Reviewerly/exHarmony huggingface dataset:
Application and Implementation of The Grounding Wire Information Management System Based on SM2

The intelligence and automation of grounding wire information management are improved by the grounding wire management system based on network communication in maintenance of catenary, but the security problem of information transmission existed in this system. For this reason, the SM2 algorithm is used to digitally sign the grounding wire information, and ensure it can not be tampered and non-repudiation. Under the C/S architecture, the grounding wire information management system with digital signature technology is established. The server realizes the information interaction between the grounding wire monitoring unit and the client. The client visually displays the status, location, and picture information of the grounding wire through the visual interface. The results show that the application of the SM2 digital signature technology for the grounding wire information management system insure the security of the grounding wire information.
A Dynamic Memory Allocation Approach Based on Balloon Technology on Virtualization Platform

In virtualization platforms, Virtual Machines(VMs) have different memory demand when running different applications. Applications with high memory demand often result in guest swapping and performance degradation of the VM. Because of the semantic gap between the Virtual Machine Monitor(VMM) and the guest Operating System(OS), it is impossible to achieve reasonable allocation and use of memory resources between the guests. Our goal is to monitor the memory information in real time and achieve memory recovery and replenish of each VM in case of memory fluctuation. This method builds up a reasonable swapping mechanism, which can achieve automatic balance of memory allocation among multiple VMs, optimize guest swapping and improve memory utilization. We display it by visualization module. Based on ballooning technology, this paper designs a dynamic memory allocation method in KVM virtualization platform, which consists of memory monitoring, memory dynamic allocation, memory resource visualization and other modules. The results of many tests and analysis demonstrate that our method has the ability to basically achieve our expected goals and requirements.
Multi-spectral image level-by-level registration algorithm based on A-KAZE feature

Aiming at the problem that he gray level of different spectral images varies greatly and the traditional feature extraction algorithm is difficult to maintain the local precision and edge detail of the image, a multi-channel multi-spectral image registration method based on A-KAZE algorithm. In the registration process, the Fast Explicit Diffusion (FED) numerical analysis framework is used to solve the nonlinear diffusion filter equation, and the nonlinear scale space is constructed. The feature points are obtained by calculating the Hessian matrix of each pixel; The invariant image feature vectors are constructed by the Modified-Local Difference Binary (M-LDB) descriptor. Then, the feature vectors are matched by KNN using Hamming distance, and the mismatched points are eliminated by M-estimator Sample Consensus (MSAC). Finally, the transformation matrix is calculated based on projection transformation model. For multi-channel multi-spectral images, the optimal registration route is calculated by level-by-level registration method, and the image registration is realized by registration strategy and transformation matrix. Multispectral phenological observation data were selected to verify the image registration effect of the algorithm, and compared with SIFT, SURF, KAZE algorithm. Experimental results show that this method can achieve sub-pixel registration accuracy on any two images, and has strong robustness and faster speed.
‘Berrypicking’ in the Formation of Ideas About Problem Drinking Amongst Users of Alcohol Online Support Groups

Beliefs held by individuals about the illnesses or problems that affect them have been shown to impact upon the health and other outcomes that they achieve. Online support groups (OSGs) are one source of information used by those with health problems which may influence or determine what they think about their particular issue and how to resolve it. Problem drinking remains a major source of significant costs to society. This article explores whether the discussion forums of alcohol OSGs that do not follow the 12-step philosophy of Alcoholics Anonymous influence the formation of these beliefs, reporting on the outcome of thematic analysis of interviews with 25 users from five groups. It argues that Bates’ ‘Berrypicking’ model of information searching is helpful in illuminating group members’ information seeking activities. It looks at the four key aspects of berrypicking identified by Bates – the nature of the search query, the information ‘domains’ drawn on, the information retrieved and the search techniques used. The study finds that users are typically berrypickers, selecting information from different sources and forming their own interpretations.
A Steganography Embedding Method Based on Hamming Coding and Histogram-Preserving

This paper proposes an information hiding algorithm using matrix embedding with Hamming codes and histogram preservation in order to keep the histogram of the image unchanged before and after hiding information in digital media. First, the algorithm uses matrix embedding with Hamming codes to determine the rewriting bits of the original image, rewrite and flip them, and successfully embed the secret information. Then, according to the idea of a break-even point, a balanced pixel frequency adaptive algorithm is proposed and each embedded bit of secret information is detected and compensated by the adjacent bit of histogram data, so that the histogram change of the image before and after information hiding is minimized. At present, most of the histogram distortion values after steganography are generally over 1000 or even higher. As a contrast, the method proposed in this paper can keep the histogram distortion values to be less than 1000. The feasibility and effectiveness of the algorithm are verified by relative entropy analysis as well. The experimental results also show that the algorithm performs well in steganographic analyses of images.
Balancing Exploitation and Exploration via Fully Probabilistic Design of Decision Policies

