Reviewerly/exHarmony huggingface dataset:
Evaluating visual analytics for text information retrieval

Retrieving information from document collections is necessary in many contexts, for example, researchers wish to retrieve papers on a research topic, physicians search for patient records related to a certain condition, police investigators seek for relationships in criminal reports. Common to these scenarios are users in need of identifying relevant textual information in a document collection. The task is challenging, especially when users hope for a retrieval process that misses none or very few of the relevant documents. Visual Analytics (VA) approaches are often advocated to support document retrieval tasks. VA relies on integrating interactive visualizations and machine learning algorithms so that a domain expert can gradually steer a system into identifying the relevant documents. As an example, TRIVIR is a state-of-the-art system that allows exploring a corpus while providing feedback to a classifier that suggests potentially relevant documents to a reference query document. Assessing VA-supported Information Retrieval (IR) strategies is also challenging, as using these systems typically involves many conceptual and practical aspects and text retrieval tasks can demand considerable cognitive effort. In this paper, we present results from observational studies on VA-supported text information retrieval. We conducted sessions with graduate students and researchers using TRIVIR to explore scientific papers for purposes of literature review. A first study allowed us to collect opinions and identify some usability issues and practical limitations of the available implementation. After handling some critical issues observed at the interface level, we conducted a second round of sessions in order to collect further user opinions regarding a retrieval process assisted with VA. We concluded that most users have a very positive view of the system's usability and its ability to facilitate their retrieval tasks. Nonetheless, we also learnt that a proper introduction to the role of the interface elements is important and that conveying the underlying conceptual model and its limitations can be difficult. We observed considerable variation in user assessment of the specific functionalities and some users may face practical difficulties in using the system autonomously in an optimal way.
Deep Learning with Data Augmentation to Add Data Around Classification Boundaries

Data augmentation methods are used as a technique to improve generalization by increasing the number of training data in image classification. However, most of these methods are not a data driven algorithm, the degree of improvement of generalization ability by performing these data augmentation methods differs between the domains of image data for training. Generative models are researched to use for augmenting data recently. In particular, Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) that can generate clean image get attention as an excellent innovation in machine learning. As GANs extension method, there is a method called CGANs (Mirza and Osindero, 2014) that can be used for data augmentation. When enough training data for each class are not prepared for classification model, the same is true for training CGANs. In such case, CGAN generates noisy images. This makes a classification model to underfit to the original training data. Moreover, when a CGAN approximates the training data distribution, the CGAN generates new training data in the same region where training data densely exist. In such case, augmented data can’t reduce overfitting on the original training data. Therefore, our research contributes to augment data which meets these two requirements. In this study, we propose a method to generate data by the class specific GAN with small training data and selectively add generated data to the training data set that improves classification accuracy by using the entropy of the classification model. The feature of the proposed method is that it focuses on the positional relationship between data and the classification hyperplane in deep learning. In the proposed method, the entropy of the classification model is used to measure the positional relationship between the classification boundary and the data. As a result, the generalization performance is improved by adding the data around the classification boundary as new training data.
TV Interaction as a Non-Invasive Sensor for Monitoring Elderly Well-Being at Home

The number of technical solutions to remotely monitoring elderly citizens and detecting hazard situations has been increasing in the last few years. These solutions have dual purposes: to provide a feeling of safety to the elderly and to inform their relatives about potential risky situations, such as falls, forgotten medication, and other unexpected deviations from daily routine. Most of these solutions are based on IoT (Internet of Things) and dedicated sensors that need to be installed at the elderly's houses, hampering mass adoption. This justifies the search for non-invasive technical alternatives with smooth integration that relying only on existent devices, without the need for any additional installations. Therefore, this paper presents the SecurHome TV ecosystem, a technical solution based on the elderly's interactions with their TV sets-one of the most used devices in their daily lives-acting as a non-invasive sensor enabling one to detect potential hazardous situations through an elaborated warning algorithm. Thus, this paper describes in detail the SecurHome TV ecosystem, with special emphasis on the warning algorithm, and reports on its validation process. We conclude that notwithstanding some constraints while setting the user's pattern, either upon the cold start of the application or after an innocuous change in the user's TV routine, the algorithm detects most hazardous situations contributing to monitor elderly well-being at home.
Model-based Data-Complexity Estimator for Deep Learning Systems

