Reviewerly/exHarmony huggingface dataset:
Adaptive technologies for training of specialists

Abstract The article is devoted to the organization of the educational process of specialists’ training by means of the adaptive teaching technologies. We have described the philosophy of instability that underlies such technologies: from chaos to self-organization. By showcasing the concepts of adaptation and adaptability, we define the content and stages of adaptive technology. The article presents the results of monitoring the trends of respondents’ awareness of the technologies of using the Bloom’s taxonomy in professional activities, which are necessary for the formation of the educational program of specialists’ training. Considering that the Bloom’s taxonomy strengthens the learning processes of applicants.
HBD-Authority: Streaming Access Control Model for Hadoop

Big data analytics, in essence, is becoming the revolution of business intelligence around the world. This momentum has given rise to the hype around analytic technologies, including Apache Hadoop. Hadoop was not originally developed with security in mind. Despite the evolving efforts to integrate security in Hadoop through developing new tools (e.g., Apache Sentry and Ranger) and employing traditional mechanisms (e.g., Kerberos and LDAP), they mainly focus on providing encryption and authentication features, albeit with limited authorization support. Existing solutions in the literature extended these evolving efforts. However, they suffer from limitations, hindering them from providing robust authorization that effectively meets the unique requirements of big data environments. Towards covering this gap, this paper proposes a hybrid authority (HBD-Authority) as a formal attribute-based access control model with context support. This model is established on a novel hybrid approach of authorization transparency that pertains to three fundamental properties of accuracy: correctness, security, and completeness. The model leverages streaming data analytics to foster distributed parallel processing capabilities that achieve multifold benefits: a) efficiently managing the security policies and promptly updating the privileges assigned to a high number of users interacting with the analytic services; b) swiftly deciding and enforcing authorization of requests over data characterized by the 5Vs; and c) providing dynamic protection for data which is frequently updated. The implementation details and experimental evaluation of the proposed model are presented, demonstrating its performance efficiency.
A Certificateless and Privacy-Preserving Authentication with Fault-Tolerance for Vehicular Sensor Networks

The vehicular sensor network has been developed to improve traffic, enhance the driving experience and so on, and is an inevitable technology trend. In vehicular sensor network, large amounts of data generated by vehicles need to be transmitted in the open channel, so it is important to ensure the integrity of information and vehicular privacy. For such a harsh condition, the aggregate signature technology is a very suitable solution, because it not only provides message integrity and authentication, but is also well suited to resource-constrained environments. Thus, in this paper, for vehicular sensor networks, we construct a certificateless and fault-tolerant aggregate signature (CLFTAS) scheme based on the elliptic curve cryptosystem. The certificateless nature makes our scheme free from the certificate management issue in traditional public key cryptography and the key escrow issue in identity-based cryptography. Fault tolerance greatly makes up for the aggregate signature's defect that adding an invalid individual signature to a valid aggregate invalidates the entire aggregation. Moreover, our scheme can protect the privacy of the vehicle’ identity. In the end, we give the security proof and performance evaluation. In the random oracle model, our scheme is secure under the assumption that solving the elliptic curve discrete logarithm problem is hard, and has great performance optimization and functionality compared with the state-of-the-art.
Learning-Based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images

The satisfied user ratio (SUR) for a given distortion level is the fraction of subjects that cannot perceive a quality difference between the original image and its compressed version. By predicting the SUR, one can determine the highest distortion level which allows to save bit rate while guaranteeing a good visual quality. We propose the first method to predict the SUR for symmetrically and asymmetrically compressed stereoscopic images. Unlike SUR prediction techniques for two-dimensional images and videos, our method exploits the properties of binocular vision. We first extract features that characterize image quality and image content. Then, we use gradient boosting decision trees to reduce the number of features and train a regression model that learns a mapping function from the features to the SUR values. Experimental results on the SIAT-JSSI and SIAT-JASI datasets show high SUR prediction accuracy for H.265 All-Intra and JPEG2000 symmetrically and asymmetrically compressed stereoscopic images.
Processing of building subsidence monitoring data based on fusion Kalman filtering algorithm

