Reviewerly/exHarmony huggingface dataset:
Steered coherence and entanglement in the Heisenberg XX chain under twisted boundary conditions*

We study steered coherence (SC) and entanglement in a three-spin Heisenberg XX model under twisted boundary conditions and show that their strengths can be significantly enhanced by tuning the twist angle. The optimal twist angle θ opt for achieving the maximum l 1 norm of SC is π in the region of weak field B and decreases gradually from π to 0 when B increases after a critical value, while for the relative entropy of SC, θ opt equals π in the weak field region and 0 otherwise. The entanglement and the critical temperature above which the entanglement vanishes can also be significantly enhanced by tuning the twist angle from 0 to π .
Automatic Following Robot Based on Optical Flow Sensor and Laser Sensor

Based on optical flow sensor and laser sensor technology, an automatic following robot is designed by this paper. L-K optical flow method optimized by Gaussian-Laplace operator and laser algorithm to obtain the coordinates and speed of the following target, and the Kalman filter is used to remove noise. The simulation results show that the robot has good robustness and adaptability by processing the adaptive weighting algorithm and fuzzy algorithm on the data.
An Alternative Lightness Control with GAN for Augmenting Camera Data

To build an autonomous car, many technologies have to be taken into application. The most important component of a fully self-driving car is the object detection system. This system is responsible for detecting obstacles on the street. However, these detection models still face many difficulties such as unable to work on extreme conditions (storm, night, chaotic road,...). To tackle one aspect of this problem, in this paper we propose an augmentation method that creates more data by generating night images from day images and vice versa using LCcycleGAN, a Lightness conditional Unpaired Image-to-Image Translation approach, this framework is the fusion of CycleGAN [1] and conditional GAN [2]. To evaluate our method, we measure performance of YoloV3 [3] on our collected dataset (and augmented data) consists of day and night images of Vietnamese streets which are often highly chaotic and extreme. Our method increases AP of base vehicle detection model's performance from 0.5 to 0.56.
Fuzzy Multi-Manifold Classifier for One-sample Face Identification

With the continuous development of image recognition technology, various classification algorithms have been proposed, which makes the accuracy of prediction increasingly improved. However, in many practical applications, there is only ’one sample per person (OSPP)’ that can be obtained. Thus, many popular methods fail to work well. To address this problem, we explore a Fuzzy Multi-Manifold Classifier (FMMC) method which transforms OSPP into the manifold matching problem. First, we construct the sub-manifold by partitioning each enrolled image of several non-overlapping patches. Then, in order to specify the class-membership degree of each patch, we introduce the theory of fuzzy set to define the membership matrix. Such that each patch has a big enough membership degree, even if it is surrounded by the inter-class neighbors. Lastly, the classification is turned into a problem of finding a category with a maximum decision membership degree of manifold. Experimental results on the YALE and AR face databases show that our method outperforms some other classifiers.
Parameterized Pseudo-Differential Operators for Graph Convolutional Neural Networks

We present a novel graph convolutional layer that is conceptually simple, fast, and provides high accuracy with reduced overfitting. Based on pseudo-differential operators, our layer operates on graphs with relative position information available for each pair of connected nodes. Our layer represents a generalization of parameterized differential operators (previously shown effective for shape correspondence, image segmentation, and dimensionality reduction tasks) to a larger class of graphs. We evaluate our method on a variety of supervised learning tasks, including 2D graph classification using the MNIST and CIFAR-100 datasets and 3D node correspondence using the FAUST dataset. We also introduce a superpixel graph version of the lesion classification task using the ISIC 2016 challenge dataset and evaluate our layer versus other state-of-the-art graph convolutional network architectures.The new layer outperforms multiple recent architectures on graph classification tasks using the MNIST and CIFAR-100 superpixel datasets. For the ISIC dataset, we outperform all other graph neural networks examined as well as all of the submissions to the original ISIC challenge despite the best of those models having more than 200 times as many parameters as our model.
Research on safety protection technology of containerization application based on probe detection

With the continuous development of container technology, the scale of dispatching control cloud is expanding, and the relationship between container applications on cloud platform is becoming more and more complex. The intelligent probe technology is used to detect the health status of the container to ensure the normal operation of the container application, thus ensuring the safe and stable operation of the dispatching control cloud platform, and providing strong support for the scheduling of the smart grid. In this paper, the health status of containerized application is detected by the container probe detection technology. The results show that the container probe detection technology has an obvious role in prolonging the life cycle of containerized application.
Triposes as a generalization of localic geometric morphisms

