Reviewerly/exHarmony huggingface dataset:
A security ensemble framework for securing the file in cloud computing environments

Scalability and on-demand features of cloud computing have revolutionised IT industry in a large scale. Cloud service providers takes the entire burdens of computing, especially managing resources and file storage. Securing the file stored in cloud storage from internal and external attacks has become a primary concern for cloud storage providers as well as cloud users. In this paper, the threats involved in file storage and a secure way of protecting the stored files using a novel ensemble of security strategies is presented. This proposed strategy helps service providers as well as users to secure the file in cloud storage more efficiently.
Design of urban landscape visual simulation system based on three-dimensional simulation technology

Aiming at the problems of inaccurate visual modelling of urban landscape and high cost of the system, a visual simulation system of urban landscape based on three-dimensional simulation technology is proposed and designed. For the hardware part, the overall framework of the system is designed, and the static landscape modelling and dynamic landscape driving are analysed, respectively. For the software part, the development process of the visual simulation system for urban landscape is described, the urban landscape plan is acquired, and the urban landscape model library is constructed. The experimental results show that the accuracy of the visual modelling system for urban landscape designed by the proposed method is over 80%, and the accuracy is good. The highest cost coefficient used in the visual simulation system for urban landscape is 0.7, the cost is low, and the users are satisfied with the system.
A Pupil-Positioning Method Based on the Starburst Model

Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction, disease diagnosis, and psychological and physiological studies.Gaze-tracking systems are an important research topic in the human-computer interaction field.As one of the core modules of the head-mounted gaze-tracking system, pupil positioning affects the accuracy and stability of the system.By tracking eye movements to better locate the center of the pupil, this paper proposes a method for pupil positioning based on the starburst model.The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye.In this paper, we propose a method for detecting the feature points of the pupil edge based on the starburst model, which clusters feature points and uses the RANdom SAmple Consensus (RANSAC) algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center.Our experimental results show that the algorithm has higher precision, higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.
Methods of Image Retrieval Based Cloud

Content Based Image Retrieval (CBIR) introduces for the time being the most used system allowing detecting the visual features of images by using processing techniques due to the challenges of recovering photos depending on the text.The essential mechanism of content based image retrieval system is analysis picture information with low-level feature of an image, including colour, texture, determining edges, that has stages which includes the stage of classification of images, then the stage of extracting features and the stage of finding similar images and finally, evaluating the system and according to the technique or techniques used in each stage from stages of content based image retrieval system to increase the accuracy and speed of recovery for images ,together can using advantages cloud computing, which can provide a number of integrated computer services without being restricted to local resources, which include storage space, processing capabilities, expansion, flexibility and other various services through the Internet and deal with cloud through the platform as a user interface such as Microsoft Azure , Google Cloud platform, Amazon Web Services and others. In this paper, an analytical study of many CBIR techniques in terms of their behavior, feature extraction and work on Cloud Computing. All these techniques have their own avails as well as certain limitations, that means, there is not a one technique that suits superior all sorts of user’s needs,but using of advantages of cloud increase the improvement of performance. The focus be on the technique (features of Principle Component Analysis (PCA)) with advantages of Cloud Computing as a helpful way to extraction the features as shown in the results derived from the comparison.
Development of a Scalable Coding for the Encryption of Images using Diagonal Min-Max Block Truncation Code

This paper proposes a scalability coding on encrypted images, especially resolution scalability on lossy compression images. In the compression stage, the input gray level image is compressed using Diagonal Min-Max Block Truncation Coding technique. The compressed input image is encrypted using pseudorandom numbers masked by modulo-256, and then the encoded bit streams are transmitted. The pseudorandom numbers generated will be the encrypted key and the same is shared to the receiver. In the receiver side, the encoded bit stream is decrypted by using the shared encrypted key, which gives the compressed pixel value. Then the original image is reconstructed by using Diagonal Min-Max Block Truncation Coding Technique.
Distributed On-Chip Power Supply for Security Enhancement in Multicore NoC

We propose a distributed DC/DC converter against correlation power analysis and input voltage glitch injection attacks in emerging multicore applications. Moving towards Internet-of-Things, heterogeneous integration and Network-on-Chip architectures, protecting data privacy and critical operation information for every device become crucial. The proposed DC/DC converter is designed to mitigate correlation power analysis by weakening the input-output relationships for various types of workloads. For steady-state workloads, the correlation factors between input and output currents are reduced to only 0.05. For digital workloads, the correlation factors can vary by 10 times when there are only a few nanoseconds delay of the load current, which greatly prevents attackers from deriving the circuit real operations. Meanwhile, the converter is able to resist 20% input voltage glitches without generating additional spikes or droops while controlling the voltage ripples within 7.5% of the output. Implemented and simulated in 32nm CMOS technology, single channel peak efficiency reaches 85% based on post-layout models. The converter is also able to provide up to 19.3V/µs reference tracking capability for dynamic voltage scaling requests. The security enhancement induces about 20% area overhead and an average 3% efficiency loss. Overall, the proposed converter is a promising countermeasure solution for side-channel attacks in multicore systems without sacrificing too much performance.
Modeling entity and event relations in scientific documents for supporting knowledge discovery and organization

