Reviewerly/exHarmony huggingface dataset:
Efficient Computation of the Continuous-Discrete Extended Kalman Filter Sensitivities Applied to Maximum Likelihood Estimation

In this paper, we present and compare different methods for computing the likelihood function and its gradient. We consider nonlinear continuous-discrete models described by a system of stochastic differential equations (SDEs) with discrete-time measurements. The problem of maximum likelihood estimation (MLE) is formulated as a nonlinear program (NLP) and it is solved numerically using a gradient-based single shooting algorithm. The estimates of the mean and its covariance are computed using a continuous-discrete extended Kalman filter (CDEKF). We derive analytical expressions for the gradient of the likelihood function. We discuss some aspects of the implementation of MLE for non-stiff systems. In particular, we present an efficient way of computing the state covariance matrix and its gradient using explicit Runge-Kutta schemes. We verify our implementation using a numerical example related to type 1 diabetes and demonstrate how to apply it for nonlinear parameter estimation.
Optical Monitoring of Pollution on MV Bushings

The proposed research aims to optically monitor the dry pollution level on transformer bushings and determine the possible leakage current should the dry polluted surface be critically wetted. The research involves the implementation of an image capturing system with appropriate image processing. A lighting array that surrounds the camera, required for one of the imaging techniques, is designed and implemented. Calibration of light position data of the array is detailed. The HV test setup used to acquire leakage current and the methods used to determine bushing pollutant conductivity are presented. The relationship between Equivalent Salt Deposit Density (ESDD) and leakage current is shown to have a logarithmic fitting, as expected from the SANS 60815 grading of pollution severity. The leakage current is characterised at various known pollution levels under wetted conditions as a reference for the neural network to correlate the predicted dry pollution level (from the images) to leakage current under wetted conditions (from test measurements). An example data-set, linking leakage current, conductivity, salinity, pollutant surface coverage and saliency, and ESDD is presented. Furthermore, the methodology relating to implementation and verification is outlined. The standard methods used to classify pollution types and severity are discussed. A brief overview of the dynamics governing bushing flashover under polluted conditions is presented. The actual pollution level and type is quantified using ESDD and Non-Soluble Deposit Density (NSDD). Image segmentation and border extraction are illustrated to output four variables related to surface pollutants: area ratio, coverage, shape factor and eccentricity. The first two parameters are proposed as measures of surface pollution density, while the latter two may assist in pollution type identification. For more accurate pollution type identification, reflectance transformation imaging (RTI) is used. With a saliency mapping resolution of approximately 100 µm, the feature recognition between salt deposits and dust deposits is more readily attained.
Optimization system at Vehicle Control Department for the control and management of the company's vehicles

This project proposes a solution to an internal problem of an organization when managing its vehicles. A web system will be developed for the control and management of information, procedures and functions of the company’s vehicles in an efficient way and thus have a better development optimizing times. The objective is to facilitate the management of information, improving the control of activities, internal functions and support, in order to speed up processes through the use of the computer system. The methodology to be used is Scrum, since it provides optimal options, such as Sprint, work boards at a certain time for the development and proper functioning of the system, as well as constant revision, avoiding errors and improving development times. The Scrum methodology provides an agile process to develop software; it was first applied by Ken Schwaber and Jeff Sutherland, who documented it in detail in the book Agile Software Development with Scrum. 1. To analyze and investigate the different development tools. 2. To select the tools that generate the greatest advantages for the project. 3. To make a work plan for the implementation of the System. 4. To perform tests and carry out adjustments for the final implementation.
Unsafe Action Recognition of Miners Based on Video Description

The Recognition of miners' unsafe action is of great significance to coal mine safety production. However, its bottleneck lies in the lack of effective model to recognize miners' actions, interactive objects of actions as well as spatio-temporal multidimensional information in video. This paper puts forward a novel and effective description model of miners' insecure actions, aiming to comprehensively describe the above multidimensional information, which can be used in video recognition of miners' actions and video annotation. The multi-dimensional information in video is automatically extracted by Real-Time Object Detection with Region Proposal Networks (faster RCNN) and converted into vector representation, and then the feature vector is input into the two-layer Long Short- Term Memory (LSTM) model to generate language description. Finally, the model is verified by experiments on the data set of miners' insecure actions collected by ourselves, and the model achieves high accuracy.
A Framework for Dynamic Access Control System for Cloud Federations Using Blockchain

