Reviewerly/exHarmony huggingface dataset:
Investigation of the influence of optical range sensor noise on the characteristics of the description of objects in cancer diagnosis

Abstract The influence of digital noise of the optical range sensor on the characteristics of microscopic objects of study was experimentally evaluated. A reference sample of smears, from which images of medical objects of research were obtained, was formed for the research. Blood cells were used as such objects. As a result of research textural signs are calculated and the analysis of their change is carried out. The obtained estimates allow to draw preliminary conclusions about the degree of noise influence.
Innovations in Quantum Optics and Quantum Computing

Longdom conferences welcome you to join Conference on “Innovations in Quantum Optics & Quantum computing” during March 23-24, 2020, London, UK. We heartily demand every one of the members who are keen on sharing their insight in Current Trends in Quantum optics, Quantum Computing and Quantum Photonics.
The Practical Resources of English Writing in Higher Vocational Colleges Based on the SQL Database

Writing is the core of the English teaching. English teaching aims at the quality education and promoting students’ development. It focuses on training students’ application levels and abilities of English knowledge, and effectively improves the English writing levels of vocational college students especially through giving full play to the application advantages of the educational resources. The SQL database is a kind of the database software with the function of the data query and update. Through reasonable operations, it can effectively solve the problems in the process of the English writing and improve the writing levels.
Research on Assisted Driving Technology based on Improved YOLOv3

Abstract In the era of automobile popularization, China and the developed countries such as Europe and the United States are facing the same problem of high car accidents. This paper uses deep learning technology to detect and identify lane lines, traffic lights, vehicles and pedestrians during driver driving. Improve the efficiency and accuracy of convolutional neural network training through migration learning and data enhancement techniques. Based on the current advanced YOLOv3 network, we have improved the network structure and loss function. The KITTI dataset has achieved the highest 2D target recognition accuracy, and the target recognition speed is higher than 36 frames/sec. The price of the assisted driving equipment we developed does not exceed RMB 5, 000, which has a good market prospect.
Towards a Novel Generalized Chinese Remainder Algorithm for Extended Rabin Cryptosystem

This paper proposes a number of theorems and algorithms for the Chinese Remainder Theorem, which is used to solve a system of linear congruences, and the extended Rabin cryptosystem, which accepts a key composed of an arbitrary finite number of distinct primes. This paper further proposes methods to relax the condition on the primes with trade-offs in the time complexity. The proposed algorithms can be used to provide ciphertext indistinguishability. Finally, this paper conducts extensive experimental analysis on six large data sets. The experimental results show that the proposed algorithms are asymptotically tight to the existing decryption algorithm in the Rabin cryptosystem with the key composed of two distinct primes while maintaining increased generality.
ON THE -ERROR LINEAR COMPLEXITY OF SEQUENCES FROM FUNCTION FIELDS

The linear complexity and the error linear complexity are two important security measures for stream ciphers. We construct periodic sequences from function fields and show that the error linear complexity of these periodic sequences is large. We also give a lower bound for the error linear complexity of a class of nonperiodic sequences.
Object Recognition Using SBMHF Features

In this research work we have achieved an object recognition from an image, instead of using feature extraction methods, directly we have considered five different namely SURF (S), MINEIGEN (M), BRISK (B), FAST (F) and HARRIS (H). To prove the efficacy of the proposed method, experimentation is carried out on our own dataset containing seven different images in which every image containing 11 objects which are both oriented and also occluded. From the experimentation, it is observed that mineigen gives good results.
Solving DNA motif discovery problem using improved Clonal selection algorithm with tournament selection operator

Identifying the regulatory elements in deoxyribonucleic acid (DNA) is a challenging area of research. The order of nucleotides in a DNA sequence determines the genetic functionality. The common nucleotides called motifs which have short sizes are placed in large number of sequences. Because of the difficulty to obtain the motifs among the large number of sequences, Motif Discovery Problem (MDP) is referred as a NP-hard problem. Since this problem is considered an open area for researchers, the evolutionary algorithms can be used to bring new solutions to this field. In this paper, Improved Clonal Selection Algorithm with Tournament Selection operator (ICSAT) is adapted to solve MDP by using real DNA sequences and compared with some other algorithms which are particularly designed to solve it. The results denote that ICSAT obtains motifs between the large sequences and it produces efficient results in terms of motif length and similarity values with respect to the compared algorithms. It can be used as a good candidate for solving these kinds of problems in bioinformatics.
A New Classification Method of Signature Network Node Based on Potential Space Projection