Verification of deep learning (DL) systems in various situations is growing in importance as they are being used in wider areas than ever before. To prevent unintentional behavior of a DL system, it is important to feed an unfamiliar input to simulate situations in which a model in the DL system can behave abnormally. Such an input is different from outliers since we consider only statistical characteristics to detect outliers and the response of the model is not taken into account. This paper proposes a method of evaluating a complexity of an input without a label for a given trained model for analyzing a dataset to find inputs unfamiliar to the model and estimating the similarity between two datasets. Specifically, we use an output of neurons in the model as an embedded representation of data. We regard typical patterns in the representations of data in a training dataset as features the model obtained from the training dataset. We then calculate a complexity of an input as a distance between the representation of the input and combination of the features. The obtained complexity can be applied for multiple purposes such as test guiding by extracting inputs that are complex for the model from a dataset, analyzing a training dataset to find an inappropriate input, and measuring the similarity between two datasets using complexity distribution. Experimental evaluations showed the effectiveness of the complexity calculated using the proposed method in analyzing a dataset.
Efficient binary descriptor-based implementation of fuzzy image registration algorithms on LabVIEW

Image registration algorithm is the process of establishing a mapping between image in a sequence and another overlapping one. Sufficient overlap ratio in the frames of the sequence is a necessary condition to find that mapping. Image mosaic is one of the important applications for which image registration may be used. Also, object tracking is another interesting image processing task that deals with finding mapping relation between successive images. Local features such as points are generally used to simplify images processing through taking significant pixels in the image and applying some operations for getting information about the whole image. Several techniques were proposed for finding corresponding features between images; among which descriptors-based matching is the most efficient approach. The suggested floating-points descriptors such as scale invariant feature transform and speed up robust feature are generally good in term of performance but require a long time; in most cases, they imply refinement methods for removing false matches, thus increasing the time cost. Therefore, we propose a fuzzy matching method that combines two binary descriptors, which are local binary patterns and Fast REtinA key point (FREAK) for image registration purpose. Our algorithms are tested on Matlab for different real scenes and satisfying results were obtained. To make program execution simple and to easily do numerical comparisons, a graphical interface has been implemented on LabVIEW platform.
Using social networks for social upbringing

The article addresses the potential of using social networks in social education and for solving the problems of youth socialization. There is an urgent social need for the harmonious development of the child's personality, including in the Internet environment. At the same time, there is insufficient knowledge about the organization of education in social networks in science. The article identifies the experience of successful interaction between teachers and students on the Internet and ways of organizing event situations in social networks. The study was conducted in February-October 2020 and analysed the content of open official accounts of educational organizations in the social networks ‘VKontakte’, Instagram and documents of educational organizations. The complex use of observation methods, quantitative data processing, expert assessments was aimed at identifying forms of educational activity that are promising for implementation online in social networks. The study showed that, despite the variety of topics and styles, the content of official school accounts resembles a list of news about holidays and other public events. The accounts do not contain materials that could cause vivid emotions and sensations in children, become a source of experiences, value attitudes, experience of interacting with people. Consequently, schools do not provide children with opportunities for self-knowledge, self-determination, self-realization, and do not support their initiatives in social networks. The article recommends that schools expand the practice of organizing networked educational events. For this, they need to create groups of different ages for children and adults for joint planning, organizing, conducting, summing up creative deeds. The maintenance of a thematic Instagram account of a school is offered as an example of a successful subject-subject interaction between a teacher-educator and students in social networks.
Stochastic quartet approach for fast multidimensional scaling

