Reviewerly/exHarmony huggingface dataset:
Hierarchical Refined Attention for Scene Text Recognition

Recent years have witnessed increased interests in scene text recognition (STR). Current state-of-the-art (SOTA) approaches adopt sequence-to-sequence (Seq2Seq) structure to leverage the mutual interaction between images and textual information. However, these methods still struggle to recognize texts in arbitrary shapes. The leading cause is that it brings about information loss and negative noises when directly compressing two-dimension image features into one-dimension vectors. This paper proposes a novel framework named hierarchical refined attention network (HRAN) for STR. HRAN obtains refined representations with the hierarchical attention, which localizes the precise region of current character from two-dimension perspective. Two novel co-attention mechanisms, stacked and guided co-attention, explicitly leverage dependency between spatial-aware contextual features and region-aware visual features without extra character annotations. Experiments show that both on regular and irregular texts, HRAN achieves highly competitive performance compared to SOTA models.
Protecting User Data Privacy with Adversarial Perturbations

The increased availability of on-body sensors gives researchers access to rich time-series data, many of which are related to human health conditions. Sharing such data can allow cross-institutional collaborations that create advanced data-driven models to make inferences on human well-being. However, such data are usually considered privacy-sensitive, and publicly sharing this data may incur significant privacy concerns. In this work, we seek to protect clinical time-series data against membership inference attacks, while maximally retaining the data utility. We achieve this by adding an imperceptible noise to the raw data. Known as adversarial perturbations, the noise is specially trained to force a deep learning model to make inference mistakes (in our case, mispredicting user identities). Our preliminary results show that our solution can better protect the data from membership inference attacks than the baselines, while succeeding in all the designed data quality checks.
Game technologies in the process of mastering theoretical musical disciplines

The purpose of the work is to describe game technologies used in the process of mastering theoretical musical disciplines by students of Russian musical universities and faculties, which are proven the most effective in the course of the study of history and theory of music. The main results of the study state that changing the format of the presentation of information and the preference for interactive forms of teaching and game technologies lead to higher teaching outcomes. The use of game technologies allows modern students to more effectively master theoretical musical disciplines. As representatives of the information society, they experience problems with the development of analytical thinking and learn worse than their predecessors. The novelty of the work is the presentation of the results of the development and practical application of specific game situations, which are proven an effective method of teaching history and theory of music. The main findings of the study: in the conditions of the post-industrial world, the world of total synthesis, globalization, and rapid flow of information, modern people have poorly developed analytical thinking. Game technologies partially solve these problems, allowing students to form competencies that are relevant in modern life.
Single-shot detection of 8 unique monochrome fringe patterns representing 4 distinct directions via multispectral fringe projection profilometry

Abstract Spatial resolution in three-dimensional fringe projection profilometry is determined in large part by the number and spacing of fringes projected onto an object. Due to the intensity-based nature of fringe projection profilometry, fringe patterns must be generated in succession, which is time-consuming. As a result, the surface features of highly dynamic objects are difficult to measure. Here, we introduce multispectral fringe projection profilometry, a novel method that utilizes multispectral illumination to project a multispectral fringe pattern onto an object combined with a multispectral camera to detect the deformation of the fringe patterns due to the object. The multispectral camera enables the detection of 8 unique monochrome fringe patterns representing 4 distinct directions in a single snapshot. Furthermore, for each direction, the camera detects two π-phase shifted fringe patterns. Each pair of fringe patterns can be differenced to generate a differential fringe pattern that corrects for illumination offsets and mitigates the effects of glare from highly reflective surfaces. The new multispectral method solves many practical problems related to conventional fringe projection profilometry and doubles the effective spatial resolution. The method is suitable for high-quality fast 3D profilometry at video frame rates.
THE EYE-TRACKING USAGE FOR TESTING CUSTOMERS’ GAZE ON CONFORMITY MARKS PLACED ON PRODUCTS PACKAGES

