Reviewerly/exHarmony huggingface dataset:
On the convex cones arising from classifications of partial entanglement in the three qubit system

In order to classify partial entanglement of multi-partite states, it is natural to consider the convex hulls, intersections and differences of basic convex cones obtained from partially separable states with respect to partitions of systems. In this paper, we consider convex cones consisting of X-shaped three qubit states arising in this way. The class of X-shaped states includes important classes like Greenberger-Horne-Zeilinger diagonal states. We find all the extreme rays of those convex cones to exhibit corresponding partially separable states. We also give characterizations for those cones which give rise to necessary criteria in terms of diagonal and anti-diagonal entries for general three qubit states.
An End-to-End LSTM-MDN Network for Projectile Trajectory Prediction

Trajectory prediction from radar data is an example of a signal processing problem, which is challenging due to small sample sizes, high noise, non-stationarity, and non-linear. A data-driven LSTM-MDN end-to-end network from incomplete and noisy radar measurements to predict projectile trajectory is investigated in this paper. Traditional prediction algorithm usually uses Kalman Filter (KF) or the like to estimate target’s position and speed, then uses the numerical integral, such as Runge-Kutta, Adams, etc. to extrapolate the launch point or impact point, which mainly relies on the accuracy of dynamic models. A Long Short-Term Memory (LSTM) network is designed to estimate the real position from sampled and noisy radar measurements series, and a Mixture Density Network (MDN) is developed for trajectory extrapolation and projectile launch point prediction. These two subnetworks are integrated into an end-to-end network, which is trained by the radar measurement samples of a projectile and the corresponding ground truth of its launch point. Compared with the traditional methods, amount of experiments show that our proposed method is superior to the traditional model-based methods, and its adaptability to the range of initial launch angle is significantly better than the traditional method.
Multi-task Gated Contextual Cross-Modal Attention Framework for Sentiment and Emotion Analysis

Multi-modal sentiment and emotion analysis have been an emerging and prominent field nowadays at the intersection of natural language processing, deep learning, machine learning, computer vision, and speech processing. Sentiment and emotion prediction model finds the attitude of a speaker or writer towards any discussion, debate, event, document or topic. It can be expressed in different ways like the words spoken, energy and tone while delivering words, accompanying facial expressions, gestures, etc. Moreover related and similar tasks generally depend on each other and are predicted better if solved through a joint framework. In this paper, we present a multi-task gated contextual cross-modal attention framework which considers all the three modalities (viz. text, acoustic and visual) and multiple utterances for sentiment and emotion prediction together. We evaluate our proposed approach on CMU-MOSEI dataset for sentiment and emotion prediction. Evaluation results depict that our proposed approach extracts co-relation among the three modalities and attains an improvement over the previous state-of-the-art models.
Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.
Word2vec’s Distributed Word Representation for Hindi Word Sense Disambiguation

Word Sense Disambiguation (WSD) is the task of extracting an appropriate sense of an ambiguous word in a sentence. WSD is an essential task for language processing, as it is a pre-requisite for determining the closest interpretations of various language-based applications. In this paper, we have made an attempt to exploit the word embedding for finding the solution for WSD for the Hindi texts. This task involves two steps - the creation of word embedding and leveraging cosine similarity to identify an appropriate sense of the word. In this process, we have considered two mostly used word2vec architectures known as Skip-Gram and Continuous Bag-Of-Words [2] models to develop the word embedding. Further, we have chosen the sense with the closest proximity to identify the meaning of an ambiguous word. To prove the effectiveness of the proposed model, we have performed experiments on large corpora and have achieved an accuracy of nearly 52%.
On Sobolev tests of uniformity on the circle with an extension to the sphere

Circular and spherical data arise in many applications, especially in biology, Earth sciences and astronomy.In dealing with such data, one of the preliminary steps before any further inference, is to test if such data is isotropic, that is, uniformly distributed around the circle or the sphere.In view of its importance, there is a considerable literature on the topic.In the present work, we provide new tests of uniformity on the circle based on original asymptotic results.Our tests are motivated by the shape of locally and asymptotically maximin tests of uniformity against generalized von Mises distributions.We show that they are uniformly consistent.Empirical power comparisons with several competing procedures are presented via simulations.The new tests detect particularly well multimodal alternatives such as mixtures of von Mises distributions.A practically-oriented combination of the new tests with already existing Sobolev tests is proposed.An extension to testing uniformity on the sphere, along with some simulations, is included.The procedures are illustrated on a real dataset.
The Measurement Method for the Size of the Hole on the Part Surface Based on Grating Image Processing

