Reviewerly/exHarmony huggingface dataset:
Modeling and representation of intangible cultural heritage knowledge using linked data and ontology

ABSTRACT Intangible cultural heritage (ICH) contains rich knowledge, and the effective organization and management of ICH knowledge is the foundation for the protection and inheritance of ICH. The development of semantic Web technology changes the way of knowledge representation and expression and provides new ideas and methods for the organizing and sharing of ICH knowledge. This paper describes a research project: to model and manage the ICH knowledge by publishing the knowledge as linked data. Firstly, a knowledge ontology was designed to reveal and standardize the concept, attribute and relationship of ICH knowledge elements. Then, we converted the knowledge to RDF data basted on the knowledge ontology and linked the entities with DBpedia and GeoNames. Finally, taking the ICH in Hubei province as an example, the methodology was used to create an RDF dataset which published as linked open data, and develop a knowledge service platform based on the linked data service. The research has promoted the standardization of knowledge management of ICH in China, which is of great significance for the protection and dissemination of ICH.
GPS-assisted Aerial Image Stitching Based on optimization Algorithm

Splicing aerial images makes it faster and more comprehensive to detect the environment of the target area. In this paper, a GPS-assisted image stitching method based on optimization algorithm is proposed for the aerial images of UAV. Predicting the feature points to be extracted in the next key frame by using GPS, the searching time can be greatly reduced and the registration rate can be improved. After obtaining the matched feature point pairs, the pixel difference value of the feature set center is calculated as the initial value of the translation amount, and the re-projection error is minimized by the L-M iterative algorithm to establish the best affine transformation, which is more accurate and less time-consuming than the traditional RANSAC method. The algorithm adjusts the threshold of selecting key frames to adjust the number of feature points between frames, so that it is adapted to a variety of outdoor scenes in real-time.
Secure Service Provisioning Scheme for Lightweight Clients with Incentive Mechanism Based on Blockchain

The Internet of Things (IoT) industry is growing very fast to transform factories, homes and farms to make them automatic and efficient. In the past, IoT is applied in different resilient scenarios and applications. IoT faces a lot of challenges due to the lack of computational power, battery and storage resources. Fortunately, the rise of blockchain technology facilitates IoT devices in security solutions. Nowadays, blockchain is used to make reliable and efficient communication among IoT devices and emerging computing technologies. In this paper, a blockchain-based secure service provisioning scheme is proposed for Lightweight Clients (LCs). Furthermore, an incentive mechanism based on reputation is proposed. We used consortium blockchain with the Proof of Authority (PoA) consensus mechanism. Furthermore, we used Smart Contracts (SCs) to validate the services provided by the Service Providers (SPs) to the LCs, transfer cryptocurrency to the SPs and maintain the reputation of the SPs. Moreover, the keccak256 hashing algorithm is used for converting the data of arbitrary size to the hash of fixed size. The simulation results show that the LCs receive validated services from the SPs at an affordable cost. The results also depict that the participation rate of SPs is increased because of the incentive mechanism.
The paradox of big data

Background The data deluge seemingly makes it more likely that data mining will discover new, heretofore unknown relationships. Findings Monte Carlo simulations demonstrate the paradox of big data: the data deluge makes it more likely that the patterns and relationships discovered by data mining are spurious. Conclusion Models are more likely to be reliable if expert opinion is used in their specification, instead of viewing human expertise as an unhelpful constraint on knowledge discovery.
To Effectively Control the Workload by Hierarchical Approach

Abstract The productive administration of geologically dispersed server farms has turned into an imperative issue not just for huge organizations that claim a few destinations, yet additionally because of the rising of between Cloud foundations that permit heterogeneous server farms to coordinate. These conditions open phenomenal roads for the help of a colossal measure of workload, yet they require the meaning of novel calculations and methodology for their administration, where adaptability is a need. The intricacy determines by the span of the framework and by the requirement for achieving a few and some of the time clashing objectives, among which: stack adjusting among various destinations, anticipation of dangers, workload union, and decrease of costs, expended vitality and carbon outflows. In this paper a various leveled approach is displayed, which protects the self-rule of single server farms and in the meantime takes into consideration a coordinated administration of heterogeneous platforms. The system is deliberately nonexclusive yet can be customized to the particular prerequisites of single situations. Exhibitions are examined for a particular Cloud framework made out of four server farms.
A Radar False Track Suppression Algorithm based on Logistic Regression