Background.Scientific documents often contain knowledge about what one entity did to another entity under what conditions (such as time, place, and method), which is related to another statement of what one entity did to another entity under what conditions.Such knowledge can be represented as relations between entities and events.Here what one entity did to another entity under what condition is defined as an event, which expresses the relationship between two entities under a condition.Objective.The objective of this paper is to design a model of entity and event relationship that can be used to represent knowledge identified from scientific documents and to facilitate knowledge discovery and organization.Method.The paper first presents a brief literature review on causal relationships, then evaluates four existing knowledge organization models and five event ontologies for their commonalities and differences in representing entity relationships and event relationships.The paper then proposes a combined entity and event relationship model based on the strengths of the existing event ontologies.Five main kinds of entity and event relationships are identified from an oil spill document set.Results.The three domain event ontologies, CIDOC CRM, Event Ontology and NewsML-G2, are only useful in serving specific purposes.The two generic event ontologies, DOLCE+DnS and Event Model F, must be enriched to be useful for representing knowledge for discovery.An entity and event model is proposed based on the strengths of these event models for representing knowledge in scientific documents.
Ring under fire over weakness in video device security

Ring, the Amazon-owned vendor of home video surveillance devices, has been coming under intense scrutiny due to security flaws in its products and what many see as a cavalier attitude to privacy.
Implementation of Hybrid ACO-PSO-GA-DE Algorithm for Mammogram Classification

Breast Cancer is one of the fastest growing cancer that causes women to death in the world. The early detection of breast cancer improves the chances of its cure. The malignant tumor that is the sign of breast cancer can be detected by mammography. This paper develops a technique to classify the mammogram images as normal, benign or malignant. This paper applies HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm to texture features extracted from the mammogram image. The analysis has been done on the DDSM and MIAS dataset by using classification accuracy, specificity, and sensitivity as the parameter with three state of art algorithms i.e. SVM classifier (without any optimization technique), Firefly (SVM with Firefly optimization), ACO-PSO-GA (SVM with hybrid ACO-PSO-GA optimization). The improvement in the performance measures against three state of art techniques shows the significance of the algorithm.
ECOLOGICAL SAFETY IN THE ECOLOGICAL CONSCIOUSNESS OF PEDAGOGICAL UNIVERSITY STUDENTS

Despite the availability of scientific materials related to social well-being, environmental awareness and a sense of environmental safety in it among the population, there is still not enough research, especially domestic, on the example of individual groups of the population, for example, students. However, this is also necessary for developing solutions to the global environmental problem and its local manifestations. It is especially important in this regard to appeal to the ecological consciousness of students of a pedagogical university, who in the near future will carry their knowledge and feelings into the environment of the younger generation of the country. Ecological safety is considered by us as the basis of ecological consciousness in the structure of public consciousness.&#x0D; The aim of the work was to assess the feeling of environmental safety and readiness to combat possible environmental hazards among first-year students of a pedagogical university. The main methods used in the study are the dialectical method, methods of mass survey and comparative data analysis.&#x0D; Results. With the severity of the affective-evaluative component of environmental consciousness, students of the pedagogical university found a greater readiness for environmental behavior than other social groups.&#x0D;
Evaluation of augmented reality (AR) displays performance based on human visual perception

Conventional display metrics are currently used to evaluate the performance of augmented reality (AR) displays: the maximum luminance it can reach, the image uniformity, the contrast ratio and others. However, these metrics initially defined for non-transmissive displays, do not consider the impact of the see-through environment as it exists in AR applications. These measurements (usually obtained in a dark laboratory) are used to quantify the performance of AR display devices, but do not necessary relates to the perceived quality of images rendered by such devices, due to the non-linearity of human perception. Upon this, the projected image (Fig. 1) is an overlay upon the see-through environment. The intensity balance between these two "layers" becomes critical as the perceived scene (Fig. 2) is resulting from color mixing. Here, we want to highlight the fact that the brightness (perceived intensity) and color uniformity of the projected image becomes less important whereas the contrast ratio between the image and the environment becomes very critical. Other metrics are also less significant once the outside scene is added such as pixel sharpness (MTF) and the generated halo effects. These metrics are still important to evaluate the display performance for comparison purpose, but higher thresholds should be permitted for an AR display. The performance requirements must be adjusted in accordance to the AR application itself and not as it is done for conventional displays.
Tensor Tucker Decomposition based Geometry Compression on Three Dimensional LiDAR Point Cloud Image

Data Visualization in static images is still dynamically growing and changing with time in recent days. In visualization applications, memory, time and bandwidth are crucial issues when handling the high resolution three dimensional (3D) Light Detection and Ranging (LiDAR) data and they progressively demand efficient data compression strategies. This shortage is strongly motivating us to develop an efficient 3D point cloud image compression methodology. This work introduces an innovative lossless compression algorithm for a 3D point cloud image based on higher-order singular value decomposition (HOSVD) technique. This algorithm starts with the preprocessing method which removes the unreliable 3D points and then it combines the HOSVD together with the normalization, predictive coding followed by Run Length encoding to compress the HOSVD coefficients. This work accomplished lower mean square error (MSE), high (infinitive) Peak signal noise ratio (PSNR) to produce the lossless decompressed 3D point cloud image. The storage size has been reduced to one by fourth of its original 3D LiDAR point cloud image size.
Secret Image Sharing using Visual Cryptography Shares with Acknowledgement