Social network node classification is widely used in solving practical problems. Most of the existing network node classification algorithms are concentrated on the unsigned social network, or the network containing only positive links. Rare works study the node classification on signed social networks, where each link is assigned a positive or negative sign. Recently, the research of the signed network has proved that the negative link has more effect on the signed network analysis than the positive links. Therefore, negative links play a key role in many tasks of signed network analysis. In this paper, we study the classification of nodes in the signed network. We project the positive and negative network to the corresponding latent space, and propose a mathematical model based on latent space. An iterative algorithm is proposed to optimize the model, and the iterative optimization of latent space matrix and projection matrix is used to classify the nodes in the network. The experimental results of the data set of the signed social network show that the classification results of the algorithm have high accuracy.
Sparse Multi-Graph Ranking towards Social Image Retrieval

Graph ranking is one of popular and successful technique for information retrieval. However, conventional graph ranking has two shortcomings when deployed for social image search. First, as social tags are noisy and incomplete, using that, the initial ranked list of images is inaccurate and impacts the following visual re-ranking. Another tough issue is how to conduct query-sensitive image re-ranking when multiple visual feature sets are available. In this work, we propose a sparse multigraph ranking framework, in which multiple graphs built on different visual features are integrated to simultaneously encode the image ranking. In particular, a sparse constraint is imposed on the fusion of different features, hoping to select a compact yet informative combination of features for different queries. To deal with the highly noisy issue inherent in social tags, a tag refinement solution along with word embedding is utilized to derive the more accurate initial ranking list, which services as the supervision signal for the proposed graph ranking framework. Extensive experimental analyses and evaluations on NUS-WIDE dataset demonstrate the proposed method can achieve state-of-the-art performance.
The New Approach for Creating the Knowledge Base Using WikiPedia

Wikipedia is recognized as one of the largest repositories in the Web. The term knowledge base was in connection with the expert systems as it is the part of Artificial Intelligence. A knowledge base can be created for any entity. The existing system like YAGO, MediaWiki tries to convert Wikipedia into a structured database to provide a vast knowledge base across the domains. It is very difficult to get the information which we want across the domains. So, the solution would be to get a systematic automated approach to build a knowledge base using Wikipedia on entity which we are interested in. The proposed system provides a knowledge base built upon the location as its entity. The system is feeded with seed data, by using these seed data it traverse through the Wikipedia graph and builds knowledge base using similarity measurement between seed data and traversed upcoming pages of wiki graph. Any expert AI systems uses gold standard knowledge base to take any decisions.
Clutter Filtering Algorithm in Dense Clutter Environment

Multi-Target Tracking (MTT) in dense clutter environment has always been a research difficulty in the field of radar target tracking, the key is to effectively combine state filtering with data association. In the dense clutter environment, in addition to the echo of the target point, there are also a large number of clutter interference from unknown scatters, so it is difficult to process the data. In this paper, we propose a clutter filtering algorithm in dense clutter environment based on Track-Oriented Multiple Hypothesis Tracking (TOMHT) and Support Vector Machine (SVM), which is used to filter clutters, and to provide prior environmental information for subsequent target tracking. It reduces the density of clutter and improves the efficiency of data association under the premise of satisfying the tracking accuracy. The results show that the algorithm can effectively suppress clutter and improve tracking performance.
Analysis of RSA variants in securing message

Abstract Message security is an important thing to prevent the interference from third parties. RSA cryptographic algorithm is believed to be a powerful algorithm in securing message. However, RSA computing process takes a long time so it takes several variants of RSA, namely R prime RSA and Multi-factor RSA which can reduce time and computing cost on the encryption and decryption side. In this paper, the author use three prime numbers to improve better security than only use two prime numbers and minimize the value of private key “ d ” is by using Chinese Reminder Theorm (CRT).
Application of Improved Long-short-term Memory Network in Human Morphology Detection

With behavior recognition technology playing an increasingly important role in many fields such as intelligent monitoring, human-computer interaction, video sequence understanding, medical and health care, researchers are trying to improve the accuracy of behavior recognition technology. By combining LSTM with GRU, this paper simplifies the network structure on the basis of retaining the basic idea of LSTM (forgetting and updating mechanism), and uses update gate to make each unit learn long-term and short-term characteristics, thus reducing the risk of gradient dispersion. Experiments show that this optimization method has better resolution than traditional LSTM.
Aggregate Searchable Encryption With Result Privacy

