Reviewerly/exHarmony huggingface dataset:
An Privacy Preserving and Multi-copy Supporting PDP Algorithm and its Application in Component Testing Tool

Aiming at the problem of malicious data deletion or tampering in the untrusted server-side storage, an improved PDP (Provable Data Possession) algorithm supporting privacy protection and multiple copies is proposed in the paper. BLS signature and encrypted copies with random mask are adopted in the algorithm. The implementation of the algorithm is described in detail. Experimental results show that the proposed algorithm achieves better performance compared with the MR-PDP algorithm proposed by Cutmola [6]. Finally, the algorithm is applied in a component testing tool to verify the integrity of component source files before component downloading and deployment.
Visual object tracking based on adaptive multi-feature fusion in complex scenarios

The major problems of object tracking by the traditional particle filter include low robustness and high computational load. To solve this problem, an object tracking method based on multi-features fusion and the MeanShift is proposed. The method represent the candidate objects with color and edge features, which efficiently avoids the unstable problems when the single color feature is applied in complex scenarios. The MeanShift is used in the particle-sampling steps, and the number of optimal particles is online determined by measuring the difference among the positions of the particles which not only improves the accuracy of the particle but also keeps the diversity. Experimental results show the effectiveness on high robustness and computational efficiency in complex scenarios.
RTMDC for Effective Cloud Data Security

In today’s world, almost everything has been digitised and hence the cloud computing is emerging as a boom in its field. But the most disturbing fact is that the cloud computing also appears as a vulnerable platform and therefore the security becomes the most important aspect in the cloud computing applications. Security issues are mainly dealt with known and unknown threats, malware and network access by unknown or unauthorised users. Hence, it has become a fundamental feature to create a secure platform for the cloud computing technologies. In this scenario, we proposed a RTMDC (Reputation based Trust Management system with Data Colouring) systems, which builds and tests a secure platform using Reputation Based Trust Management technique. Reputation based trust factor is then calculated using the collective reviews given by the users. This research work focuses on the images that has been related to the cloud users.
An Efficient Algorithm for Computing the Derivative of Mean Structural Similarity Index Measure

Many inverse problems in imaging can be addressed using energy minimization. The Euclidean distance is traditionally used in data fidelity term of energy functionals, even though it is not an optimal measure of visual quality. Recently the use of Mean Structural Similarity Index Measure (MSSIM) in data fidelity expressions has been examined. Solving such problems requires derivative of MSSIM. We propose an efficient algorithm for computing this derivative using convolutions. We indicate how the computational cost will be reduced to $${\mathcal {O}}(N \log {} N)$$ from the cost of traditional scheme $${\mathcal {O}}(m~ N \log {} N)$$ where N is the size of an input image and m is the window size. The proposed algorithm can be used for any inverse problem that traditionally applies L2 norm as a data fidelity measure. We apply the proposed numerical scheme to the inverse problem of image denoising.
Catering System Design of Smart Park Based on Blockchain Technology

Smart Park becomes the main development direction of new-type industrial park. For the operation target of efficient, transparent and intelligent park, blockchain technology is applied to realize tamper-proof, traceability and high security. In this paper, the characteristics of blockchain type are analyzed, and consortium blockchain is proposed to construct the Smart Park System. Taking the catering system of Smart Park as an example, this paper explores the application of blockchain technology in the practical scene of Smart Park, analyzes the blockchain-based catering system process model and key technologies in detail, and designs the implementation method of smart contract.
Semantic Segmentation of Satellite Images Using a U-Shaped Fully Connected Network with Dense Residual Blocks

Semantic segmentation is the task of clustering pixels into an object class. In the field of remote sensing semantic segmentation has wide applications ranging from scene cover classification to change detection for scene understanding. With the success of deep learning algorithms for classification tasks, there has been much work to apply convolutional neural networks in remote sensing with much success. However, feature extraction of high resolution remote sensing imagery poses a challenge when applying such networks. In particular, there is a need to extract high level features while maintaining an objects resolution in the networks feature space. This work proposes an efficient deep fully convolution architecture that obtains high level features without loss of spatial resolution by replacing the standard convolutional layers in U-Net with dense residual blocks. By stacking identity blocks, we allow the input to flow through the network at every proceeding layer. Our network is termed DRU-Net, and is shown to outperform standard U-Net.
Cloud Service Prediction Using KCFC Approach

