Reviewerly/exHarmony huggingface dataset:
A Two-Level Morphological Description of Bashkir Turkish

In recent years, the topic of Natural Language Processing (NLP) has attracted increasing interest. Many NLP applications including machine translation, machine learning, speech recognition, sentiment analysis, semantic search and natural language generation have been developed for most of the existing languages. Besides, two-level morphological description of the language to be used is required for these applications. However, there is no comprehensive study of Bashkir Turkish in the literature. In this paper, a two-level description of Bashkir Turkish morphology is described. The description based on a root word lexicon of Bashkir Turkish is implemented using Extensible Markup Language (XML) and appended to Nuve framework. The phonetic rules of Bashkir Turkish are encoded using 41 two-level rules. This two-level morphological description is promising to be used in Bashkir Turkish oriented NLP applications.
Enhanced GPU-Based Anti-Noise Hybrid Edge Detection Method

Today, there is a growing demand for computer vision and image processing in different areas and applications such as military surveillance, and biological and medical imaging. Edge detection is a vital image processing technique used as a pre-processing step in many computer vision algorithms. However, the presence of noise makes the edge detection task more challenging; therefore, an image restoration technique is needed to tackle this obstacle by presenting an adaptive solution. As the complexity of processing is rising due to recent high-definition technologies, the expanse of data attained by the image is increasing dramatically. Thus, increased processing power is needed to speed up the completion of certain tasks. In this paper,we present a parallel implementation of hybrid algorithm-comprised edge detection and image restoration along with other processes using Computed Unified Device Architecture (CUDA) platform, exploiting a Single Instruction Multiple Thread (SIMT) execution model on a Graphical Processing Unit (GPU). The performance of the proposed method is tested and evaluated using well-known images from various applications. We evaluated the computation time in both parallel implementation on the GPU, and sequential execution in the Central Processing Unit (CPU) natively and using Hyper-Threading (HT) implementations. The gained speedup for the naïve approach of the proposed edge detection using GPU under global memory direct access is up to 37 times faster, while the speedup of the native CPU implementation when using shared memory approach is up to 25 times and 1.5 times over HT implementation.
Information Theoretic Optimal Learning of Gaussian Graphical Models

What is the optimal number of independent observations from which a sparse Gaussian Graphical Model can be correctly recovered? Information-theoretic arguments provide a lower bound on the minimum number of samples necessary to perfectly identify the support of any multivariate normal distribution as a function of model parameters. For a model defined on a sparse graph with $p$ nodes, a maximum degree $d$ and minimum normalized edge strength $\kappa$, this necessary number of samples scales at least as $d \log p/\kappa^2$. The sample complexity requirements of existing methods for perfect graph reconstruction exhibit dependency on additional parameters that do not enter in the lower bound. The question of whether the lower bound is tight and achievable by a polynomial time algorithm remains open. In this paper, we constructively answer this question and propose an algorithm, termed DICE, whose sample complexity matches the information-theoretic lower bound up to a universal constant factor. We also propose a related algorithm, SLICE, that has a slightly higher sample complexity, but can be implemented as a mixed integer quadratic program which makes it attractive in practice. Importantly, SLICE retains a critical advantage of DICE in that its sample complexity only depends on quantities present in the information theoretic lower bound. We anticipate that this result will stimulate future search of computationally efficient sample-optimal algorithms.
Classifying and Measuring Hate Speech in Twitter Using Topic Classifier of Sentiment Analysis

The aim and objective of this research are to create a model to measure the hate speech and to measure the contents of hate speech. The descriptive analysis method of data science was used to describe and summarize raw data from a dataset. We used Twitter as the social networking Web site for this research to analyze and measure the hate speech and its classifications. A dataset from kaggle datasets was applied for this research. To produce statistical results, we used monkey learn machine learning libraries which are incorporated with Python program to design and develop a model to classify and measure hate speech and its types that could be trained and tested using sentiment analysis. Researchers have found that the majority of the tweets are based on racist and ethnicity, sex and religion-based hate speech are also widely available.
A Novel Approach of OTP Generation Using Time-Based OTP and Randomization Techniques

