Reviewerly/exHarmony huggingface dataset:
Classification weight of new born baby at Sayang Ibu maternity Hospital with probabilistic neural network (PNN)

The purpose of this research is to classify baby weight before baby was born by considering maternal factors such as maternal weight, maternal height, maternal hemoglobin (Hb), maternal blood pressure, maternal age, total parity and fetal factors such as twin status using PNN (Probabilistic Neural Network). PNN is one of the models in Artificial Neural Networks (ANN) that exists for classification. PNN can be used in classification because it can classify with optimal results, higher accuracy and faster than other neural network models. PNN is structured by four layers, namely the input layer, pattern layer, summation layer and output layer. From classification with PNN on weight of new born baby using 7 factors, we obtained that the best proportion of accuracy classification on weight of new baby born is 90:10 with σ value is 0,2. The accuracy of classification within training data is 100% whereas on testing data is 92%. Based on classification on testing data we obtain eleven data are classified into the same as original class and one data os predicted to the other class.
The Methods for Reducing the Number of OOVs in Chinese-Uyghur NMT System

Recently, neural machine translation (NMT) has made significant achievements in multiple language pairs, and surpassed traditional statistical machine translation. However, NMT has strict restrictions on vocabulary, which leads to out-of-vocabulary (OOV) problems. Agglutinative languages, such as Uyghur, which are rich in morphological changes, theoretically have unlimited vocabulary, and we confront with serious OOV problems with these languages in NMT. In a quest of how to reduce OOVs in Chinese - Uyghur pair NMT, we present two different solutions on this study: In the first solution, with regard of the key feature of agglutinative languages, the declension, we segment Uyghur words into stems and affixes. The NMT test we ran on the stem-affix segmented data showed that the number of OOVs reduced from an original 1526 to only 121. In the second solution, we ran a Similar Word Replacement test on low-frequency words from Chinese corpus after training and achieved an even more reduced OOV result of 98. The mass reduction of OOVs from 1.5 thousand to only a hundred signifies the effectiveness of the solutions in this study.
High-resolution imaging of space target based on compressed sensing

Different degradation factors such as Poisson noise, blurring effect, different contrast and different reflectivity and so on will impose severe influences on the imaging process of the non-cooperative space targets with low light intensity and the corresponding image quality is usually poor. In this paper, a two-step reconstruction framework based on compressed sensing (CS) theory is proposed to deal with these degradation factors to improve the quality of the space target images. The proposed algorithm is divided into two steps, the first step is standard compressed sensing based reconstruction, and the second step is super-resolution based on the theory of compressed sensing. Specifically speaking, when the sparsely sampling are obtained, the total variation augmented Lagrangian alternating direction algorithm (TVAL3) is first used to recover the 2D image, which only obtain 25% of the number of pixels in the original image instead of all the pixels in the traditional sampling. Subsequently, the single-frame image super-resolution reconstruction is performed on the captured 2D image, and the super-resolution algorithm based on the dictionary learning is used to realize super-resolution reconstruction, which makes the image resolution doubled.
Semi-supervised semantic image segmentation using dual discriminator adversarial networks

Semantic image segmentation approaches based on convolutional neural networks require a large amount of pixel-level training data, but the labeling process is time consuming and laborious. In this paper, we propose a semi-supervised semantic segmentation method that can leverage unlabeled data in model training to alleviate the task of labeling. A novel GAN framework comprised of a generator network and a dual discriminator network is proposed, and the entire network is trained by coupling the standard multi-class cross entropy loss with the adversarial loss. To further improve the localization of object boundaries, a self-attention layer is added to the generator network to model long-range dependencies in images, and a skip layer is also added to combine deep layer with highly abstract information and shallow layer with detailed appearance information. The dual discriminator network includes a fully convolutional discriminator and a typical GAN discriminator, so that the input image can be discriminated on both pixel level and image level. For semi-supervised semantic segmentation, the predicted segmentation results of unlabeled images are selected by image-level discriminator, and then their trustworthy regions are generated by pixel-level discriminator to provide additional supervisory signals. Extensive experiments on PASCAL VOC 2012 dataset demonstrate that our approach outperforms existing semi-supervised semantic image segmentation methods on accuracy.
Mining Quantitative Temporal Dependencies Between Interval-Based Streams