Cloud computing is an emerging paradigm where the user can benefit many efficient services. Since many services resemble the same functionality, the user faces relevant and un-relevant information as data burden. So, Recommender System (RS) is getting used to suggest the user only the information that suits their search. Here (CFC)-Collaborative Filtering Coefficient is used as RS which functions by analyzing user history and similar service from neighbor users. Pearson coefficient is used to calculate the association between the services. But, it works for existing users not for new users because the further user details are not sufficient to recommend a service. To overcome this, the KNN approach is utilized to classify the recommendation from a k-nearest neighbor by finding the resemblance between various client ratings using Euclidean Distance measure. Thus, the KNN-CFC hybrid novel approach can create a new efficient RS framework which supplies the client a most relevant service information with low execution time for various data densities and different users and services.
Subspace Controllability of Quantum Ising Spin Networks with a Central Spin

We consider a class of spin networks where each spin in a certain set interacts via Ising coupling with a single central spin. This is a common situation for instance in NV centers in diamonds. Due to the permutation symmetries of the network, the system is not globally controllable but it displays invariant subspaces of the underlying Hilbert space. The system is said to be subspace controllable if it is controllable on each of these subspaces. We characterize the given invariant subspaces and the dynamical Lie algebra of this class of systems and prove subspace controllability.
Human wrist motion classification using decision tree and principle component analysis

This study investigates and acts as a trial clinical outcome for human motion and behaviour analysis in consensus of health related quality of life in Malaysia. Both methods were developed in MATLAB platform by managing and analyzing motion data in graphical user interface (GUI) form. By attaching the wearable sensors to each segments of interest, the collected data can be reconstructed in real time basis. Artificial intelligence technique is proposed to classify the subjects’ motions. Data reconstruction technique can be used to regenerate the motion of subjects’ limbs movement in 2D representation. Corresponding tests are performed to validate the accuracy of the application. The application is potentially to be used to access the quality of human motion in hospitals, clinics and human motion research. An experiment was set up in a laboratory environment for data collecting. Subjects were asked to perform three shape drawings using left and right hands. The instruments demonstrate adequate internal consistency of optimum average accuracy of 52 % for decision tree and 80 % for principle component analysis. Results were presented in tabular, image and graphical forms. The expected output of the study can be used to estimate joint angles in predicting Parkinson Disease for early stage symptoms, to track the moving behaviour in clinical gait analysis, to analyse limb movement in detecting resting tremor, to trace wrist motion in regenerating children handwriting difficulty, and to measure joint motion in evaluating patient rehabilitation progress.
A Fast Two-Stage Extreme Learning Machine

In order to realize the automatic adjustment of the network structure of the extreme learning machine (ELM), inspired by the two-stage extreme learning machine (TS-ELM), a fast two-stage extreme learning machine (FTS-ELM) is proposed by making the nodes added follow the arithmetic progression and using the principal component analysis (PCA) for pruning the redundant nodes. In the growing stage of hidden nodes, the nodes are added into network according to the arithmetic progression to reduce the number of iterations. In the pruning phase, PCA is used to delete redundant nodes. The hidden nodes with low contribution rate are quickly reduced by continuously reducing the cumulative contribution rate threshold, until the error (accuracy) achieves its maximum (minimum), which makes the network structure more compact. The empirical studies show that compared with ELM, EM-ELM, OP-ELM and TS-ELM algorithms, FTS-ELM leads to a compact network structure with good generalization performance, and its training time is far less than TS-ELM.
User Oriented Protocols for Data Sharing and Services Management

In this paper will be presented a new user-oriented approach for creation of cryptographic protocols dedicated for efficient and secure data sharing and services management. Such new procedures will be proposed with application of mathematical linguistic formalisms and application of personal characteristics, involved in creation of the division and management procedures. Such new protocols can be created with application of different data representation classes. Some possible application will also be described.
Research on Image Super-Resolution Reconstruction of Optical Image

Currently, the image super-resolution reconstruction method based on sparse representation has limited ability to process the details of the edge. Therefore, based on the dictionary learning, the local variance feature edge gradient estimation image fast super-resolution reconstruction is improved and optimized based on dictionary training. The dictionary training process includes cluster analysis of high-resolution images, local variance extraction, and sparse filtering. The reconstruction process includes local variance detection of the low-resolution image and threshold judgment, and then the image is reconstructed according to the gradient value.
Research and Application of Big Data in E-government

Abstract The wide use of modern electronic information technology, represented by big data technology, has brought people into the era of big data. Big data is affecting our work, life, even economy and the development of the society. Nowadays, the application of big data technology in e-government speeds up the development of e-government, which is important for the overall construction of e-government. This paper summarizes the application of the big data, introduces the application of big data technology in e-government. Then this paper analyzes the current situation and problems of e-government development. Finally this paper proposes the solutions of big data platform in e-government.
Towards a Semiautomatic Tool to Support the Integration of Feature Models