Adaptive decision making learns an environment model serving a design of a decision policy. The policy-generated actions influence both the acquired reward and the future knowledge. The optimal policy properly balances exploitation with exploration. The inherent dimensionality curse of decision making under incomplete knowledge prevents the realisation of the optimal design. This has stimulated repetitive attempts to reach this balance at least approximately. Usually, either: (a) the exploitative reward is enriched by a part reflecting the exploration quality and a feasible approximate certainty-equivalent design is made; or (b) an explorative random noise is added to the purely exploitative actions. This paper avoids the inauspicious (a) and improves (b) by employing the non-standard fully probabilistic design (FPD) of decision policies, which naturally generates random actions. Monte-Carlo experiments confirm its achieved quality. The quality stems from methodological contributions, which include: (i) an improvement of the relation between FPD and standard Markov decision processes; (ii) a design of an adaptive tuning of an FPD-parameter. The latter also suits for the tuning of the temperature in both simulated annealing and Boltzmann’s machine.
System design optimization

Analytics are used to extract information from data. Meta-analytics are often used to extract information from systems. In this chapter, we apply the simple methods of entropy and error variance to the broad problem of system design optimization. These simple analytics can be used to guide our decisions on system architecture—whether to split, merge, or substitute a module; whether to pull out entirely a subsystem, where we should be most concerned with test and measurement for reliability, robustness, etc. It is not only the structure of the architecture that can be guided by meta-analytics. Additionally, the trafficking between subcomponents can be analyzed, and an approach to optimization of the messaging can be suggested by sensitivity analysis and other approaches. On the data architecture side, we provide a means of optimizing clustering extending the Eeyore method of Chapter 9. We finish with additional means of optimizing the analytics themselves.
Wheelchair Balancing Control System

The study of stabilization systems based on a spring accumulator with a sinus torque characteristic based on cylindrical compression or extension springs ensures the development of basic principles for designing mechatronic systems with position stabilization modules. In the course of the study, the mathematical apparatus of the balancing system (stabilization) was determined, the necessary efforts of the springs and torques of the gear motors were calculated. Built stand to confirm the theoretical calculations. A control system with stabilization in one plane has been developed. The stand of the system confirmed the correctness of the calculations. Further research is related to the development and production of a stand with a large number of suspension modules and the design and implementation of a control system for stabilization in two planes.
Commodore: Fail Safe Container Scheduling in Kubernetes

Kubernetes is a tool to facilitate deployment of multiple virtualized applications using container technology. Kubernetes scheduling mechanism orchestrates computing resources per application at runtime. However, resource allocation is static, as the maximum amount of computing resources that each application can use, must be reserved in advance. If the application requests more resources than the maximum, a fail scheduling event is generated. Although solutions to the problem of automatic scaling in Kubernetes are known to exist and automatic scaling is supported by cloud providers such as Amazon and Google, these solutions are fully proprietary and not generic (e.g. do not apply to all Kubernetes distributions). Our solution, referred to as “Commodore”, is capable of allocating (or de-allocating) resources based on the actual demands of running applications. Taking advantage of the virtualization features of cloud computing, applications are deployed on worker machines (nodes) as Virtual Machines (VMs). This not only results in better utilization of computing resources (i.e. CPU, memory and network are defined virtually) but also, in enhanced software security by isolating services or applications from each other. The experimental results demonstrated that Commodore responds to the increasing (or decreasing) resource demands of each application leading to significantly faster response times compared to a non-auto scaled implementation.
Mini gesture detection using neural networks algorithms

