Reviewerly/exHarmony huggingface dataset:
A Pairing Free Attribute-Based Authenticated Key Agreement Protocol Using ECC

This paper presents a two party attribute based authenticated key agreement protocol without using bilinear pairing. Attribute based key agreement is required in several applications wherever a fine grained access control is required like cloud storage, military applications, broadcast encryption etc. In several applications a secure session between the participants is established without knowing their actual identities. In that case in place of identities a descriptive set of attributes (called as access policy) is used. The participants having sufficient set of attributes that satisfy the access policy are considered as authenticated and eligible for key establishment. The present paper uses Shamir Secret Sharing, Diffie Hellman Key exchange and elliptic curve arithmetic instead of bilinear pairing computations. The participant is authenticated based on the access policy defined over the set of attributes. The participant who satisfies the set of attributes perform Diffie Hellman key exchange with the other participant. Thus, a secure key is established between the participants. The authentication process of this protocol is information theoretically secure while the confidentiality rely on the intractability of Elliptic Curve Discrete Logarithm Problem (ECDLP). abstract environment.
The Positive Effect of PMI on the Selection of Meaningful Words

The aim of this study is to enhance the quality of automatic created dictionary. As far as we know, PMI (Pointwise Mutual Information) is the first time applied in order to determine representative words in the literature. A previously developed system is revised to experiment PMI effect on the selection of representative words additionally. Firstly, the meaningful words of the seed document/s are determined by Helmholtz Principle. Then, PMI value is calculated for each of the meaningful words. Meaningful words are sorted by using the total PMI value in decreasing order. Finally, the top n meaningful words are added to the dictionary, where n is experimentally worked. Hash similarity is used to measure the performance of the dictionary. PMI enhances the dictionary quality for all n value. The dictionary hash similarity increases from 40.46% up to 68.75% for the best n value in the experiments.
Real-time Data Collection and Management System for Emergency Spatial Data Based on Cross-platform Development Framework

Geospatial data collection is the most critical and fundamental part of Geographic Information System (GIS), and the tempo-spatial characteristics of geospatial data largely determine the value of GIS. Particularly, in the field of public safety, the emergency spatial data with strong standard and high timeliness is an important guarantee for the emergency command system to make rapid and accurate emergency command decisions. In the traditional emergency spatial data collection process, not only expensive data collection equipment needs to be purchased, but also complicated data post-processing is required to obtain emergency spatial data in standard geographic information data format. In this paper, a real-time emergency spatial data collection and management system based on cross-platform technology is developed to solve the problems in the traditional emergency spatial data collection process. The system is divided into two subsystems: mobile data collection subsystem and data management subsystem. The mobile data collection subsystem adopts Uni-app, a cross-platform development framework, which can develop application that can be used in the form of native application on the Android and IOS platform, mobile web application and Mini program. At the same time, it provides the real-time data editing operation mode, avoids the tedious and complicated data post-processing process, and significantly improves the emergency spatial data collection efficiency. The data management subsystem is based on Vue, a progressive JavaScript framework. Data managers can easily customize the collection template, and complete the work of data audit, management, sharing and format exchange, ensuring the standard and consistency of emergency spatial data. At the same time, this paper takes the collection of emergency spatial data in Shenzhen University Town as an example to demonstrate the basic usage process of our system.
Clustering with statistical error control

Abstract This article presents a clustering approach that allows for rigorous statistical error control similar to a statistical test. We develop estimators for both the unknown number of clusters and the clusters themselves. The estimators depend on a tuning parameter α which is similar to the significance level of a statistical hypothesis test. By choosing α , one can control the probability of overestimating the true number of clusters, while the probability of underestimation is asymptotically negligible. In addition, the probability that the estimated clusters differ from the true ones is controlled. In the theoretical part of the article, formal versions of these statements on statistical error control are derived in a baseline model with convex clusters. A simulation study and two applications to temperature and gene expression microarray data complement the theoretical analysis.
Improved K-means clustering algorithm for screw locking classification

The traditional K-means clustering algorithm is difficult to solve the nonlinear classification problem. In view of this defect, the kernel function is introduced to transform the nonlinear problem into a linear problem, and then the K-means clustering algorithm is used to classify it. This paper take data average as the central point of the kernel function, simplifying the calculation of the kernel K-means. Compared with the traditional K-means clustering algorithm, the algorithm in this paper has higher accuracy.
The Apprentice Gaze - AR Experience on Serralves Museum