The integration of feature models plays a key role in many software engineering tasks, e.g., adding new features to software product lines (SPL) of information systems. Previous empirical studies have revealed that integrating design models is still considered a time-consuming and error-prone task. Unfortunately, integration approaches with tool support are still severely lacking. Even worse, little is known about the effort invested by developers to integrate models manually, and how correct the integrated models are. This paper proposes FMIT, which is a semiautomatic tool to support the integration of feature models. It comes up with a strategy-based approach to reduce the effort that developers invest to combine feature models and increase the amount of correctly integrated models. A controlled experiment was run with 10 volunteers through six realistic integration scenarios. Our results, supported by statistical tests, show that our semiautomatic approach not only reduced the integration effort by 73.01%, but also increased the number of correctly integrated feature models by 43.01%, compared with the manual approach. Our main contributions are a semiautomatic, strategy-based approach with tool support, and empirical evidence on its benefits. Our encouraging results open the way for the development of new heuristics and tools to support developers during the evolution of feature models.
Computation of the Three-Dimensional Electric Field Using Particle Swarm Optimization Charge Simulation Method

In this paper, particle swarm optimization (PSO) and charge simulation method (CSM) are combined to calculate the three-dimensional electric field of sphere-plane electrode, rod-plane electrode and simplified high-voltage insulator string. The optimization variable of PSO is the coordinate or charge quantity of the simulation charge, and the fitness function is the potential error of the matching point. In the calculation of the sphere-plane electrode, the calculation results verify the effectiveness of PSOCSM; the comparison with GACSM highlights the advantages of short calculation time and high precision of PSOCSM. In the calculation of the rod-plane electrode, a method to reduce the optimization variables of PSO is proposed to further improve the computational accuracy and optimization efficiency of PSOCSM. In the calculation of the rod-plane electrode and the high voltage insulator string, the comparison results of GACSM and PSOCSM reflect the excellent optimization performance of PSO. The research results provide a new calculation method for electric field calculation of high voltage electrical appliances.
Mobile phones in physics teaching – an overview of development and research activities

In Graz, a lot of activities concerning the implementation of mobile phones in physics teaching have been carried out on different levels during the last years. These activities can be regarded as exemplary for the common progress in the field of teaching and learning with digital media: Videos of experiments, motion analysis, measurements with internal sensors, WhatsApp and finally the technology of the smartphone itself. All of the presented results have been developed and partially evaluated within final theses on different levels.
Optimal Path Planning for ω-regular Objectives with Abstraction-Refinement

This paper presents an abstraction-refinement based framework for optimal controller synthesis of discrete-time systems with respect to ω-regular objectives. It first abstracts the discrete-time “concrete” system into a finite weighted transition system using a finite partition of the state-space. Then, a two-player mean payoff parity game is solved on the product of the abstract system and the Büchi automaton corresponding to the ω-regular objective, to obtain an optimal “abstract” controller that satisfies the ω-regular objective. The abstract controller is guaranteed to be implementable in the concrete discrete-time system, with a sub-optimal cost. The abstraction is refined with finer partitions to reduce the suboptimality. In contrast to existing formal controller synthesis algorithms based on abstractions, this technique provides an upper bound on the trajectory cost when implementing the suboptimal controller. A robot surveillance scenario is presented to illustrate the feasibility of the approach.
Anisotropic moveout correction using a Hough transform neural network

