Reviewerly/exHarmony huggingface dataset:
Using GPUs for Determining the Energy Configuration of Superconducting Vortex Lattices with Stochastic Methods

By fabricating Nb films on top of array of Ni nanodots with different geometries, the vortex lattice for specific values of the external applied magnetic field is modified by the array of periodic pinning potentials. In this work, a GPU-based code developed from scratch simulating this phenomenon is presented. It evaluates the vortex-vortex and the vortex-nanodot interactions providing the total interaction between vortices and pinning sites, as well as the position of the vortices in the array unit cell. This final position is obtained with two stochastic processes (simulated annealing, Basin Hopping) being able to simulate square, rectangular, or triangular arrays of nanodefects of different size. A computational performance study is also made.
Quality Enhancement of The Compressed Image Using Super Resolution

Bilal, Mohammed Naveen, V. C. Chetan, D. Shaikh, Tajuddin Chachadi, Kavita Kamath, ShilpaAdvancement in computing power and the accessibility to huge training data sets have gained interest in the implementation of deep learning CNN’s (Convolution neural networks) to define image recognition and image processing operations. The enhancement of the visual quality of the compressed image and video compression has been considered a significant problem. The paper fundamentally focuses on the visual quality improvement of a compressed image using super resolution as the up-sampling technique. To address the problem of visual quality of the compressed image, the input image is bicubic down sampled. The downsampled image is encoded using image codec (JPEG). Finally, the decoded image is up-sampled using proposed up-sampling technique to reconstruct the original image with high quality. Experiments show our proposed up-sampling technique can improve the image both qualitatively and quantitatively and also the model achieves reconstruction performance with less computation resources and processing time compared to DCSCN [1].
Multiparty Quantum Cryptography with Block Cipher RC6 (B- MQKD)

The proposed B-MQKD method uses a quantum key distribution to generate a key and provide an authentication amongst many parties. It uses concepts of block cipher by using RC6 algorithm for encryption. The mix concept in B-MQKD (quantum and block cipher) gives the algorithm more authentication, randomness, and security which make it difficult to find the original message by attackers. In this paper using BB84 among three parties make it possiple to give the key to many users in a secret way.
A Shape-inference-based Approach to Enhance Constraint Independence Optimization

Constraint independence optimization is one of the most important constraint solving optimizations and widely used by symbolic executions. Constraint independence optimization divides the set of constraints into multiple independent subsets as the symbolic variables of constraint. But existing constraint independence optimization is not efficient for binary symbolic execution because the symbolic variables are coarse-grained. In this paper, we propose a shape-inference-based approach to enhance constraint independence optimization.We propose a shape inference algorithm to split the symbolic variable into multiple fields and perform constraint independence optimization as the fields of symbolic variables. We implement a prototype system, angr-shape, based on the proposed approach and verify its effectiveness according to experimentation. Compared with state-of-the-art approach, the experimental results demonstrate that the number of generated test cases increases by 2.37% and the time of constraint solving reduces by 14.00%.
Development of human movement measurement device for long-term measurements

Human movement data is useful in improving industrial products related to humans. The purpose of this study is to analyze the characteristics and trends of human movement. To measure human movement in daily life, a wireless device that can measure the acceleration, angular velocity, and quaternion for long-term was developed. It was necessary to use small and cheap parts in its construction because it must not be burdened when wearing and aid in enabling the mass production. For that reason, the processing speed was not sufficient for saving sampling data, so this study changed the method of data storage. Additionally, the method of classifying the posture of device using quaternions was proposed. To verify the usefulness of the device, the arm movement of one participant were examined, and the characteristic and trend of the human movements was shown to be analyzed well using the data that acquired by the device.
Absolute Value of Number in Mathematical Olympiads Tasks

The article focuses on the application of tasks containing a variable under the sign of the module in problems of mathematical olympiads. The results are obtained: the topics of the section are determined, on the basis of which the conditions for the olympiad problems of the republican olympiad are compiled, the goals and requirements for studying the absolute value in the olympiad program are determined, 5 main methods for solving equations with a module are identified: methods for sequentially opening modules, intervals, graphical, determining the dependencies between numbers a and b, their modules and squares, geometric interpretation of the module. In the course of the study, conclusions were drawn: due to the increasing complexity of the olympiad problems, there is a need to familiarize students with different methods for solving the olympiad tasks with a module in the system of additional education.
MagicGlove: Estimation for Flexion Angle of Fingers