Abstract In Hyland et al. (1980), Hyland, Johnstone and Pitts introduced the notion of tripos for the purpose of organizing the construction of realizability toposes in a way that generalizes the construction of localic toposes from complete Heyting algebras. In Pitts (2002), one finds a generalization of this notion eliminating an unnecessary assumption of Hyland et al. (1980). The aim of this paper is to characterize triposes over a base topos ${\cal S}$ in terms of so-called constant objects functors from ${\cal S}$ to some elementary topos. Our characterization is slightly different from the one in Pitts’s PhD Thesis (Pitts, 1981) and motivated by the fibered view of geometric morphisms as described in Streicher (2020). In particular, we discuss the question whether triposes over Set giving rise to equivalent toposes are already equivalent as triposes.
Dehaze Model to Improve Object Visibility Under Atmospheric Degradation

Optically captured images from the outdoor scenes will be humiliated by natural occurrences of Fog, Mist and Haze. This is due to atmospheric absorption and scattering of visible information, resulting in poor object visibility. It is necessary to estimate the quantifiable parameters of this atmospheric degradation to improve the visibility. Dehazing models attempt to estimate scattering parameters. Single image dehazing models are observed to possess estimation of inaccurate textures, thereby leading to blocking artifacts. The imbalances in the concentration of atmospheric particles and air-light are crucial, that should be mitigated. The available models have to be improved in terms of image parameters such as contrast, saturation and color information. The work reported in this paper emphasized the model that estimates intensified transmission map from the hazy images with color distortions and thereby exploiting scattering parameters for dehazing.
An Imbalanced Data Classification Method Based on Multi-Kernel Extreme Learning Machine Fusion

To solve the problem of low classification accuracy in imbalanced data classification, a method based on Multi-Kernel Extreme Learning Machine Fusion (MKELMF) is proposed. By introducing the “learning to learn” idea of meta-learning algorithm, the ability of Multi-Kernel Extreme Learning Machine (MK-ELM) to classify local areas of data samples is fully mined. For each test sample, the meta-learning algorithm selects a group of Kernel Extreme Learning Machine (KELM) with sufficient classification ability for combined decision making, avoiding the problems such as insufficient classification of single KELM and the problem that ordinary MK-ELM can easily introduce wrong classification information by making weighted decisions based on all KELM results. According to the characteristics of KELM, five evaluation criteria are proposed as meta-features for training meta-learner. We construct a serial regulating circuit and collect data under normal and fault conditions to verify the proposed method. The experimental results show that compared with two cost-sensitive SVM methods and two local multi-kernel weighted fusion methods, the MKELMF method improves the test accuracy, Macro-F1 value and G-mean by 8.53%, 4.59% and 5.37% respectively. In addition, the method has higher training and testing efficiency.
Unified Road Infrastructure Safety System using Visible Light Communication

Detection of dangerous events and situations is becoming an important direction in the field of road safety, intelligent traffic, and monitoring and control systems. Traditional ways of highlighting and detecting traffic hazards have reached a peak based on intelligent systems. Their detection is done through applications, based on high-resolution video cameras with and without the feedback of LIDAR sensors. The current approach is based on a detection threshold and focuses on the transmission of information from devices that identify danger to intelligent traffic management systems, and other vehicles. By using computer vision the camera feed is analyzed and relevant data is classified to the driver and then transmitted promptly to an intelligent traffic light system capable of changing its status depending on the message received from the installed systems either at the level of road infrastructure or embedded in participant cars. On this approach, we propose a module to enable bidirectional communication with a traffic light through modulation of the available light sources via a microcontroller and a light receiver to enable data transmission and reception to achieve data transmission on the identified objects, type, direction, back and forth to the road infrastructure and other participants. The proposed system takes part in a larger under development safety system that includes several modules dedicated to the automotive safety sector.
Stopping the Unstoppable - Termination and Unwinding of Smart Contracts

Whether smart contracts can in fact live up to their purported potential will be examined with view to the legal issues surrounding their termination and unwinding. The revolutionary aspects of this new technology lie with the automatic execution of contractual agreements through the aid of computer codes, leaving human operators without ultimate control. Smart contracts perform the programmed process in a precise, secure and invariable manner. Once set in motion, their mechanism can no longer be unilaterally intercepted. This applies without exception, even if for legal reasons the performance of obligations between the two parties is unenforceable, e.g. because the underlying contract is void. Smart contracts are thus sometimes performed even though, in the particular case, they should not be. The question then arises: How to stop the unstoppable, and how to unwind the immutable?
Popularization of Graphic Novels in Russia and the Role of the Russian State Library for Young Adults in Formation of Loyal Library Environment

