Reviewerly/exHarmony huggingface dataset:
Asynchronous Multi-Radar Tracking Fusion with Converted Measurements

In tracking applications, multiple radars used to observe target motion usually work asynchronously due to different sampling rates and initial sampling time instants, and the fusion time instant at the fusion center can be designated arbitrarily. In this paper, the estimation fusion problem for target tracking with asynchronous multi-rate multi-radar measurements is investigated. Two asynchronous fusion algorithms are proposed, i.e., batch time-aligned asynchronous fusion with unbiased converted measurements and sequential linear minimum mean square error (LMMSE) asynchronous fusion with converted measurements. The batch time-aligned asynchronous fusion algorithm is suboptimal because of the correlation between measurement error covariance and measurement itself. The sequential LMMSE asynchronous fusion algorithm is theoretically optimal in the sense of minimizing the mean square error within the set of all linear estimators. Numerical examples are provided to demonstrate the effectiveness of the proposed two asynchronous fusion algorithms.
Application of Big Data in the Reform of Physical Education Teaching Mode

With the continuous development of information technology and technology, the Internet has penetrated into all walks of life. “Internet plus” has become an important means to promote the development of the times. In the era of Internet plus love, there has been a change in the teaching objects of physical education in Colleges and universities. Under the influence of the Internet, significant changes have taken place in the education industry. As far as physical education is concerned, the most important problem is to achieve the maximum effect of “Internet plus physical education”. The reform of physical education teaching is mainly to combine the psychology and behavior of contemporary students to make appropriate changes. It has become a proposition of the times to mobilize students’ interest in the realization of technology, information and data. Through the data platform, this paper carries out a comparative experiment on two classes of physical education major in a university in this city, and analyzes the help of the two classes in traditional education mode and education mode based on big data platform for students’ physical education. The conclusion is as follows: the sports teaching mode based on big data can stimulate students’ enthusiasm, and has obvious effect on students’ sports performance and sports interest, at the same time, the attendance rate of the course has been increased to 85%. Through the statistics of heat consumption and movement distance, the big data platform has significantly improved the enthusiasm of students, and 85% of students support the introduction of big data platform.
Detection and Intelligent Control of Cloud Data Location Using Hyperledger Framework

Cloud Computing and its security issues have always drawn technical attention. Specifically, the compliance issues associated with the location of cloud data have always been a concern of a Cloud Service Consumer (CSC). On the other hand, blockchain based smart contracts have become very popular nowadays for automatizing legal transactions. They eliminate the need of mediators to verify a process and secure the same using digital signatures. Moreover, the in-built immutable nature of the blockchain makes it to compromise its security. A “Hyperledger Fabric” (Androulaki et al., 2018) based blockchain framework is used in this work to solve the locational constraints of cloud based services and guarantee a compliant geographical transfer of the cloud data when required. Our contribution towards configuring the hyperledger network and designing the chaincodes guarantee an organization wise segregation of the location information and prevent illegitimate users from accessing the same. Add-on security measures including port segregation, TLS (Transport Layer Security) certificates and access control rules are implemented to secure the configuration files, chaincodes and ledgers, respectively. The simulation results reflect a scalable system as denoted by the performance graphs. Security effectiveness value is calculated based on the NIST threat model.
Mutation detection dual correlation filter with an object-awareness module for real-time target tracking

Discriminative correlation filter (DCF) based methods have recently been widely used for visual tracking tasks. The adaptive spatiotemporal-regulation based tracker (AutoTrack) can only partially solve some limitations of the DCF framework including filter degradation and the boundary effect, but its application scenarios need to be broadened, and performance improvements are also required. To further surmount these difficulties, this paper provides an object-awareness-module based mutation detection dual correlation filter (MDDCF-OAM). The main innovation points of this work are: (1) an object-mask based context enhancer is proposed to formulate a more robust appearance model; (2) a dual filter training-learning structure is adopted to allow the dual filters to restrict each other and suppress the filter degradation effect; (3) a Gaussian label map is updated with the refined joint response map to detect and attenuate the response mutation effects. Exhaustive experiments have been conducted to test the efficiency of the suggested MDDCF-OAM on four benchmarks, namely, OTB2015, UAV123, TC128, and VOT2019. The results indicate that: (1) the introduced MDDCF-OAM surpasses nine state-of-the-art trackers; (2) the MDDCF-OAM has a real-time speed of 32 frames per second, which is sufficient for target tracking tasks in numerous scenarios, especially unmanned aerial vehicles and camera tracking.
Real-Time Vehicle Detection Method Based on Aerial Image in Complex Background