With the flourishing of wearable techniques, a future of interaction getting rid of bulky external equipment can be envisioned. This work explores the use of stretchable wearable sensors for sensing the flexion angle of fingers. Different from the traditional hand gesture interaction, the goal of this work is to achieve the fine-grained precise interaction to the exact angle level in real-time. We design a glove-based prototype, MagicGlove, integrating the advanced flexible sensor, wireless communication technique and hand pose reconstruction to provide the high functionality with robustness and reusability. The detachable fabrication design makes it possible to meet the customized requirements. We describe our system in detail with the elaborate experimental data as well as its limitations for further investigations.
Issues and Paths to Improve the Policy in the Higher Professional Education Within the Innovative Development of Modern Society

The chapter is dedicated to the topical Russian issue of improvement of public policy in the reformation of the higher professional education system in order to raise its quality and ensure the competitiveness of Russian professional staff on the domestic and international labor markets. The authors reveal and analyze the main system problems related to the improvement of education quality in Russian universities, studying in detail the particular points of this issue. We consider possible ways to raise the quality of education associated with Russia’s accession to the Bologna process and the transition to a two-stage model of education in Russian universities. The authors suppose that insufficient provision of education quality in higher education institutions of the mass segment is the main factor of the inconsistency of graduates’ training with the modern labor market demands. Particular attention is paid to the prospects for the development of creativity and innovative thinking using teaching methods used in higher education institutions.
Deep Similarity Fusion Networks for One-Shot Semantic Segmentation

One-shot semantic segmentation is a new challenging task extended from traditional semantic segmentation, which aims to predict unseen object categories for each pixel given only one annotated sample. Previous works employ oversimplified operations to fuse the features from query image and support image, while neglecting to incorporate multi-scale information that is essential for the one-shot segmentation task. In this paper, we propose a novel one-shot based architecture, Deep Similarity Fusion Network (DSFN) to tackle this issue. Specifically, a new similarity feature generator is proposed to generate multi-scale similarity feature maps, which can provide both contextual and spatial information for the following modules. Then, a similarity feature aggregator is employed to fuse different scale feature maps in a coarse-to-fine manner. Finally, a simple yet effective convolutional module is introduced to create the final segmentation mask. Extensive experiments on $$\mathrm {PASCAL-5^{i}}$$ demonstrate that DSFN outperforms the state-of-the-art methods by a large margin with mean IoU of 47.7%.
Cellular V2X – Hybridization of Long and Short Range Capabilities

Cellular V2X, initially defined under 3GPP Rel 14, establishes a baseline of direct device to device connectivity over cellular networks. Whereas traditional use cases may be enhanced with extended warning times, in fact a hybrid long range and short range approach requires further management of message timing and position tracking, combining differing latencies and multiple sensors into a seamless environmental view. Furthermore, the distances feasible in the presence of cellular infrastructure change the use cases, allowing for new far to near transitional cases, and providing impetus for a more integrated HMI combining information, warning, and safety messages.
Performance Improvement of Blind Image Restoration Using Ringing Removal Processing

Blur is a representative feature of image degradation. The restoration of a degraded image can be successfully achieved using a method in which point spread function estimation and ideal image estimation processing are alternately repeated. In this paper, we introduce a ringing removal method using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> regularization and propose a highperformance restoration method that can estimate sharper images. It is known that the prior probability of the gradient is effective in suppressing deterioration such as ringing. Thus, ideal image estimation is performed only using gradient information. In the proposed method, a bilateral filter is used to compare the estimated ideal image obtained in previous studies with that obtained using gradient information. By subtracting the difference map from the final deconvolution result, a clear image without ringing could be obtained.
Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems

Machine-learning techniques have been widely applied for solving decision-making problems. Machine-learning algorithms perform better as compared to other algorithms while dealing with complex environments. The recent development in the area of neural network has enabled reinforcement learning techniques to provide the optimal policies for sophisticated and capable agents. In this paper, we would like to explore some algorithms people have applied recently based on interaction of multiple agents and their components. We would like to provide a survey of reinforcement-learning techniques to solve complex and real-world scenarios.
Analysis of Time Series Generated By Long Short-Term Memory Trained with Adversarial Imitation Learning

Generally, birds gradually develop complicated songs by learning how to sing a song from each other. Thus, we assume that adversarial imitation learning (AIL) plays a major role in the development of complex songs and the emergence of syntax. In our previous work, we proposed an AIL using a recurrent neural network with Long Short-Term Memory to use historical information. Although the results shows that the generated time series gradually becomes complex on spatio-temporal dimensions through the AIL method, the generated time series has not been adequately analyzed. In this paper, we analyzed the generated time series with the maximum Lyapunov exponent, the zip compression and the visualization of internal state spaces. The results show that the generated time series is chaotic, which indicates syntax-like structures.
Topic Modelling for Extracting Behavioral Patterns from Transactions Data