Data streams gathered from sensor systems can contain a significant amount of noise and are challenging to sequential pattern mining algorithms. A majority of existing approaches deals with such data as time point events to find before/after relations that induces loss of information when dealing with events lasting in time, i.e intervals. Other interval-based approaches focus on qualitative patterns and are sensitive to temporal variability. We consider that quantitative patterns maintaining lag information permit to deal better with this problem. In this work, we propose an efficient algorithm devised to extract quantitative patterns from interval streams: Interval Time Lag Discovery (ITLD). It is based on an intersection-based confidence that is automatically assessed with a statistical $$\chi ^2$$ test. Experimental results, on both synthetic and real-life data, show that our method is more suitable for sensor interval streams and provides more precise information in comparison with existing approaches.
UFJF-MLTK: a framework for machine learning algorithms

Machine learning techniques have become increasingly common due to the extension of their application domains and because they can improve their performance when exposed to new data. Several methods have been proposed to address problems of the area, bringing the challenge of comparing different methods to find the one that best solves a problem. Frameworks and libraries focused on learning algorithms can reduce this effort. This paper describes the UFJF-MLTK, an object-oriented framework that helps to choose between different methods, in the development of new algorithms through the instantiation of a C++ class architecture that covers various types of learning algorithms and also helps in teaching the subject. We discuss the problems faced in the project architecture, the components of the framework, the algorithms that currently compose it, how it was documented and examples of its instantiation.
Travelling salesman problem concerning to manipulator Kawasaki FS03N trajectory formation

Abstract An idea of travelling salesman problem (TSP) application to an optimal manipulator path finding was implemented. A set of known poses robot should pass is given. The poses sequence may vary. A traveling time is a performance criteria to be minimized. This task was realized concerning to a special case of Kawasaki FS03N manipulator movement by dynamic programming method. A special Bellman function state notation was used that in some cases gives an ability to solve the task at industrial robot controller. The state notation rule is one-one mapping rule between TSP state and array index it is stored.
An improved self-supervised framework for feature point detection

The robust image feature point is a critical component of image matching. In order to detect feature points that are robust to illumination changes and viewpoint changes, an improved self-supervised learning framework for feature point detector is proposed. Firstly, feature point detector is trained in simple synthetic datasets. Then, the labeled dataset is generated by applying the Homographic Adaptation to automatically label the unlabeled area image. Finally, the full convolution network is trained with the labeled dataset. In this paper, the convolutional neural network in the selfsupervised learning framework is improved, mainly by increasing the number of layers of the neural network, from the original 8 layers to 11 layers. Experiments on the HPatches dataset show that the improved self-supervised feature point detector has achieved good results
Intraframe interpolation based on edge detection

In order to eliminate the effect of edge aliasing and blurring caused by deinterlacing algorithm based on traditional edge detection, this paper proposes a novel edge direction determination method. On the basic of the preliminary direction judgment obtained from the absolute difference, the reliability judgment of the direction is added. The correlation between the vertical direction of the current pixel and the pixel mean of the remaining four directions is used to obtain a direction determination criterion, and then interpolation is performed by means of median filtering. The experimental results indicate that the PSNR and visual quality of the proposed algorithm outperforms other existing methods, whether its objective evaluation or subjective evaluation.
RESEARCH ACTIVITIES OF FUTURE TEACHERS OF TECHNOLOGIES AT THE ELECTRICAL ENGINEERING CLASSES

The article proves that the formation of research competence of future technology teachers depends on the methodology and organization of educational, cognitive and research activity of students and on the formation of their educational and research skills. It is substantiated that the importance in the system of future technology teachers training belongs to laboratory, practical, laboratory-practical and individual occupations, as well as consultations with the teacher. At these classes, consolidation of theoretical knowledge, the formation of skills in the installation and operation of electrical equipment, the mastery of the apparatus and algorithms for scientific research. It is established that in the laboratory and practical classes it is necessary to focus the attention of future teachers on problem tasks for activating their search activity. Under such conditions, a situation arises when choosing the knowledge that enables the task to be properly resolved. Creative tasks encourage students to independently search for different ways of solving problems of educational and production character, assumptions, formulating hypotheses, implementing purposeful attempts to confirm them, rejecting some unsuccessful hypotheses, and replacing others with consistent considerations. It is noted that modern methodological and technical support of laboratory classes in different disciplines is designed for an abstract average student and does not always meet the requirements for organizing a developing, creative process. Significant potential for the formation of research competence of future technology teachers is concentrated in the system of laboratory classes in electrical engineering.
Visual Exploratory Activity under Microgravity Conditions in VR: An Exploratory Study during a Parabolic Flight