Gesture recognition is defined as non-verbal motions used as a means of communication in Human Computer Interaction. It is one of the significant aspects of HCI, both in the device interfaces and interpersonally. In a virtual reality system, gestures can be used to navigate, control or interact with a computer. The aim of gesture recognition is to capture gestures that are formed in a certain way and are detected by a device such as a camera. Hand gesture recognition is one of the logical ways to generate a convenient and high adaptability interface between devices and users. In this paper, a system is created for hand gesture recognition using image processing tools, namely Wavelets Transform (WT), Empirical Mode Decomposition (EMD) methods, Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), for gesture classification. These methods are evaluated based on many factors such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood, receiver operating characteristic, area under roc curve and root mean square. Preliminary results indicate that WT had less execution time than EMD and CNN. CNN had the ability to extract distinct features and classify data accurately while EMD and WT were less effective. Hence, the classification accuracy is improved dramatically.
Detection Threshold of the Height Difference between a Visual and Physical Step

In recent years, virtual reality (VR) applications that accompany real-space walking have become popular. In these applications, the expression of steps, such as a stairway, is a technical challenge. Preparing a real step with the same scale as that of the step in the VR space is one alternative; however, it is costly and impractical. We propose using a real step, but one physical step for the expression of various steps, by manipulating the viewpoint and foot position when ascending and descending real steps. The hypothesis is that the height of a step can be complemented to some extent visually, even if the heights of the real step and that in the VR space are different. In this paper, we first propose a viewpoint and foot position manipulation algorithm. T hen we measure the detection threshold of the height difference between the visual and physical step when ascending and descending the physical step using our manipulation algorithm. As a result, we found that the difference can be detected if there is a difference of approximately 1.0 cm between the VR space and the real space, irrespective of the height of the physical step.
Methane in sulphides from gold-bearing deposits, Britain and Ireland

The direct measurement of gases trapped in sulphide minerals shows that samples from gold-bearing deposits in Britain and Ireland are anomalously rich in methane. Chalcopyrite samples in deposits of Palaeozoic age sited in Neoproterozoic basement (Caledonides greenschist), Munster Basin (Variscides greenschist) and the Carboniferous cover (diagenetic) were compared using mass spectrometry of cold-crushed gases. All host sequences contain sources of organic matter. The content of non-aqueous gas is greater in both sets of greenschist-hosted deposits than in the diagenetic-hosted deposits. However, chalcopyrite accompanying gold in the Neoproterozoic is methane-rich, but in the low-gold Munster Basin it is methane-poor. These gas data from opaque minerals complement fluid inclusion data from gangue minerals, and add support to models for the involvement of organic species in orogenic gold mineralisation.
LIBRARIES OF THE URAL CLOSED NUCLEAR ADMINISTRATIVE-TERRITORIAL FORMATIONS IN THE PERIOD OF SOCIAL TRANSFORMATIONS OF THE 1990S

The article provides a retrospective analysis of the changes, which affected the activity of the libraries of the Ural closed administrative-territorial formations in the 1990s. Relying on published sources and archival data the author examines the most significant changes in library services that influenced the everyday life of such closed administrative-territorial formations as Lesnoi, Novouralsk, Ozersk, Snezhinsk and Trekhgornyi. The chosen approach has allowed the author to show that for the libraries of the Ural closed cities the 1990s became a period of essential organizational and technological changes (transfer to municipal property, structural changes, informatization, and integration into the Russian cultural space).
Mathematical Modeling of Group Authentication Based on Isogeny of Elliptic Curves

In this paper, we consider ways of organizing group authentication, as well as the features of constructing the isogeny of elliptic curves. The work includes the study of isogeny graphs and their application in postquantum systems. A hierarchical group authentication scheme has been developed using transformations based on the search for isogeny of elliptic curves.
A Reference-Dependent Model of Search Evaluation

Most of the existing IR studies employed final values of search behavior measures in building evaluation metrics. However, according to the theories and empirical evidences from Behavioral Economics studies, in people's evaluations of actions and outcomes, the carriers of the values of different actions are gains and losses defined relative to a reference point, rather than the absolute final assets. Based on this idea, I will first explore how users' levels of search satisfaction are affected by the gains and losses defined relative to the pre-search expectations of system performance or reference levels in a controlled lab study. Then, based on the data collected from a field study, I will test the predicative power of my reference-dependent models (built upon delta-value-based behavioral features given the corresponding reference points) in predicting user satisfaction in naturalistic settings, aiming to examine the extent to which the reference-dependent approach can approximate real users' search evaluations. The findings of this work can help us better understand the subjectivity, bias, and variation in users' evaluation of search experience and thus have implications for user modeling and system recommendations design.
A Privacy-Preserving Feature Extraction Method for Big Data Analytics Based on Data-Independent Reusable Projection

