Reviewerly/exHarmony huggingface dataset:
SVM-PCA Based Handwritten Devanagari Digit Character Recognition

Objective: The blended fusion of Support Vector Machine (SVM) and Principal Component Analysis (PCA) have been widely used in recognizing handwritten digit characters of Devanagari script. The feature information from the character is extracted using its skeleton structure which optimally reduce data dimensionality using PCA. There is ample information available on handwritten charac-ter recognition on Indian and Non-Indian scripts but very few article emphasized on recognition of Devanagari scripts. Therefore, this paper presents an efficient handwritten Devanagari character recognition system based on block based feature extraction and PCA-SVM classifier. Methods: We have collected samples of handwritten Devanagari characters from different handwritten experts for classification. Results: For experimental work, total of 100 images having Devanagari digit characters been used for the purpose of training and testing. The proposed system achieves a maximum recognition accuracy of 96.6 % and 96.5% for 5 &amp; 10 fold validations with 70% training and 30% testing data using block based feature and SVM classifier having different kernels. Conclusion: The obtained results achieve maximum accuracy using SVM classifier for digit character recognition. In future deep learning networks will be considered for accuracy enhancement and precision.
Multiple Barcode Detection with Mask R-CNN

Almost all products on the market today have a unique code or ID associated with them. This special identification is called a barcode. Barcodes have been the subject of extensive research in recent years due to the high demand for automation in various industrial environments. Fast and accurate reading of barcodes, where all details about the product used in many commercial applications can be learned, is very important. In this study, Mask R-CNN algorithm was used to determine the regions of the 1B barcodes in the image. In the Mask R-CNN, barcodes in the image have been detected, as well as the bounding box position of each barcode, as well as the pixel information corresponding to this class in the bounding box. Colored barcodes on various products taken at different ambient lights and at different angles were collected and a data set of 1114 images was prepared. Using this dataset, 74.41 % accuracy was achieved with Mask R-CNN.
Techniques and Tools for Advanced Software Vulnerability Detection

Software is frequently deployed with vulnerabilities that may allow hackers to gain access to the system or information, leading to money or reputation losses. Although there are many techniques to detect software vulnerabilities, their effectiveness is far from acceptable, especially in large software projects, as shown by several research works. This Ph.D. aims to study the combination of different techniques to improve the effectiveness of vulnerability detection (increasing the detection rate and decreasing the number of false-positives). Static Code Analysis (SCA) has a good detection rate and is the central technique of this work. However, as SCA reports many false-positives, we will study the combination of various SCA tools and the integration with other detection approaches (e.g., software metrics) to improve vulnerability detection capabilities. We will also study the use of such combination to prioritize the reported vulnerabilities and thus guide the development efforts and fixes in resource-constrained projects.
Business-Oriented Approach to Requirements Elicitation in a Scrum Project

As agile methods allow requirements to emerge throughout the development process, requirements engineering activities span the entire life cycle of a system. However, requirements elicitation is poorly performed in agile teams. Instead, agile teams rely fundamentally on the use of validation. In this paper, we demonstrate that on the contrary to what agilists say, creating a requirements document up front may not necessarily be a waste of time and can be done in a light way. We report on a Scrum project in which we adopted the Business-Oriented approach to Requirements Elicitation (BORE) proposed by Przybyłek [12]. In this approach, system requirements are derived from business process models. Accordingly, the system requirements meet real business needs and there are no superfluous requirements.
The System of Additional Professional Education of the University as a Platform for Implementation of the Federal Project «New Opportunities for Everyone»

The global task constantly represented in the documents adopted by the government is the breakthrough development of the Russian economy. The results of the “New Opportunities for Everyone” Federal project in the framework of the national “Education” project are aimed at developing human capital - a cross-cutting condition for achieving national development goals of the country (increasing life expectancy, accelerating technological development of the Russian Federation, ensuring accelerated implementation of digital technologies in the economy and social sphere, etc.). Additional professional education at Kazan National Research Technological University is considered as an integral and real part of the lifelong learning system. The University is actively involved in the implementation of the Federal project. For this purpose, about 20 additional professional development programs have been developed and implemented.
Arabic Dialects Identification for All Arabic countries