Purpose:The purpose of this article is to perform the idea of eye-tracking method based research, which will allow us to testing the customers gaze on the conformity marks placed on products packages.The aim of this article is to present the possible way of eye-tracking based research method, an experiment the results of which can be used for future research and statistical analyzes.Design/methodology/approach: To perform the eye-tracking based research, the path and methodology of conducting this research was shown.The basic research method proposed in this paper is experiment, which output should be heat maps with areas, on which people look will be marked.Additionally, the project of short survey, which respondents should complete after eye-tracking examination was presented.The subject of paper is theoretical, but shown methodology, which makes it possible to carry out the same or similar research in reality. Findings:The main finding of this article is the creation of eye-tracking method based research proposal way, which should allow us to analyze both on conformity marks gazing by customers and other elements of packages.Additionally, the survey conducted after experiment can give researchers an ability to accordance assessment to real conformity marks and other etiquettes/packages marks, on which customers are looking at, and what they think are looking at.It may also allow us obtaining other information, such as: awareness of the types and concept of compliance marks, the voluntary nature of their placement, and also whether consumers care what marks of compliance are marked with the purchased product.Research limitations/implications: This is the proposition of the way to conduct an experiment.The survey after eye-tracking experiment is created by an author and questions were not prepared with use specific methodology.It is possible to repeat this research with other things placed on products packages and elements of etiquettes.Practical implications: The practical implication is the ability to develop and use presented path for different things placed on packages, showing on screens etc.It can also leads to conducting other similar researches with usage of this path/way/method.Social implications: This research may allow us to know, on what conformity and also safety signs customers look most common.These research can also lead to user safety improvement.Originality/value: Using eye-tracking technique in different researches etc. is well known and is not a new method, but in this paper it is transposed especially to conformity marks gazing in specific and practical way of conduct.Basing on literature review the research problem was stated: there are not many or no studies on the use and technical application of eye tracking to study the perception of conformity marks.
Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology

Tracking is a fundamental task in Augmented Reality (AR) technology which requires robust real-time to properly adjust real and virtual objects in a single alignment, so that, both objects appear to coexist in the same world. Marker-less tracking has been explored to overcome the limitations of conventional marker-based tracking in AR. By capturing real surroundings to produce the features, the marker-less tracking will recognize these features to overlay the virtual objects on the top of the captured features. The features have been tracked in real-time by the display device, based on the real environment. Therefore, this article aimed to explain the features detection using Features Accelerated Segment Test (FAST) to detect corner features. Related works were reviewed and the features extraction for AR framework using Grayscale Image Generation (GIG) were presented. In addition, to enhance details of grayscale images, a comprehensive study was performed on the three techniques of Contrast Enhancement (CE), namely, Colormap, HE and CLAHE to determine the best method for robust features detection. The findings showed Colormap to be the best technique, compared to HE and CLAHE, in terms of noise, the accuracy of the corner, distributed histogram and amount of features.
DeepSampling: Image Sampling Technique for Cost-Effective Deep Learning

Deep learning is beneficial from big data while facing computationally expensive, with an increase in data size. Some severe data issues, such as the presence of highly skewed, sparse, and imbalanced data, would substantially influence the findings of machine learning. Due to the complexity of such data, the ability to assess and evaluate the data is central to cost-effective deep learning. More specifically, in Deep Learning, choosing the right validation method is vital to ensure the accuracy and biases of the validation process. Current validation techniques, including k-fold cross-validation or random split of training and testing datasets, are hampered by the lack of systematic sampling with a comprehensive understanding of the data.In this thesis, we proposed a sampling technique called DeepSampling that aims at achieving cost-effective deep learning for a given application. For the proposed DeepSampling framework, two sampling schemes are designed [1] to resolve the imbalanced data issues using Generative Adversarial Networks (GANs), [2] to develop an effective sampling technique based on clustering. The clustering techniques are based on Mahalanobis distance metric and use t-SNE (T-distributed Stochastic Neighbor Embedding), to overcome the data skewness and sparseness issues.The proposed DeepSampling technique for cost-effective deep learning has been evaluated with three Deep Learning models and four benchmark datasets, including MNIST, Breast Histology, Malaria cell images, and Stanford dog. The results confirm that the accuracies obtained by DeepSampling are improved by approximately 2-3% for image classification, compared to traditional evaluation techniques on the same dataset.
Mean-square stability of Riemann–Liouville fractional Hopfield’s graded response neural networks with random impulses