In the field of military and security, the vehicle target has high capture value because of its high mobility, large load and high threat. In recent years, with the development of UAV technology and the improvement performance, as well as the rapid development and wide application of deep convolutional neural network in the field of object detection, vehicle detection based on UAV has become an important task, which has application value and practical significance that cannot be ignored. Compared with the traditional ground monitoring system, the UAV has high mobility, the background of the aerial image is more complex, and the target scales vary widely. Therefore, based on deep convolutional neural network, this paper proposes a feature fusion method combining context, which enhances the feature extraction ability of small targets in complex background. Then, a multi-scale region proposal matching method based on location clustering is proposed to enhance the matching degree between target features and region proposal. Finally, this paper designs a lightweight backbone based on DenseNet [1], which effectively reduces the computational cost. Experimental results show that our model reached the accuracy of 86.7% on PASCAL VOC (only car and bus are selected) dataset, which has a 3.1% bonus compared with the baseline SSD [2]. Moreover, in order to show the effect on aerial images, an additional experiment is conducted on the UAV123 dataset, which gets a 5.7% bonus. The speed of our model has a 180 fps on the NVIDIA RTX 2080.
Rendered Image Superresolution Reconstruction with Multichannel Feature Network

In the process of film and television production, clear images can give the audience a real sensory experience, but high-resolution images require a massive amount of production time and highly specialized imaging equipment, which is not a cost-effective solution at the moment. To achieve a better cost efficiency during video production, we propose a multichannel featured superresolution network model that utilizes rendered low-resolution images according to their characteristics. This model includes a feature extraction layer, a series of subnetworks, and a reconstruction module. Inside the network model, a series of subnetworks are cascaded to improve the information flow from coarse to fine, which helps to fully extract the depth, normal vector, edge, and texture features from low-resolution rendered images to reconstruct the high-resolution image. Additionally, residual learning is introduced at each stage to further improve the reconstruction performance. We experiment with the model on the classic Disney Monte Carlo datasets and compare it with several related algorithms. The results show that our algorithm is able to reconstruct the image with clearer details and texture. Thus, our research not only helps to maintain the audience’s sensory experience but also increases the efficiency of film and television production, which also brings considerable economic benefits.
Online Robust Specific and Consistent Hashing

Most of the existing cross-modal hashing (CMH) methods are trained in a batch-based manner, which is time-consuming and unable to handle streaming data. Recently, online CMH methods have attracted increasing attention. However, existing online CMH methods face two limitations: 1) they usu-ally excavate the common semantic information by learning modality-specific projection matrix for each modality, while ignoring the intrinsic relationship among different modali-ties; 2) they often suffer from learning less discriminative hash codes because of insufficiently exploiting the pairwise similarity. To tackle these challenges, we propose a novel Online Robust Specific and Consistent Hashing (ORSCH) method. Specifically, ORSCH decomposes the projection ma-trices into consistent and modality-specific ones, which ef-fectively exploits intrinsic semantic information of streaming data in different modalities. Furthermore, we utilize both dis-crete and continuous labels to construct affinity matrices to improve the discrimination of hash codes. Experiments on three benchmark datasets show the superiority of ORSCH.
Relation Reasoning for Video Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is a challenging and important task in many applications, which aims to predict future pedestrians' trajectory coordinates from the input historical data. The existing methods usually use ready-made trajectory coordinates as inputs, which is, however, unavailable in video-based scenarios. In this paper, we propose a relation reasoning hypergraph (RRH) model to directly predict multiple pedestrian trajectories from raw videos. It is a challenging issue for the input and output are in different modalities and a video may contain multiple pedestrians. Our model integrates historical trajectory tracking, pedestrian relation reasoning, and future trajectory prediction into one framework. For capturing the subtle social relationships among pedestrians, we design a relation reasoning hypergraph network. We tested the proposed method on two public pedestrians datasets and the performance demonstrates the power of the model.
Self-Guided Video Super-Resolution Based on a Fast Deformable ConvGRU Model