Arabic dialects are among of three main variant of Arabic language (Classical Arabic, modern standard Arabic and dialectal Arabic). It has many variants according to the country, city (provinces) or town. In this paper, several techniques with multiple algorithms are applied for Arabic dialects identification starting from removing noise till classification task using all Arabic countries as 21 classes. Three types of classifiers (Naive Bayes, Logistic Regression, and Decision Tree) are combined using voting with two different methodologies. Also clustering technique is used for decreasing the noise that result from the existing of MSA tweets in the data set for training phase. The results of f-measure were 27.17, 41.34 and 52.38 for first methodology without clustering, second methodology without clustering, and second methodology with clustering, the used data set is NADI shared task data set.
BSS: Blockchain Enabled Security System for Internet of Things Applications

In the Internet of Things (IoT), devices can interconnect and communicate autonomously, which requires devices to authenticate each other to exchange meaningful information. Otherwise, these things become vulnerable to various attacks. The conventional security protocols are not suitable for IoT applications due to the high computation and storage demand. Therefore, we proposed a blockchain-enabled secure storage and communication scheme for IoT applications, called BSS. The scheme ensures identification, authentication, and data integrity. Our scheme uses the security advantages of blockchain and helps to create safe zones (trust batch) where authenticated objects interconnect securely and do communication. A secure and robust trust mechanism is employed to build these batches, where each device has to authenticate itself before joining the trust batch. The obtained results satisfy the IoT security requirements with 60% reduced computation, storage and communication cost compared with state-of-the-art schemes. BSS also withstands various cyberattacks such as impersonation, message replay, man-in-the-middle, and botnet attacks.
Multi-Dialect Arabic BERT for Country-Level Dialect Identification.

Arabic dialect identification is a complex problem for a number of inherent properties of the language itself. In this paper, we present the experiments conducted, and the models developed by our competing team, Mawdoo3 AI, along the way to achieving our winning solution to subtask 1 of the Nuanced Arabic Dialect Identification (NADI) shared task. The dialect identification subtask provides 21,000 country-level labeled tweets covering all 21 Arab countries. An unlabeled corpus of 10M tweets from the same domain is also presented by the competition organizers for optional use. Our winning solution itself came in the form of an ensemble of different training iterations of our pre-trained BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask at hand. We publicly release the pre-trained language model component of our winning solution under the name of Multi-dialect-Arabic-BERT model, for any interested researcher out there.
Evaluation and Promotion of M-Learning Accessibility for Smart Education Development

Young people’s involvement with their mobile devices is often considered a problem nowadays. However, with the new “learning on the go approach” it can be used for good: for learning purposes, in particular in higher education. The m-learning tools have the potential to turn learning into a more attractive, interesting and motivating process of acquiring new knowledge and developing competencies. At the same time, m-learning can be inclusive of people with special needs, who used to be detached from traditional educational services. In our paper we purpose an evaluation approach to mobile learning software accessibility based on accessibility guidelines and standards. We suggest a formalization of the approach and reducing it to a controlled process. The dedicated usage of mobile devices and m-learning software can assist in improving mental operations speed, executive control of information selection and decision-making, as well as optimize the memorization system. Such enhancements are particularly important for facilitating effective learning in the elderly and the people with disabilities.
Domain Adaptation for Eye Segmentation

Domain adaptation (DA) has been widely investigated as a framework to alleviate the laborious task of data annotation for image segmentation. Most DA investigations operate under the unsupervised domain adaptation (UDA) setting, where the modeler has access to a large cohort of source domain labeled data and target domain data with no annotations. UDA techniques exhibit poor performance when the domain gap, i.e., the distribution overlap between the data in source and target domain is large. We hypothesize that the DA performance gap can be improved with the availability of a small subset of labeled target domain data. In this paper, we systematically investigate the impact of varying amounts of labeled target domain data on the performance gap for DA. We specifically focus on the problem of segmenting eye-regions from eye images collected using two different head mounted display systems. Source domain is comprised of 12,759 eye images with annotations and target domain is comprised of 4,629 images with varying amounts of annotations. Experiments are performed to compare the impact on DA performance gap under three schemes: unsupervised (UDA), supervised (SDA) and semi-supervised (SSDA) domain adaptation. We evaluate these schemes by measuring the mean intersection-over-union (mIoU) metric. Using only 200 samples of labeled target data under SDA and SSDA schemes, we show an improvement in mIoU of 5.4% and 6.6% respectively, over mIoU of 81.7% under UDA. By using all available labeled target data, models trained under SSDA achieve a competitive mIoU score of 89.8%. Overall, we conclude that availability of a small subset of target domain data with annotations can substantially improve DA performance.
Reducing Speckle Noise from Ultrasound Images Using an Autoencoder Network

