Reviewerly/exHarmony huggingface dataset:
An Introduction to Deep Learning Techniques in Ultrasound Image Modality

Deep learning has recently developed as quickly rising field for the analysis of different medical images. Ultrasound (US) has developed as one of the most frequently clinically used imaging modalities. Although, it is a quickly developing technology but it also has challenges like low imaging quality and high variability. So, it is desirable to progressively develop techniques for automatic analysis of US images for diagnosis. Now a days, Deep learning is also widely used technique for analysis of many US images. In this review, we surveyed different deep learning techniques used for classification, detection, and segmentation along with the challenges of deep learning in US image analysis.
Research on Rainstorm Weather Detection Method Based on Weighted Density Adaptive A-DBSCAN Algorithm

Accurate detection of regional extreme weather is a research hotspot. Therefore, based on the real-time image of the camera, this paper proposes a new method for real-time detection of rainstorm weather based on the camera image by combining Laplacian operator and gaussian filter to calculate the image feature value (LOG value), and then establish the regression model of the LOG value and the recorded rainfall data. In addition, aiming at the characteristics of high noise in images collected by cameras, this paper proposes an adaptive method of A-DBSCAN denoising based on weighted density. The experiment in this paper proves that A-DBSCAN method can greatly improve the accuracy of real-time detection of rainstorm information from camera images.
Contents of Professional Training of Future After-School Teachers in Pedagogical Universities of Ukraine

The article addresses the main issues of future teachers’ professional training after-school education system in Ukraine. The training of after-school teachers in pedagogical University discover on the example of the specialty “Secondary education (Design and Technology)” at the educational level of bachelor. Particular attention is paid to implementation of the content of training of future teachers of after-school education through the subjects of “After-school education pedagogy”, “Introduction to after-school education”, “History of technology”, “History of after-school education”, “Practic tools in after-school education”, “Theory and metods of teaching in workshops”, “Organizational and masswork of after-school institutions”, “Psychology of creative activity”, “Nanotechnology”, etc.
Bayesian Learning for Classification using a Uniform Dirichlet Prior

In Bayesian learning, designs based on noninformative priors are appropriate when the user cannot confidently identify the data-generating distribution. While such learners cannot achieve the performance of those based on a well-matched subjective prior, they impart a robustness against poor prior selection. The uniform Dirichlet distribution is the true non-informative prior as it has full support over the space of candidate distributions; additionally, it leads to closed-form posteriors. This work applies such a prior to classification using the 0-1 loss, determines the optimal Bayes classifier and the corresponding minimum probability of error, and analyzes the results.
Object Recognition Based on Improved Zernike Moments and SURF

Since single global or local features can only describe objects partly or unilaterally that may lead to a low recognition rate, object recognition algorithm based on improved Zernike moments and Speeded-up Robust Features (SURF) is proposed. Firstly, the seven improved Zernike moments and SURF descriptor of objects are extracted, and then the two features are fused together with the weights in term of their contribution to the recognition. Secondly, Euclidean distance is calculated to determine the recognition result. Finally, the performance of algorithm is tested by some image data. Experimental results show that the proposed method is robust to scaling transformation, rotation change and noise variation. Compared with the other three ones, the results show that the proposed method has better recognition performance.
The Precise Location Method for Initial Target Based on Fused Information

If there is a large error in the initial position of the tracking method, the tracking performance will decrease. This paper proposes an accurate locating algorithm for initial tracking target with fusion of object-like information, saliency information, and priori information to provide accurate initial tracking target for further tracking method. Firstly, the probability distribution of object-like information is calculated by Edge Boxes which is used to detect the object-like regions. Then, the probability distribution of saliency information is calculated by spectral residual method. And the priori probability distribution is calculated according to the target initial position determined by the target detection. Finally, these three probabilities are adaptively fused to build multiple clues fused probability distribution. The precisely located initial tracking target area is the Edge Boxes object-like box with the highest average probability of fusion probability distribution. Experiments show that the accurate location method in this paper can achieve accurate relocation of the target in the case of selection box drift and scale changes and has good real-time performance.
Locality—Aware Scheduling for Containers in Cloud Computing