Project CHIC(Cooperative Holistic View on Internet and Content) aims to develop a set of digital platforms, based on open formats and interoperable technologies that promote and increase the dynamics of Portuguese media content creation. One of the platforms is a georeferenced augmented reality platform capable of interconnecting and retaining different sources of information and users, with the objective of providing an application capable of generating contextual information about heritage and tourism, specifically attributed to a user and a concrete experience. This article presents the development of three augmented reality experiences within the context of a museum.
Ischemic Stroke Identification by Using Watershed Segmentation and Textural and Statistical Features

The algorithm presented in this paper identifies the ischemic stroke from CT brain images by extracting the textural and statistical features. Our algorithm starts by preprocessing of our CT images, and then image enhancement is performed. The brain CT images are segmented by Marker Controlled watershed. We obtained the Grey Level Co-occurrence matrix (GLCM) to extract the textural and statistical features. The experimental results showed that the over-segmentation due to noise is resolved by Marker controlled watershed. The textural and statistical features showed that the values of contrast, correlation, standard deviation and variance of normal CT images are less than those of abnormal CT images (contains ischemic stroke), where the values of homogeneity, energy and mean are bigger in normal CT images than those of abnormal CT images.
Colored Structured Light Patterns with Binocular Cameras for Fast and Low-cost 3-D Reconstruction

Gray code is a commonly used coding strategy in structured light techniques. However, projecting the large number of patterns required by it slows down the reconstruction. In this paper, a colored Gray code was generated to reduce the number of projected patterns and cut down the projecting time, which appeared to be a major time-consuming part especially for low-cost projectors. Meanwhile, two cameras were used to improve the performance of color stripes matching. Therefore, the complex considerations about the spectral characteristics of devices and the calibration of projectors could be avoided, making the system easy to use. Inverse colored patterns were also projected to assist the decoding procedure. The effects of the combination of colored line-shifting methods and different threshold parameters were also studied. A 130mm*100mm statue was reconstructed into a dense point cloud with 214,4848 points using our approach. Evaluations showed that the system had an average deviation of 0.02mm, and its speed can be further elevated with GPUs.
ECPNet: An Efficient Attention-Based Convolution Network with Pseudo-3D Block for Human Action Recognition

Human action recognition has became an important task in computer vision and has received a significant amount of research interests in recent years. Convolutional Neural Network (CNN) has shown its power in image recognition task. While in the field of video recognition, it is still a challenge problem. In this paper, we introduce a high-efficient attention-based convolutional network named ECPNet for video understanding. ECPNet adopts the framework that is a consecutive connection of 2D CNN and pseudo-3D CNN. The pseudo-3D means we replace the traditional 3 × 3 × 3 kernel with two 3D convolutional filters shaped 1 × 3 × 3 and 3 × 1 × 1. Our ECPNet combines the advantages of both 2D and 3D CNNs: (1) ECPNet is an end-to-end network and can learn information of appearance from images and motion between frames. (2) ECPNet requires less computing resource and lower memory consumption than many state-of-art models. (3) ECPNet is easy to expand for different demands of runtime and classification accuracy. We evaluate the proposed model on three popular video benchmarks in human action recognition task: Kinetics-mini (split of full Kinetics), UCF101 and HMDB51. Our ECPNet achieves the excellent performance on above datasets with less time cost.
Comparative Analysis of Stereo Matching Algorithms

Computer stereo vision (CSV) is the extraction of 3D data from two pictures of a similar scene taken from two parallel vantage focuses. Stereo vision has a great importance in the robotics, image analysis, object recognition etc. The correspondence problem in stereo vision refers to the problem of ascertaining similar features in stereo images. The major challenges in corresponds are due to change in viewpoints, camera noise and difference in bias. To solve this matching problem along with these challenges, many algorithms are proposed so far. This paper investigates the current stereo vision applications. The stereo vision algorithms are classified and the comparative analysis is presented. For comparison, middle bury database are used. The grapgh cut based algorithm has least bad pixel of 4.06 % and found to be best among all the available methods.
Vehicle INS/Odometer Integrated Navigation Algorithm Based on Factor Graph