There are many ways of generating OTP in and performing user authentication, earlier authentication in a traditional way happened using username and password which became way popular in the digital world. Then, OTP came into the picture, and one-time password is the technique which is two-way authentication. This paper proposes an algorithm that will give an easy way to implement as well as a very secure OTP generation method. This OTP will be a combination of current time value and back-end server-generated value with some random operations on that combined value. This value is 32 bit in size which will give a combination of 8-digit numbers. The proposed OTP algorithm is easier to develop on the other side difficult to reverse engineer and gives efficient performance.
Facial Expression Recognition Using Improved Local Binary Pattern and Min-Max Similarity with Nearest Neighbor Algorithm

In human-to-human interaction, important role is played by facial expression recognition (FER). Various modes of understanding and expressions out of verbal world are allowed by this. In many applications, it is a challenging task to analyze human facial expression automatically. For better human machine interaction, robust facial expression recognition (FER) system is proposed in this work. In this work, improved local binary patterns (ILBPs) and min-max similarity with nearest neighbor (MMSNN) algorithm are proposed for efficient training and recognition. Initially, the face detection is performed using Haar classifier method. From images, useful information is extracted by applying improved local binary pattern (ILBP) method. Finally, min-max similarity with nearest neighbor (MMSNN) algorithm is applied to train various expressions of face. Traditional methods are used to make a performance comparison of proposed MMSNN method. Mean recognition rate is achieved by proposed method, which shows its ability in comparison with existing methods. With respect to accuracy, f-measure, recall, and precision, better results are produced by the proposed MMSNN method.
Inferring Firewall Rules by Cache Side-channel Analysis in Network Function Virtualization

Network function virtualization takes advantage of virtualization technology to achieve flexibility in network service provisioning. However, it comes at the cost of security risks caused by cache side-channel attacks on virtual machines. In this study, we investigate the security impact of these attacks on virtualized network functions. In particular, we propose a novel cache-based reconnaissance technique against virtualized Linux-based firewalls. The proposed technique has significant advantages in the perspective of attackers. First, it enhances evasiveness against intrusion detection owing to the ability of source spoofing. Second, it allows inference on a wide variety of filtering rules. During experiment in VyOS, the proposed method could infer the firewall rules with an accuracy of more than 90% by using only a few dozen packets. We also present countermeasures to mitigate cache-based attacks on virtualized network functions.
‘A Little Knowledge is a Dangerous Thing’: A method to automatically detect knowledge compartmentalization and oversimplification

Simplification is a common practice to allow students to understand complex concepts. However, such practice may lead to oversimplification and knowledge compartmentalization problems. This paper proposes a method to minimize the effects of oversimplification and knowledge compartmentalization through a five-step processing chain, following instructional design principles, to foster advanced knowledge acquisition. The proposed method was applied to online courses and a discussion for an example of a Data Science course is provided to show its applicability.
Stabilization of Complementarity Systems via Contact-Aware Controllers

We propose a control framework which can utilize tactile information by exploiting the complementarity structure of contact dynamics. Since many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatorial structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. This framework can close the loop on tactile sensors and it is non-combinatorial, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on multiple numerical examples, including quasi-static friction problems and a high dimensional problem with ten contacts. We also validate our results on an experimental setup and show the effectiveness of the proposed method on an underactuated multi-contact system.
A HIGH-EFFICIENT LOW BUDGETARY APPROACH TO CYBER-SECURITY EXERCISES

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 3051-3059ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0901Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Mind-Reading Chatbots: We Are Not There Yet

Chatbots are getting increasingly popular in businesses, and particularly in customer services, but there is also a growing interest in developing artificial conversational agents able to coach people for medical or social purposes. Yet in too many cases, they remain frustrating to use when it comes to actual conversation going beyond simple question-answer interactions. In this paper, we show that this inability to sustain conversations is mostly caused by the lack of consideration of the user’s expectations, intentions, and current knowledge by the chatbot: a lack of a Theory of Mind. We investigated this hypothesis by designing an experiment using 5 chatbots having won the Loebner prize, in two different kinds of interaction: one relying heavily on implicit information, and the other not. As expected, no chatbot was able to keep conversing in the implicit condition.
High-Resolution Neural Texture Synthesis with Long-Range Constraints