The cutting edge scheduler of containerized cloud administrations considers load balance as the main rule, numerous other imperative properties, including application execution, are ignored. In the period of Big Data, applications advance to be progressively more information escalated and subsequently performed inadequately when conveyed on containerized cloud administrations. With that in mind, this paper means to enhance the present cloud administration by considering application execution for the cutting edge compartments. The more explicitly, in this work we fabricate and break down another model that regards both burden equalization and application execution. Dissimilar to earlier examinations, our model edited compositions the predicament between burden equalization and application execution into brought together steaming issue and after that utilizes a factual technique to effectively settle it. The most difficult part is that some sub-issues are amazingly unpredictable (for instance, NP-hard) and heuristic calculations must be formulated. To wrap things up, we actualize a framework model of the proposed planning procedure for containerized cloud administrations. Exploratory outcomes demonstrate that our framework can fundamentally support application execution white safeguarding generally high burden balance.
Image Description Generation Using Deep Learning

Over the years, various attempts have been made to make social media and devices more disabled friendly. Screen readers and other assisting technologies have made it easier for people with visual disabilities like low vision and blindness to interact with social media. However, images on the social network are still inaccessible to them. Developing the description of an image in natural language is an upcoming problem which is at the intersection of disciplines of artificial intelligence, computer vision, and natural language processing. The image captioning task, also known as visual captioning, makes the technical chassis of many important applications of semantic visual search and visual questioning, photo and video sharing in social media, visual intelligence in chatting robots, and aid for visually impaired people to perceive surrounding objects and happenings. To describe an image effectively, it involves the detection of objects in images, identifying scenes and attributes of the objects. Then, these labels are used to construct semantically meaningful sentences to generate paragraphs which describe the images. Our research makes an attempt to carry out the task to produce a paragraph like a description of images.
Malay Manuscripts Transliteration Using Statistical Machine Translation (SMT)

Natural Language Processing (NLP) is a vital field of artificial intelligence that automates the study of human language. However for Malay manuscripts (MM) written in old jawi, its exposure on such field is limited. Besides, most of the studies related to MM studies and NLP were focused on rule based or rule based machine transliteration (RBMT). Hence the objective of this study is to propose a statistical approach for old jawi to modern jawi transliteration of Malay manuscript contents using Phrase Based Statistical Machine Translation (PBSMT) as its model. In order to achieve such purpose, quality score of Word Error Rate (WER) was computed on the transliteration output. Besides, the issues formerly encountered by rule based approach such as vocals limitation and homograph, reduplication, letters error and combination of multiple words were observed in the implementation. Moreover, this paper utilized exploratory approach as its research strategy and mixed method as its research method. The data for the analysis were extracted from a MM titled Bida̅yat al-Mubtadi̅ biFaḑlilla̅h al-Muhdi̅. Quality score of WER was computed for the evaluation of SMT output. Afterwards, related issues were identified and assessed. The research found that quality score of PBSMT for old jawi to modern jawi transliteration was high in terms of WER, however the issues of rule based were generally addressed by PBSMT except homograph. The research is however limited to the approach of SMT that solely focused on PBSMT as its model. Moreover, the corpus size was limited to one manuscript while SMT relies on corpus size. Nevertheless the research contributes to the wider coverage on Malay language as one of the under resource languages in NLP, in form of old and modern jawi. Besides, to the best of the researcher's knowledge, it is also the first to apply SMT (PBSMT) approach on old jawi transliteration. Most importantly, the study is to contribute on MM's.
Bitcoin and Global Political Uncertainty - Evidence from the U.S. Election Cycle

In this study, we investigate the impact of political uncertainty on Bitcoin. Introducing the U.S. federal election cycle as a proxy for political uncertainty, we find that (i) an increase in political uncertainty leads to a decrease in Bitcoin return, (ii) political uncertainty has the strongest impact on Bitcoin six and three months prior the election and decreases as the election date approaches, and (iii) the effect is more pronounced in the left and right tail of the distribution. The results shed a new light on the property of Bitcoin being a safe haven asset and provide important information for investors and policymakers.
Low-Complexity Adaptive Switched Prediction-Based Lossless Compression of Time-Lapse Hyperspectral Image Data

Time-lapse hyperspectral image (HSI) data has an enormous size and demands lossless compression for most of the high fidelity applications. In literature, spatial and spectral correlations in HSI are widely studied and used for compression. We propose a novel adaptive switched prediction-based scheme, which efficiently exploits temporal correlations in addition to spatial, and spectral correlations. The predictor switching uses a threshold, which is chosen based on the residual error distribution of already encoded band. Hence, our method does not need any overhead to be transmitted. The proposed scheme outperforms other state-of-the-art methods in bit-rate, and the method is computationally efficient too.
Security Analysis of Fountain V1