Abstract In this paper a model of Hopfield’s graded response neural network is investigated. A network whose neurons are subject to a certain impulsive state displacement at random times is considered. The model is set up and studied. The presence of random moments of impulses in the model leads to a change of the solutions to stochastic processes. Also, we use the Riemann–Liouville fractional derivative to model adequately the long-term memory and the nonlocality in the neural networks. We set up in an appropriate way both the initial conditions and the impulsive conditions at random moments. The application of the Riemann–Liouville fractional derivative leads to a new definition of the equilibrium point. We define mean-square Mittag-Leffler stability in time of the equilibrium point of the model and study this type of stability. Some sufficient conditions for this type of stability are obtained. The general case with time varying self-regulating parameters of all units and time varying functions of the connection between two neurons is studied.
Development of Soft Skills While Teaching English to Master’s Students

Besides the knowledge and skills directly related to the profession, the so-called soft or transferable skills are essential for future specialists in all areas. Soft skills involve communication, problem-solving, decision-making, computer, leadership, teamwork, critical and creative thinking, presentation, reflection and other skills, which help people to be employed and develop a successful career. To develop these skills while teaching English at University the following activities are useful: projects, presentations, discussions, debates, keeping portfolios, writing reflective diaries, role play and business games. A mixed paradigm was applied: quantitative research (self-assessment questionnaire survey) with 60 MA/MS students and, for triangulation, a focus group interview (a qualitative approach) with 6 of those respondents who volunteered to take part in it. The goal of the research was to find out whether activities supporting the development of soft skills were applied in MA/MS English classrooms and whether the respondent students realized their usefulness for their future careers. Two corresponding research questions were asked. The research held applied a researcher-made Likert-scale questionnaire and a focus group interview. The questionnaire was validated and piloted. The interview asked the same questions, trying to get students’ insight on the issue. A conclusion was made that presentations, discussions and debates, as well as role-play were often applied in their classes, while portfolios, diaries and business games were more or less applied and projects were almost not used. However, the students realized the importance of these activities for their future careers. &#x0D; Keywords: soft skills, teaching English as a foreign/second language (EFL, ESL), activities, Master’s students
The Effect of E-Learning on Students&amp;#39; Learning Interest in the Equivalence Education Program

The Equivalence Education Program is non-formal education that applies learning with the e-learning system to substitute conventional learning in formal education school. This study aims to identify and analyze (1) e-learning conducted by students, (2) students &amp;#39;interest in learning, and (3) the effect of e-learning on students&amp;#39; learning interest in the Package C Equivalence Program at PKBM Pioneer Karanganyar. The method used is a quantitative approach focusing on the ex post facto approach. The research sample was 45 students of Package C at PKBM Pioneer Karanganyar, out of a total population of 141 Package C students. The data were collected using questionnaire and documentation. The analysis technique used is statistical and inferential analysis techniques focusing on simple linear regression analysis techniques. The results showed that (1) Package C students at PKBM Pioneer Karanganyar appreciated that the e-learning was equivalent to online learning (2) Package C students at PKBM Pioneer Karanganyar had high learning interest, (3) there was a positive influence and e-learning was significant for students&amp;#39; interest in learning in the Package C Program at PKBM Pioneer Karanganyar. The benefit of this research is to provide an overview of online learning that is fun to students.
On Keystroke Pattern Variability in Virtual Desktop Infrastructure

The paper investigates the impact of network traffic and network characteristics on the effectiveness of keystroke dynamics for continuous and one-time authentication in the remote desktop applications and virtual desktop infrastructure.The context presented in the paper contributes to the area of biometric authentication systems by identifying virtual desktop environment as a specific application domain that uses a keystroke pattern for user verification and identifying at what extent the context influences on permanence and the immunity of the keystroke data which was never studied before.The results showed that the keystroke pattern is not affected by network latency, but standard deviation, jitter, and packet loss have a significant combined impact onto it.It can also be concluded that RDP packets are not prioritized during transmission over the network, and therefore, we can say that any other competing traffic is likely to render keystroke dynamics unusable for continuous authentication during remote access.
Microcalcification Detection in Mammograms Using Particle Swarm Optimization and Probabilistic Neural Network

Breast cancer is the most typical form of cancer among the female population and the most common form of cancer-related death. However, if the cancer is detected at an early stage, treatment may be more effective. Mammography is one of the most used imaging modalities for the early breast cancer diagnosis. The present paper proposes an intelligent system for the detection and analysis of microcalcifications in mammography using the region growing algorithm, the particle swarm optimization algorithm (PSO), and the Probabilistic neural network (PNN) to detect the presence of breast cancer as early as possible and to avoid resorting to ablation of the breast.
Reachability in big graphs: A distributed indexing and querying approach