With the development of manufacturing industry, the high precision size measurement for the holes on the surface of mechanical parts is required. A method for measuring size of the hole based on double projection is proposed in this paper. The CCD camera separately collects grating images which projected onto the part surface in two directions. The phase-shift method is used to calculate the phase of grating images in two directions, and the reconstructed three-dimensional shapes in the two directions are merged to measure the hole dimension. Experiment results show that the measurement accuracy of the thickness is 0.04 mm, and the hole size can reach to 0.1 mm.
A Novel Semantics-Preserving Hashing for Fine-Grained Image Retrieval

With the advent of the era of big data, the storage and retrieval of data have become a research hotspot. Hashing methods that transform high-dimensional data into compact binary codes have received increasing attention. Recently, with the successful application of convolutional neural networks in computer vision, deep hashing methods utilize an end-to-end framework to learn feature representations and hash codes mutually, which achieve better retrieval performance than conventional hashing methods. However, deep hashing methods still face some challenges in image retrieval. Firstly, most existing deep hashing methods preserve similarity between original data space and hash coding space using loss functions with high time complexity, which cannot get a win-win situation in time and accuracy. Secondly, few existing deep hashing methods are designed for fine-grained image retrieval, which is necessary in practice. In this study, we propose a novel semantics-preserving hashing method which solves the above problems. We add a hash layer before the classification layer as a feature switch layer to guide the classification. At the same time, we replace the complicated loss with the simple classification loss, combining with quantization loss and bit balance loss to generate high-quality hash codes. Besides, we incorporate feature extractor designed for fine-grained image classification into our network for better representation learning. The results on three widely-used fine-grained image datasets show that our method is superior to other state-of-the-art image retrieval methods.
Over-Professional Skills and Professional Knowledge of Library Specialist: Demands of the Time

Library specialists having competencies in the field of modern information technologies and knowledge of information resources, capable to analyse and synthesize heterogeneous information, process data, solve non-standard tasks, are able to develop innovative trends, increase the importance and competitiveness of libraries in the information space. The purpose of this study is to determine the most important skills and knowledge of librarians for the development of new forms and trends in the activities of research libraries: assistant services to scientists, work with research data, creation of intellectual centres, centres of intellectual leisure, organization of communication platforms, etc. The author highlights the key knowledge necessary for librarian: knowledge of modern and advanced information technologies (social networks, cloud, mobile technologies, new generation analytics, etc.), knowledge of the world market of information resources, as well as technologies of collection and processing of information/data. The article presents competences of librarians in the research data management, who provide consulting and assistant services to scientists in the life cycle of research. It is determined that the research data management librarian should know the methods of data management plan preparation, management methods, categories, metadata standards and schemes, data classifications and identifiers, data citation requirements, copyright, data repositories, long-term data preservation technologies, etc. The author concludes that the possession of non-specialized over-professional (“soft”) skills (communication skills, emotional intelligence, thinking by “results” and “processes”, etc.) along with the complex of professional knowledge is the key to the improvement of efficiency and demand of libraries in the conditions of intensively developing environment.
IMPLEMENTATION OF MOBILE LEARNING USING A SOCIAL NETWORK PLATFORM: FACEBOOK

This research set out to determine the use of a social networking platform, Facebook, as a medium for learning and teaching. Using a combination of the unified theory of acceptance and use of technology (UTAUT) model, Information Systems (IS) success model and the educational use of Facebook theory, a conceptual framework was developed for implementation of mobile learning and teaching. Structural equation modelling (SEM) was used to determine a model for the implementation of mobile learning and teaching. A sample of 330 respondents was used in this research. The data were analysed using structural equation modelling. The aim was to determine to what extent mobile learning is acceptable to colleges of education in Nigeria. Lecturers from a federal college of education in Nigeria were used to test the implementation of mobile learning using Facebook as a medium of communication. Four courses were selected as part of the intervention programme. The research revealed that m-learning significantly improves the teaching and learning conditions in colleges of education, specifically by reducing the inadequacies of physical facilities, and by improving the reading culture and performance of students. The results further indicate that most of the stakeholders are satisfied with the benefits of m-learning and wish to continue using the Facebook and their mobile phones. Keywords: educational system, quantitative approach, mobile learning, Facebook, SEM.
Accuracy evaluation of 3D velocity estimation by multi-frequency phase-sensitive motion estimator under various specifications of matrix array probe