In this paper, an Inertial navigation system (INS)/Odometer (OD) integrated navigation algorithm based on factor graph is proposed for the vehicle integrated navigation system composed of inertial navigation system and odometer. The factor graph method is used to model the inertial navigation system and odometer, and the factor graph model of INS/OD integrated navigation system is constructed.The key parameters of the inertial sensors and the scale factor of the odometer are estimated and fed back in real time to correct the information of the inertial navigation system and odometer.At the same time, aiming at the problem that the accuracy of the integrated navigation system is degraded due to the abnormality of the odometer measurement information in complex road conditions, a fault detection and isolation method based on factor graph is proposed.The test results show that the proposed method can output higher precision navigation results continuously and steadily compared with the traditional loose integrated navigation algorithm, and can be well adapted to vehicular navigation under complex and harsh road conditions.
Assessing Impact Of Explicit And Implicit Relationships On User’s Interests

A user's interests are influenced by his relationships. A social network can be imagined to be made up of explicitly declared relationships as well as implicit relationships. Direct or explicit networks consist of explicitly declared friendship or follower relation. Hidden or implicit networks consist of similar users based on their similarity as decided by their footprints in the social network. In this work, the impact of user's implicit network as compared to explicit network is investigated with respect to user's behavior. Understanding this impact is of paramount significance for product driven websites such as Amazon, Yelp etc. These websites offer social networking features such as option to follow another user to enable product recommendation. In spite of such features, explicit networks on such websites deliver limited benefit for reasons such as very small fraction of users forming such explicit relationships and rapid staling of such explicit social links. Hence, these websites have to depend mostly on classical collaborative filtering techniques. In such scenario, an implicit network based on user activity footprints can be a viable alternative. In this work, an appropriate data set is chosen for this investigation based on the richness of the data set. The user's interest is interpreted as the business that he frequents and the task of finding user's interest is modelled as a link prediction problem between user and business. After detection of the networks, the network information (users' relations) is captured using node embedding and then link prediction is performed. The baseline is provided by the explicit network. This investigation has looked at various combination of implicit and explicit networks to retain maximum information about users and their implicit/explicit relationships. It is observed that a certain combination that includes implicit network outperforms the baseline.
An Aggregation Degree Based PSO Algorithm

In order to improve the global searching ability of PSO, the weighted aggregation based on a redefined similarity is reconstructed to describe the degree of diversity of the population. The improved PSO also adjusts the particle searching space with an adaptive decision. The experimental analysis shows the effectiveness of the algorithm in terms of optimization ability, convergence speed and stability.
Understanding Hadoop Framework Through Single-Node Cluster Installation

Hadoop is a Java-based open source programming model that becomes a pillar in recent distributed computing by providing massive storage and multiprocessing of data. Hadoop becomes a simple and easy to implement programming model through the usage of commodity hardware. Hadoop open-source became a foundation for massive parallel processing of big data which includes scientific analytics, e-commerce data and sales planning, and processing enormous volumes of sensor from various sensors. Since Hadoop plays vital role in distributed parallel processing of big data, it is good to know the technical details behind the hadoop framework. This paper focus on detailing the necessary steps for the successful implementation of a Hadoop single-node cluster in a individual computer which provide a foundation for the new hadoop users.
Monitoring real time data and secure retrieval for Telemedicine systems

In the biomedical and healthcare domain, most the patients, who have undergone vital surgeries require regular monitoring of their health parameters. There they are vault to physical boundaries that restrict their movement. We use a belt, which is tied to the wrist and continuously monitor the temperature and heart rate of the patient. This data is sent to the hospital server and is taken care by the physician. They are alerted when there is a drastic change in the observed parameters. Based on the BSID technique, any sort of intrusion is identified by differentiating the observed readings with the state table which contains the standard values for the parameters. The health records of the patient are stored in a centralized cloud and can be fetched from anywhere in the globe. Distributed cloud computing allows exclusive treatment of the persons during medical consultation by sharing patient's health records among different hospitals. Here, the secrecy of the data and patients' identity privacy are preserved by applying a novel AAPM (Authorized Accessible Privacy Model). Patients have the sole right to provide access rights to the doctors on their wish based on their requirement and priority. By using PSMPA (Patient Self controllable and Multilevel Privacy preserving Authentication) technique we can establish three security levels. The doctor who is responsible to care for the patient will have full access to the medical records.
Design and Implementation of Application Classification Based on Deep Learning