Image enhancement aims to obtain a clear image from a noisy image and it also uses for ultrasound images. In the experimental study, unlike classical image enhancement methods, deep learning method was used. Different levels of speckle noise added to the ultrasound images of the brachial plexus, which is known as the large nerve community under the armpit, were tried to be removed with the help of the convolutional denoising autoencoder network, which is one of the deep learning methods. The results obtained from the experimental study were compared with classical methods results and the proposed method was found to be more successful than classical methods.
Mobile Learning Application for Helping Pupils in Learning Chemistry

&lt;p&gt;Elementary school pupils can face different difficulties to learn different topics. Among the different topics, learning chemical elements and their different properties and interactions. A way to make the learning process easier and effective is using mobile learning. Mobile learning can contribute a new method to educate pupils different topics. Also, mobile learning has different advantages over classical learning materials. Among the different advantages of using mobile learning is the fact that it enables pupils to interact with learning materials, visualize different learning concepts, and learning anytime and anywhere. This article presents an exploration study related to investigating the possible use of educational mobile App to learn chemistry course in elementary school. The article presents also a qualitative research methodology implemented by including 7 pupils. � The findings of this research show that mobile learning technology can be adopted for learning chemistry course in order to enable pupils to understand periodic table, chemical elements properties and their chemical interaction. Also, this study has shown that the proposed mobile application can be integrated in learning chemistry course for elementary school pupils.&lt;/p&gt;
NRC Systems for the 2020 Inuktitut-English News Translation Task.

We describe the National Research Council of Canada (NRC) submissions for the 2020 Inuktitut-English shared task on news translation at the Fifth Conference on Machine Translation (WMT20). Our submissions consist of ensembled domain-specific finetuned transformer models, trained using the Nunavut Hansard and news data and, in the case of Inuktitut-English, backtranslated news and parliamentary data. In this work we explore challenges related to the relatively small amount of parallel data, morphological complexity, and domain shifts.
Taxonomy of numeral classifiers

In a numeral classifier language, a classifier (C) or measure word (M) is needed to link a noun (N) and its numerical quantifier (Num). This chapter first illustrates the confusions surrounding the convergence and divergence between Cs and Ms and offers some clarification. From the multiplicative mathematical perspective proposed in Greenberg (1990[1972]: 172), Au Yeung (2007), and Her (2012a), C and M converge as the multiplicand, with Num as the multiplier. Yet, crucially, a C’s value is numerical and necessarily 1, while an M’s value can be anything but 1, thus ¬1. An M’s value can therefore be numerical, for example, 2, 6, 12, and so on, or non-numerical, for example, kilo, meter, liter, bottle, and so on. This chapter then proposes a new and comprehensive taxonomy based on the different kinds of mathematical values encoded in C/Ms and further proposes a formal semantic account of this taxonomy.
Emergency Powers in Response to COVID-19: Policy Diffusion, Democracy, and Preparedness

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Virtuality in university teaching-learning versus COVID-19

The research aimed to analyze the use of virtual resources in teaching-learning in times of pandemic at the Daniel Alcides Carrion National University. The methodology used was of qualitative approach through field work to explain the behaviors assumed in the processes of learning management by students and teachers. The information was collected through various techniques such as observation of virtual classes, documentary analysis, and focus groups with students, unstructured interviews and, in depth, with experts in the subject that allowed us to conduct the discussion and triangulation. The results allowed us to affirm that students recognize the importance of technology and the use of ICTs for their professional training; in addition, students and teachers have been demonstrating high levels of value, responsibility and commitment to their learning.
From Virtual Reality to Neuroscience and Back: a Use Case on Peripersonal Hand Space Plasticity