The field of texture synthesis has witnessed important progresses over the last years, most notably through the use of convolutional neural networks. However, neural synthesis methods still struggle to reproduce large-scale structures, especially with high-resolution textures. To address this issue, we first introduce a simple multi-resolution framework that efficiently accounts for long-range dependency. Then, we show that additional statistical constraints further improve the reproduction of textures with strong regularity. This can be achieved by constraining both the Gram matrices of a neural network and the power spectrum of the image. Alternatively, one may constrain only the autocorrelation of the features of the network and drop the Gram matrices constraints. In an experimental part, the proposed methods are then extensively tested and compared to alternative approaches, both in an unsupervised way and through a user study. Experiments show the advantage of the multi-scale scheme for high-resolution textures and the advantage of combining it with additional constraints for regular textures.
Self-attention encoding and pooling for speaker recognition

The computing power of mobile devices limits the end-user applications in terms of storage size, processing, memory and energy consumption. These limitations motivate researchers for the design of more efficient deep models. On the other hand, self-attention networks based on Transformer architecture have attracted remarkable interests due to their high parallelization capabilities and strong performance on a variety of Natural Language Processing (NLP) applications. Inspired by the Transformer, we propose a tandem Self-Attention Encoding and Pooling (SAEP) mechanism to obtain a discriminative speaker embedding given non-fixed length speech utterances. SAEP is a stack of identical blocks solely relied on self-attention and position-wise feed-forward networks to create vector representation of speakers. This approach encodes short-term speaker spectral features into speaker embeddings to be used in text-independent speaker verification. We have evaluated this approach on both VoxCeleb1 & 2 datasets. The proposed architecture is able to outperform the baseline x-vector, and shows competitive performance to some other benchmarks based on convolutions, with a significant reduction in model size. It employs 94%, 95%, and 73% less parameters compared to ResNet-34, ResNet-50, and x-vector, respectively. This indicates that the proposed fully attention based architecture is more efficient in extracting time-invariant features from speaker utterances.
Relação entre trombose venosa profunda e seus fatores de risco na população feminina

The aim was to identify the risk factors related to the development of deep venous thrombosis in the female population of Maringa. This is a quantitative research, carried out with the female population attending two vascular surgery outpatient clinics in a municipality in the northwest of Parana, from November 2019 to March 2020, with a structured collection instrument. Data analysis was performed with the aid of the software and description of absolute and relative frequency. The results found showed a high prevalence of deep venous thrombosis in women over 66 years of age, and a significantly higher number of cases in white people. Systemic arterial hypertension was shown to be the main comorbidity in the population studied, followed by venous insufficiency and heart disease. Were concluded that there is a significant relationship between several risk factors and deep vein thrombosis in the female population, as well as an important understanding of each of these factors, a fact that is necessary for an adequate transfer of information to this target population.
Embedding asymmetric backdoors into the RSA key generator

This article describes the class of asymmetric backdoors in the RSA key generator. The algorithms are based on the classical Anderson’s backdoor, but unlike it, are resistant to attacks on the backdoor key embedded in the implementation, and therefore can be used in open source systems. The similarity of the constructed SETUP mechanisms with the ROCA vulnerability is also demonstrated.
Enhancing the Organisational Culture related to Cyber Security during the University Digital Transformation

The digital transformation (digitalisation) becomes an important item in strategies and plans for development and improvement of higher education The implementation of new approaches in education, new ways of information sharing and group work are expected to improve and transform all processes and services within the higher education institutions The digital transformation should not underestimate the security aspects of ICT use and specific Cyber Security Culture (CSC), part of the wider organisational culture should be directed, shaped and supported CSC of organizations refers to the knowledge, beliefs, perceptions, attitudes, assumptions, norms and values of people regarding cybersecurity and how they are manifested in people's behaviour with information technologies The attitude towards security measures in academic organisations is usually oriented towards free and open sharing of information and knowledge This positive direction has to be maintained and preserved, but also to be changed and adapted to current threats and security environment The balance between openness and security has to be analysed, rationally implemented, and monitored through establishment of organisational programme dedicated to CSC as a measure to influence the human factor in cybersecurity This article presents best practices of universities' digitalisation from the cyber security and CRC point of view ENISA's CSC development guidance was used as the main tool for developing the general CSC programme for universities The required changes of CSC and possible programme implementation are considered based on cases from several Bulgarian universities
Emotions Comparison of Commuter Line and Transjakarta Users Based on Twitter Using Multinomial Naïve Bayes Classifier