False track suppression is the difficult problem of data processing for radar. The two are contradictory to ensure the true track starts smoothly and that all possible false tracks are suppressed. This paper presents a radar false track suppression algorithm based on logistic regression. First of all, the evaluation index is given according to engineering experience. And then the logistic regression is used to classify the true and false track on the basis of a large number of test data. The quality management method is referred to suppress the false track further and reduce the misjudgment. Through the radar measured data validation shows the efficacy of the proposed method.
Two-terminal wide range SQUID amplifier with hysteretic quasi linear transfer characteristics

To implement wide range measurement using direct current Superconducting Quantum Interference Device (dc SQUID), we presented a simple two-terminal SQUID amplifier consists of a shunt resistor and a conventional SQUID in series with feedback coil. Only two terminals are led out with coaxial cable for high speed and low heat loss. Periodically repeated quasi linear flux-to-voltage transfer characteristics are achieved by internal flux feedback. Wide range flux input is modulated by the SQUID amplifier into voltage signal with rapid transient edges. The reconstructed output is the synthesis of measured voltage and counts of flux-quanta counting. Working principles of linearized SQUID amplifier were simulated and experimentally verified. Wide range measurement with error within ±0.1Φ0 was demonstrated in the experiment.
Eye Tracking based Cognitive-Centered User Models

State-of-the-art predictive user models, like Goals, Operators, Methods, and Selection Rules (GOMS) or Keystroke-Level Model (KLM), do not consider human differences in information processing, and do not model user interaction by also considering visual behavior patterns of users during task execution. This can be accredited mainly to insufficient methods and approaches on how to model such interdependencies and thus correlate users' cognitive characteristics with their interaction and visual behavior during task execution, and ultimately considering such cognitive-centered user models practically within current state-of-the-art information systems' personalization and adaptation approaches. In this paper, we elaborate on such an endeavor and propose a seven-step, gaze-based human cognitive-centered user model as a basis of synthesizing user cognitive styles along with their interaction and visual behavior patterns. Aiming to prove the validity of the suggested user model, we applied it in the context of an eye tracking study that investigated influences of users' human cognitive differences on their visual behavior in user authentication within traditional desktop and mixed reality contexts. Initial results of the study are also reported.
Forecasting the Intrinsic Viscosity of Polyester Based on Improved Extreme Learning Machine

Polymerization process plays an important role in the entire production process of polyester fibers. Polyester, the product of polymerization process, directly determines the quality of the final fiber. Since intrinsic viscosity is a key performance index of polyester, its prediction is necessary and meaningful. In this paper, a forecasting model of intrinsic viscosity of polyester based on extreme learning machine is proposed. The model inputs are the technological parameters of the polymerization process, including the temperatures and pressures of the esterification reactor, the prepolymerization reactor, the final polycondensation reactor, and the additive amount of titanium dioxide. The model output is intrinsic viscosity of polyester. In order to enhance the prediction accuracy of the model, the combination function of ReLu and Softplus is used as the activation function of extreme learning machine (ELM). And particle swarm optimization (PSO) is used to optimize the weights of the input layer and thresholds of the hidden layer of ELM. The experimental results show that the proposed prediction model has better performance than ELM, Support Vector Machine (SVM) , back propagation neural network (BPNN) and ELM based on PSO (PSO-ELM).
Object Detection in Satellite Images Based on Active Learning Utilizing Visual Explanation

Convolutional neural networks (CNNs) have attracted much attention for object detection in satellite images. However, creating an annotated dataset requires a lot of time and user workload due to which the remote sensing domain has insufficient labeled datasets for training CNNs. We exploit an active learning (AL) framework for training CNNs with a small labeled dataset. AL obtains the training dataset by asking a human user to label the samples. For efficient AL, an intelligent query strategy is essential because the performance of a CNN depends on the collected dataset. Thus, in this study, we propose a query strategy to train CNNs effectively; this is done by choosing effective samples for training both the classifier and feature extractor. The strategy selects samples according to the gap between the classifier's prediction and visual explanation, which is the class discriminative part of an image derived from the extracted feature maps. Experimental result shows that a CNN trained with samples queried by our strategy had a 95% reduction in training samples requirement while maintaining 94% detection performance compared to a CNN trained with a complete dataset. Furthermore, the proposed strategy also reduced the required training samples by 30% compared to the conventional strategy to yield the same performance.
Stability of Random-Projection Based Classifiers. The Bayes Error Perspective

