Reviewerly/exHarmony huggingface dataset:
Image Captioning with Clause-Focused Metrics in a Multi-modal Setting for Marketing

Automatically generating descriptive captions for images is a well-researched area in computer vision. However, existing evaluation approaches focus on measuring the similarity between two sentences disregarding fine-grained semantics of the captions. In our setting of images depicting persons interacting with branded products, the subject, predicate, object and the name of the branded product are important evaluation criteria of the generated captions. Generating image captions with these constraints is a new challenge, which we tackle in this work. By simultaneously predicting integer-valued ratings that describe attributes of the human-product interaction, we optimize a deep neural network architecture in a multi-task learning setting, which considerably improves the caption quality. Furthermore, we introduce a novel metric that allows us to assess whether the generated captions meet our requirements (i.e., subject, predicate, object, and product name) and describe a series of experiments on caption quality and how to address annotator disagreements for the image ratings with an approach called soft targets. We also show that our novel clause-focused metrics are also applicable to other image captioning datasets, such as the popular MSCOCO dataset.
Empirical Study to Evaluate the Performance of Classification Algorithms on Public Datasets

In today’s world, a huge amount of data is stored in the form of electronic documents in the World Wide Web. Text classification algorithms have been widely used for classifying those text documents into a fixed number of predefined classes. The applicable scopes and their performances of these algorithms are different. Therefore, finding an appropriate algorithm for a dataset is becoming a significant emphasis for researchers to solve practical problems quickly. This paper puts forward an experimental evaluation of five significant text classification algorithms with each other and with TF and TF-IDF feature selection methods built using decision tree (C5.0), support vector machine, K-nearest neighbor, Naïve Bayes, and neural network on four public datasets, namely 20news-bydate, ohsumed-first-20000-docs, Reuters 21578-Apte-90 Cat, and 20 Newsgroup. The experimental results are examined from multiple perspectives and summarized to provide usefulness of different algorithms on different datasets.
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions

Networks are powerful data structures, but are challenging to work with for conventional machine learning methods. Network Embedding (NE) methods attempt to resolve this by learning vector representations for the nodes, for subsequent use in downstream machine learning tasks. Link Prediction (LP) is one such downstream machine learning task that is an important use case and popular benchmark for NE methods. Unfortunately, while NE methods perform exceedingly well at this task, they are lacking in transparency as compared to simpler LP approaches. We introduce ExplaiNE, an approach to offer counterfactual explanations for NE-based LP methods, by identifying existing links in the network that explain the predicted links. ExplaiNE is applicable to a broad class of NE algorithms. An extensive empirical evaluation for the NE method `Conditional Network Embedding' in particular demonstrates its accuracy and scalability.
Capturing Discriminative Attributes Using Convolution Neural Network Over ConceptNet Numberbatch Embedding

A semantic representation of text helps us understand the lexical association between words. Capturing these associations becomes an integral part of perceiving any language. One such fundamental property that expresses these associations is ‘similarity.’ This property shows consistency in comprehending similar words in the same manner. However, seldom it falls short on specific tasks where the lexical similarity in itself is not sufficient enough to validate the semantic representations. In this paper, the objective is to capture such semantic distinctions. It is based on the shared task ‘capturing discriminative attributes’ conducted in SemEval-2018. Our team participated in the task and held an F1 score of 0.658 with GloVe representation. An extension to this work is taken up in this paper where a new embedding known as ConceptNet Numberbatch is explored. The ConceptNet word embedding in overall showed improvement in the scores with the previous rule-based feature representation. The model is further tuned over certain hyperparameters, which improved the score to as much as 6%. A comparison is also put forth here with another prominent embedding like FastText. The ConceptNet model achieved a near par score with the state of the art, based only on a simple ensemble of features as representation.
Nanoconfinement: 1D Nanoconfined Ordered‐Assembly Reaction (Adv. Mater. Interfaces 8/2019)

In article number 1900104, Shijie Liu, Xiqi Zhang, and Lei Jiang summarize the recent advances in 1D nanoconfined chemical reactions. A new concept of ordered-assembly reaction is proposed, by combining quantum-confined superfluid with frontier molecular orbital theory, to understand the inherent mechanism of high-performance 1D nanoconfined chemical reactions. The prospective of ordered-assembly reaction is also presented.
The Analysis of the Learning Efficiency of Bidikmisi Students

