Reviewerly/exHarmony huggingface dataset:
Semantic linkage of source content dynamically with virtual documents using Wikipedia in Hadoop

In recent years, the World Wide Web has developed enormously and become more multifaceted because of the rising number of users and emerging technologies. Web 2.0 has modified this by enabling the users to create and share the content and remain collaborative. As result of this there is enormous growth of web content. Hence relevant information retrieval became a challenging task. To handle this problem application of semantic web is very much essential. The proposed model, document-based content management system (DSCMS) extracts the meta-content of the link using Wiki application programming interface (API) and store it as repository based on mongo DB. The meta content matching is implemented by Jaccard similarity measure with alchemy API. Finally the exact source documents for our virtual document will be retrieved for future use. Graphical analysis with metrics recall and precision is calculated to prove the efficiency of the system compared to conventional systems.
A tree structure-based blockchain

Due to the limitation of the capacity of each block, when the number of transactions increases at the same time, it will face congestion problems and reduce the efficiency of accounting. Collaborative computing and data slicing are effective methods to improve performance, and they have been widely used in various fields. This paper proposes a tree chain architecture that adds peer-to-peer blockchain to main chain of the Ethereum. When there is a task to be processed in the main chain, the architecture assigns the task to "branch" or "leaf". The "branch" first determines whether the task can be sliced. If the task can be sliced, divide the task into leaf nodes for processing, otherwise let the branch nodes handle. By assigning tasks in this way, it is possible to help the main chain reduce the computational pressure and increase the system throughput.
A Geometric Approach for Unsupervised Similarity Learning

Metric learning groups similar examples together, while moving away dissimilar ones. This is a crucial task in image processing and computer vision. However, existing metric learning approaches require huge number of labeled examples for their success. In this paper, we propose a novel, unsupervised metric learning approach, that learns a similarity metric without making use of class labels. Using a graph-based clustering approach, we form a set of tuples, to provide constraints for metric learning. To efficiently handle high-dimensional data, we learn the metric in a lower dimensional latent space. A confidence function is devised to aid the convergence by appropriately weighting the loss functions. The parameters of our approach are jointly learned using Riemannian optimization.
Sketch-based Deep Generative Models Conditioned on a Background Image

Thanks to modern image generation techniques using deep learning, we can easily create images from simple sketches. Unfortunately, previous work focuses on generating a foreground only and does not consider composing the foreground and an arbitrary background image. Therefore, it suffers from unnaturalness arising from a composition between the generated foreground and the original background image. In this paper, we present a method that not only generates a foreground image from sketches but also composes the foreground and background naturally using a generative adversarial network (GAN). In particular, we propose three approaches to conditioning the GAN on sketches and a background. Evaluation experiments confirm that the proposed models obtain different results according to our design concepts and can synthesize more natural images than a baseline method.
A Security Situational Awareness Method for Cloud Platform

In order to grasp the overall security status of the cloud platform, it is necessary to conduct security situational awareness research on cloud platform. Firstly, a three-layer security situation index system including static and dynamic is proposed. Then, the order relation method is used to calculate the static indexes, and the BP neural network method based on particle swarm optimization is used to calculate the dynamic indexes. Finally, the variable weight comprehensive analysis method is used to calculate the overall security situation value. The analysis and experiment results show that, the situation indicator system and situation awareness method proposed by us are generally more scientific and reasonable, and can identify the security situation of cloud platform more effectively.
OUTFIT - An optimal data storage hosting model using Sugeno-type fuzzy inference system for multi-cloud environments

The unprecedented growth in personal, business and research data motivates users to lease storage from multiple cloud storage providers like Amazon, Azure, etc. Selection of cost-effective cloud storage service by considering different pricing policies along with their performance characteristics i s a challenging task. This research proposes a model named as OUTFIT (Optimal sUgeno Type Fuzzy Inference sysTem) an optimal data storage hosting model by suggesting an appropriate storage type based on user demands. In the first phase, we have surveyed Amazon, Google Cloud, Azure and Rackspace cloud storage providers and consolidated the different cloud storage types supported by them. In the second phase the cloud service providers are ranked by using Sugeno fuzzy inference system based on the user preference. The third phase designates the appropriate service that incurs minimal estimated storage usage cost. The proposed approach is able to categorize various cloud service providers with an optimal grading process by including multiple decision criteria for fine-grained storage type selection. The observed results prove it to be a more favourable selection tool in comparison with its counterpart tools like Cloudorado, RightCloudz in terms of cost.
Kernelized Graph-based Multi-view Clustering on High Dimensional Data