With searchable encryption (SE), the user is allowed to extract partial data from stored ciphertexts from the storage server, based on a chosen query of keywords. A majority of the existing SE schemes support SQL search query, i.e. 'Select * where (list of keywords).' However, applications for encrypted data analysis often need to count data matched with a query, instead of data extraction. For such applications, the execution of SQL aggregate query, i.e. 'Count * where (list of keywords)' at server is essential. Additionally, in case of semi-honest server, privacy of aggregate result is of primary concern. In this article, the authors propose an aggregate searchable encryption with result privacy (ASE-RP) that includes ASearch() algorithm. The proposed ASearch() performs aggregate operation (i.e. Count *) on the implicitly searched ciphertexts (for the conjunctive query) and outputs an encrypted result. The server, due to encrypted form of aggregate result, would not be able to get actual count unless having a decryption key and hence ASearch() offers result privacy.
Towards a UMLsec-Based Proctored Examination Model

Electronic examination systems are becoming increasingly complex and intensive to develop with the introduction of virtual invigilator in proctored examinations. In order to address this complexity issue, there is evident need to have a global model that is extensible. Modeling software systems enables developers to better understand the system they are building and offers opportunities for simplification and reuse. This article presents an extension to UMLsec, by introducing three (3) new stereotypes which were added to the UMLsec Profile for a Proctored e-Exam model. The model was validated and converted to a platform specific model using the Java stereotype available on Papyrus. This enabled the model generate Java classes which can be used for the implementation of a secure proctored e-exam system. The model allows developers with little or no knowledge in security to use the model to build proctored e-exam systems and to incorporate all known security requirements. The model can also be extended to accommodate new security solutions for e-exam systems as they are discovered.
Image Copy-Paste Tampering Detection Based on Superpixel Segmentation and Feature Extraction Algorithm

With the rapid advancement of the network and the popularity of image editing software, digital image forgery has been performed increasingly. People are paying more and more attention to the authenticity of the images. In the paper, a method based on superpixel segmentation and SURF algorithm is proposed for image copy-paste tampering detection. Firstly, SLIC superpixel segmentation is used to extract SURF features and feature descriptors. K-d tree is combined with BBF algorithm when feature points are matched. RANSAC algorithm removes mismatches. Morphological corrosion expansion operation shows copy-paste tampered areas. Experiments show that this method improves the accuracy and efficiency of detection.
Prediction of Solar Radiation in Qinghai Lake Area Based on BiLSTM-Attention Method

Short-term solar radiation prediction plays a crucial role in production and life. There is much room for improvement in the prediction accuracy and stability of traditional models. In order to solve this problem, this paper uses a method based on deep learning to predict solar radiation. A short-term solar radiation prediction model is established for Qinghai Lake combined with a bidirectional long-term memory network and attention mechanism. Based on the historical solar radiation in the past month and the average temperature at 1.5 meters, the solar radiation prediction in the next two weeks is made prediction. The experimental results show that the prediction model combined with the bidirectional long-term memory network and the attention mechanism is superior to the traditional prediction method in predicting accuracy, convergence speed and root mean square error and average absolute error, which can effectively improve. The accuracy and stability of the short-term solar radiation prediction model in local areas.
Model Selection for Data Analysis in Encrypted Domain: Application to Simple Linear Regression

In the big data era, data scientists explore machine learning methods for observed data to predict or classify. For machine learining to be effective, it requires access to raw data which is often privacy sensitive. In addition, whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model from the given dataset. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction. To address this issue, we develop new techniques to provide solutions for running model selection over encrypted data. Our approach provides the best approximation of the relationship between the dependent and independent variable through cross validation. After performing 4-fold cross validation, 4 different estimates of our model’s errors are calculated. And then we use bias and variance extracted from these errors to find the best model. We perform an experiment on a dataset extracted from Kaggle and show that our approach can homomorphically regress a given encrypted data without decrypting it.
Cloud-based Adaptive Quantum Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem

The quantum genetic algorithm is improved for the flexible shop scheduling problem. In this paper, a quantum genetic algorithm adaptively adjusting the rotation angle is designed to solve the flexible shop scheduling problem. The randomness and stability tendency of the cloud model are used to adaptively adjust the rotation angle to improve the optimization ability of the algorithm. At the same time, quantum crossover and quantum variability are used to improve population diversity and prevent premature convergence. Numerical experiments show that the algorithm has good performance.
ASIC Design and Power Characterization of Standard and Low Power Multi-Radix Trivium