Video super-resolution (VSR) aims at recovering a natural and realistic high-resolution (HR) video frame from the corre-sponding low-resolution (LR) counterpart and its consecutive neighboring frames. The challenge is how to make full use of spatio-temporal coherence among the input LR frames. In this work, we propose a self-guided deformable convolutional gated recurrent unit (GRU) framework for VSR. Specifically, convolutional GRU can efficiently extract temporal features of the input LR frames. Deformable convolution (DConv) is utilized to spatially align the hidden states of GRU cells with the input feature maps. Moreover, we argue that the reference LR frame itself is efficient to guide the aggregated features learning frame-specific feature maps, which are then used to generate rich and more realistic textures towards the corresponding HR frame. Extensive experimental results on benchmark datasets demonstrate that the proposed framework achieves better performance than state-of-the-art methods and has higher model efficiency.
Hierarchical continuous-time inhomogeneous hidden Markov model for cancer screening with extensive followup data

Continuous-time hidden Markov models are an attractive approach for disease modeling because they are explainable and capable of handling both irregularly sampled, skewed and sparse data arising from real-world medical practice, in particular to screening data with extensive followup. Most applications in this context consider time-homogeneous models due to their relative computational simplicity. However, the time homogeneous assumption is too strong to accurately model the natural history of many diseases including cancer. Moreover, cancer risk across the population is not homogeneous either, since exposure to disease risk factors can vary considerably between individuals. This is important when analyzing longitudinal datasets and different birth cohorts. We model the heterogeneity of disease progression and regression using piece-wise constant intensity functions and model the heterogeneity of risks in the population using a latent mixture structure. Different submodels under the mixture structure employ the same types of Markov states reflecting disease progression and allowing both clinical interpretation and model parsimony. We also consider flexible observational models dealing with model over-dispersion in real data. An efficient, scalable Expectation-Maximization algorithm for inference is proposed with the theoretical guaranteed convergence property. We demonstrate our method’s superior performance compared to other state-of-the-art methods using synthetic data and a real-world cervical cancer screening dataset from the Cancer Registry of Norway. Moreover, we present two model-based risk stratification methods that identify the risk levels of individuals.
A novel maximum likelihood and moving weighted average based adaptive Kalman filter

Abstract For the state estimation with inaccurate noise statistics, the existing adaptive Kalman filters (AKFs) usually have substantial computational complexity or are not easy to estimate online. Inspired by the fact, a new computationally efficient AKF based on maximum likelihood and moving weighted average (MMAKF) is proposed. Firstly, to reduce computational complexity, instead of estimating the noise covariance matrixes, the maximum likelihood principle is introduced to directly estimate the prediction error covariance matrix and innovation covariance matrix. Subsequently, a new moving weighted average algorithm is designed to optimize the estimated results. Then, a computationally efficient AKF is derived, and its convergence performance and application are discussed. Simulation results for the target tracking example illustrate that the proposed AKF can effectively reduce error caused by inaccurate noise statistics and basically keep simplicity and elegance of the classical KF.
Blockchain adoption: value creation or insider selling?

Using a sample of listed firms in China, we aim to examine the motivation of blockchain adoption and propose two opposite hypotheses, namely value creation hypothesis and insider selling hypothesis. The empirical results demonstrate that initial blockchain adoption announcement leads to approximately 2% CAAR over the three-day interval around the release, whereas the abnormal return quickly reverses after the announcement, implying that the blockchain adoption can only provide short-term value and against the value creation hypothesis. The finding remains robust for largest industries (manufacturing and information technology) and release channels. On the contrary, we observe a significant positive effect of insider selling on the blockchain adoption announcement, which supports the insider selling hypothesis. Our findings can provide a better understanding on firms’ motivation of blockchain adoption, warn the investors to keep a rational attitude to ‘blockchain hysteria’ when making investment decisions, and motivate the regulators to propose strict information disclosure mechanism and prevent insider selling behaviours.
HyperAST: Enabling Efficient Analysis of Software Histories at Scale

Abstract Syntax Trees (ASTs) are widely used beyond compilers in many tools that measure and improve code quality, such as code analysis, bug detection, mining code metrics, refactoring. With the advent of fast software evolution and multistage releases, the temporal analysis of an AST history is becoming useful to understand and maintain code.
Face Super-resolution Based on Multi-source References

This paper proposes a multi-source references (MSR) based face super-resolution (FSR) model. More specifically, to enhance the low-quality large-scale reconstruction of faces without the involvement of face prior knowledge, we propose a multi-source references based FSR framework exploiting a constructed reference library of nonidentity faces and an information mining module for external and internal references. Experimental results show that the proposed model can provide more satisfactory and reliable face super-resolution results than the-state-of-the-art methods.
UAV path planning based on improved ant colony algorithm with multiple heuristic factors