A cloud federation is a network of cloud computing environment consisting of two or more providers. In this framework, the user classification is done dynamically depending on their user’s blockchain transactions. The data classification is done in the cloud using the suitable methods. The data access from the cloud will be given to the users who have the access or permission to use the data depending on their access/privilege rights. For the new user, the access will be through voting based on the new users from different organizations. The votes are given by the trusted parties, and their votes are valid only after their blockchain transaction verification. For a new user to access the data, he requires minimum of votes from the trusted parties.
A Knowledge Graph Based Approach for Automatic Speech and Essay Summarization

Every day, big amounts of unstructured data is generated. This data is of the form of essays, research papers, speeches, patents, scholastic articles, book chapters etc. In today's world, it is very important to extract key patterns from huge text passages or verbal speeches. This paper proposes a novel method for summarizing multilingual vocal as well as written paragraphs and speeches, using semantic Knowledge Graphs. Using the proposed model, big text extracts or speeches can be summarized for better understanding and analysis. The method uses speech recognition as well as Named Entity Recognition to identify entities from spoken content to create optimized Knowledge Graphs in the English Language.
Digitization of the Mechanical Articulator: Virtual Articulator

This study presents the digitalization of the dental articulator, which is a device used by dentists as a replica of the patient’s mouth. For this purpose, the digitalization has been carried out applying reverse engineering to obtain a three-dimensional model that totally simulates the behavior of the virtual model with the same measurements as the physical articulator Panadent PSH. First, the physical dental articulator has been scanned using the ATOS Compact three-dimensional scanner. Then, the processing was carried out using the reverse engineering Geomagic software, where the necessary geometric elements were obtained. The parts have been modeled in 3D in the Solid Edge program and the assembly has been carried out. In this same CAD program, the parameterization of the adjustable parts, such as condylar pits, has been carried out. Finally, variables have been introduced that a physical dental articulator does not allow, for example the intercondylar distance. By digitizing and parameterizing the dental articulator, the virtual articulator has been obtained. This virtual device allows the user to have greater control of the design, plan more accurately, enter more patient data, for example the 3D face of the patient, and data management is better.
A New Method of System-level EMC Evaluation Based on Deep Learning

As a new deep learning model, antagonistic network generation is one of the most promising methods of unsupervised learning in complex distribution in recent years. In this paper, the electromagnetic compatibility evaluation method of complex systems is studied and analyzed by means of generative countermeasure network technology. Firstly, the theoretical model of generative antagonistic network is introduced; Secondly, by analyzing the types and characteristics of the indexes affecting the system level EMC, the network structure is selected as the system level EMC evaluation index structure; Finally, a system evaluation approach degree model based on the generation of antagonistic network is established, and a fuzzy mathematics method is used to realize the comprehensive evaluation of EMC performance.
Development of a System of Anonymizing Mobile Calls

Voice over Internet Protocol (VoIP) is a technology that allows people to use the Internet, rather than a traditional telephone network. VoIP is becoming more and more popular due to its significant advantages. VoIP telephony has become the most developed and frequently used cellular service. It also created new threats to caller's privacy. Due to the high degree of interest in ensuring anonymization of calls in the GSM network. Previous research on the development of algorithms to increase anonymization in IP-telephony and their implementation. In this work, we developed a new mechanism for achieving anonymity in the Global System for Mobile Communications (GSM - Global System for Mobile Communications). The lowest probability of anonymity disclosure in this algorithm is achieved by redirecting calls from one mobile operator to another. In addition, GPSS-Studio implements a model for redirecting incoming calls using multi-channel modeling. Probabilistic-temporal characteristics of the distribution of incoming calls to specific nodes.
A Framework for Using Radar Measurements of Unknown Targets in Hierarchical Classification

Real-life target recognition often requires appropriate processing of unknown targets. Such targets are the ones that the automatic target recognition system has not been trained to identify. These targets may, however, be interesting whereupon they should be further analyzed. In this paper, we propose a novel framework for analyzing radar measurements of unknown targets in order to incorporate them into a hierarchical target class taxonomy for the target recognition. Besides the preliminary information, a vital part in the analysis of the radar measurement is the comparison between the measured signature and the signatures of the known target types and categories. We use the results of such analysis to indicate potential spots in the class taxonomy where to add the unknown target. The framework allows identification of unknown target types that have been previously observed, when they are encountered again. We demonstrate the proposed framework through an experiment using the real data of a multi-radar system. In the experiments, we show the feasibility of our approach by examining target recognition in two cases: using our framework and without it. We find that the proposed framework enables enhanced processing of unknown targets in radar target recognition.
WhatsApp as a Support Tool for Language Learning Among Immigrants