In this paper we investigateSkubalska-Rafajłowicz, Ewa the Bayes error and the stability of Bayes’ error when the dimension of the classification problem is reduced using random projections. We restrict our attention to the two-class problem. Furthermore, we assume that distributions in classes come from multivariate normal distributions with the same covariance matrices, i.e., differing only in the means. This is one of the few situations when the Bayes error expression can be written in a simple form of a compact final formula. The bias and the variance of the classification error introduced by random projections are determined. Both full-dimensional normal distributions and singular distributions were considered with a real dimension smaller than the ambient dimension. These results allow for the separation of the impact of random dimension reduction from the impact of the learning sample and provide lower bounds on classification errors. Relatively low variance of the Bayes error introduced by random projections confirms the stability of the random-projection based classifiers, at least under the proposed assumptions.
A Speech Interaction System Based on Cloud Service under ROS

With the recent development of technologies such as artificial intelligence and cloud computing, intelligent service robots are more and more widely used, and current users are paying more attention to the speech interaction function of robots. In this paper, we design a cloud-based intelligent speech interaction system under ROS to enhance the human-robot interaction experience, and carry out related tests on the laptop. The system we designed runs stably and smoothly, and has high real-time performance, which can satisfy user's requirements.
Benchmarking for Transaction Processing Database Systems in Big Data Era

Benchmarking is an essential suite supporting development of database management systems. It runs a set of well defined data and workloads on a specific hardware configuration to gather the results to fill the measurements. It is used widely for evaluating new technology or comparing different systems so as to promote the progress of database systems. To date, under the requirement of data management, new databases are designed and issued for different application requirements. Most of the state-of-the-art benchmarks are also designed for specific types of applications. Based on our experiences, however, we argue that considering the characteristics of data or workloads in big data era, benchmarking transaction processing databases (TP) must put much effort for domain specific needs to reflet 4V properties (i.e. volume, velocity, variety and veracity). With the critical transaction processing requirements of new applications, we see an explosion of designing innovative scalable databases or new processing architecture on traditional databases dealing with high intensive transaction workloads, which are called SecKill and can saturate the traditional database systems by high workloads, for example “11 $$\cdot 11$$ ” of Tmall, “ticket booking” during China Spring Festival and “Stock Exchange” applications. In this paper, we first analyze SecKill applications and the implementation logics, and also summarize and abstract the business model in details. Then, we propose a totally new benchmark called PeakBench for simulating SecKill applications, including workload characteristics definition, workload distribution simulating, and logics implementing. Additionally, we define new evaluation metrics for performance comparison among DBMSs under different implementation architecture from the micro- and macro- points of views. At last, we provide a package of tools for simulating and evaluating purpose.
Mallard: Turn the Web into a Contextualized Prototyping Environment for Machine Learning

Machine learning (ML) can be hard to master, but what first trips up novices is something much more mundane: the incidental complexities of installing and configuring software development environments. Everyone has a web browser, so can we let people experiment with ML within the context of any webpage they visit? This paper's contribution is the idea that the web can serve as a contextualized prototyping environment for ML by enabling analyses to occur within the context of data on actual webpages rather than in isolated silos. We realized this idea by building Mallard, a browser extension that scaffolds acquiring and parsing web data, prototyping with pretrained ML models, and augmenting webpages with ML-driven results and interactions. To demonstrate the versatility of Mallard, we performed a case study where we used it to prototype nine ML-based browser apps, including augmenting Amazon and Twitter websites with sentiment analysis, augmenting restaurant menu websites with OCR-based search, using real-time face tracking to control a Pac-Man game, and style transfer on Google image search results. These case studies show that Mallard is capable of supporting a diverse range of hobbyist-level ML prototyping projects.
A Segmentation-Driven Approach for 6D Object Pose Estimation in the Crowd

The task of estimating the 6D pose of object instances in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has been a research hotspot in recent years since it is very common in industrial applications. In this work, we present a segmentation-driven approach to recover the 6D object pose in the crowd. Firstly, a convolution neural network framework Mask R-CNN is applied to segment masks and bounding boxes of target object instances from the scene image in this stage. Then, the bounding boxes are segmented into smaller patches with slide windows. After that, a Sparse Auto Encoder is employed to extract invariant features of these patches, and we can obtain several candidate rough poses by Hough Voting. Finally, Iterative Closest Point (ICP) method is used to refine the 6D object pose for a better result. We tested our approach on the commonly used LINEMOD dataset [1]. Experimental results show that our approach achieves high accuracy and robustness under foreground occlusions and background distractors.
Optimized Dimensionality Reduction Methods for Interval-Valued Variables and Their Application to Facial Recognition