Subsidence monitoring is an important means to ensure the safety of buildings. After subsidence monitoring, it is necessary to mine key information from the monitored information, interpret the subsidence through in-depth analysis of the information, and predict the subsidence of buildings, especially the residential buildings. To improve the accuracy of subsidence interpretation and prediction, this paper applies the tracking fusion Kalman filtering algorithm to process the building subsidence monitoring data. Tracking fusion algorithm is a global suboptimal weighted state fusion algorithm. This algorithm not only can significantly improve the local estimation error, but also has the advantages of small computational burden and good fault tolerance, which is very convenient for practical engineering application. The simulation results show that the proposed tracking fusion Kalman filtering algorithm outperformed the local Kalman filtering algorithm adopted by Deng et al. in estimation accuracy of building subsidence. The research results provide reference for the protection of building safety.
Segmentation of brain magnetic resonance images using a novel fuzzy clustering based method

Segmentation of tissues in brain magnetic resonance (MR) images has a crucial role in computer-aided diagnosis (CAD) of various brain diseases. However, due to the complex anatomical structure and the presence of intensity non-uniformity (INU) artefact, the segmentation of brain MR images is considered as a complicated task. In this study, the authors propose a novel locally influenced fuzzy C-means (LIFCM) clustering for segmentation of tissues in MR brain images. The proposed method incorporates local information in the clustering process to achieve accurate labelling of pixels. A novel local influence factor is proposed, which estimates the influence of a neighbouring pixel on the centre pixel. Furthermore, they have introduced the kernel-induced distance in LIFCM, which deals with complex brain MR data and produces effective segmentation. To evaluate the performance of the proposed method, they have used one simulated and one real MRI data set. Extensive experimental findings suggest that the authors' method not only produces effective segmentation but also retains crucial image details. The statistical significance test has been further conducted to support their experimental observations.
Storage Efficient Data Security Model for Distributed Cloud Storage

Digital Data or information is multiplying in a tremendous volume nowadays. Due to storage capacity restriction in local devices we use in our daily life, this concern was getting a great importance for on demand new innovation to eliminate the restriction. The development of Storage as a Service (STaaS), a cloud-based distributed storage framework fulfilled all the necessities of consumers. It can handle a huge amount of data by its scalable computing power and resources. Because of the expanding measure of data, a few important security issues arise for information security, privacy, confidentiality, trustworthiness and authentication. These issues the significant barrier in the adoption of cloud computing as STaaS. As indicated by a survey by EMC, practically 75% of current digital data is the copied duplicates that hold up a lot of extra storage space in the cloud. So, efficient uses of storage are also important. In this paper, we proposed an approach that is storage efficient and eliminates the issue of data security for distributed cloud-based storage (STaaS) with the use of data compression and cryptography procedures. The transferred data to the cloud will be compacted and encoded with compression and Encryption algorithm and afterwards, the output data will be chunked and stored onto the distributed storage which will make the transferred data hard to get any access even for the cloud service providers without data owner's permission. If data compression and encryption are executed simultaneously, at that point it requires less processing time and more speed.
MITOS: Optimal Decisioning for the Indirect Flow Propagation Dilemma in Dynamic Information Flow Tracking Systems

Dynamic Information Flow Tracking (DIFT), also called Dynamic Taint Analysis (DTA), is a technique for tracking the information as it flows through a program’s execution. Specifically, some inputs or data get tainted and then these taint marks (tags) propagate usually at the instruction-level. While DIFT has been a fundamental concept in computer and network security for the past decade, it still faces open challenges that impede its widespread application in practice; one of them being the indirect flow propagation dilemma: should the tags involved in an indirect flow, e.g., in a control or address dependency, be propagated? Propagating all these tags, as is done for direct flows, leads to overtainting (all taintable objects become tainted), while not propagating them leads to undertainting (information flow becomes incomplete). In this paper, we analytically model that decisioning problem for indirect flows, by considering various tradeoffs including undertainting versus overtainting, importance of heterogeneous code semantics and context. Towards tackling this problem, we design MITOS, a distributed-optimization algorithm, that: decides about the propagation of indirect flows by properly weighting all these tradeoffs, is of low-complexity, is scalable, is able to flexibly adapt to different application scenarios and security needs of large distributed systems. Additionally, MITOS is applicable to most DIFT systems that consider an arbitrary number of tag types, and introduces the key properties of fairness and tag-balancing to the DIFT field. To demonstrate MITOS’s applicability in practice, we implement and evaluate MITOS on top of an open-source DIFT, and we shed light on the open problem. We also perform a case-study scenario with a real in-memory only attack and show that MITOS improves simultaneously (i) system’s spatiotemporal overhead (up to 40%), and (ii) system’s fingerprint on suspected bytes (up to 167%) compared to traditional DIFT, even though these metrics usually conflict.
History entanglement entropy

