Reviewerly/exHarmony huggingface dataset:
Bayesian spatially constrained Fernandez-Steel Skew Normal Mixture model for MRI-based brain tumor segmentation

Brain scanning using Magnetic Resonance Imaging (MRI) can be used to detect the brain tumor. MRI could detect the soft tissue abnormalities better than the other radiological devices. However, the noise in the image of the MRI sometimes appears randomly, so that it is difficult to detect the tumor more precisely. The image segmentation, therefore, is needed to be able to diagnose the location of the brain tumor by separating the tumor as the Region of Interest (ROI) from other regions. Gaussian Mixture Model (GMM) is commonly used for image segmentation. This method, however, frequently provides a poor result since it is less able to explain the skew pattern of MRI data. Moreover, the GMM is not considering the spatial dependencies between pixel, therefore it is less capable of handling noise. This study tries to employ the Fernandez Steel Skew Normal (FSSN) distribution as the replacement of the Gaussian in the GMM. The FSSN distribution could accommodate symmetrical and even asymmetrical pattern of the MRI data adaptively. In order to increase the noise robustness, the spatial dependencies with Markov Random Field (MRF) are used as a prior in Bayesian Markov chain Monte Carlo. The proposed model is called as the Spatially Constrained FSSN mixture model (Sc-FSSNMM). The results show that by applying the Sc- FSSNMM, the segmentation result is better than the GMM. In additions, the Sc-FSSNMM is more robust to noise while also more parsimony.
UNAS: Differentiable Architecture Search Meets Reinforcement Learning

Neural architecture search (NAS) aims to discover network architectures with desired properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has demonstrated promising results while maintaining a search cost orders of magnitude lower than reinforcement learning (RL) based NAS. However, DNAS models can only optimize differentiable loss functions in search, and they require an accurate differentiable approximation of non-differentiable criteria. In this work, we present UNAS, a unified framework for NAS, that encapsulates recent DNAS and RL-based approaches under one framework. Our framework brings the best of both worlds, and it enables us to search for architectures with both differentiable and non-differentiable criteria in one unified framework while maintaining a low search cost. Further, we introduce a new objective function for search based on the generalization gap that prevents the selection of architectures prone to overfitting. We present extensive experiments on the CIFAR-10, CIFAR-100, and ImageNet datasets and we perform search in two fundamentally different search spaces. We show that UNAS obtains the state-of-the-art average accuracy on all three datasets when compared to the architectures searched in the DARTS space. Moreover, we show that UNAS can find an efficient and accurate architecture in the ProxylessNAS search space, that outperforms existing MobileNetV2 based architectures. The source code is available at this https URL .
Musical Enlightenment as a Way of Preserving the Cultural Identity of Students Foreign Russian Universities

The concept of the research is based on the idea of a high educational mission of universities in the Russian Diaspora, using the example of the Pridnestrovian State University named after T. G. Shevchenko, which is the bearer of the spiritual values of Russian culture, including Russian musical classics. The article highlights the problem of preserving the cultural identity of students of foreign Russian-speaking universities in the post-Soviet space in the context of educational science. The authors consider the historical origins of music education in the Russian Diaspora and analyze the typical features of educational activities that are closely related to the traditions of Russian culture and the cultural and typological foundations of Russian musical art as a phenomenon of the centuries-old Orthodox civilization. The article substantiates the relevance of the formation of the cultural environment of foreign Russian-language universities by means of musical enlightenment, analyzes the maintenance of Russian students’ identity based on the development of musical and educational projects tested on the basis of Pridnestrovian State University named after T. G. Shevchenko, shows the importance of including classical heritage of Russian music in the training program for music students. The authors proceed from the idea of creating a pedagogical system of musical and educational work in an overseas Russian-language university, operating according to the educational standards of the Russian Federation. The article describes the levels of gradual professional training of music students for musical education activities that can ensure the effective development of multi-ethnic student contingent of cultural typological dominants of Russian music and Russian culture; a complex of special methods is presented that initiates the motivation of future music teachers to various forms of enlightenment, activating their intellectual and creative resources, promoting the mastering of the technology of organizing and implementing musical educational projects.
Infrared spectroscopy characteristics of mount Sinabung volcanic materials, North Sumatra, Indonesia