The article presents the attempt to generalize the experience of working with graphic novels (comics, manga, etc.) available in the domestic book sector. The author considers the main stages of development in the historical perspective of forms and methods of popularization and distribution of graphic novels in Russia. At the first stage, formation of the segment of graphic novels in Russia was associated with the development of the festival movement. At the second stage — with the publishing boom and distribution of publications. Later — with work with a reader of graphic novels in libraries. Development of the festival movement was carried out due to the interest, first, in Japanese cartoons (anime) and Japnese graphic novels (manga), and secondly, in American comics and European graphic novels. The author presents the festival movement in the context of the main directions: anime festivals and comics festivals (with special emphasis on the work of the ‘KomMissia’ [ComMission] festival). The article gives general characteristics of these types of festivals and reveals the degree of their involvement in the processes of forming the readership and professional environment at a certain evolutionary stage. Bookstores and public libraries are the traditional operators of book dissemination and popularization of knowledge. The author explains why the integration of graphic novels into these spaces was delayed. The article notes the importance of reader communities in social networks in creating alternative channels for selling the products of publishers of graphic novels. In 2010, the Russian State Library for Young Adults (RSLYA) established the Centre for comics and visual culture, collecting graphic novels and providing methodological assistance to libraries that use graphic novels in their work. At present, it is the Centre for graphic novels and images that provides comprehensive support to libraries. The author highlights the role of the RSLYA as a methodological centre for library services to young people in the formation of loyalty to the new phenomenon among Russian library professionals. Based on the studied processes, the article gives characteristics of graphic novels as a new segment of the Russian book market with its inherent features of positioning in the reader’s environment.
Quantum Implementation for Comparing Sets of Data

Abstract By simultaneously analyzing sets of data, we propose a quantum implementation for handling a large amount of data. Our research may be useful in big data analysis.
Joint Sparse Convolutional Coding for Image Super-Resolution Restoration

The globality of the image is a strong constraint in the process of image super-resolution reconstruction. Convolutional sparse coding uses the global features of image in superresolution reconstruction, and decomposes image wholly into convolution sum of filters and feature map instead of encoding the image patches. However, sparsity guided by the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm cannot represent high-order structured information. In this paper, the joint sparse convolution coding (JSCC) proposed to further extract high-order structured information, leading to a compact dictionary. Our model involves the three sets of parameters to learn, the decomposition filters, mapping function and reconstruction filters. The model utilizes structured sparse regularization term ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> regularization to constrain feature maps instead of single ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm. Meantime, extract edge prior information for registration with high frequency components. Extensive experiments demonstrate JSCC model can achieve competitive PSNR results, while reconstruction image illustrates better texture preservation performance and edge information. ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> regularization term can also avoid the influence of noise, reflecting the superior robust performance.
VBSSR: Variable Bitrate Encoded Video Streaming with Super-Resolution on HPC Education Platform

In online HPC education platforms, a large amount of educational resources are in the form of video. It is desirable to provide better QoE (Quality of Experience) for students when they are viewing these educational resources. Compared to traditional constant bitrate (CBR) encoding, variable bitrate (VBR) encoding can achieve better video quality and reduce network bandwidth. However, previous adaptive bitrate (ABR) schemes were commonly designed for CBR encoded videos. Such ABR schemes are not suitable to stream VBR encoded videos whose chunk sizes fluctuate rapidly. In this paper, we propose a novel ABR scheme call VBSSR, which takes the characteristics of VBR encoded video into consideration. The basic idea of VBSSR is to stream video chunks with complex scenes at a low bitrate level to reduce bandwidth consumption, and then boost the video quality by leveraging the technique of super-resolution (SR) at the client-side. VBSSR trains a deep reinforcement learning (DRL) based neural model to jointly make bitrate selections and decide which chunks to be enhanced. We conduct extensive trace-driven evaluations to compare VBSSR with other state-of-the-art methods. The experiment results show that our method significantly outperforms existing approaches with improvements in the average video quality by at least 37.1% while reducing the rebuffering time by 24.3%–75.9%.
Automatic Classification of Research Papers Using Machine Learning Approaches and Natural Language Processing

This paper shows the automatic classification of research papers published in Scopus. Our classification is based on the research lines of the university. We apply the K-nearest neighbor classifier and linear discriminant analysis (LDA). Various stages were used from information gathering, creating the vocabulary, pre-processing and data training, and supervised classification in this work. The experiment involved 596 research articles published in SCOPUS from 2003–2017. The results show an overall accuracy of 88.44%.
The Use of a Fixed Height Binary Tree to Accelerate the Determination of the Medians of Subarrays