Abstract A formalism is proposed to describe entangled quantum histories, and their entanglement entropy. We define a history vector, living in a tensor space with basis elements corresponding to the allowed histories, i.e. histories with nonvanishing amplitudes. The amplitudes are the components of the history vector, and contain the dynamical information. Probabilities of measurement sequences, and resulting collapse, are given by generalized Born rules: they are all expressed by means of projections and scalar products involving the history vector. Entangled history states are introduced, and a history density matrix is defined in terms of ensembles of history vectors. The corresponding history entropies (and history entanglement entropies for composite systems) are explicitly computed in two examples taken from quantum computation circuits.
Research and Application of Immersive Virtual Reality System Based on 3D Geographic Information System

Three-dimensional Geographic Information System (GIS) is the basic platform for data management and visualization of the internet of energy. The display medium of three-dimensional scene is generally a two-dimensional screen. The visualization of three-dimensional scenes lacks visual immersion and reality, which is difficult for two-dimensional screen to meet the requirements of large-scale, high-dimensionality and Multi-type energy internet data visualization. Combining the features of three-dimensional Geographic Information System and Cave Automatic Virtual Environment(CAVE) virtual reality system, this paper discusses the key technology of developing the Cave Automatic Virtual Environment immersive virtual reality system with three-dimensional Geographic Information System as the basic platform, the immersive virtual reality system and environment based on three-dimensional Geographic Information System is realized to help users understand the data and present the spatial knowledge more effectively. Finally, combined with the promotion and application of the system, the next research direction is discussed.
Probability-Based Framework to Fuse Temporal Consistency and Semantic Information for Background Segmentation

The fusion of temporal consistency and semantic information with limited foreground information for background segmentation using deep learning is an underinvestigated problem. In this paper, we explore the relation between temporal consistency and semantic information based on the law of total probability. A highly concise framework is proposed to fuse these two types of information. A theoretical proof is given to show that the proposed framework is more accurate than either the temporal consistency-based model or the semantic information-based model and that each model is a special case of the proposed framework. The proposed framework is a white-box framework that can easily be embedded into a deep neural network as a merging layer. In the proposed model, only a few parameters must be learned, which substantially reduces the need for a large dataset. In addition, these interpretable parameters reflect our understanding of the background and can be applied to a wide range of environments. Extensive evaluations indicate the promising performance of the proposed method. Our code and trained weights for the experiments are available at GitHub. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> <uri>https://github.com/zengzhi2015/SS_TC_BS</uri> </fn> (We encourage the reader to run the program for a better understanding of the proposed method).
Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model’s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.
Range-Doppler Hand Gesture Recognition Using Deep Residual-3DCNN with Transformer Network

Recently hand gesture recognition via millimeter-wave radar has attracted a lot of research attention for human-computer interaction. Encouraged by the ability of deep learning models in successfully tackling hand gesture recognition tasks, we propose a deep neural network (DNN) model namely, Res3DTENet that aims to classify dynamic hand gestures using the radio frequency (RF) signals. We propose a scheme that improves the convolutional process of 3DCNNs with residual skip connection (Res3D) to emphasize local-global information and enriches the intra-frame spatio-temporal feature representation. A multi-head attention transformer encoder (TE) network has been trained over the spatio-temporal features to refine the inter-frame temporal dependencies of range-Doppler sequences. The experiments are carried out on the publicly available Soli hand gesture data set. Based on our extensive experiments, we show that the proposed network achieves improved gesture recognition accuracy than the state-of-the-art hand gesture recognition methods.
Perception Analysis and Early Warning of Home-Based Care Health Information Based on the Internet of Things