This work explores the human visual exploratory activity (VEA) in a microgravity environment compared to one-G. Parabolic flights are the only way to experience microgravity without astronaut training, and the duration of each microgravity segment is less than 20 seconds. Under such special conditions, the test subject visually searches a virtual representation of the International Space Station located in his Field of Regard (FOR). The task was repeated in two different postural positions. Interestingly, the test subject reported a significant reduction of microgravity-related motion sickness while experiencing the VR simulation, in comparison to his previous parabolic flights without VR.
OnToCode: Template-based code-generation from ontologies

In support of research into software-based methods for spacecraft development and manufacturing, ontologies have been (e.g.(Hennig, Viehl, & Benedikt Kämpgen, 2016)) and are being developed.These ontologies serve as domain models in model-driven engineering processes for the development of software tool prototypes.
Efficient and Secure Data Storage CP-ABE Analysis Algorithm

In the contemporary digital environment, secure search over encrypted data is necessary to prevent the unauthorized data usage practices. To provide data privacy and security fine grained access control is important. For having data privacy data have to be stored onto the cloud in an encrypted form and have to be decrypted before retrieving the message. Encrypting the information is a tedious process for mobiles and also its recovery is a significantly challenging task since the mobile devices have limited bandwidth and battery life. Searching the encrypted data over mobile cloud is a tedious process. To tackle this issue, Cipher text Policy Attribute Based Encryption (CP-ABE) mechanism is proposed. The proposed system will perform computation in cloud rather than the mobile device. This mechanism could effectively prevent malicious searching and decrypting of files. Also, the proposed system support many number of attributes and flexible multiple keyword search patterns in which query file order will not affect the search result. This indicates CP-ABE scheme to improve the efficiency and security.
Private and Secure Mixing in Credit Networks

In this paper, we propose a system for mixing transactions in payment networks such as credit networks. Credit networks like Ripple and Stellar are increasingly popular, and can facilitate crosscurrency transactions in a fraction of the time it would take for banks or other financial institutions to process the same transaction, and at a fraction of the cost. Unlike for cryptocurrencies, there has been little work in the area of designing secure and private mixers for credit networks. Mixers for cryptocurrencies such as Bitcoin cannot be directly applied to the credit network domain because credit networks have an inherently different structure and purpose than cryptocurrencies. We design a system that uses cryptographic constructs such as ring signatures, commitments, and zero knowledge proofs to provide security/integrity of all transactions, ensures privacy of the users involved in a transaction, as well as privacy of the amount transacted. We also provide preliminary experimental results.
LaNa2

Many tasks in various areas of science and society depend on interaction with the computer. The more transparent and simple the interaction, the better and faster the results will become. The gain in productivity is most evident when the tasks become more complex and the amount of data available increases exponentially. For example, amount of data produced in social networks. In these case, the manual evaluation is almost impossible. Thus, the study of techniques and applications that extract and interpret this data automatically has become an essential task in the decision making of the most diverse areas. This work aims at the extension and demonstration of LaNa2: a web platform for the collection, processing, analysis and visualization of textual contents in a clear and objective way. The main differential in comparison with other tools is its free access, in addition to the adoption of Machine Learning models for textual classification. We compare LaNa2 with other tools available in the market and run experiments to demonstrate their functionality.
A Cloud Based Augmented Reality Framework - Enabling User-Centered Interactive Systems Development

Concepts for user interfaces are developed in order to enable humans exchanging information and having a better understanding of different processes. With the rise of technology, concepts of enriching our environments with computer-generated graphics appeared in the 1960s. The expansion of visual perception of the surrounding is the core of Augmented Reality (AR). Many AR applications have been appearing in different areas, opening the door for revolutionizing and changing interactivity models and the way people perform tasks. Even though AR systems’ structures are well defined, applications are not yet widespread. One of the main reasons for this is that AR system developers face difficulties with developing their applications and adapting them to different hardware. Each hardware set (i.e. AR device) requires different software development kits and different technical requirements. That leaves AR concepts hard to be scaled and implemented in real life scenarios. One more additional challenge facing the implementing of AR is that AR mostly relies on complex vision-based algorithms. Based on that, this paper explores a lightweight, modular, and cross-platform solution. It presents a novel framework based on redesigning the AR pipeline, introducing a prototype of a solution where AR can operate within the cloud, converting all AR devices into thin clients with high performance capability. This also contributes directly to the Industry 4.0 aim of preparing the way for digitalized and optimized processes in a connected and integrated network.
Adaptive Bingham Distribution Based Filter for SE (3) Estimation