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Anisotropic moveout correction using a Hough transform neural networkAuthors: Janaki VamarajuMrinal K. SenJanaki VamarajuUTIG — The University of Texas at AustinSearch for more papers by this author and Mrinal K. SenUTIG — The University of Texas at AustinSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215883.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractSeismic anisotropy is present in different geological settings at various scales. This significantly affects the standard hyperbolic reflection moveout approximation that is based on the assumption that layers are isotropic. Conventional traveltime picking and velocity analysis picking can take up a significant amount of time to process anisotropic seismic data. Here we develop a velocity-independent Hough transform neural network based technique for normal moveout correction in VTI media. This technique offers advantages when compared to the time and computational costs required in conventional anisotropic normal moveout correction. Using a Hough transform based neural network, we simultaneously fit all the non-hyperbolic reflection moveout curves using intermediate to long offsets. The functionality of the unsupervised neural network is analogous to the classical Hough transform. However, the presented form is an efficient representation with reduced storage space and is capable of handling noisy datasets. The network is applied to two synthetic VTI datasets and we demonstrate the practical feasibility of anisotropic moveout correction that is independent of traveltime picking and velocity analysisPresentation Date: Wednesday, September 18, 2019Session Start Time: 8:30 AMPresentation Time: 11:00 AMLocation: 221DPresentation Type: OralKeywords: NMO, anisotropy, VTI, neural networks, moveoutPermalink: https://doi.org/10.1190/segam2019-3215883.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Janaki Vamaraju and Mrinal K. Sen, (2019), "Anisotropic moveout correction using a Hough transform neural network," SEG Technical Program Expanded Abstracts : 2383-2387. https://doi.org/10.1190/segam2019-3215883.1 Plain-Language Summary KeywordsNMOanisotropyVTIneural networksmoveoutPDF DownloadLoading ...
On the Evolutionary Neural Network Creation Using Native Visibility Graph

In this article, a novel way of creating the feed forward neural network is presented. This model is inspired by native visibility graph, and the networks are created by well-known differential evolution algorithm. In the experiment parts, we compared these networks with multi-layer perceptron. We have found that our novel approach creates smaller networks comparing to multi-layer perceptron and also, the decision space differs. The decision space was similar for multi-layer perceptron for almost all training runs from 51 repetitions, but for our novel approach, the decision space differs and also creates more complex patterns.
Real-Time and Accurate Target Location Method Based on Artificial Mark

A real-time and accurate target location method based on the artificial mark is proposed in the applications of wheelchair nursing bed docking. The artificial mark consists of the lines and the circles. Target-oriented edge detection is realized by using the modified Sobel operator, and morphology filtering is used to realize the edge extraction of the artificial mark area. The image segmentation is achieved based on the extracted edge image. In addition, location accurately on the segmented image is realized by using the method of the gravity center. The experimental results show that the subpixel level location is achieved under the complex background. The accuracy of the location method is less than 5 mm, and the location process takes 36 ms.
Learning to Retrieve Related Resources in a Bibliographic Information Network

With the advancement in semantic technologies, structured data in different domains is being modelled as knowledge graphs. Bibliographic information has proven rather acquiescent to being formalized as knowledge graphs or information networks. In this paper, we address the problem of retrieving related resources in a bibliographic information network like SciGraph. Discovery of path patterns representing a set of example pairs of related resources forms the basis of our solution. We have adopted a bi-directional search strategy to accommodate state-of-the-art similarity measure (viz. HeteSim), relevant to Heterogeneous Information Networks (HIN). The proposed method has been evaluated based on precision and execution time. On experimenting with different datasets (YAGO and Springer Nature SciGraph), we observe an improvement in performance, compared to forward search algorithm, which is based on Path Constrained Random Walk similarity measure.
Lossy compression of color images based on discrete Tchebichef transform

The Discrete cosine transform (DCT) plays an important role in image and video coding. It is used as the first step in lossy/lossless image compression. Consequently, many algorithms have been developed to efficiently compute it. In the image compression community, an alternative to the DCT, known as the discrete Tchebichef transform (DTT), is increasingly used. It possesses an excellent energy compaction property and a low implementation complexity. However, the DTT has not been investigated in the compression of color images. In this paper, the theory behind the DTT and its pruned version (PDTT) are investigated and a lossy compression method of color images based on the DTT is proposed. Experimental results demonstrate that the proposed method exhibits almost the same performance as the DCT-based technique, in terms of objective measures such as the PSNR and SSIM while reducing the arithmetic complexity by more than 37%.
Adaptive Scale Mean-Shift Tracking with Gradient Histogram

The mean-shift (MS) tracking is fast, is easy to implement, and performs well in many conditions especially for object with rotation and deformation. But the existing MS-like algorithms always have inferior performance for two reasons: the loss of pixel’s neighborhood information and lack of template update and scale estimation. We present a new adaptive scale MS algorithm with gradient histogram to settle those problems. The gradient histogram is constructed by gradient features concatenated with color features which are quantized into the 16 × 16 × 16 × 16 bins. To deal with scale change, a scale robust algorithm is adopted which is called background ratio weighting (BRW) algorithm. In order to cope with appearance variation, when the Bhattacharyya coefficient is greater than a threshold the object template is updated and the threshold is set to avoid incorrect updates. The proposed tracker is compared with lots of tracking algorithms, and the experimental results show its effectiveness in both distance precision and overlap precision.