This study aims to determine the factors that influence student learning outcomes and the level of efficiency of the use of these inputs. Respondents in this study were the recipients of the 2015 - 2017 Bidikmisi class at the PIPS Department of the University of Jambi FKIP, namely 53 people. This study uses quantitative methods. Data were analyzed using the Stochastic frontier production function approach. The results showed that, first the use of books / modules had a positive and significant effect on the learning output of bidikmisi recipient students. Second the use of material photocopy material has a positive and significant effect on learning output. And, third, the use of pulses for internet access has a positive and significant effect. The level of student learning efficiency in using learning inputs averaged 94 percent. Student learning inefficiency is influenced by organizational activities and interaction with Bidikmisi managers, significant at Î± = 5 percent.&#x0D;
Location Based Services (LBS) for Searching Tire Repair Location in Banjarbaru City Based on Android

The tire repair location is one of the determining factors sought by motorized vehicle users if they are experiencing a tire leak in the middle of a trip. This makes vehicle users panic and think to immediately look for the nearest tire repair location. Banjarbaru City has 5 sub-districts and 12 villages, and is the center of the South Kalimantan Province administration. Given the large number of settlements, especially now that there are many housing complexes in Banjarbaru, this automatically makes the number of new roads opened by the local government. Actually this can be an opportunity for tire repair entrepreneurs to open tire repair locations in several places, because nowadays there are still very few workshops or tire repair locations in the new residential area.Location Based Service System, or better known as LBS, combines the process of a mobile service with the geographical position of its users. The important point is when the target position, where a target might be an LBS user or another entity that is incorporated in a service. This research leads to android based applications and services that provide information of tire repairlocation, to make it easier for motorbike riders to find tire repair locations based on available facilities.
Deep-Learning-Based Stance Detection for Indian Social Media Text

Stance detection is one step ahead of sentiment analysis where author’s stance for certain topics such as an event, personality, or a government policy is considered. The author’s stance could be in “favor” or “against” the topic under consideration. A myriad amount of data is being accumulated via various social media platforms. This work considers the Kannada–English code-mixed aspect of social media text. The corpus was collected based on various trending topics such as “Bengaluru molestation,” “currency ban”, etc. using particular word phrases. The user comments on social media platform Facebook was used to collect the corpus. The collected dataset was represented using different techniques such as bag of tricks, word embedding like Word2vec and GloVe, and pre-trained embeddings. These representations were further used in combination with various deep learning architectures such as convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM). The results for various combinations are listed.
STUDENTS’ COORDINATION SKILLS TESTING IN PHYSICAL EDUCATION: ICT APPLICATION

The informatisation of modern education necessitates the development of a new model of physical education, based on interdisciplinary integrative processes using ICT. This paper considers using ICT for testing coordination skills in physical education of students. The main idea of integrating ICT into the monitoring is to intensify the pedagogical control process in order to reinforce its information capabilities. The purpose of the paper is to justify and show how to implement ICT in students’ coordination skills monitoring, while they undergo physical education. Foremost, it concerns a personalist approach to education and training, and an integrated approach in the implementation of monitoring and evaluating its results. The device that measures movement is the result of a research aimed at integrating ICT into students’ coordination skills testing. Its function is to provide a high level of control and assessment of students’ coordination skills. The device is based on the latest advances in energy-saving technologies, using optical sensors, multifunctional microcontroller development boards and software developed in Proteus Design Suite. The developed infrastructure of the device of movement control provides quick processing of received testing results using the methods of multidimensional mathematical analysis as well as the formation of integrated database for coordination skills controlling. The interface used for this provides a high level of ergonomic properties of the developed device and the possibility of effective work of specialists with the data of testing. The effectiveness of using the designed device in testing students' coordination skills is achieved through the ease of use and compactness of the device, student-friendly testing procedure and the efficiency and reliability of control. Implementation of the unique possibilities of ICT provides state-of-the-art didactic method of intensification of control process in physical education of students.
Electrical conductivity of the crust and mantle of the East European platform in the western part of Ukraine from 2D inversion