With the increasing popularity of cashless payment methods for everyday, seasonal and special expenses popular banks accumulate huge amount of data about customer operations. In the article, we report a successful application of topic modelling to extract behaviour patterns from the data. The resulting models are built with BigARTM framework: flexible and efficient tool for topic modelling. The framework allows us to experiment with various models including PLSA, LDA and beyond. Results demonstrate ability of the approach to aggregate information about behaviour patterns of different customer groups. The results analysis allows to see the topics of such people clusters varying from travellers to mortgage holders. Moreover, low-dementional embeddings of the customers, which was given with topic model, were studied. We display that the client vector representations store demographic information as well as source data. We also test for a best way of preparing data for the model with metric above in mind.
Collapse Resistant Deep Convolutional GAN for Multi-object Image Generation

This work introduces a novel system for the generation of images that contain multiple classes of objects. Recent work in Generative Adversarial Networks have produced high quality images, but many focus on generating images of a single object or set of objects. Our system addresses the task of image generation conditioned on a list of desired classes to be included in a single image. This enables our system to generate images with any given combination of objects, all composed into a visually realistic natural image. The system learns the interrelationships of all classes represented in a dataset, and can generate diverse samples including a set of these classes. It displays the ability to arrange these objects together, accounting for occlusions and inter-object spatial relations that characterize complex natural images. To accomplish this, we introduce a novel architecture based on Conditional Deep Convolutional GANs that is stabilized against collapse relative to both mode and condition. The system learns to rectify mode collapse during training, self-correcting to avoid suboptimal generation modes.
Modal logics with hard diamond-free fragments

Abstract We investigate the complexity of modal satisfiability for a family of multi-modal logics with interdependencies among the modalities. In particular, we examine four characteristic multi-modal logics with dependencies and demonstrate that, even if we restrict the formulae to be diamond-free and to have only one propositional variable, these logics still have a high complexity. This result identifies and isolates two sources of complexity: the presence of axiom $D$ for some of the modalities and certain modal interdependencies. We then further investigate and characterize the complexity of the diamond-free, 1-variable fragments of multi-modal logics in a general setting.
Independent Construction System of the Knowledge Graph of Network Literature Resources

Constructing a general knowledge graph is a systematic and huge work. Among the existing knowledge graphs of network literature resources, the domain-oriented knowledge graphs are more common. Because they cover less knowledge data than the general knowledge graphs, they are easier to construct. Up to now, there is no standard general knowledge graph system in the field of the knowledge graph of the network literary resources, and the knowledge graph system constructed in this paper strives to fill this gap. Through practice, it is found that the construction of the domain knowledge graph can be completed on the basis of the construction of the general knowledge graph. Because when the general knowledge graph has a certain scale, it can form more accurate and effective domain knowledge graph by classifying the data in it, forming various kinds of the sub-knowledge graphs, and then manually modified by a certain professional field.
A latent-label denoising method for relation extraction with self-directed confidence learning

Distant supervision for relation extraction aims to automatically obtain a large number of relational facts as training data, but it often leads to noisy label problem. In this paper, we propose a self-directed confidence learning based latent-label denoising method for distantly supervised relatio n extraction. Concretely, a self-directed algorithm that combines the semantic information of model prediction and distant supervision is designed to predict the confidence score of latent labels. Since this mechanism utilizes the obtained latent labels of easy examples to produce the latent labels of hard examples step by step, it is a robust and reliable learning process. Besides, it facilitates dynamic exploration of the confidence space to achieve better denoising performance. Moreover, to cope with the common imbalance problem in large corpus where the negative instances account for a much larger percentage, we introduce a discriminative loss function to solve the misclassification between non-relational and relational instances. Empirically, in order to verify the generality of the proposed denoising method, we use different neural models – CNN, PCNN and BiLSTM for representation learning. Experimental results show that our method can correct the noisy labels with high accuracy and outperform the state-of-the-art relation extraction systems.
Cost-Effective Real-Time Aerial Surveillance System Using Edge Computing