Kernelized graph-based learning methods have gained popularity because of its better performances in the clustering task. But in high dimensional data, there exist many redundant features which may degrade the clustering performances. To solve this issue, we propose a novel multi-view kernelized graph-based clustering (MVKGC) framework for high dimensional data that performs the clustering task while reducing the dimensionality of the data. The proposed method also uses multiple views which help to improve the clustering performances by providing different partial information of a given data set. The extensive experiments of the proposed method on different real-world benchmark data sets show a better and efficient performance of the proposed method than other existing methods.
Lqaid: Localized Quality Aware Image Denoising Using Deep Convolutional Neural Networks

In this paper we propose the Localized Quality Aware Image Denoising (LQAID) technique for image denoising using deep convolutional neural networks (CNNs). LQAID relies on local quality estimates over global cues like noise standard deviation since the perceptual quality of a noisy image is typically spatially varying. Specifically, we use localized quality maps generated using DistNet, a spatial quality map estimation method. These quality maps are used to augment the noisy image and guide the denoising process. The augmented noisy image is denoised using a deep fully convolutional network (FCN) trained using mean square error (MSE) as the loss function. The proposed approach shows state-of-the-art performance both qualitatively and quantitatively on two vision datasets: TID 2008 and BSD500. We also show that the proposed approach possesses excellent generalization ability. Lastly, the proposed approach is completely blind since it neither requires information about the strength of the additive noise nor does it try to explicitly estimate it.
Anonymous Authentication for Privacy Preserving of Multimedia Data in the Cloud

Cloud computing technology provides cost-saving and flexibility of services for users. With the explosion of multimedia data, more and more data owners would outsource their personal multimedia data on the cloud. In the meantime, some computationally expensive tasks are also undertaken by cloud servers. However, the outsourced multimedia data and its applications may reveal the data owner's private information because the data owners lose control of their data. Recently, this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data. Anonymous Authentication Scheme will be proposed in this chapter as the most relatable, applicable, and appropriate techniques to be adopted by the cloud computing professionals for the eradication of risks that have been associated with the risks and challenges of privacy.
Parsing C and Fortran code to SymPy Expressions

SymPy is a tool written in Python programming language that can be used to perform complex mathematical operations symbolically. C and Fortran programming languages are also used to perform complex mathematical operations. The syntax for other programming languages that are used to perform mathematical operations is not compatible with SymPy. The paper provides a framework to convert source code from these programming languages into symbolic expressions compatible with SymPy. It also provides implementations for converting mathematical expressions written in C and Fortran programming languages into SymPy Expressions using C and Fortran parsers. The parsers take C and Fortran source code as input, process it and return a list of SymPy Expressions. The SymPy Expressions can be used to generate symbolic expressions compatible with SymPy or source code for other programming languages.
Weak Reverse attention with Context Aware for Person Re-identification

Abstract Person re-identification is a difficult topic in computer vision. Some study think that current deep learning methods is biased to capture the most discriminative features and ignore low-level details, more serious is it pay too much attention on relevance between background appearances of person images. It might limit their accuracy or makes them needlessly expensive for a not best performance. In this paper, we carefully design the Weak Reverse attention with Context Aware Network (WRCANet). Specifically, by merging weak reverse attention network and content aware module, the model can not only remove the background noise to extract the main information of persons, but also suppress the loss of local detailed information as the network deepens. We experiment on the Market-1501, DukeMTMC-reID and CUHK03, and the results show that our method achieves the state-of-the-art performance.
Research on Construction and Evolution of Relational Graph of the Science and Technology Data

In recent years, the amount of scientific and technological data has been increasing, and the requirements for data analysis have been continuously improved. Among of them, the mining of data association relations and the construction and evolution of relational models are the research hotspots in recent years. How to obtain entities and their relationships from massive data, and then use the relational model to construct the relational graph and support the evolution of the graph is the key problem to be solved. Aiming at the above problems, this paper proposes a method for constructing and evolving relational graphs based on structured data, including source data preprocessing, entity and attribute identification, information extraction, relationship matching, and relational graph construction and evolution. The evolution and update algorithm of the graph based on Simhash and Hamming distance is proposed, and the update strategy of the entity similarity in the graph is analyzed. Combined with experimental verification, this method can automatically realize the extraction of structured data, the construction of relational graph and the evolution and update process of the graph, and the accuracy of the results was about 97%.
Extremely High-Capacity, Secure Image Steganography Based on Composite Edge Detection, with Message Integrity