We are presenting the experimental measurements of the power consumption and the maximum frequency in an ASIC prototype of 12 versions of the Trivium cipher: one standard version and two low power versions (FPLP and MPLP) with four different radix (radix-1, radix-2, radix-8 and radix-16). It is also described the mechanism for measuring power consumption in each Trivium implemented in the ASIC prototype. The clock tree of the ciphers has been designed in such a way that the clock signal of each Trivium can be cut independently. The experimental setup uses the Agilent 93000 testing system. The results show that the higher radix versions have a lower operating frequency and that the lower radix low-power versions have a very high power reduction. However, the Trivium radix-16 versions generate 16 bit/clock cycle so the measurements conclude that the MPLP version is the one with the lowest power consumption per bit (0.69 pJ/bit at 50 MHz).
A Real-Time Update Approach for Visualizing Multidimensional Big Data

Multidimensional data in different fields (e.g., science and technology resources, medicine, finance and transportation) have emerged in the big data era. Technology of visualization is critical to analyze multidimensional data which is large-scale. Thus, the highly variability of multidimensional data leads to the necessity of improving data management efficiency by updating data in real time. Nevertheless, traditional real-time communication technology suffers from low efficiency and large bandwidth consumption. Based on WebSocket, this paper proposes a multidimensional data service platform architecture that is scalable, real-time and versatile. First, on the platform, we compare the network load and network latency above Ajax polling and WebSocket. Secondly, for multidimensional data, we further validate the effectiveness of WebSocket in the application scenarios of frequent updating and multi-user concurrent requests. WebSocket-based platform greatly shortens the time for people to understand and analyze massive and complex multidimensional data. The application results show that the design is effective for real-time updating of multidimensional data.
Robot Awareness of Human Behaviors and Interacting with Humans by A Two-Step Identifying Approach combining LSTM and SSD

Robot cooperating with humans is a hot research topic recently. And the first challenge is how to specifically identify humans' intention. In this paper, we propose a two-step approach based on LSTM and SSD for identifying humans' specific intention. A LSTM with attention mechanism is firstly presented for recognizing humans' actions based 3D skeleton data; then, the hand-held objects are detected by a SSD neural network. Finally, a human-robot interaction framework are constructed based on the proposed intention identification model and DMP model. The experiments show that the proposed method can effectively identifying humans' intention. And the robot accomplishes to interact with the human with the built motion database and DMP.
An Technique for Big Statistics Security Based on Hadoop Distributed File System

Cloud computing is used to store large amount of data because is low cost and flexible but in cloud data security important issue. In this paper we proposed approach encryption or decryption file using algorithm are AES and OTP using Hadoop here we encrypted file within HDFS and decrypted within maptask in existing system encrypted/decryption the usage of AES but report length degree accelerated with the aid of 50% from unique record however now enhance the ratio as length of the encrypted record accelerated by means of 20% from original file length and also we carried out new technique called HDFS.
Permutation Polynomials from two piecewise functions

Permutation polynomials are an interesting subject of mathematics and have applications in other areas of mathematics and engineering. In this paper, we present two classes of explicit permutation polynomials over $\mathbb{F}_q$ from two piecewise functions.
Trajectory Correction and Optimization of a Flexible Manipulator Based on a Visual Servo

The tokamak in-vessel robot is a 10-DOF flexible in-vessel inspection robot designed for carrying out scanning and maintenance of a tokamak in a complicated environment. Due to the large size of the TIR, the flexible deformation of the TIR is significant, which leads to a low end positioning accuracy and trajectory accuracy. This paper analyzes the factors leading to the deformation of the TIR and establishes a model of the pose error caused by these factors. Based on the model, an error compensation method is proposed based on an optimization method. According to the the principle of visual servo, the end position deviation is calculated by using the image obtained by the visual unit. The detected deviation is used to control the end position of the TIR and optimize and reduce the deviation. Experiments are carried out on a 1:1 model of a tokamak vessel, and the results show that the proposed method can effectively improve the end positioning accuracy.
Pimiento: A Vertex-Centric Graph-Processing Framework on a Single Machine

Here, we describe a method for handling large graphs with data sizes exceeding memory capacity using minimal hardware resources. This method (called Pimiento) is a vertex-centric graph-processing framework on a single machine and represents a semi-external graph-computing system, where all vertices are stored in memory, and all edges are stored externally in compressed sparse row data-storage format. Pimiento uses a multi-core CPU, memory, and multi-threaded data preprocessing to optimize disk I/O in order to reduce random-access overhead in the graph-algorithm implementation process. An on-the-fly update-accumulated mechanism was designed to reduce the time that the graph algorithm accesses disks during execution. Our experiments compared external this method with other graph-processing systems, including GraphChi, X-Stream, and FlashGraph, revealing that Pimiento achieved $$7.5\times , 4\times , 1.6\times $$ better performance on large real-world graphs and synthetic graphs in the same experimental environment.