Abstract Infrared spectroscopy has the capability to determine the mineralogical composition of geologic materials. Its reflectance spectra contain diagnostic spectral absorption feature characteristics that can be used for analyzing the mineral chemistry and chemical composition of volcanic material samples. In this research, we analyze the infrared spectral shapes and properties of volcanic materials at specific wavelength ranges in order to identify compositional information of the samples. We used volcanic material samples such as ash and rocks (tuffs) collected from Sinabung volcano, North Sumatra, Indonesia. Reflectance spectra of the samples were characterized using infrared spectroscopic method. Variation in depth of spectral absorption features and reflectance values of ash and rock samples related to grain size, chemical and mineralogical composition, and surface roughness of the samples. The spectroscopic characteristics showed that these volcanic samples exhibit a strong Al-OH absorption feature centered at ~2200 nm. It indicates that the samples contain predominantly clay or other phyllosilicate minerals. Reflectance spectroscopy can be used to extract compositional information of mineralogy in volcanic material samples.
Phoneme Boundary Analysis Using Graphs

In this paper, a novel approach for phoneme boundary detection is proposed. The method uses a graph based structural analysis to understand the changes in different phonemes so that a clear boundary can be drawn between the phonemes. The features of speech segments are represented in a graph data structure and the changes in signal are observed by using a Graph Edit Distance (GED) algorithm. Finally the edit distances that are computed for the speech segments are used as criteria for the boundary detection. The proposed method with results are presented.
TEXTURE IMAGE CLASSIFICATION BY STATISTICAL FEATURES OF WAVELET

An unidentified image sample is assigned to a recognized texture class is known as Texture Classification (TC).The main challenging task in TC is the non uniformity changes in orientation, visual appearance and scale.Texture is an important feature in computer analysis for the purpose of classification.In this paper, an efficient TC system based on Discrete Wavelet Transform (DWT) is presented.The performance of the system is evaluated by Brodatz database.At first, the DWT is used to decompose the input texture image for feature extraction at a particular decomposition level.From each sub-band coefficients statistical features are extracted.Finally, k-Nearest Neighbour (kNN) classifier is used for classification.Results show that a better classification accuracy of 94.72% is achieved by the features of 3 rd level DWT and KNN classifier.
Parameter-wise co-clustering for high-dimensional data

In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the literature. A parameter-wise co-clustering model, for (possibly high-dimensional) data modelled via continuous random variables, is presented. The proposed model, although allowing more flexibility, still maintains the very high degree of parsimony and interpretability achieved by traditional co-clustering. More precisely, the keystone consists of dramatically increasing the number of column-clusters while expressing each as a combination of a limited number of mean-dependent and variance-dependent column-clusters. A stochastic expectation-maximization algorithm along with a Gibbs sampler is used for parameter estimation and an integrated complete log-likelihood criterion is used for model selection. Simulated and real datasets are used for illustration and comparison with traditional co-clustering.
Recovering lost areas of the underlying image surface using a method based on similar blocks

The article presents the relevance of developing a method for recovering lost areas of a map of the area. The object of research is the methods of building a map of the area and restoring images. The algorithm for recovering images of lost areas is investigated and its software implementation is developed. The effectiveness of the developed algorithm was evaluated on a test example using a statistical criterion.
Anomaly detection on flight route using similarity and grouping approach based-on automatic dependent surveillance-broadcast

Flight anomaly detection is used to determine the abnormal state data on the flight route. This study focused on two groups: general aviation habits (C1)and anomalies (C2). Groups C1 and C2 are obtained through similarity test with references. The methods used are: 1) normalizing the training data form, 2) forming the training segment 3) calculating the log-likelihood value and determining the maximum log-likelihood (C1) and minimum log-likelihood (C2) values, 4) determining the percentage of data based on criteria C1 and C2 by grouping SVM, KNN, and K-means and 5) Testing with log-likelihood ratio. The results achieved in each segment are Log-likelihood value in C1Latitude is -15.97 and C1Longitude is -16.97. On the other hand, Log-likelihood value in C2Latitude is -19.3 (maximum) and -20.3 (minimum), and log-likelihood value in C2Longitude is -21.2 (maximum) and -24.8 (minimum). The largest percentage value in C1 is 96%, while the largest in C2 is 10%. Thus, the highest potential anomaly data is 10%, and the smallest is 3%. Also, there are performance tests based on F-measure to get accuracy and precision.
PλωNK: functional probabilistic NetKAT