Image steganography is a technique for obscuring secret message within image pixel matrix. It provides means for secretly communicating between two entities with the third party totally unaware of the embedded message. One of the most important methods for image steganography is edge detection technique, wherein secret data is inserted in LSBs of image edge pixels. Data embedding in edge pixels does not create notable distortion in image when compared to embedding data in non-edge pixels. Different types of edge pixel detectors are available including most renowned Canny edge detector, Sobel edge detector, Prewitt edge detector and many others. Hybrid edge detection uses a combination of different edge detectors for generating edge pixels of image. Message to be secretly communicated is inserted in the LSB of these edge pixels, and the image thus formed called stego image is sent to the concerned party. Major drawback of hybrid edge detection technique is its capacity. The number of edge pixels is very less in any image. In the last decade, many papers have focused on increasing capacity of edge detection by using different techniques. In this work, we have proposed an extremely high-capacity hybrid edge detection technique by using data compression. Also, data is encrypted and then inserted in the cover image pixels. For message integrity, SHA512 hash function is used. Subsequently, this work not only improves capacity of edge detection while maintaining the image quality but also provides security and message integrity.
Detecting Wireless Intrusions With RF Watermarks

Validation of devices at the physical layer can be accomplish with radio frequency (RF) watermarks to enhance wireless network security. For this application, RF watermarks are implemented as small signal offsets below the noise floor which are superimposed onto the signal transmitted from a wireless device. A receiver equipped to detect the offsets is then utilized to determine whether the anticipated RF watermarks are present in the received signal. Through this process, the integrity of the originally transmitted data is maintained while simultaneously validating the authenticity of the transmitting device.
Evaluating the Effectiveness of Notations for Designing Security Aspects

Existing software design approaches used to design security aspects lack notations that can be used to design the security requirements of a system which may result in more complex and dense designs. However, literature review has shown that there are some proposed notations to represent security aspects within a system. Other researchers have proposed evidence-based principles that provide basis for constructing Visual Notations in Software Engineering. The principles include: Graphic Economy, Perceptual Discriminability, Semiotic Clarity, Cognitive Integration, Visual Expressiveness etc. In this research, we have evaluated a set of proposed notations in accordance with principles for designing cognitively accepted graphics by conducting a human subject study. A questionnaire was distributed among a group of graduate and post-graduate students. The goal was to validate the notations based on theory of “How Visual Notations Communicate?”. This study will help researchers in developing cognitively accepted notations and set a course for designing new notations for UMLsec that are perceptually, semantically and visually expressive.
A New LCS-Neutrosophic Similarity Measure for Text Information Retrieval

The reality of human existence and their interactions with various things that surround them reveal that the world is imprecise, incomplete, vague, and even sometimes indeterminate. Neutrosophic logic is the only theory that attempts to unify all previous logics in the same global theoretical framework. Extracting data from a similar environment is becoming a problem as the volume of data keeps growing day-in and day-out. This chapter proposes a new neutrosophic string similarity measure based on the longest common subsequence (LCS) to address uncertainty in string information search. This new method has been compared with four other existing classical string similarity measure using wordlist as data set. The analyses show the performance of proposed neutrosophic similarity measure to be better than the existing in information retrieval task as the evaluation is based on precision, recall, highest false match, lowest true match, and separation.
Office Document Search Engine

Search engine have been widely used to find some documents for many reasons. One of frequently used kind of document is office document. Office document is classified as semi-structured document because sometimes they have consistent structure in a document category. Office document also has various categories and formats. To build a search engine, there are two main processes that must be implemented. Those processes are indexing process and query process. Every process consists of some methods that has some function for each of them. Not all kind of methods can be used and implemented for that processes. A suitable method needs to be selected in order to produce an optimal search engine for a specific defined domain. This paper will explain how to recognize office document's pattern that will be used to build a search engine. It will also explain about selection of methods that were used to build an optimal search engine in office document domain. This search engine will be evaluated with some testing scenario to calculate its precision for some queries and to know how optimal it is. This proposed search engine more focus on having effective result than efficiency of processing. However, the evaluation still covers both of effectiveness and efficiency of the system.
Implementation of SIMON & SPECK Algorithm