The advent of Big graphs characterized by their enormous number of nodes, with multiple edges between them makes the existing reachability query indexing approaches unable to guarantee a reasonable time for both the index construction and query steps. Therefore a novel approach that takes into account these new characteristics during the graph processing is needed. In this paper, we propose an Overlay Graph-based Distributed Reachability Indexing approach (ODRI), an indexing scheme through which the index construction and reachability query are processed in a parallel and distributed manner. The key idea of ODRI is to process a Big graph as a set of smaller subgraphs (partitions) interconnected to each other through an overlay graph. In this way, the partitions can be indexed in parallel and, at the same time, the reachability information can also be extracted. Hence, the index construction and query processing time will be reduced significantly. Therefore, ODRI ensures the scalability of Big graphs, which is a challenge for the existing reachability approaches. Besides, we formally prove that this strategy preserves the reachability properties. Using real-life data, we experimentally verify that our approach outperforms the state-of-the-art methods, and is scalable in terms of the number of partitions, regardless of how graphs are distributed.
Automatic Generation of German Translation Candidates for SNOMED CT Textual Descriptions

We present an approach called MTP (multiple translation paths) aiming at assisting human translation in SNOMED CT localisation projects based on free, web-based machine translation tools. For a chosen target language, MTP generates a scored output of translation candidates (TCs) for each input concept. This paper describes the basic idea of MTP, the distribution of its output TCs and discusses typical examples with German as target language. The MTP approach capitalises on combinatorial growth by the combination of input languages, support languages, and translation engines. We applied MTP on the SNOMED CT Starter Set, using Google Translator, DeepL and Systran, together with the four source languages English, Spanish, Swedish and French, and Danish, Dutch, Norwegian, Italian, Portuguese, Polish and Russian as support languages. The descriptive assessment of TC variety, together with an analysis of typical results is the focus of this paper. MTP defines, for each input concept, TPs by the combination of input languages, support languages and translation engines, resulting in 91 translation results with various degrees of co-incidence (cardinality). The most configurations produce an average number of TCs indicating that the same TC is often derived via different translation paths. Combinations of translation engines result in distributions with a higher number of distinct TCs per concept. We present work in progress on using machine translation (MT) for terminology translation, by leveraging several free MT tools fed by different languages and language combinations. A first qualitative analysis was promising and supports our hypothesis that a majority voting applied to many translation candidates yields higher quality results than from one single engine and input language.
Transfer Learning for Classifying Spanish and English Text by Clinical Specialties

Transfer learning has demonstrated its potential in natural language processing tasks, where models have been pre-trained on large corpora and then tuned to specific tasks. We applied pre-trained transfer models to a Spanish biomedical document classification task. The main goal is to analyze the performance of text classification by clinical specialties using state-of-the-art language models for Spanish, and compared them with the results using corresponding models in English and with the most important pre-trained model for the biomedical domain. The outcomes present interesting perspectives on the performance of language models that are pre-trained for a particular domain. In particular, we found that BioBERT achieved better results on Spanish texts translated into English than the general domain model in Spanish and the state-of-the-art multilingual model.
Exploring Awareness for Technological Pedagogical and Content Knowledge (TPAC) in Pre-Service Teacher Education Programme

The purpose of the present study is to explore the prospective teacher’s awareness about Technological Pedagogical and Content Knowledge (TPACK) framework in their ongoing teacher education programme. The sample comprised 80 B.Ed. students from two colleges of Jammu division and 20 M.Ed. students from one prominent university of the Jammu division. The investigator employed a self-constructed semi-structured tool as per the objective of the study. The study employed a descriptive exploratory design. The findings revealed that both at UG and PG level, students have individualised knowledge about technology, pedagogy and content but, the least number of students were familiar with this framework. The study included some concrete suggestions and recommendations for meaningful integration of Technology-mediated pedagogy in the ongoing teacher education programme at the regional and national level. The findings of the study help in strengthening the policy formulation for teacher education in the current perspective.
Are we losing the fight against cybercrime?

Cyber security researchers, governmental bodies, independent threat hunters, private cyber security organisations – we all do our jobs as part of a broader mission of building a safer world. Yet, somehow, we are failing.