The human brain does not represent space homogeneously, but it constructs multiple representations of it depending on the source of sensory stimulation and the nature of interaction between the body and the environment. The peripersonal space is defined as an imaginary area coded as separated sector of space, as if there were a boundary between what the body might or might not interact with. We present an experimental pattern that combines the use of virtual reality (VR) and functional magnetic resonance imaging (fMRI) to investigate human behavior and neural basis in case of training of the plasticity of the peripersonal space around the hand. The expected results may provide knowledge on a phenomenon interesting for behavioral neuroscience as well as for the interaction of embodied self-avatars in virtual environments.
C-3PO: Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation

With the emergence of mobile and wearable devices, push notification becomes a powerful tool to connect and maintain the relationship with app users, but sending inappropriate or too many messages at the wrong time may result in the app being removed by the users. In order to maintain the retention rate and the delivery rate of advertisement, we adopt deep neural network (DNN) to develop a pop-up recommendation system “Click-sequence-aware deeP neural network (DNN)-based Pop-uPs recOmmendation (C-3PO)” enabled by collaborative filtering-based hybrid user behavioral analysis. We further verified the system with real data collected from the product security master, clean master, and CM browser, supported by Leopard Mobile Inc. (Cheetah Mobile Taiwan Agency). In this way, we can know precisely about users’ preference and frequency to click on the push notification/pop-ups, decrease the troublesome to users efficiently, and meanwhile increase the click-through rate of push notifications/pop-ups.
Pre-training Text-to-Text Transformers for Concept-centric Common Sense

Pretrained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks that require a syntactic and semantic understanding of the text. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked span infilling (for T5-style PTLMs) do not explicitly model the relational and compositional commonsense knowledge about everyday concepts, which is crucial to many downstream tasks requiring commonsense reasoning. To augment PTLMs with common sense, we propose generative and contrastive objectives as intermediate self-supervised pre-training tasks between general pre-training and downstream task-specific fine-tuning. We also propose a joint training framework to unify generative and contrastive objectives so that these objectives can be more effective. Our proposed objectives can pack more commonsense knowledge into the parameters of a pre-trained text-to-text transformer without relying on external knowledge bases, yielding better performance on both NLU and NLG tasks. We apply our method on a pre-trained T5 model in an intermediate task transfer learning fashion to train a concept-aware language model (CALM) and experiment with five commonsense benchmarks (four NLU tasks and one NLG task). Experimental results show that CALM outperforms baseline methods by a consistent margin.
Prober

Heap-based overflows are still not completely solved even after decades of research. This paper proposes Prober, a novel system aiming to detect and prevent heap overflows in the production environment. Prober leverages a key observation based on the analysis of dozens of real bugs: all heap overflows are related to arrays. Based on this observation, Prober only focuses on array-related heap objects, instead of all heap objects. Prober utilizes static analysis to label all susceptible call-stacks during the compilation, and then employs the page protection to detect any invalid accesses during the runtime. In addition to this, Prober integrates multiple existing methods together to ensure the efficiency of its detection. Overall, Prober introduces almost negligible performance overhead, with 1.5% on average. Prober not only stops possible attacks on time, but also reports the faulty instructions that could guide bug fixes. Prober is ready for deployment due to its effectiveness and low overhead.
Shape-Driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning

We present a neural optimization model trained with reinforcement learning to solve the coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set. To find the optimal coordinate order for the given set, we train a neural network using reinforcement learning to reward orderings with high silhouette coefficients. The network consists of an encoder and a decoder with an attention mechanism. The encoder employs a recurrent neural network (RNN) to encode input shape and class information, while the decoder together with the attention mechanism employs another RNN to output a sequence with the new coordinate order. In addition, we introduce a neural network to efficiently estimate the similarity between shape context descriptors, which allows to speed up the computation of silhouette coefficients and thus the training of the axis ordering network. Two user studies demonstrate that the orders provided by our method are preferred by users for perceiving class separation. We tested our model on different settings to show its robustness and generalization abilities and demonstrate that it allows to order input sets with unseen data size, data dimension, or number of classes. We also demonstrate that our model can be adapted to coordinate ordering of other types of plots such as RadViz by replacing the proposed shape-aware silhouette coefficient with the corresponding quality metric to guide network training.