Acquiring a second language is a challenging task for everyone, especially for immigrants. In this case study, we examined the use of the WhatsApp application as a support tool for language learning among immigrants. The study took place at an Ulpan in Tel Aviv. The participants were new immigrants from different countries who study or have studied Hebrew as a second language at the Ulpan (special schools for immigrants for Hebrew learning). This qualitative research, conducted through personal interviews. The participants were asked to describe how they use the WhatsApp group opened for them by the teacher during a Hebrew language course at the Ulpan. The interviews raised many insights relating to the perceived affordance of WhatsApp use by students and their Ulpan teacher. The findings reveal interesting and unique WhatsApp usage: Textual-based as well as Voice-based and visual-based communication.
Some methods for generating sequences with run property

In this paper, we calculate the number of sequences with run property and propose two methods for generating sequences with run property. One of them, generating sequences in order, is useful for exhaustive search, and another, ranking and unranking method, is useful for scatter search.
Online System Evaluation and Learning of Data Source Models: a Probabilistic Generative Approach

This paper introduces a method for reasoning about the quality and relevance of data sources supplying inputs to information fusion systems. The emphasis is on probabilistic inference using causal Bayesian networks representing stochastic data generation processes. Modelling patterns that implement explaining away support simultaneous reasoning about (i) hidden states of the domain, (ii) status/quality of individual sources and (iii) the suitability of a specific source type in a given situation. Through such reasoning, a fusion system can estimate the quality and relevance of data sources at runtime which, in turn, is used for automatic adaptation of the source models. This can significantly improve the performance of the overall fusion process. Moreover, the source models feature latent variables representing the status of an individual source and the operational conditions influencing the source, respectively. Consequently, the parameters of such models must be extracted from incomplete training data sets (i.e. there are no observations of the latent states). It is shown that the Expectation Maximization algorithm (EM) in combination with special modelling patterns can cope with this challenge. The effectiveness of the approach is validated with the help of experiments using synthetic data.
An Improved Similarity Algorithm Based On Deep Hash and Code Bit Independence

Abstract Deep convolutional neural networks have been widely used in image retrieval because of their powerful feature representation capabilities. Due to the high efficiency of hash space, many image retrieval algorithms based on deep hashing emerge. Aiming at the high correlation between the codes bits of hash code, this paper proposes a new end-to-end trainable deep hash algorithm to implement the feature grouping so that each code bit can express its unique partial image information. We add slice layers to reduce the repetitive expression of information and add the pairing loss to expand the difference between different categories of images and improve the recognition ability of the model for different categories. After training, we use the hierarchical search strategy to test the retrieval ability of the classification model. The mean average precision and recall of our algorithm can reach 87.5% and 92% respectively, which achieves great result in the related field and has important guiding significance for the future research of hash algorithm.
Performance Optimization of Aero Turboshaft Engine Based on Bayesian Network

The aero turboshaft engine is mainly used in helicopters. As a power unit that drives the rotor to generate lift and propulsion, it has been rapidly developed in recent years. When the power of the turboshaft engine meets the conditions of use, the key section temperature often exceeds the threshold. As another important indicator of engine performance, it will affect the safety of the whole machine. This situation has become the primary problem for the current turboshaft engine manufacturers. In this paper, based on the collected data of a certain type of turboshaft engines, according to the manufacturer's suggestions, three component size variables are extracted firstly. They have been confirmed to affect the engine power and the key section temperature. Then, based on Bayesian network, the engine performance models are established for power and the key section temperature respectively. Finally, after validity verification, the production optimization table and transition optimization matrix are proposed. From them, some effective suggestions are also given for the optimization of engine performance.
Whitelists that Work: Creating Defensible Dynamic Whitelists with Statistical Learning

Blacklists are a fundamental component of many cyber security products. Typically populated from a number of sources including machine learning algorithms and malware sandboxes, large automated blacklists carry the risk of blocking access to necessary, benign Internet resources and negatively impacting users. Whitelists reduce this risk by identifying items that should not be blocked. Unfortunately, whitelists are often poorly constructed and ill-maintained, failing to provide the proper counter-balance and potentially weakening the security ecosystem. This paper introduces the first published algorithm for automated whitelist creation. Based on Bayesian statistical learning methods and incorporating network and threat information, it is both defensible and responsive to the environment. For comparison, we describe two additional algorithms for whitelisting and evaluate all three over a six-week period using multiple data sources. Further, we demonstrate how the proposed algorithm can be used as a quality assurance mechanism for blacklists, a concept not previously described in the literature.
Target Localization Using Mobile Sensors and a Decentralized and Distributed Variational Estimator