Multidimensional scaling is a statistical process that aims to embed high-dimensional data into a lower-dimensional, more manageable space.Common MDS algorithms tend to have some limitations when facing large data sets due to their high time and spatial complexities.This paper attempts to tackle the problem by using a stochastic approach to MDS which uses gradient descent to optimise a loss function defined on randomly designated quartets of points.This method mitigates the quadratic memory usage by computing distances on the fly, and has iterations in O(N ) time complexity, with N samples.Experiments show that the proposed method provides competitive results in reasonable time.Public codes are available at https://github.com/PierreLambert3/SQuaD-MDS.git. Multidimensional scaling and its limitationsDimensionality reduction (DR) is the process of mapping high-dimensional (HD) observations into a lower-dimensional (LD) space such that the LD embedding is a faithful representation of the HD data.The main DR uses are in machine learning, to curb the curse of dimensionality, and in visualisation.Mapped data can reveal structures that would lay hidden from the human perception if left in HD.Typically, some information is lost by the DR and, therefore, each DR method has a take on what kind of information should be preserved and what can be lost.Used frequently in visualisation, t-SNE [1] aims at retaining the neighbourhood of each point according to a distance metric and a perplexity, which reflects the size of the neighbourhood to preserve.While t-SNE excels at retaining local structures, sufficiently remote points tend to be considered equally distant by the algorithm and, therefore, the larger-scale structures can be distorted.Such distortions can lead to erroneous conclusions by the human user, who might overestimate the dissimilarity between two clusters that are distant in the LD embedding.For this reason, using multiple DR paradigms in conjunction is a good practice in visualisation: another embedding that preserves distances instead of neighbourhoods would have prevented this erroneous conclusion.This paper considers metric multidimensional scaling (MDS): a DR technique that produces a LD embedding such that the pairwise distances in LD reflect those in HD.MDS minimises a cost function which, in its simplest form, is the sum of the squared differences between distances in HD and the Euclidean distances in LD.A common strategy to optimize this cost function is based on 417
SAR Image Registration Based on ROEWA-Blocks and Multiscale Circle Descriptor

Given the imaging characteristics of synthetic aperture radar (SAR) images and the inherent speckle noise in them, scaleinvariant feature transform (SIFT) based algorithms are unable to perform satisfactorily.To improve registration efficiency between SAR images, we propose a robust and efficient registration method with three main contributions.First, considering sudden dark patches appearing in SAR images, we propose the ratio of exponentially weighted average blocks to suppress the sudden dark patches and better adapt to different test images.This new operator called blocks of the ratio of exponentially weighted averages (ROEWA-B), divides the processing windows of ROEWA into blocks, which can not only reduce speckle noise but also retain more edge details compared to ROEWA when sudden dark patches appear.Second, for outlier removal, we present an approach using the minimum moment map to remove erroneous keypoints.Finally, based on the gradient location orientation histogram descriptor, we propose a novel multiscale circle descriptor, which combines scale change information to give weights to feature points at different scales.Experimental results for various thresholds and evaluations demonstrate the advantage and robustness of our method in registration.
Culture-inspired Multi-modal Color Palette Generation and Colorization: A Chinese Youth Subculture Case

Color is an essential component of graphic design, acting not only as a visual factor but also carrying cultural implications. However, existing research on algorithmic color palette generation and colorization largely ignores the cultural aspect. In this paper, we contribute to this line of research by first constructing a unique color dataset inspired by a specific culture, i.e., Chinese Youth Subculture (CYS), which is an vibrant and trending cultural group especially for the Gen Z population. We show that the colors used in CYS have special aesthetic and semantic characteristics that are different from generic color theory. We then develop an interactive multi-modal generative framework to create CYS-styled color palettes, which can be used to put a CYS twist on images using our automatic colorization model. Our framework is illustrated via a demo system designed with the human-in-the-loop principle that constantly provides feedback to our algorithms. User studies are also conducted to evaluate our generation results.
RETRACTED ARTICLE: Deep Learning-Assisted Heuristic Data Management in the E-Commerce Recommendation System

Nowadays, recommendation systems are being used by an ever-increasing number of E-commerce platforms to help customers locate goods to purchase. Recommendation systems aid e-commerce sites in increasing sales and analysis at market-leading locations. Recommendation systems leverage product information, expert knowledge from customer behavior to direct customers around the often challenging task of locating products they want. Further, this paper proposed the Deep Learning-Assisted Heuristic Data Management (DLHDM) for customer purchasing data management to improve e-commerce sales. The proposed DLHDM to identify customer data correctly described, categorized, and stored for customers can quickly find what they are looking for be positive in buying decisions. Heuristic data management is useful for the development of buyer individuals or shopper profiles. This helps users to decide which product they like best and when users typically buy customer preferences. The results show the effectiveness of recommender systems and consumer recommendations for their corresponding trade-offs.
Ecommerce Chatbot (Online Shopping App)

The main aim of the paper is to introduce a online shopping app with integrated chatbot that saves the time of the user and help to resolve their query at a moment. Online shopping app consist of different functionalities and features such as proper categorization of the product , trending results, recent searched product etc. that will save the time of the customer and customer/user will have clear idea about the product and will have the benefit to choose the product wisely. The chatbot will give the reply to the customer query in the form of “voice message”, “chat support” and hence visually impaired people can use the app easily and will have the clear idea about the product. The chatbot will provide relevant information to the user as per their query. This application works in various types of browser as well as in various network.
Distance education during the COVID-19 pandemic