Aiming at the problem of insufficient health monitoring of the elderly in the existing home care system, this paper designs a health information analysis and early warning system based on the Internet of Things (IoT) technology, which can monitor the physiological data of the elderly in real time. It also can be based on the elderly real-time monitoring data, physical examination data, and other types of health data, which can be used to predict diseases, so as to achieve “early detection and early treatment” of diseases. First, analyse and design the architecture and content of the home care monitoring system based on the Internet of Things. Secondly, based on the collected heart rate, blood pressure, and three-axis acceleration information of the elderly, it is analysed to determine whether the elderly are in danger of falling, and the designed system is used for early warning. Finally, this paper analyses the prediction algorithm theory of the disease prediction module in the health monitoring software of the home care system. In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network (BPNN) algorithm and conduct experimental tests. The test results show that the health information analysis and early warning software of the home care system meet actual needs and achieve the expected goals.
A Deep Learning Approach for Text Segmentation in Document Analysis

Text segmentation plays an essential role in both page segmentation and document reading comprehension. In this manuscript, we present a system to separate the page into homogeneous regions that can serve to extract information. Our approach is based on the U-Net network platform to extract text-lines, then the text lines will be read by an OCR system which is developed based on Convolutional Recurrent Neural Network (CRNN). We group the text-lines and OCR results simultaneously based on the idea from the DBSCAN algorithm. Our system also contains the support modules such as template matching and deskew to improve the performance. To materialize and evaluate ideas, we built a complete Vietnamese data set for training and testing. As a result, we get over 90% accuracy in both Vietnamese and English languages.
A platform to test and automation on gesture and motion controls of play station 4 using robotic arm

Abstract Gesture and motion play an important role in computer games. This paper reveals about automation testing for gesture and motion-based interaction on play station4 and to provide a single automation test setup for multiple devices of same family. Generally, play station 4 gesture controls are tested manually by using move motion controller which leads to tedious effort of building a gesture and motion test. The proposed system provides low cost and effective automation test solution for gesture and motion-controlled devices. The created platform ‘TAGMED’ - Test Automation for Gesture and Motion Enabled Devices, a test automation tool to provide automation testing through the gesture and motion input simulation to play station4 gaming consoles. The automation test solution has robotic arm tweaked to handle PS4 MOVE controller and simulate gesture input on testing device, the platform captures the corresponding output, compares the captured output with the expected output and provides the report. This helps in bringing better accuracy of recognition, repeatability and defect less testing.
Image Watermarking with Region of Interest Determination Using Deep Neural Networks

Watermarking is a popular technique used in various applications, such as copyright protection of digital media, including audio, video, and image files. Proper watermarking should satisfy multiple criteria, such as robustness and transparency. While a successful watermarking needs to meet these criteria, there is a tradeoff between the two opposing criteria of robustness and transparency. This paper proposes a method for determining the appropriate locations for embedding watermarks with high strength factors. For this purpose, a deep neural network, known as Mask R-CNN, is used, which is pre-trained on the COCO dataset. This neural network finds a good strength factor for those sub-blocks of the host image selected for embedding. The proposed technique can be used in conjunction with most DWT and DCT based semi-blind watermarking approaches. Experiments show that the proposed method is robust against different attacks and demonstrates good transparency.
Data Right Confirmation Mechanism Based on Blockchain and Locality Sensitive Hashing

In the era of big data, the ownership of digital asset has become an important issue that people concern about. Traditional means of data right confirmation adopt the mode of submitting the ownership evidence and authority reviewing, but there are uncontrollable factors such as potential tampering. In order to solve this problem, a data right confirmation mechanism based on blockchain and locality sensitive hashing is proposed. First, blockchain technology is used to handle the matter of trust in data right confirmation, and a user identity management scheme based on Hyperledger Fabric is proposed to ensure the credibility of the identity of the confirmation entity. Secondly, a data fingerprint extraction scheme based on locality sensitive hashing is proposed, and it is used to maintain the consistency of the fingerprints stored on the blockchain and the data disseminated off the blockchain. Finally, the chaincode deployed on Hyperledger is developed to support the submitting, verification and notarization of confirmation transactions. And the security SDK components are developed to interact with the chaincode. The result of experiment shows that this mechanism can effectively discover the infringement of data resources with the appropriate parameters.