, accurate, and robust for 3-D segmentation of the prostate on CT images.
Ant Lion Optimizer for Texture Classification: A Moving Convolutional Mask

Texture classification is an important issue for a number of applications in machine vision, which could be addressed through learning texture features by using a convolutional mask. However, the traditional convolutional mask has a single orientation, and the learning ability is limited. In addition, the optimization process often falls into the local optima, and the discrimination capacity of the learned mask is unsatisfactory. Thus, a novel moving convolutional mask is presented to enhance the discrimination of specific texture features in the proposed approach. Furthermore, how to achieve the satisfactory convolutional mask is considered as a combinatorial optimization problem and acquired by maximizing the texture energy by using ant lion optimizer (ALO). The proposed approach was tested on some public images, and the results were compared with those of the state-of-the-art approaches. The experimental results showed that ALO has strong optimization ability, and the proposed method is robust, adaptive and superior to the improved grey level co-occurrence matrix (GLCM), directional statistical Gabor filter and Tuned convolutional mask in terms of the fitness value and classification accuracy which has reached 27 and 91%, respectively, for all images.