This paper analyses the security of the lightweight cryptographic algorithm Fountain (V1), which is a candidate in the current NIST competition for such ciphers. We examine the Boolean functions used in Fountain for state update and output. We show that correlations exist between S-box functions and some register stages that may lead to correlation attacks if certain update functions are detectable. We also show that the state update function avoids state convergence in any phase of cipher operation, but state collisions may be forced in one bit position, for select states, by manipulating the associated data or plaintext.
Multimedia Content's Brokerage

Metadata-based similarity measurement is far from obsolete nowadays, despite research's focus on content and context-based information. It allows for aggregating information from textual references, measuring similarity when content is not available, traditional keyword searches in search engines, merging results in meta-search engines, etc. Existing similarity measures do not take into consideration neither the unique nature of multimedia metadata nor the requirements of metadata-based information retrieval of multimedia. This work presents a commonly available author-title multimedia metadata hybrid similarity measure customised that has been shown to be experimentally significantly more effective than baseline measures. In addition, the work presents an architecture and a web-based implementation of an information system for data collection and validation by expert users that allows distributed, binary and scalar result ground-truth definition for a similarity measurement that can be used in digital content's identification and sales.
Palestra sobre o Tempo: John Cage e Henry David Thoreau

This essay is based on Lecture on the Weather (1976), a lecture performance by John Cage (1912–1992) drawn from texts by Henry David Thoreau (1817–1862). Cage’s poetics helps to reveal the affinities between the two authors and underscores the importance of empirical experience to achieve a broader understanding of the world, cultivating present moment awareness. ResumoEste ensaio parte da peça Lecture on the Weather (1976), uma lecture performance de John Cage (1912–1992) elaborada a partir de textos de Henry David Thoreau (1817–1862). A poética de Cage ajuda a revelar as afinidades existentes entre os dois autores e ressalva a importância da experiência empírica para uma compreensão mais alargada do mundo, partindo do fluxo duracional do momento presente. Palavras-chave: John Cage; Henry David Thoreau; Performance; Experiência; Tempo
Categorical vehicle classification using Deep Neural Networks

Categorical vehicle classification on sequence of image data being a task of great importance and challenging as well for automated traffic surveillance and for the autonomous vehicles building up nowadays. For an autonomous vehicle, it demands for an approach that can precisely detect and classify the vehicle or object around it while moving to avoid any accident. Deep neural networks approach for object detection and classification overpowers other machine learning algorithms that lags in accuracy and computational complexity. In this paper we have classified different categories of vehicles as HMV, LMV and Two-wheelers using deep neural networks. The implementation utilizes nine-layer network to faithfully detect and classify the vehicle category. The results showcase that we can rely on the deep neural networks approach for the vehicle category detection and classification for the urban traffic surveillance and for the autonomous vehicles.
Distinguishing Photographic and Computer Generated Images using Local Rank Transform

In recent times, the availability of multimedia in digital format and their sharing through Internet has brought forth issues like authenticity. When light passes through a digital camera, it undergoes various processing steps such as sensor nonlinearities, compression, color filter array interpolation, white balance, auto exposure, etc. The camera fingerprints left by these steps can be used to effectively distinguish between photographic and photorealistic images. In this paper, we describe a novel technique using local rank transform to distinguish between photographic and synthetic image. The key to distinguishing between these two classes is to detect the presence of demosaicing, which is carried out using the local rank transform followed by estimating a statistical feature namely coefficient of variation which is used to train a C-SVM classifier for training and testing purposes. Good accuracy has been observed for the proposed method in comparison to other existing methods in this domain.
Semi-automated detection of airbases in satellite images using Deep Convolutional Neural Networks

Manual inspection of satellite images for detection of objects of interest is very cumbersome and time consuming. In this paper we report the use of the YOLO Deep Convolutional Neural Network(CNN) architecture for automatic detection of air-bases in satellite images. We have trained the YOLO network with a custom dataset of about 360 air-bases. The trained network has demonstrated a recall value of 0.7716 on the test Images.
On Mathematical Visualization in Education