The center method, which was first proposed in Rev. Stat. Appl. 1997 by Cazes et al. and Stat. Anal. Data Mining 2011 by Douzal-Chouakria et al., extends the well-known Principal Component Analysis (PCA) method to particular types of symbolic objects that are characterized by multivalued interval-type variables. In contrast to classical data, symbolic data have internal variation. The authors who originally proposed the center method used the center of a hyper-rectangle in R m as a base point to carry out PCA, followed by the projection of all vertices of the hyper-rectangles as supplementary elements. Since these publications, the center point of the hyper-rectangle has typically been assumed to be the best point for the initial PCA. However, in this paper, we show that this is not always the case, if the aim is to maximize the variance of projections or minimize the squared distance between the vertices and their respective projections. Instead, we propose the use of an optimization algorithm that maximizes the variance of the projections (or that minimizes the distances between the squares of the vertices and their respective projections) and finds the optimal point for the initial PCA. The vertices of the hyper-rectangles are, then, projected as supplementary variables to this optimal point, which we call the “Best Point” for projection. For this purpose, we propose four new algorithms and two new theorems. The proposed methods and algorithms are illustrated using a data set comprised of measurements of facial characteristics from a study on facial recognition patterns for use in surveillance. The performance of our approach is compared with that of another procedure in the literature, and the results show that our symbolic analyses provide more accurate information. Our approach can be regarded as an optimization method, as it maximizes the explained variance or minimizes the squared distance between projections and the original points. In addition, the symbolic analyses generate more informative conclusions, compared with the classical analysis in which classical surrogates replace intervals. All the methods proposed in this paper can be executed in the RSDA package developed in R.
A Survey on Load Balancing in Cloud Systems for Big Data Applications

Today’s ever-growing information world, in which we witness the juggernaut of information explosion stemming from social networks, medical records, diverse medias, IoT, and so forth, has called for a solution—encompassing boundless resources for this voluminous information’s storing as well as processing in a distributed manner. To do so, although cloud computing has come up with an applicable remedy, it has overwhelmingly required a well-defined load-balancing mechanism, lifeblood of any given distributed system; a load-balancing algorithm has consistently strove to pinpoint overloaded nodes so as to disseminate and shift the burden of extra workload towards the under-loaded ones—by which the overall system performance in terms of resource utilization, throughput, cost, and response time will be guaranteed after all. In the interests of placing a high premium on load-balancing issue in distributed systems, in this study, we have provided a review concerning load-balancing algorithms in cloud environment for Big Data environment.
Performance Analysis of Convolutional encoder based on FPGA

This paper focuses on performance analysis of convolution encoder based on FPGA. With the big quantity of applications now a days, that use digital communication to store and retrieve data, this information should sent and received with smallest errors. Forward Error Correction (FEC) schemes unit a necessary an area of wireless communication systems. There square measure several styles of cryptography techniques accustomed correct utterly totally different error. Convolution secret writing methodology, an honest technique used for correcting errors that occur throughout data transmission. A The performance analysis is implemented by using Vivado 2016.1 and Xilinx Simulation. FPGA device used for hardware realization of Convolutional encoder performed with the help of XC7Z020clg4843. Convolutional encoders area unitenforced within the sort of a register style of circuit with explicit locations of the register completely ORed along to supply AN output. The locations that area unit completely ORed along is also mentioned as faucets. The location of those faucets defines potential state transitions wherever the amount of states for a selected code is outlined by 2(L+1). During this case, L is that the length of the register. Using this results in paper it is found that error correcting codes are the best medium to triumph data corruption in digital communication channels.
Flight Envelope Predicting Algorithm for UAV Based on UKF

In this paper, the Envelope Prediction Algorithm (EPA) is presented based on the Unscented Kalman Filter (UKF) method for the Unmanned Aerial Vehicle (UAV). Firstly, the flight envelope of UAV extended system is calculated by the Attainable Equilibrium Set (AES) algorithm. The discrete Newton iteration method is used to solve the nonlinear algebraic equation of UAV, and then the AES is obtained at the current state. Secondly, the UKF has the characteristics of high accuracy and strong stability, which can effectively to predict the next moment state value of the nonlinear system. Therefore, the UKF in this paper is used to predict the state variables of the UAV system at the next moment. Finally, the AES algorithm is adopted to calculate the next moment flight envelope of the UAV. The simulation results of the UAV indicate that the EPA algorithm can effectively predict the next moment AES of the UAV based on the predict the results.
Fringe pattern filtering using convolutional neural network

Fringe pattern denoising is an important process for fringe pattern analysis. In this paper, fringe pattern denoising using the convolutional neural network (CNN) is introduced. We use Gaussian functions to generate the various phase distributions, and then the required training samples are simulated according to theoretical formulas. The noisy fringe pattern can directly obtain the clean fringe pattern using the trained model. The denoising performance has been verified, which can recover high-quality fringe pattern.