Big data analytics is one of the key research areas ever since the advancement of internet technologies, social media, mobile networks, and internet of things (IoT). The volume of big data creates a major challenge to the data scientist while interpreting the information from raw data. The privacy of user data is an important issue faced by the users who utilize the computing resources from third party (i.e., cloud environment). This chapter proposed a data independent reusable projection (DIRP) technique for reducing the dimension of the original high dimensional data and also preserves the privacy of the data in analysis phase. The proposed method projects the high dimensional input data into the random low dimensional space. The data independent and distance preserving property helps the proposed method to reduce the computational complexity of the machine learning algorithm. The randomness of data masks the original input data which helps to solve the privacy issue during data analysis. The proposed algorithm has been tested with the MNIST hand written digit recognition dataset.
Study on Mistype Classification in Japanese Input Using Machine Learning

When considering usability of electronic devices such as computers and smartphones, how to input characters without mistakes is a very important point in the interface. Therefore in this paper, we examine classification of mistypes for Japanese input using machine learning. First, we classified the category of mistypes in Japanese input into six types (replacement, removal, exchange, insertion, involvement, repetition). We asked 12 subjects to input the words displayed and saw a trend. Experimental results showed that there was a common trend such as many replacement errors in all subjects, and characteristic mistypes were found for each subject. Next, we made the neural network (RNN · LSTM) to learn the mistype data of all subjects and classified. As a result, the correct answer rate of RNN was 10%, and the correct answer rate of LSTM was 88%. We are planning to use LSTM for correcting mistype in the future.
Natural Language Based SQL Query Verification Against Relational Schema

Writing SQL queries for database is a complex and skill requiring task especially for the new users. The situation becomes more critical when a low skilled person want to access and analyze his data from a relational database. These scenarios require expertise and skills in terms of understanding and writing the accurate and functional queries. However, these complex tasks can be simplified by providing an easy interface to the users. In order to resolve all such issues, automated software tool is needed, which facilitates both users and software engineers. In this paper we present a novel approach with name Que-Gen (Query Generator) that generates SQL queries based on the specification provided in National English Language. Users need to write the requirements in simple English in a few statements. After a semantic analysis and mapping of the associated information. Que-Gen generates the intended SQL queries that can be executed directly on the database. An experimental study has been conducted to analyze the performance and the accuracy of the purposed tool.
Vehicle recognition using multi-task cascaded network

Vehicle attribute recognition mainly contains two tasks: vehicle object location and vehicle category recognition. We propose a multi-task cascaded model MC-CNN, which integrates the improved Faster R-CNN and CNN. The first stage uses the improved Faster R-CNN network (IFR-CNN) to process the object location, and the second stage uses the improved CNN network (ICNN) to realize the object recognition. In IFR-CNN sub network, a max pooling and the deconvolution operation are added to the shallow layers of Faster R-CNN network. IFR-CNN can extract features from the different levels and increase the location information of shallow object. In ICNN sub network, we optimize the information extraction ability of high-level semantics in the middle layers and the deep layers of CNN network. The experimental results show that MC-CNN network proposed in this paper has better attribute recognition accuracy on BIT-Vehicle dataset and SYIT-Vehicle dataset than the single Faster R-CNN and CNN network models.
Smartphones, Grounds, Satellites, UAVs for Earthquake Nowcast

Smartphones augmented with smart sensors together with ground geological survey stations, and Satellites (communication Iridium) system can easily generate billion byte vector time series. Despite of lacking of the synchronization, all these data sources is generated from the Earth molten core of the size of the moon. The constantly thermal neutrons decay of iron isotopes generates an enormous nonlinear underground thermal Bernard convection. This gigantic convection force is measured at the Richter scale of 10 to 5th~9th powers. These gravitational and nuclear energy become the necessary and sufficient conditions of the Earthquake Calamities happened pseudo-periodically over decades at any mantle cracks and falls whether in the continental drifts, tectonic plates, or dynamic cooking pot theories. Since digital memory becomes recently dirt cheap, it is possible to design big databases analyses (BDA) based on Matrix Associative Memory from thousands vector times series for all the mobile phone users at local cellular tower. The consistent data will be piece-wisely sent from a tower to another to the USGS data center where fusion is possible with USGS local seismic stations and with NASA satellite gravity data. The novelty detection will Nowcast for the early warning of the imminent Earthquake. At same time, the disaster area without any human or equipment can be surveyed by μ-UAV. It can help us to verify the convergence of Nowcast as well as to increase the reliability to protect the infrastructure and save the life.