This article treats the use of modern information technologies in the classroom educational process. It emphasizes the need to combine symbolic and visual mathematics, describes the problems associated with this issue, provides a review of the existing systems and a list of the requirements a modern mathematical visualization system must meet. The article is conceived as a manifesto for the use of mathematical visualization in education. The article then goes on to describe the developments of the authors’ research group. The functionality of visualmath.ru website is described. This resource contains an ample collection of visual and text modules for teachers to create presentations based largely on visual materials. The most important part of the article is the description of fast and powerful JavaScript visulatization libraries developed specifically for the project: Skeleton and Grafar. The former is designed to display two-dimensional graphs, while the latter visualizes three-dimensional objects with transparency and illumination effects. Both libraries are capable of processing large element sets in near-real time. In conclusion, selected examples of visualizations created using the libraries use are provided, including the ones used in courses on mathematical analysis and analytical geometry.
Risk Assessment Method Based on Cloud Model for Distribution Network

To improve the safe level of operation and security guarantee for distribution network. This paper proposes a risk assessment method based on cloud prediction model for distribution network. First, the cloud theory is used in distribution network main equipment failure prediction to build prediction rule based on cloud generator. On the basis of the prediction rule, a cloud-based distribution network main equipment failure rate prediction model is built. Then, failure rate and power outage user-hours of load were calculated based on distribution network main equipment failure rate and net topology with minimum path method. The assessment method could be used to estimate the risk of distribution network in order to scan the weakness in distribution operation. Finally, the test results of IEEE-RBTS BUS6 indicate the failure rate, failure time, risk of the load point can be calculated. The results demonstrate the effectiveness of the proposed method.
Research on yhe Approximation Properties of Two-Dimensional Stochastic Bases in Image Reconstruction Problems

This paper discusses the problems of constructing a basis for two-dimensional stochastic functions, which are samples of random numbers with a given distribution law. The algorithm for constructing an approximation model with the use of this basis is described. The general algorithm for two-dimensional signal recovery using the stochastic basis by the least squares method with a given weight function is given. The approach to solving the problem of recovery of blurred images with a known point scattering function by constructing an inverse filter model is proposed. The approbation of the algorithms was performed on model examples and in the processing of real images obtained by remote sensing of the Earth. To quantify the quality of the recovery, we used a relative root mean square measurement of the difference between the reference and reconstructed images.
On Fusing Multiple Instance Selection Results

Instance selection is an important preprocessing step in data mining. Its aim is to filter out unrepresentative data samples from a given training dataset, which allow the classifier to perform better than the one without instance selection. Since various instance selection algorithms have been proposed in the literature, no study considers applying the information fusion principle to combine multiple instance selection results. This paper uses three well-known instance selection algorithms, which are IB3, DROP3, and GA, and their selection results are combined via the union, intersection, and multi-intersection strategies. Our experimental results based on 50 various domains of datasets show that the union between GA and DROP3 performs the best. However, it does not produce the largest reduction rate. On the other hand, if both classification accuracy and reduction rate are considered, the union between DROP3 and IB3 is the better choice.
Implementation of Digital Images Using the Chain Code Method to Calculate the Area and Circumference of 2-Dimensional Objects

Geometry is one of the oldest branches of science in mathematics that studies the shape of fields and spaces applied by architects and building technicians. Studying geometry provides many basic skills and helps to build thinking skills in analytical reasoning and problem-solving logic. Geometry is useful for understanding space in a real-life that helps in understanding better concepts. With the help of Smartphones as an information technology tool, today can simplify the calculation of a flat build. In this study will be presented the use of computer vision to calculate several types of flat shapes. The automatic calculation of the flat shape formula becomes a solution to calculate quickly to correct the answers to mathematical questions about regular or irregular flat shapes. The calculation process is done by detecting a flat shape that is taken using a Smartphone camera. Once detected, the image is converted into a binary image. Then the measurement of each pixel in the object image is measured, then the formula to be used and the calculation are processed by the application submitted. This study will utilize the Chain Code technique as a digital image processing which functions to generate codes in the form of several numbers based on the direction of the wind. This chain code can represent curves, lines, or contours of a field, determine circumference and area, and can determine the form factor of an object. From this method, it is used to detect objects and then scaling the size of objects so that the formula of the flat building that will be identified can be applied. The results showed that the detection of 2-dimensional objects was quite accurate with a percentage of 57.57% of 33 tests, and with 3 models of variation testing.