Nowadays there is an emerging need for surveillance in order to maintain the public places more secure and ensure the safety and security of the people. Many government agencies require some autonomous system for surveillance of the large areas which can give them precise and real-time information like number of vehicles, people, and other objects. An aerial surveillance system will be very effective in this scenario and platform like Unmanned Aerial vehicle (UAV) will be very reliable and cost-effective option for this task. To make the system fully autonomous, we require real-time object detection that is computationally complex and time consuming due to the heavy load on the limited processing and payload capacity of low-cost UAV. In this paper, we propose a cost-effective approach for aerial surveillance in which we move the heavy computation tasks to the cloud while keeping limited computation on-board of UAV system using Edge computing technique. Further this will maintain the minimum communication between UAV and the cloud thus proposed system will reduce the network traffic and also delay. Proposed system is based on the state-of-art technique YOLO (You Look Only Once) for real time object detection.
Impact of Distance Measures on Imbalanced Classes for Rule Extraction

This paper identifies the supervised and unsupervised learning algorithms for imbalanced classes to extract rules for them. Most Machine learning algorithms work best when the number of instances of each class is roughly equal, but there are only specific algorithms to deal with the imbalanced classes. Imbalanced classification problems mean that the dependent (response) variable has an imbalanced proportion of classes. This results in biased predictions, misleading inaccuracies, and fluctuating performances in the datasets and henceforth, imbalanced classes need attention in machine learning. In the field of machine learning, the traditional model evaluation methods do not accurately measure the model performances when faced with imbalanced classes. However, in an imbalanced dataset, the minor class does not significantly contribute towards accuracy. Hence, accuracy should not be used to evaluate the models' performance for an imbalanced dataset. Thus, this paper discusses several data mining methodologies like Sampling, Clustering, Distance measures and Performance measures to extract more rules for the imbalanced classes.
FPGA Implementation of 8-bit SSA Multiplier for designing OFDM Transceiver

In recent technology of digital signal processing applications, we have number of modulation and demodulation techniques. In this domain multiplexing technique is having greatest importance as it has a transmission and reception part with secure systems for communication. Thus orthogonal frequency division multiplexing (OFDM) is superior to other multiplexing techniques, which has multiple carriers with very much robust frequency selective distorted channels. OFDM will have a number of modules such as serial to parallel converter, modulator, IFFT, FFT, demodulator and so on. The Proposed work will concentrate on arithmetic operations in OFDM with re-modified technique using large integer values such as addition, and multiplications. This large integer based arithmetic operation technique were designed using Schönhage-Strassen algorithm (SSA). Thus the proposed method will focus on this SSA algorithm based arithmetic operations with Number Theoretic Transform (NTT) and Inverse Number Theoretic transform (INTT) method. Proposed method of OFDM will replace FFT-IFFT to NTT-INTT method and this application is developed in Verilog HDL and synthesized in Xilinx vivado 15.4. It proved better results in terms of area and delay when compared with conventional design.
Asymptotic reduced density matrix of discrete-time quantum walks

In this article we show that for any quantum walker with \textit{m}-dimensional coin subspace, we have $m^2\times m^2$ specific constant matrix $\mathcal{C}$ where it completely determines the asymptotic reduced density matrix of the walker. We show that for any initial state with $P_0$ projector, reduced density matrix, can be obtained by $Tr_1\left(P_0\otimes I\;\mathcal{C}\right)$ or equivalently $Tr_2\left(I\otimes P_0\;\mathcal{C}\right)$. It is worth to mention that characteristic matrix $\mathcal{C}$ is independent of the initial state and just depends on coin operator, so by finding this matrix for specific type of QW the long-time behavior of it, such as local state of the coin after a long time walking and asymptotic entanglement between coin and position will be completely known for any initial state. We have found the characteristic matrix $\mathcal{C}$ for general coin operator, $U\left(2\right)$, as well as exact form of this matrix for local initial state.
Transpose-based Integrated Data Reduction Techniques for Speeding up Classifier Training

The dramatic increase in dataset volumes available to train learning models has led to great advances in machine learning, but at the cost of slowing down training. This paper addresses the effect of data reduction on speeding up training while keeping or improving the accuracy performance of classification. Since many studies have focused on feature selection, but did not adequately consider instance selection, our work focuses on both instance reduction and feature reduction, integrated into a holistic reduction approach. We examined in prior work Simple Random Sample Selection without Replacement, integrated with the Information Gain-based Feature Selection method, and compared its performance with the unintegrated instance selection and feature selection individually, applied at various reduction rates. Our results proved that the integration of instance and feature selection performed much better than instance or feature selection alone, in terms of both training speedup and even accuracy improvement. In this paper, a novel transpose-based instance selection approach is introduced and integrated with feature selection, and its performance is investigated on classification and compared with the top-performing results of our prior work. Our results show that our new integrated method leads to significant increase in training speedup without impacting classification accuracy, and in fact, for some classifiers like Naive Bayes, the accuracy goes up considerably.