This work presents PλωNK, a functional probabilistic network programming language that extends Probabilistic NetKAT (PNK). Like PNK, it enables probabilistic modelling of network behaviour, by providing probabilistic choice and infinite iteration (to simulate looping network packets). Yet, unlike PNK, it also offers abstraction and higher-order functions to make programming much more convenient. The formalisation of PλωNK is challenging for two reasons: Firstly, network programming induces multiple side effects (in particular, parallelism and probabilistic choice) which need to be carefully controlled in a functional setting. Our system uses an explicit syntax for thunks and sequencing which makes the interplay of these effects explicit. Secondly, measure theory, the standard domain for formalisations of (continuous) probablistic languages, does not admit higher-order functions. We address this by leveraging ω-Quasi Borel Spaces (ωQBSes), a recent advancement in the domain theory of probabilistic programming languages. We believe that our work is not only useful for bringing abstraction to PNK, but that—as part of our contribution—we have developed the meta-theory for a probabilistic language that combines advanced features like higher-order functions, iteration and parallelism, which may inform similar meta-theoretic efforts.
Classification of AI Powered Social Bots on Twitter by Sentiment Analysis and Data Mining through SVM

In this paper, the behavior of twitter bots and their influence on the social media is investigated.As the user population increased on Twitter, it became an ideal platform for social manipulation and influencing perspectives.There has been a rise in autonomous entities, which are known to exploit Twitter's API feature by performing actions such as tweeting, retweeting, liking, following, or messaging other users, that engage in social engineering.In this research, a framework based on existing research to detect these autonomous entities on Twitter is presented.For detection, tweet syntax analysis, user behavior along with sentiment analysis is performed.Sentiment analysis is an opinion mining technique which analyzes people's opinions or sentiments.Crawling on Twitter is performed for random tweets, user specific tweets and features are extracted by aggregating the tweets by their senders.Based on the resultant information the human or bot training and classification is made.After successfully training with SVM, this model was able to detect Twitter bots with a precision of 0.75.
A Demographic Perspective of Smartphone Security and Its Redesigned Notifications

Billions of smartphones, globally, are running out-of-date Operating Systems (OS) which make them vulnerable to cyberattacks. Behaviours of users in updating their OS vary between different geographic locations considering various demographic factors. For instance, developing countries have a very different stance compared to developed ones on how their users perceive device updates. To assert our claim, we first investigated security behaviours among different demographics in Japan and Tanzania. The results indicate that demographic factors such as culture, income, and geographic location highly impact behaviours of participants on OS updating. However, education and awareness do not seem to have significant impact on security behaviours. Consequently, insecure behaviours were equally exhibited among most participants regardless of their education levels or awareness. We also found that most participants do not update their application software on their smartphones despite being aware. Moreover, in the developing country settings, most participants tend to avoid certain security advice because they necessitate incurring data charges that take up a high percentage of their incomes. Then, we surveyed and evaluated the participants' preferences for different re-designed security notifications for improving update compliance. Finally, we propose color-coded fear-appeal designs for persuading users into updating their devices' application software.
The Reciprocal Teaching Model in the Development of Writing in Tenth Graders

This article presents an action research study which purpose was to determine the extent to which the Reciprocal Teaching model (RT), proposed by Palincscar and Brown (1984), impacts tenth grader’s English writing skills. The research work included: (1) A diagnostic stage to determine the participant’s initial difficulties in regards to their writing skills; (2) An action stage that consisted of the implementation of some workshops based on the RT model and focused on reading strategies, namely predicting, clarifying, questioning, and summarizing. (3) An evaluation stage where key findings indicated the usefulness of RT for building confidence in the students and for developing their writing skills as they were able to expand their vocabulary spectrum, reduce syntax errors, and improve the content, organization and punctuation of their writing. Findings also suggest that assessing writing performance through portfolios is useful to enhance the school curriculum because students engaged in their own learning and participated actively in their learning process.&#x0D; Keywords: curriculum, Reciprocal Teaching Model, reading strategies, portfolios, writing.
Control-Tutored Reinforcement Learning

We introduce a control-tutored reinforcement learning (CTRL) algorithm. The idea is to enhance tabular learning algorithms so as to improve the exploration of the state-space, and substantially reduce learning times by leveraging some limited knowledge of the plant encoded into a tutoring model-based control strategy. We illustrate the benefits of our novel approach and its effectiveness by using the problem of controlling one or more agents to herd and contain within a goal region a set of target free-roving agents in the plane.
A Novel Approach for Improving Breast Cancer Risk Prediction using Machine Learning Algorithms : A Survey

Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women's deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. Here, a common misconception is that predictive analytics and machine learning are the same thing where in predictive analysis is a statistical learning and machine learning is pattern recognition and explores the notion that algorithms can learn from and make predictions on data. In this paper, we are addressing the problem of predictive analysis by adding machine learning techniques for better prediction of breast cancer. In this, a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer (original) datasets is conducted. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of hybrid algorithm in terms of accuracy, precision, sensitivity and specificity.
Data Mining with R: An Applied Study

Purpose -The aim of this study is to analyze different classification algorithms with R programming and to determine the accuracy rates.It also encourages the use of the R program by giving readers the opportunity to experiment.Method -For the purposes mentioned above, different data sets were obtained from the UC Irvine Machine Learning Repository (2019), which was suitable for classification.After preparing data set and R program for data mining, performance evaluation was made with classification algorithms (J48, Random Forest, Naïve Bayes).The 'accuracy' criterion was taken into consideration when interpreting the results.Results -At the end of the study, the accuracy rates were determined for three data sets.Looking at the "wine" data, the performance of all three algorithms is quite successful.The results of the other two data sets (lenses and liver) are parallel.Only the 'liver' dataset gave a slightly lower accuracy than expected with the Naïve Bayes algorithm (0.55). 202Conclusion -In this study, performance comparison of algorithms has been made within the scope of data mining with R program.The accuracy rate was taken as a criterion.All codes are given with their outputs in order to be an example especially for young researchers or students.It is thought that this study can be a source for other researchers, will encourage the use of R and the researchers or students will try new papers by trying the codes.Recommendations -In subsequent studies, a similar study can be done by developing the given codes.Or how to make classification analysis in R with different algorithms can be examined.
One-Shot Imitation Filming of Human Motion Videos

Imitation learning has been applied to mimic the operation of a human cameraman in several autonomous cinematography systems. To imitate different filming styles, existing methods train multiple models, where each model handles a particular style and requires a significant number of training samples. As a result, existing methods can hardly generalize to unseen styles. In this paper, we propose a framework, which can imitate a filming style by "seeing" only a single demonstration video of the same style, i.e., one-shot imitation filming. This is done by two key enabling techniques: 1) feature extraction of the filming style from the demo video, and 2) filming style transfer from the demo video to the new situation. We implement the approach with deep neural network and deploy it to a 6 degrees of freedom (DOF) real drone cinematography system by first predicting the future camera motions, and then converting them to the drone's control commands via an odometer. Our experimental results on extensive datasets and showcases exhibit significant improvements in our approach over conventional baselines and our approach can successfully mimic the footage with an unseen style.
Modelling Synthetic Non-Linguistic Utterances for Communication in Dialogue

Non-Linguistic Utterances (NLUs) present a potentially useful additional communication channel between humans and machines. NLUs may be appropriate in situations such as assisting tourists with various language backgrounds and needs. An experiment was done to model NLU interpretation as dialogue parts. 1000 sounds were generated and evaluated for dialogue relevance to reduce the number to 53, used in the NLU evaluation experiment. 31 Subjects listened to the sounds and rated the following communicative acts for each on a 5-point scale: Greeting, Reject, Question, Thanking, Accept, Apology, Non-Understanding, and Exclamation. Factor analysis yielded three factors: 1) Interrogative vs Apologetic, 2) Appreciative vs Negative, and 3) Positive vs Non-Understanding. Prosodic features such as average pitch, pitch contour, timbre, and duration influenced the interpretation of the sounds. Future work will apply spectral-based speech and emotion recognition techniques along with prosodic features to allow machines to augment dialogue with effective NLUs.
Weakly Supervised Fine-Grained Visual Recognition via Adversarial Complementary Attentions and Hierarchical Bilinear Pooling

Learning subtle and distinctive features is the key to fine-grained object recognition. Previous approaches use attention mechanisms to localize discriminatory regions and learn fine-grained details, which often treat each object part attention independently while neglecting the correlations among them and may lead them attentive to the same distinctive features. In this paper, we proposed a novel fine-grained visual recognition method to solve this problem by adversarial complementary attention (ACA) strategy and hierarchical bilinear pooling. Our method learns an attention-based classification branch through an attention module, and then train a counterpart classifier branch with adversarial complementary attention parts by erasing attentive regions in the first network branch from the feature maps. To capture different attention features interactions, a hierarchical bilinear pooling framework is used. Our method can be trained end-to-end without any bounding box/part annotations. Extensive experiments indicate that our approach is efficient and achieves the state-of-the-art performance on widely used fine-grained classification datasets, including CUB-200-2011, Stanford Cars, and FGVC-Aircraft datasets.