Aiming at the problems of long search time and easy to fall into deadlock when using the traditional ant colony algorithm in the path planning process of UAV in two-dimensional space, this paper first introduces the advantages and disadvantages of the traditional algorithm, and puts forward an improved ant colony algorithm. Select A*, potential field method, traditional heuristic search strategy and obstacle avoidance heuristic search, improve pheromone update algorithm, and dynamically modify the dependence of transition probability on pheromone. The simulation results show that the improved ant colony algorithm has better global search ability, the path superiority is more prominent, the number of iterations and calculation time are optimized, and the stability of path search is greatly improved.
A Framework for Blockchain-, AI-, and IoT-Driven Smart and Secure New-Generation Agriculture

Agriculture is a fundamental need for survival of humankind. To cater to the current needs and decrease food wastage, smart agriculture using innovative technologies like Internet of Things (IoT), artificial intelligence(AI), and Big Data is the current trend. Blockchain technology is recently being used to solve the challenges of transparency, neutrality, security, and reliability in agriculture. In this chapter we present a picture of smart agriculture using Blockchain, IoT, and AI together. A framework for smart agriculture is proposed where IoT devices will collect data related to agricultural production, transport, and storage. AI is used to gather specific knowledge from collected data and for future prediction. Blockchain is used in every step of storage of data after sensing or transaction, and all the authorized parties can verify data from the Blockchain. In comparison with existing methods, our framework is having more features in many useful operational parameters.
Novel Online Censoring Based Learning Algorithm For Complex-Valued Big Data Streams

This study introduces a novel online censoring (OC) strategy based learning algorithm for complex-valued big data streams. First of all, the kurtosis-based cost function is redefined considering the OC strategy, and then the OC-based complex-valued least mean kurtosis (OC-based complex-valued least mean kurtosis, OC-CLMK) learning algorithm is designed by minimizing it. The learning performance of the proposed OC-CLMK is compared with the performances of the well-known learning algorithms in the literature in terms of mean square error and correlation coefficient on a real-world linear regression problem. Simulation results show that the proposed algorithm thanks to its OC strategy reduces the computational complexity by censoring noninformative training data and produces satisfactory mean square error, training and test regression performances. As a result, this study shows that the proposed algorithm can be easily used in the processing of big data streams because it uses only informative data in the training process with the help of its OC mechanism and reduces the input data size to half by working in the complex domain.
Investigating Data Mining Trend in Cybercrime Among Youths

This study investigated the emerging trend in cyber dynamics among youths using statistical tools. The study adopted a descriptive survey design. The population of the study comprised all cybercriminals in police custody in Ibadan, Nigeria. A simple random sampling procedure was used to select four hundred and eighteen participants for the study. The results of the analysis revealed that there was a significant and positive relationship between social influence and the prevalence of cybercrime among youth in the study area, there was a significant and positive relationship between economic influence and prevalence of cybercrime among youth in Africa, there was a significant and negative relationship between political influence and prevalence of cybercrime among youth in Africa and there was a significant and negative relationship between religious influence and prevalence of cybercrime among youth in Africa. Based on the findings of the study, the conclusion was reached that data mining and machine learning have a direct pattern on the prevalence of cybercrime and associated factors. The recommendation was made that authority in charge of cybercrime should ensure that data are available in the database for easy accessibility and quality conclusion on the causes of the prevalence of cybercrime among Nigeria youth.
A robust method of IC seating inspection in burn-in sockets using Hough transform

In IC chips manufacturing process, burn-in test is one of the most important testing methods. They are subjected to a temperature and voltage load in a burn-in machine to detect defective chips. A serious problem may occur if IC chips are not placed correctly in burn-in sockets in the machine. To ensure correct IC chips seating, there is an inspecting method to investigate the relationship between two straight lines on the surface projected by a line laser beam, one is on the IC chip and another one is on the socket. We propose a robust image analysis method even when the laser beam is diffusely reflected. The proposed method detects laser line using the Hough transform to achieve fast and stable seating inspection. We have conducted experiments using images of IC chips used in actual tests and evaluated our method.
Information System Architecture and APP for Passenger Notification of Specific Events in MRT Cabins Based on Artificial Intelligence Assistance

To grasp MRT cabin situations as soon as possible, the best solution is to incorporate instant feedback from passengers. In addition to the walkie-talkie in the cabin, the mobile app is a very good way. This study proposes an information system architecture based on artificial intelligence assistance to help passengers use apps to notify MRT companies in real time. An APP prototype was developed to demonstrate the effectiveness of this system architecture.