2D inversion of the magnetotelluric and magnetovariational data along 40 profiles made it possible to create overview model of electric resistivity for the territory between eastern longitudes 28° and 36° and by the northern latitudes 47.8° and 52°. Analysis of sections of resistances in the depth interval from 5 to 105 km indicate the absence of objects of low resistance (less than 100 Ohm × m — OLR), the dimensions of which would allow them to be called a layer. The geometrical parameters of the OLR indicate their locality. Along the border of the East European platform are geoelectric objects of low and high (more than 4000 Ohm × m — HO) resistances, at low depths of 20 km low-resistance objects prevail. Comparison of anomalous geoelectrical objects (OLR and HO) with fault tectonics, metallogeny, oil and gas content and modern activation shows that most of the OLR are confined to fault nodes, in which nodes and areas are often concentrated of minerals having an electronic type of conductivity (graphite, polymetallic ore and noble metals). The areas promising for oil and gas content, identified on the maps of various authors, are partially consistent with the Yavoriv, Lokachi, Ternopil and Chernivtsi zones of modern activation, and in this case are accompanied by conducting objects. It is interesting to note the consistency of geological and geophysical conclusions about the prospects of the North-West oil and gas province with the presence of the OLR. Depth and power of conducting objects allows to assume the presence of a gene-tic connection of various minerals not only with sedimentary metamorphosed rocks, but also with the presence of deep fluids. It seems that the results obtained can contribute to the development of ideas about the deep structure of the studied territory and its connection with both existing deposits of various minerals and during the spillage of new ones.
Evaluating Recurrent Neural Network Explanations

Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs. The goal of these methods is to understand the network's decisions by assigning to each input variable, e.g., a word, a relevance indicating to which extent it contributed to a particular prediction. In previous works, some of these methods were not yet compared to one another, or were evaluated only qualitatively. We close this gap by systematically and quantitatively comparing these methods in different settings, namely (1) a toy arithmetic task which we use as a sanity check, (2) a five-class sentiment prediction of movie reviews, and besides (3) we explore the usefulness of word relevances to build sentence-level representations. Lastly, using the method that performed best in our experiments, we show how specific linguistic phenomena such as the negation in sentiment analysis reflect in terms of relevance patterns, and how the relevance visualization can help to understand the misclassification of individual samples.
ST-Sem: A Multimodal Method for Points-of-Interest Classification Using Street-Level Imagery

Street-level imagery contains a variety of visual information about the facades of Points of Interest (POIs). In addition to general morphological features, signs on the facades of, primarily, business-related POIs could be a valuable source of information about the type and identity of a POI. Recent advancements in computer vision could leverage visual information from street-level imagery, and contribute to the classification of POIs. However, there is currently a gap in existing literature regarding the use of visual labels contained in street-level imagery, where their value as indicators of POI categories is assessed. This paper presents Scene-Text Semantics (ST-Sem), a novel method that leverages visual labels (e.g., texts, logos) from street-level imagery as complementary information for the categorization of business-related POIs. Contrary to existing methods that fuse visual and textual information at a feature-level, we propose a late fusion approach that combines visual and textual cues after resolving issues of incorrect digitization and semantic ambiguity of the retrieved textual components. Experiments on two existing and a newly-created datasets show that ST-Sem can outperform visual-only approaches by 80% and related multimodal approaches by 4%.
Distance Communication of the Lecturer and Students in the Higher Education

The article suggests an information-pedagogical model of a teacher-student interaction via telecommunication means. The necessity of information technologies implementation into the educational process is determined by the changing role of the teacher in today’s education. The suggested model may help to develop the theoretical grounds for comprehensive management of students’ educational activity distantly, the model may be also used for the work with the students who take full-time course.
Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering. The extraction of features gave a high distinguishability and helped GA reach the solution more accurately and faster.
INTEGRATED APPROACH TO THE EFFECTIVE PROVISION OF HIGHER EDUCATION EDUCATIONAL PROGRAMS WITH LIBRARY AND INFORMATION RESOURCES