The multi-frequency two-dimensional (2D) phase-sensitive motion estimator can estimate tissue motion without interpolation of ultrasonic echo signals. In real situations, biological tissues move three dimensionally and, thus, the 2D estimation may be affected by the movement in the elevational direction. In this study, we proposed a multi-frequency three-dimensional (3D) phase-sensitive motion estimator. The accuracy was verified with computer simulation and phantom experiments. In the simulation, the accuracies of the proposed 3D method with five types of matrix array probes were compared with the previous 2D method. The standard deviation in motion velocities estimated with a 32 × 8 matrix array probe was suppressed by 8.4% and 0.9% in lateral and vertical directions at (lateral, vertical, elevation). In phantom experiments, the proposed 3D method performed better than the 2D method. According to these results, the estimation accuracy was shown to be improved by considering the 3D movement.
Hybrid Microwave-Radiation Patterns for High-Fidelity Quantum Gates with Trapped Ions

We present a method that combines continuous and pulsed microwave-radiation patterns to achieve robust interactions among hyperfine trapped ions placed in a magnetic field gradient. More specifically, our scheme displays continuous microwave drivings with modulated phases, phase flips, and $\ensuremath{\pi}$ pulses. This leads to high-fidelity entangling gates that are resilient against magnetic field fluctuations, changes in the microwave amplitudes, and crosstalk effects. Our protocol runs with arbitrary values of microwave power, which includes the technologically relevant case of low microwave intensities. We demonstrate the performance of our method with detailed numerical simulations that take into account the main sources of decoherence.
Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video

Depth image-based rendering (DIBR) plays an important role in 3D video and free viewpoint video synthesis. However, artifacts might occur in the synthesized view due to viewpoint changes and stereo depth estimation errors. Holes are usually out-of-field regions and disocclusions, and filling them appropriately becomes a challenge. In this paper, a virtual view synthesis approach based on asymmetric bidirectional DIBR is proposed. A depth image preprocessing method is applied to detect and correct unreliable depth values around the foreground edges. For the primary view, all pixels are warped to the virtual view by the modified DIBR method. For the auxiliary view, only the selected regions are warped, which contain the contents that are not visible in the primary view. This approach reduces the computational cost and prevents irrelevant foreground pixels from being warped to the holes. During the merging process, a color correction approach is introduced to make the result appear more natural. In addition, a depth-guided inpainting method is proposed to handle the remaining holes in the merged image. Experimental results show that, compared with bidirectional DIBR, the proposed rendering method can reduce about 37% rendering time and achieve 97% hole reduction. In terms of visual quality and objective evaluation, our approach performs better than the previous methods.
Quality Adaptation and Resource Allocation for Scalable Video in D2D Communication Networks

Recently fifth-generation (5G) of cellular networks appeared to enable various smart bandwidth thirsty applications. Frequency re-usability via device-to-device (D2D) communication is one of the possible ways to enhance the 5G network throughput. However, despite these technological advancements in cellular networks, users' Quality of Experience (QoE) is significantly affected by video quality adaptation according to future network conditions for video streaming. In this paper, we jointly formulate D2D pair association, video quality adaptation, and network resource allocation for scalable video streaming in a D2D communication network. The formulated problem is a mixed-integer linear programming problem and has a combinatorial nature. Thus, the formulated problem is decomposed into two subproblems: D2D pair association subproblem and resource allocation subproblem. The D2D pair association subproblem is a combinatorial optimization problem, to solve it a distributed algorithm is proposed based on two sided-matching theory. On the other hand, resource allocation subproblem is solved using conventional linear optimization techniques to get the boundary solution. Furthermore, a fully distributed quality adaptation scheme is presented using the proposed resource allocation algorithm for layered video. To validate the performance of our proposal, we perform numerical simulations for various scenarios.
Explicit Representation and Enumeration of Repeated-Root (δ + <i>αu</i>²)-Constacyclic Codes Over F₂<sup> <i>m</i> </sup>[<i>u</i>]/‹<i>u</i> <sup>2λ</sup>›