Filter-based methods are a suitable option to deal with the burdensome 3D pose estimation problems for their incremental properties. The classical approaches use the Gaussian distribution to model the uncertainty of the pose parameters and recent work has begun to take advantage of the Bingham distribution, which is theoretically more suitable for modeling uncertainty on the SO(3) group. However, these algorithms are still at the very beginning and heavily rely on manual tuning. In this work we equip the Bingham distribution based filter with adaptive filtering ability, which realizes completely autonomous tuning without human interaction. The experiments demonstrate that our method is significantly laborsaving compared to the state-of-the-art methods as well as capable of maintaining high accuracy.
A Method for Under-Sampling Modulation Pattern Recognition in Satellite Communication

To solve the problem of reconnaissance and processing of broadband satellite communication signals, a kind of satellite communication signals BPSK/QPSK modulation pattern recognition method was put forward in this paper. This method deals with the satellite descending signal with BPSK/QPSK modulation in the under-sampling condition. Because the corrected spectrum of BPSK signal contains obvious crest, while QPSK signal does not contain this feature. The difference of the waveform characteristics is used to complete modulation pattern recognition. The simulation results show that this method can identify BPSK/QPSK modulation signals when SNR is greater than 1 dB. When the sampling points are reduced, the satellite communication signal under-sampling modulation pattern recognition method can still maintain good recognition performance.
Negative curves of small genus on surfaces

Let X be a smooth geometrically irreducible projective surface over a field. In this paper we give an effective upper bound in terms of the Néron–Severi rank of X for the number of irreducible curves C on X with negative self-intersection and geometric genus less than $$b_1(X)/4$$, where $$b_1(X)$$ is the first étale Betti number of X. The proof involves a hyperbolic analog of the theory of spherical codes. More specifically, we relate these curves to the hyperbolic kissing number, and then prove upper and lower bounds for the hyperbolic kissing number in terms of the classical Euclidean kissing number.
Evidence Humans Provide When Explaining Data-Labeling Decisions

Because machine learning would benefit from reduced data requirements, some prior work has proposed using humans not just to label data, but also to explain those labels. To characterize the evidence humans might want to provide, we conducted a user study and a data experiment. In the user study, 75 participants provided classification labels for 20 photos, justifying those labels with free-text explanations. Explanations frequently referenced concepts (objects and attributes) in the image, yet 26% of explanations invoked concepts not in the image. Boolean logic was common in implicit form, but was rarely explicit. In a follow-up experiment on the Visual Genome dataset, we found that some concepts could be partially defined through their relationship to frequently co-occurring concepts, rather than only through labeling.
Dynamic Re-ranking with Deep Features Fusion for Person Re-identification

State-of-the-art (STOA) person re-identification (re-ID) methods measure features extracted by deep CNNs for final evaluation. In this work, we aim to improve re-ID performance by better utilizing these deep features. Firstly, a Dynamic Re-ranking (DRR) method is proposed, which matches features based on neighborhood structure to utilize contextual information. Different from common re-ranking methods, it finds more matches by adding contextual information. Secondly, to exploit the diverse information embedded in the deep features, we introduce Deep Feature Fusion (DFF), which splits and combines deep features through a diffusion and fusion process. Extensive comparative evaluations on three large re-ID benchmarks and six well-known features show that DRR and DFF are effective and insensitive to parameter setting. With a proper integration strategy, DRR and DFF can achieve STOA re-ID performance.
Detecting Sub-Image Replicas: Retrieval and Localization of Zoomed-In Images

Zoomed-in image retrieval is a special field of near-duplicate image retrieval. It allows determining the sight, panorama or landscape image where a zoomed-in image belongs to. In addition, it can help to detect copyright violations of images that have been cropped and rescaled from panorama images. So far, only few research have been done on this problem using supervised learning techniques. We present a method to retrieve and localize zoomed-in images with respect to the whole scene based on correlating groups of features. Feature grouping is used to filter features that do not contribute to identifying relations between images. The remaining features are used to estimate the scale and location of the zoomed-in image with respect to the whole scene. We provide results of a benchmark data study using the proposed method to detect zoomed-in images and to localize them in the correlating whole scene images. We compare our method with the RANSAC model in case of zoomed-in retrieval and localization. The results indicate that our approach is more robust than the RANSAC model and can detect the relation and localize zoomed-in images even when most matched features are not correlated or only a few matches can be found.