Educational and methodological support of the main professional educational programs of higher education (hereinafter referred to as MPEP) is an integral component and an important condition for the licensing, implementation and accreditation of these programs. An analysis of the publications of the last five years shows that universities and libraries of educational institutions are in search of modern software alternatives to the book supply modules included in automated library information systems (hereinafter — ABIS). In Herzen Stage Pedagogical University the launch of the Book Supply module, created as an external online software product using data streams from various sources, was the impetus for the formation of a whole complex of automated modules that allow real-time monitoring of the effective use in the educational process of both the printed fund and the fund of electronic publications presented on the platforms of publishers and aggregators, access to which is provided by subscription. Based on the data provided by the software package, the authors show the need and importance of continuous monitoring of the state of educational and methodological support of the educational process in order to respond quickly, adjust printed and electronic funds, interact meaningfully with departments and maintain the balance of printed and electronic information appropriate to the current state of development information technology and user readiness to perceive different types of information. The technique presented by the authors allows to form a harmonious content of educational and methodological support.
A Comparative Analysis of Machine Comprehension Using Deep Learning Models in Code-Mixed Hindi Language

The domain of artificial intelligence revolutionizes the way in which humans interact with machines. Machine comprehension is one of the latest fields under natural language processing that holds the capability for huge improvement in artificial intelligence. Machine comprehension technique gives systems the ability to understand a passage given by user and answer questions asked from it, which is an evolved version of traditional question answering technique. Machine comprehension is a main technique that falls under the category of natural language understanding, which exposes the amount of understanding required for a model to find the area of interest from a passage. The scope for the implementation of this technique is very high in India due to the availability of different regional languages. This work focused on the incorporation of machine comprehension technique in code-mixed Hindi language. A detailed comparison study on the performance of dataset in several deep learning approaches including End to End Memory Network, Dynamic Memory Network, Recurrent Neural Network, Long Short-Term Memory Network and Gated Recurrent Unit are evaluated. The best suited model for the dataset used is identified from the comparison study. A new architecture is proposed in this work by combining two of the best performing networks. To improve the model with respect to various ways of answering questions from a passage the natural language processing technique of distributed word representation was performed on the best model identified. The model was improved by applying pre-trained fastText embeddings for word representations. This is the first implementation of machine comprehension models in code-mixed Hindi language using deep neural networks. The work analyses the performance of all five models implemented, which will be helpful for future researches on Machine Comprehension technique in code-mixed Indian languages.
My Safetipin Mobile Phone Application: Case Study of E-Participation Platform for Women Safety in India

Women Safety is a major public challenge in India which is the world’s second most populous nation making it the most dangerous country for women in the world. While the government and non-government organizations are trying their level best to fight against this menace, there is a new mobile-based application named “My Safetipin” which attempts to create online participation platform for women in India to help each other in identifying locations which are unsafe for them so that the situation can be improved. Use of Digital Democracy platforms in the form of a mobile application for improving societal issues shows the increasing utilization of the Internet in a country where the Internet mobile is the most favored tool for using the Internet outreach is still 20.25% in Rural India while 65% in Urban India according to Internet and Mobile Association of India (IAMAI) report based on census 2011. For our research we have used mixed methods which includes Interface Accessibility Analysis, Usability Testing as well as GIS mapping of this mobile phone availability followed by secondary data analysis of user review over of this mobile application over android play store application. This research will help us in understanding whether e-participation platforms over mobile platform really help a citizen in resolving women safety issue resolve issue democratically or simply help in providing an additional platform to create more sensitization.
Understanding Social Costs in Online Question Asking

Various social media sites and online communities provide new channels for people in need to ask questions and seek help. However, individuals may still encounter psychological barriers that deter solicitation for assistance, which is more formally described as"social costs''. For example, it can be %concerns people have when seeking help can be the concerns of burdening others, the obligation of reciprocation, etc. To understand what could reduce social costs, we conducted a study in the context of Question-Answering (QA) and investigated the following three factors inspired by literature: anonymity (posting a question anonymously), recommendation (having the system handle the question routing), and ephemerality (allowing questions to be visible only for a short period). We built a QA platform to support these three features and conducted a randomized within-subject experiment to test their effects on social costs of posting questions. Results suggest the presence of anonymity, recommendation, and ephemerality reduces the social costs which provides design implications for future community building.
Flux-qubit readout in the persistent-current basis at arbitrary bias points

Common flux-qubit readout schemes are qubit dominated, meaning they measure in the energy eigenbasis of the qubit. For various applications, measurements in a basis different from the energy eigenbasis are required. Here we present an indirect measurement protocol, which is detector dominated instead of qubit dominated, yielding projective measurements in the persistent-current basis for arbitrary bias points. We show that with our setup it is possible to perform a quantum nondemolition measurement in the persistent-current basis at all flux bias points with fidelities reaching almost $100%$.