The new method for calculation the median of an array and their subarrays using a fixed-height binary tree is described. It is shown that such tree allows to find the element, determine the median of the array, or any of its ordinal statistics, by making no more comparisons than its double height. The computation time of calculation the medians of different types of arrays and continuous subarrays by the algorithms of both the traditional and the proposed methods is analyzed. It is shown that instead of the known sorting methods it is expedient to use Hoare partitioning to determine the medians of different arrays and unrelated subarrays. In order to identify the median sequence of continuous nested subarrays, the method of two pyramids could be used. And it is advisable to use a fixed-height binary tree to find the median of neighboring subarrays.
Collaborated Cyber Security Education with Hokkaido Prefectural Police and National Institute of Technology, Tomakomai College

Hokkaido Prefectural Police and NIT Tomakomai College signed a contract about student nurture in cyber security field in 2018. After the contract, we have started collaborated cyber security education. Hokkaido Prefectural Police has spoken to students about cyber security, usage of social network service and cyber security crime. In NIT Tomakomai College, some computer science and engineering course students developed and studied some contents related to cyber security. In this paper, we report the cyber security education in 2018 and 2019.
An Efficient Blind Signature Scheme with Untraceability for Data Privacy in Smart Grid

Smart grid has been expected to provide exquisite consumption monitoring or energy trading for its equipped abundant facilities together with two-way communication, but it inevitably leads to privacy leakage of consumption data during data retransmission. Although blind signature schemes can reduce the risk of privacy leakage due to the properties of unforgeability and blindness, most of them are traceable meaning that some malicious signers can still obtain the real information of blinded signatures. In this paper, we propose an improved blind signature scheme with untraceability to further enhance privacy protection of consumption data in smart grid. We also provide security analysis and comparative performance analysis to demonstrate its feasibility.
An N-step Look Ahead Algorithm Using Mixed (On and Off) Policy Reinforcement Learning

In this paper, a new algorithm for reinforcement learning is proposed. Q-learning and SARSA are one of the most famous algorithms used in Reinforcement learning. Q-learning gives the most optimal path whereas SARSA gives the safest path. This paper proposes an algorithm that allows users to flexibly chose between safer or faster path in any given environment by altering the hyper parameter n. The approach taken to achieve this is N-step look ahead. In this the agent will update the initial states value after it has taken n steps using on- policy reinforcement learning and in adjacent step it applies off- policy RL. Using this technique, the algorithm overcomes the overestimation of value function prevalent in Q-learning as it emphasizes more on on-policy updates. This algorithm allows the user alter the dependency on bootstrapping and sampling in updating the value function.
Research on Blockchain-Based Organization Collaboration System

Abstract Collaboration log, which can be used to reflect the trust of an organization and applied to subsequent analysis and audit, is constantly generated during the organization’s collaboration. Common systems usually store them by a third-party organization, which exists at risk of tampering. Regarding this issue, blockchain can safely manage the collaboration logs owing to its decentralized and unforgeable features. In this paper, we implement an organization collaboration prototype system for spare parts procurement based on the Software As A Service(SaaS) collaboration model and Hyperledger Fabric solutions. To validate the performance of the prototype system, we conduct some performance tests on the prototype system. The result shows the feasibility of our proposed system.
On the maximum number of minimal codewords

Minimal codewords have applications in decoding linear codes and in cryptography. We study the maximum number of minimal codewords in binary linear codes of a given length and dimension. Improved lower and upper bounds on the maximum number are presented. We determine the exact values for the case of linear codes of dimension k and length k+2 and for small values of the length and dimension. We also give a formula for the number of minimal codewords of linear codes of dimension k and length k+3.
An Improved Object Tracking Algorithm Combining Spatio-Temporal Context and Selection Update

The tracking method based on spatio-temporal context has achieved great effects in real-time and robustness, but its tracking results are not satisfying while facing with strong interference such as illumination variation, occlusion, in-plane rotation and fast motion. For solving these problems, we have proposed a real-time detection and selective update spatio-temporal context tracker. The introduced detect-or is based on keypoint matching and consistent voting, so that the improved method can effectively track occluded and in-plane rotating targets; the overlap rate and Hamming distance are introduced to judge the confidence of the output consequence of the detection and tracking modules, and then the best result is selected to update the tracker. This improvement effectively avoids the situation that the tracking template introduces error information due to fast motion, illumination variation, thereby improving the tracker's robustness and precision. A large number of experimental results on the OTB-50 datasets demonstrate that this method has better performance compared with some representative advanced tracking algorithms.