Let F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(m)</sub> be a finite field of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> elements, λ and k be integers satisfying λ, k ≥ 2 and denote R = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(m)</sub> [u]/(u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2λ</sup> ). Let δ, α ∈ F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(m)</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">×</sup> . For any odd positive integer n, we give an explicit representation and enumeration for all distinct (δ +αu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-constacyclic codes over R of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> n, and provide a clear formula to count the number of all these codes. In particular, we conclude that every (δ + αu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-constacyclic code over R of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> n is an ideal generated by at most 2 polynomials in the ring R[x]/〈x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(k)n</sup> - (δ + αu <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )〉. As an example, we listed all 135 distinct (1 + u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-constacyclic codes of length 4 over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> [u]/〈u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> 〉, and apply our results to determine all 11 self-dual codes among them.
InterCriteria Analysis of Data Obtained from Patients with Behterev’s Disease

This paper continues series of research on application of the novel approach of InterCriteria Analysis (ICrA) to medical data.It describes a new method of analyzing the treatment results of patients with Behterev's disease in order to aid the decision making process for treatment course -InterCriteria Analysis.The ICrA analysis is applied on the results of medicine, physiotherapeutic treatment and kinesitherapeutical program characteristics.The main goal is an improvement of general quality of patients' life through practices of specific methodology of kinesitherapy and ergotherapy.The object of empirical study is health status of patients suffering Rheumatoid spondylitis in relation to their current life conditions.Here are analyzed the data from observation about 25 patients (14 women and 11 men, aged 25 to 67).The results confirm previously performed research, showing that the muscles in the human body are closely connected and the improving of one movement will lead to improving another.The obtained results also prove applicability of ICrA to the researched problem, give grounds for extending its application and potential of further in-depth study.
AUG-BERT: An Efficient Data Augmentation Algorithm for Text Classification

We propose a BERT-based data augmentation for labeled sentences called Aug-Bert. New sentences are generated by stochastically selecting words and replacing them with other words predicted by Aug-BERT. After a two-stage training, including CLP and L-MLM, BERT can be fine-tuned to be Aug-BERT. Aug-BERT can predict stochastically selected words according to both the context and the label. Depending on the ability of BERT’s deep bidirectional language model and the information of label incorporated via label-segment embedding, Aug-BERT can generate sentences of high quality. Experiments on six different text classification tasks show that our methods outperform most of the others and can improve the performance of classifiers obviously.
Sentiment Analysis Approach Based on Combination of Word Embedding Techniques

Sentiment analysis is a field of research that attracts the attention of companies and governments to understand the opinion of the client and citizens, about products, services, policies and more other. With the increased volume of user-generated content on the Web, especially social networks, textual information becomes freely accessible and with a gigantic quantity, which requires powerful automated analysis tools to extract such kind of information (positive or negative sentiment). In this paper, we present sentiment analysis approach depends on pre-trained word embeddings, a frilly high-quality word representation vectors, namely, AraVec and fastText models, and we proposed a combination of the two models, based on vectors concatenation of both models. Sentiment classification was executed employing six different machine learning algorithms, we find that in most of the cases, our proposed method achieves the best results in terms of accuracy, especially with NuSVC classifier which is a type of SVM.
Major Convolutional Neural Networks in Image Classification: A Survey

Deep learning is a vital technique to implement artificial intelligence and a significant part of machine learning. In the last decades, deep learning gained enormous popularity due to the remarkable enhancement in computational ability and machine learning experimentation. The high computational time taken by the processes in deep learning because of large data sets can be compensated by increased computational ability. In deep learning, Convolutional Neural Network (CNN) or ConvNet is among the eminent approaches used for image classification. In recent image recognition competitions, CNN is outperforming other techniques of image classification. In this review paper, we have been discussed the basics of CNN, and significant developments in the history of CNN concerning image classification.