Nowadys, the security of exchanging information and communication such as authentication and privacy are essentially important. Cryptographic algorithms such as block ciphers and stream ciphers are basic components for security. Security, simplicity and flexibility are the conflicting goals in the cryptographic design. and families of block cipher are designed specifically to offer security on the constrained devices, where the simplicity of design is important. and speck, each of this comes in a variety of widths and key sizes. Moreover, it is also reduces the inefficiency of encrypting slightly longer messages by supporting a variable block-size. Many lightweight block ciphers were designed to perform well on a single platform and were not meant to provide high performance. while, and each provide or offers excellent performance on hardware and software platforms, is flexible enough to admit a variety of implementation on variety of platforms and is easy to analysis using existing techniques. Both perform exceptionally well across the full spectrum of lightweight applications, but the is tuned for optimal performance in hardware, and the Speck for optimal performance in software. In our project, we analysis the Simon and speck block cipher family and then simulate and synthesize this algorithm. We are going to implement the The and Speck “the cryptography algorithm on a FPGA.
AdvMS: A Multi-Source Multi-Cost Defense Against Adversarial Attacks

Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars. Conventional defense methods, although shown to be promising, are largely limited by their single-source single-cost nature: The robustness promotion tends to plateau when the defenses are made increasingly stronger while the cost tends to amplify. In this paper, we study principles of designing multi-source and multi-cost schemes where defense performance is boosted from multiple defending components. Based on this motivation, we propose a multi-source and multi-cost defense scheme, Adversarially Trained Model Switching (AdvMS), that inherits advantages from two leading schemes: adversarial training and random model switching. We show that the multi-source nature of AdvMS mitigates the performance plateauing issue and the multi-cost nature enables improving robustness at a flexible and adjustable combination of costs over different factors which can better suit specific restrictions and needs in practice.
Dynamic Balance Net: Correlation-enhanced Two-stage Object Detection Network with IoU-Loss

The most widely used object detectors to date are based on the two-stage approach popularized by R-CNN, where two locators cooperate on estimating objects' position information. However, when a network reaches the best performance point, the first locator is under-fit and the second locator is over-fit because two locators are trained by same loss function while the first locator has harder work. This blocks two locators being fitted at the same time and limits two-stage networks' performance. In this paper, we propose a correlation-enhanced IoU-loss to tackle this problem. More precisely, our method adjusts loss functions according to IoU between output and ground-truth, which enhances first locator's loss and achieves better inner cooperation. Extensive experiments on the PASCAL-VOC and MS COCO datasets show the effectiveness of the correlation-enhanced IoU-loss, as well as its compatibility with and adaptivity to two-stage object detectors. All networks included in the experiment obtain a no less than 3 per cent enhancement.
Attitudes towards elastic language in Australian online healthcare information

Abstract Based on questionnaire data in response to six excerpts of Australian online health information, this study investigates university students’ attitudes towards elastic language (EL). The findings show that averaging all six cases a neutral attitude is found, with no strong preference for EL or non-EL. This indicates that it is unnecessary to deliberately use more or less EL – the key is to use EL appropriately when and where it is needed. Examining the reasons for participants’ choices, we identify ten frames. Often the same EL generated positive and negative comments: a phenomenon that can be explained in terms of two sides of a frame. The findings may help healthcare professionals to deliver medical information in ways most accessible to the public and to find effective ways of communicating uncertainty. A ‘one fits all’ rule for language use does not exist, and instead multiple standards guide our use of it.
Cell Image Segmentation Method Based on Convolution Neural Network

Aiming at the problem of the poor cell image segmentation accuracy of traditional segmentation method, this paper introduces a supervised machine learning approach- convolutional neural network. In this paper, we implement training and execution of convolutional neural networks using the Keras, Theano, Numpy, Scipy, and Scikit-image Python packages. And the result shows that we can achieve the image segmentation by using convolutional neural networks.
Application of 3D Laser Scanning Technology in Simulating Dam-Breaking Flood Routing

Abstract 3D laser scanning technology (3D-LST) is a new measuring technology that has been developed rapidly in recent years, which is characterized by obtaining 3D space coordinates of the object surface with its high speed, high accuracy and high density and the object and without contacting with the measured object. It has been increasingly and extensively applied in a variety of fields of engineering^. In this paper, the analysis of 3D-LST in the dam-breaking flood routing analysis and its application in emergency rescue has been discussed. And then, a dam breaking and flood routing model has been established through scanning the dam and its downstream terrain with a 3D laser scanner, so as to provide accurate data basis for dam relief and disaster relief.