An existential problem for modern society is the stability problem when the ambiguity of extended forecasts becomes characteristic even for the foreseeable future. Consideration of this problem from the perspective of the digital space is especially important for two reasons. First, the bulk of modern communication processes takes place in the network space. Second, the processes of searching, consuming, exchanging information and acquiring new knowledge are directly or indirectly connected today with digital resources. This article deals with the analysis of the specifity of the implementation of distance learning in the context of the Covid-19 pandemic. The empirical study base is a group interview with students of Kazan Federal University which has highlighted students’ attitudes over online education in the face of a pandemic. The analysis of the respondents’ viewpoints reveals several common stances towards understanding their individual educational strategy, and the results of the study enable to determine the specific features of the implementation of training for students of Kazan Federal University, to show the features of the influence of distance learning on the choice of educational technologies. The differences between the students’ attitudes over the development of remote education are revealed.
Visual Analysis of Multidimensional Big Data: A Scalable Lightweight Bundling Method for Parallel Coordinates

Varied edge bundling methods have been used to reduce visual clutter in parallel coordinates plots (PCP). However, existing edge-bundled PCP do not scale well for visual analysis of multidimensional big data and often overplot the bundles in the area near the axes. In this study, we propose a scalable lightweight bundling method to support visual analysis of multidimensional big data in PCP. It helps the users discover trends and detect outliers in the data by bundling the edges between each two adjacent axes independently. We integrate human judgments into the two-dimensional data binning by novel interactions to accelerate the clustering process of the data. We use the frequency-based representation to render the clusters as histogram-like bundles to reveal the distribution of the data and eliminate the overplotting of the bundles. Based on our method, we build a lightweight web-based visual analytics system for exploring multidimensional big data in PCP. The scalability analysis of our method shows that its clustering time increases linearly with the size of the data. Its rendering time is independent of the size of the data. It can cluster and visualize 1 million data records with 6 dimensions in about 1 second in web-based visualization without pre-computation of the data or hardware-accelerated rendering. We conduct two case studies and a user study to compare our method with classic PCP and two state-of-the-art edge-bundled PCP. The results show that our method is more efficient and effective for visually analyzing multidimensional big data.
Kannada Document Classification Using Unicode Term Encoding Over Vector Space

Today, there is a great demand for extracting useful information and ability to take actionable insights from heaps of raw textual data. The processing of regional language texts, notably with low resources is challenging. Especially for Indian Regional Languages (IRL), language text processing is in huge requirement with regard to the applications of natural language processing (NLP) and text analytics. As a part of regional language processing and text analytics text documents classification for IRL are yet to be explored. Kannada is one of the official Indian regional languages. In this paper, the new benchmark Kannada document’s dataset is created and analyzed using machine learning algorithms. This paper proposes an explicit Unicode term encoding based Kannada document classification, using the vector space model. Both term frequency (TF) and term frequency-inverse document frequency (TF-IDF), statistical measures are used for classification of Kannada documents using K-NN and SVM classifiers. SVM (linear) classifier performs better than the K-NN classifier in classifying the Kannada documents with 98.67% mean accuracy over K-Fold experiments.
Compare Encoder-Decoder, Encoder-Only, and Decoder-Only Architectures for Text Generation on Low-Resource Datasets

Natural language generation (NLG) tasks have received significant research attention in recent years. For tackling various NLG tasks, the Transformer [27] is now consensus to be employed as a fundamental building block. In the literature, there are three main Transformer variants for NLG: full Transformer, Encoder-Only (only using the encoder part of the Transformer), and Decoder-Only (only using the decoder part). A natural question to ask is: which architecture is the best choice. According to previous studies, when the amount of training dataset is sufficient, using the full Transformer is the priority choice for NLG tasks. However, for the insufficient training dataset setting, we find this is not the case. In this paper, we report experiment results of applying the three architectures to four different tasks under low-resource settings. In contrast to the conclusion by previous study, we find that there are no consistent results indicating which architecture is the best under low-resource dataset settings. Further, based on the experiment results, we comment on the architecture selection under the low-resource dataset consideration.