This work proposes a distributed and decentralized observer for position estimation (localization) of a moving target being tracked by multiple mobile sensors. The possibly time-varying set of sensors that have the target in their field of view, is used to create an energy-like quantity that depends on errors in the estimated relative positions and relative velocities of the target as measured by the mobile sensors. The relative velocities need not be measured directly, and can be obtained by filtering the observed relative positions. Each sensor then implements a local version of this distributed observer, and shares relative position information with the other sensors that are tracking the target. The observer is in the form of a variational estimator that is obtained by taking an action functional constructed from the energy-like quantity and dissipating this energy. As a result, the observer is shown to be asymptotically stable. Numerical simulations confirm this stability property and indicate robustness of the distributed observer to bounded measurement errors.
Beyond 2D: Fusion of Monocular 3D Pose, Motion and Appearance for Human Action Recognition

Human pose has proved to be an effective representation for action recognition in video. However, traditional 2D features extracted from videos suffer from the high variations caused by viewpoint changes and projection. In this paper, we investigate the recent monocular 3D pose estimation technology for action recognition and perform a cross-modality analysis by comparing 2D, monocular 3D and Kinect 3D poses in terms of action recognition, especially under cross-viewpoint settings. We show that our proposed monocular 3D pose action recognition pipeline achieves superior results even without real depth information as input. Our proposed three-stream fusion of 3D pose, motion and appearance outperforms state-of-the-art methods on sub-JHMDB, Penn Action and NTU RGB+D datasets.
Comparative Analysis of Post-Quantum Key Transfer Protocols Using Mathematical Modeling

In this paper, we will compare quantum key exchange protocols such as E91 and BB84 using mathematical modeling. This topic is relevant, because in the near future it is possible to spread quantum computers. This will make it easy to solve many mathematical problems, but also simplify the task of attackers. The results of this work will help to select a quantum key distribution protocol in the future.
RRT* Trajectory Scheduling Using Angles-Only Measurements for AUV Recovery

Sensor trajectory optimisation involves extensive search over the sensor motion space against an optimisation criterion. The search under dynamic programming or fixed grid is often computationally nontrivial even for a myopic search scenario. In this paper, we study the problem of an autonomous underwater vehicle planning its return route to a moving recovery vessel. To complicate the issue, the AUV needs to localize the vessel using angle-only measurements. Accordingly, we propose a random sampling based trajectory planning algorithm that incorporates both a dynamic goal and the need to localize that goal. More precisely, we incorporate an information theoretic cost into a rapid-exploring random tree trajectory planning framework thus allowing the AUV to both localize and reach the recovery vessel. Our experimental results show that the proposed method may achieve the same trajectory optimisation performance as that under dynamic programming method but with greater computational efficiency.
Adaptive Virtual Machine Scheduling Algorithm Based on Improved Particle Swarm Optimization

With the rapid development and popularization of cloud computing, how to reduce the energy consumption of cloud computing data centers and improve the utility of data centers is one of the urgent problems to be solved. In this paper, focusing on four dimensions of CPU, memory, network bandwidth, and disk, we establish a virtual machine(VM) scheduling model based on multi-objective optimization, which can minimize the data center energy consumption and maximize data center utility. And we propose a VM scheduling algorithm based on improved particle swarm optimization(PSO) to solve the model. The improvement includes adaptive parameter adjustment. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments. The experimental results show that the adaptive VM scheduling algorithm based on improved PSO can improve the efficiency of the data center while reducing energy consumption.
The Translation of Jiaodong’s Excellent Traditional Culture Based on Computer Network

In the 21st century, human society has entered the information age from the industrial age, and computers have penetrated into various fields. Today’s world is gradually globalizing. As a bridge to spread the world’s information and culture, translation is receiving more and more attention from the public. The current translation method has changed dramatically. Chinese excellent traditional culture is the national foundation of the core values of socialism. As an important part of Chinese traditional culture, Jiaodong’s excellent traditional culture contains rich ideological and spiritual values, cultural and educational values. In the Internet era, how to spread excellent Chinese culture to foreign countries is particularly meaningful. Based on the computer network, this paper conducts research on cultural connotation translation.