This paper uses a deep learning-based model to solve the problem of automatic classification of mobile applications. In this paper, we address the classification problem of mobile applications from the perspective of text classification. By analyzing the major mobile phone application markets, we have developed the main categories of applications, and crawled the descriptions of various mobile phone applications as needed. With analyzing the original corpus of the crawl, the semantic information is further expanded by using data augmentation methods based on both word and char. Then, we design different text classification networks and compare the experimental results, and finally select the network with the best classification effect for tuning. The results of experiments show that the classification network of Bert+Highway+GRU designed in this paper has better classification effect. The average P/R/Fl value of the classification is 0.8820/0.8892/0.8856. The classification indicators under the above all reached 0.85 or higher, which in the first level label of those applications; at the same time, it also showed better performance in network training and convergence speed. The deep learning-based mobile phone application classification network designed in this paper has high classification efficiency and can achieve higher classification accuracy.
Small Sample Meta-leaming Towards Object Recognition Through UAV Observations

Deep learning has achieved impressive results for image classification and object recognition. However, deep learning typically requires too many labeled data for model training which is not always applicable in the tasks that data collection is difficult or expensive. In this paper, we consider the problem of object recognition by a UAV that can observe a given object from different viewpoints and learn to recognize similar objects. Specifically, we have used the Recursive Cortical Network (RCN) for car recognition based on a recorded UAV video. Furthermore, we have combined meta-learning and transfer learning to further improve the training efficiency. Experiments results have demonstrated the effectiveness of the proposed method using the Ominiglot dataset.
Arnold Transformed Position Power First Mapping (AT-PPFM) for Secret Digital Image

Digital image data stored and exchanged in cloud storage can be secured using cryptographic and steganographic techniques. The information contained in a data is secured in order to avoid taking data or information from unauthorized parties. The encryption process is done by changing the original image data from what can be understood to be incomprehensible. Encryption uses Arnold Transformation Algorithm which randomizes the pixel of image data by using 4-bit Most Significant hidden images to then be inserted into the encrypted 4-bit Least Significant Cover. Peak Signal to Noise (PSNR) is used to compare image quality before and after extraction. This comparison is based on the test results of the mean error value or Mean Square Error (MSE) of the original image data and the resulting image insertion data. PSNR produced in this study is above the minimum standard value (40 dB), which is between 45.60 dB – 46.10 dB, and the resulting distortion value is very small (MSE> 2). By using a 4-bit insertion of the existing image data, the extraction results are not much different from the hidden image before insertion and the results can be identified. So that the use of Arnold Transform and Position Power First Mapping (PPFM) algorithms reduces distortion and differences as well as the possibility of data leakage from the resulting image data.
Analytical Closed-Form Solution for General Factor with Many Variables

The factor analytic triad method of one-factor solution gives the explicit analytical form for a common latent factor built by three variables. The current work considers analytical presentation of a general latent factor constructed in a closed-form solution for multivariate case. The results can be supportive to theoretical description and practical application of latent variable modeling, especially for big data because the analytical closed-form solution is not prone to data dimensionality.
A Framework for Medical Image Retrieval using Machine Learning and Directional Local Ternary Quantized Extrema Patterns

In recent days, the application of medical images has been exponentially raised and it offers comprehensive information about the patient's health condition. It is applicable to diagnose the disease and also saved in a database for research purposes to get a detailed knowledge about the reason and remedial of the diseases. To retrieve the medical images effective in real time scenario, there is a great requirement of developing an innovative medical image indexing and retrieval model. This study introduces a new medical image retrieval (MIR) model using, Directional local ternary quantized extrema pattern (DLTerQEP). Next, particle swarm optimization (PSO) with support vector machine (SVM) based classifier is applied. The main concentration is provided for parameter optimization and feature selection for SVM with no reduction in the SVM classifier accuracy by the integration of PSO in the retrieval procedure. An analysis is made with the existing models to showcase the betterment of the DLTerQEPs with PSO-SVM model. On testing the presented model on the benchmark dataset, superior results are attained in terms of precision and recall.
Gesture-based User Identity Verification as an Open Set Problem for Smartphones

The most straightforward, yet insecure, methods of authenticating a person on smartphones derive from the solutions applied to personal computers or smart cards, namely the authorization by passwords or numeric codes. Alarmingly, the widespread use of smartphone platforms implies that people are carrying around sensitive information in their pocket, making the information more available physically. As smartphone owners are often using their devices in public areas, these short numeric codes or other forms of passwords can be obtained quickly through shoulder surfing, resulting in making that restricted data far more accessible for those who are not authorized to access the device. In this paper, we address the problem of biometric verifi-cation on smartphones. We propose a new approach for gesture-based verification that makes use of open set recognition algorithms. Further, we introduce a new database of inertial measurements to investigate the user identification capabilities of this approach. The results we have obtained indicate that this approach is a feasible solution, although the precision of the method depends highly on the chosen samples of the training set.