The availability of public transportation is a serious concern of the DKI Jakarta Government. The government provides a variety of public transports such as Commuter line and Transjakarta. User satisfaction with transportation services can display in social media such as Twitter. Tweets about service satisfaction can be emotional or not, this can be known through classification. One classification method that can be used is Multinomial Naive Bayes (MNB). The research stages in this classification include data collection, data, text preprocessing, TF-IDF weighting, MNB classification using 10-fold cross validation, and evaluation. The classification consists of two stages, the first stage to classify tweets contains emotions or not, the second stage classifies emotionally tweets into five classes namely happy, angry, sad, fear and surprised. The accuracy result of first stage classification for Commuter line, Transjakarta and mixed data were 84.40%, 81.78%, and 83.52%. The accuracy results of the second stage classification for the data of Commuter line, Transjakarta and mixed data were 91.85%, 91.21%, and 91.53%. The accuracy of balanced data with Random Oversampling (ROS) for Commuter line, Transjakarta, and mixed data were 96.18%, 96.55%, and 95.63%. Furthermore, conduct a comparative analysis of emotions based on the results of accuracy, word clouds, and time series graphics. The time series results show that the increase in Transjakarta angry emotions is higher than Commuter line, this is due to more factors in Transjakarta disruption. And the word cloud result shows the most common term used for the two data set is 'tunggu' or 'wait', this indicates that both transportations have the same problem, which is a long waiting time for transportation.
Image Colorization with Deep Convolutional Neural Networks

Colorization, a task of coloring monochrome images or videos, plays an important role in the human perception of visual information, to black and white pictures or videos. Colorizing, when done manually in Photoshop, a single picture might take months to get exactly correct. Understanding the tediousness of the task and inspired by the benefits of artificial intelligence, we propose a mechanism to automate the coloring process with the help of convolutional neural networks (CNNs). Firstly, an Alpha version is developed which successfully works on trained images but fails to colorize images, and the network has never seen before. Subsequently, a Beta version is implemented which is able to overcome the limitations of Alpha version and works well for untrained images. To further enhance the network, we fused the deep CNN with a classifier called Inception ResNet V2 which is a pre-trained model. Finally, the training results are observed for all the versions followed by a comparative analysis for trained and untrained images.
A two-dimensional quantum walk driven by a single two-side coin*

We study a two-dimensional quantum walk with only one walker alternatively walking along the horizontal and vertical directions driven by a single two-side coin. We find the analytical expressions of the first two moments of the walker’s position distribution in the long-time limit, which indicates that the variance of the position distribution grows quadratically with walking steps, showing a ballistic spreading typically for quantum walks. Besides, we analyze the correlation by calculating the quantum mutual information and the measurement-induced disturbance respectively as the outcome of the walk in one dimension is correlated to the other with the coin as a bridge. It is shown that the quantum correlation between walker spaces increases gradually with the walking steps.
A Framework Development on Big Data Analytics for Terahertz Healthcare

This chapter is mainly focused on the development of big data analytics in terahertz healthcare technology. In today's world, “big data” is a very familiar term, but the way it is interpreted is modified day by day. Healthcare is one aspect in which big data can be utilized to improve the overall system of healthcare, as, in the context of healthcare, the three primary “V's” of big data definition, volume, variety, and velocity, are very well suited. According to big data analysts, error-free analysis and outcome are ensured from big data, but this is really difficult for medical data due to the issues regarding the data quality. The final V related to big data is value, i.e., how much leverage the data can provide. Thus we can conclude that healthcare is a much preferred area for expert big data analysis. But there are several challenges to be faced in every aspect, starting from data collection to storage, analysis, prediction, etc. The main challenge is the unstructured nature of the data and the organizations from where the data are collected, following no standardized rules, which forms a big gap in the processing of information. Also, a huge investment is required for resources such as high-level expertise, knowledge, technologies used for data analytics, common data warehouses (for obtaining homogeneous data), etc. Despite having so many obstructions, big data analytics has already started growing rapidly in the healthcare sector.