Visual Cryptography is an encryption technique in which the secret image is encoded and divided into n meaningless images called shares. The shares look like black and white dots embedded randomly in an image. These shares don’t reveal any information about the original image. Every share was printed on transparent paper and decrypted through the superimposition of shares without any computer decryption algorithm. When all n shares were overlapped, the original picture would appear. A (k, n)-threshold visual cryptography is a technique in which n is the maximum number of shares that are to be generated and k is the minimum number of shares that are required to decrypt the original image. If the insufficient number of shares, which are less than the k value is given to the decryption function, the decryption function will generate the output, which doesn’t reveal any clue to the original image. This paper presents how the Entropy, Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) values varies with respect to given same image of different sizes.
Correlation and Probability Based Similarity Measure for Detecting Outliers in Categorical Data

Determining the similarity or distance among data objects is an important part in many research fields such as statistics, data mining, machine learning etc. There are many measures available in the literature to define the distance between two numerical data objects. It is difficult to define such a metric to measure the similarity between two categorical data objects since categorical data objects are not ordered. Only a few distance measures are available in the literature to find the similarities among categorical data objects. This paper presents a comparative evaluation of various similarity measures for categorical data and also introduces a novel similarity measure for categorical data based on occurrence frequency and correlation. We evaluated the performance of these similarity measures in the context of outlier detection task in data mining using real world data sets. Experimental results show that the proposed similarity measure outperform the existing similarity measures to detect outliers in categorical datasets. The performances are evaluated in the context of outlier detection task in data mining.
Minerals Yearbook, volume III, Area Reports — International — Asia and the Pacific

For additional information, contact: Director, National Minerals Information Center U.S. Geological Survey 12201 Sunrise Valley Drive 988 National Center Reston, VA 20192 Email: nmicrecordsmgt@usgs.gov The U.S. Geological Survey (USGS) Minerals Yearbook discusses the performance of the worldwide minerals and materials industries and provides background information to assist in interpreting that performance. Content of the individual Minerals Yearbook volumes follows:Volume I, Metals and Minerals, contains chapters about virtually all metallic and industrial mineral commodities important to the U.S. economy. Chapters on survey methods, summary statistics for domestic nonfuel minerals, and trends in mining and quarrying in the metals and industrial mineral industries in the United States are also included.Volume II, Area Reports: Domestic, contains a chapter on the mineral industry of each of the 50 States and Puerto Rico and the Administered Islands. This volume also has chapters on survey methods and summary statistics of domestic nonfuel minerals.Volume III, Area Reports: International, is published as four separate reports. These regional reports contain the latest available minerals data on more than 180 foreign countries and discuss the importance of minerals to the economies of these nations and the United States. Each report begins with an overview of the region’s mineral industries during the year. It continues with individual country chapters that examine the mining, refining, processing, and use of minerals in each country of the region and how each country’s mineral industry relates to U.S. industry. Most chapters include production tables and industry structure tables, information about Government policies and programs that affect the country’s mineral industry, and an outlook section.The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the Minerals Yearbook are welcomed.
Next generation genetic algorithms

tutorial Share on Next generation genetic algorithms Authors: Darrell Whitley Colorado State University Colorado State UniversityView Profile , F. Chicano Colorado State University Colorado State UniversityView Profile , G. Ochoa Colorado State University Colorado State UniversityView Profile , A. Sutton Colorado State University Colorado State UniversityView Profile , R. Tinós Colorado State University Colorado State UniversityView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 1113–1136https://doi.org/10.1145/3319619.3323387Published:13 July 2019Publication History 3citation177DownloadsMetricsTotal Citations3Total Downloads177Last 12 Months10Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Cloud computing, one of the fastest growing fields, is the the delivery of computing resources and services. Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during the task computation. The load balancing optimization problem is an NP-hard problem, hence, for the optimal usage of available resources, we propose a new efficient user-priority multi-agent genetic algorithm (GA). Our algorithm takes the “users’ priority and earliest job finishing time” into consideration for minimizing the response time and energy. We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.
Blockchain Basics

This chapter will serve as your ground school before you “take off” toward development. It will introduce basic concepts that will help you to understand the blockchain technology. This chapter is split into four parts.
Sincerely, Anonymous

This paper provides an account of anonymous speech treated as anonymized speech. It is argued that anonymous speech acts are best defined by reference to intentional acts of blocking a speaker's identification as opposed to the various epistemic effects that imperfectly correlate with these actions. The account is used to examine two important subclasses of anonymized speech: speech using pseudonyms, and speech anonymized in a specifically communicative manner. Several pragmatic and ethical issues with anonymized speech are considered.
Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work