Reviewerly/exHarmony huggingface dataset:
Graphical Symbol Design System of Plane Advertisement Based on Visual Communication

The traditional graphic symbol design system of graphic advertisements contains few basic design elements. When designing graphic advertisements, the design tasks carried by the system are too large, resulting in too long response time of the design system. In response to this shortcoming, a visual communication-based Graphical symbol design system for graphic advertising. The hardware part chooses to design the core hardware of the system. According to the working requirements of the chip, the hardware connection circuit is designed, the graphic symbol form is converted, and the conversion parameters are calibrated. According to the requirements of visual communication, the graphic symbol database of graphic advertisement is constructed to complete the design of the design system. Set the experimental instrument parameters, set up the system test environment, and use two traditional design systems and the researched design system for experiments. The results show that the researched design system has the shortest response time.
Estimation of Watermark Embedding Capacity with Line Space Shifting

The article describes an analytical model of the maximum achievable embedding capacity evaluation for robust watermark based on the approach to information embedding in text data by line space shifting. The developed model allows to boundary values assessment of information amount that may contain a watermark embedded into text data printed. In the developing process of an analytical model, the dependence of maximum achievable embedding capacity on the lines amount of a text document and the used watermark embedding parameters was established. The relationship between the parameters of a text document and the lines number per page of a text document is mathematically described. Mathematical calculations of the obtained expressions and the corresponding experimental researches are conducted. The evaluation of obtained simulation results correspondence to the parameters of texts printed on paper is implemented. The simulation results are analyzed and a linear dependence of the results is established. The obtained values are approximated and analytical expressions that allow one to quantify the maximum achievable embedding capacity of the developed robust watermark depending on the embedding parameters used are received. The degree of contradictions between the following parameters of robust watermarks: embedding capacity, extractability and robustness is estimated. The relationship between the maximum achievable embedding capacity and the accuracy of the extraction of the developed watermark is determined. Quantitative estimates of the influence of the size of the watermark on the final extraction accuracy of embedded information are given. The further research directions are determined.
Analysis of flood discharge using Snyder and Melchior methods at Krueng Meureubo watershed in Pasi Mesjid village

Abstract Pasi Mesjid villages in Meureubo district are vulnerable to floods in the rainy season. Every year, the village was flooded and submerges many houses, rice fields, and plantation land owned by residents. The phenomenon of annual flooding is a concern for the local community because it causes material losses. Research on the flood discharge in the Meureubo watershed, Pasi Mesjid Village, was carried out to determine the amount of flood discharge with various return periods. The flood discharge calculation methods are using Melchior and Snyder. The scope of this study includes frequency analysis, rainfall design, flood discharge design by the Melchior and Snyder methods with return periods of 2, 5, 10, 25, and 50 years. The hydrological analysis results showed that the flood discharge values of the Snyder and Melchior methods were not much different, namely in the 2-year return period of 1607.77 m 3 /s. With the Melchior method, a 2-year return period obtained the plan flood discharge of 1813.286 m 3 /s, a 5-year return period of 2791.62 m 3 /s, a 10-year return period of 2938.14 m 3 /s, a 25-year return period of 3153.19 m 3 / s, and a 50-year return period of 3336.5 m 3 /s. With the Snyder method, the 5 (five) year return period is 2374.710 m 3 /s, the 10 (ten) year return period is 2762.09 m 3 /s, the 25 year return period is 3284.96 m 3 /s, and for the 50 year return period, the value is 3699.18 m 3 /s. Based on this analysis results, the Snyder and Melchior methods can be used in analyzing the flood discharge in the Meureubo watershed area.
An Efficient Collaborative Filtering and Graph Approach for Business-Matching Systems

The unprecedented availability of Internet and Social media led to significant changes in daily business tasks and enforced managers to get a new insight into the way of supervising organizations. Several managers and participants from all the world come to look for business opportunities to enhance the quality of work in such trade exhibitions. Hence, they implement Business-Matching based recommendation system to help attendees to find the appropriate future partner for their business, increase user’s interaction and enrich shopping potential. Recommendation System should be the backbone of any business matching concept. Since it has the potential to change the way that websites communicate with users and allows companies to maximize their Return On Investment (ROI) based on the information they can gather on each customer’s preferences and purchases. In this paper, we present a taxonomy of Business Matching features based on recommendation systems, approaches, actions, and goals, and according to these variables, we study the existing literature on recommender systems applied in different fields.
Security of GNSS

Applications of Global Navigation Satellite System (GNSS) need secure information to drive vehicles. GNSS is vulnerable to jamming, meaconing, and spoofing. The most difficult type of attack is spoofing. Spoofer can transmit false signals from a distance of several tens or hundreds of meters from the victim. Spoofing can be classified into simple, intermediate, or complex attacks in terms of their effectiveness. In this chapter, we briefly examine interference and jamming, as well as meaconing and spoofing detection algorithms based on one or two receivers. We will also briefly review antimeaconing and antispoofing issues.
The Internet of Everything—Introducing Privacy

chapter Share on The Internet of Everything—Introducing Privacy Author: David McMenemy Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 57–58https://doi.org/10.1145/3447404.3447409Online:23 February 2021Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months3Last 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
LibGuides Home: Lyman Beecher Brooks Library Virtual Assistance (ENG 102.45A): Search, Find, Write

This guide will show you how to search for various library resources to support your assignment..  Click on one of the tabs below to get more information
Visual image encryption scheme based on BTC and LSB modification

Abstract With the rapid development of Internet technology, information security issues have become an important guarantee for society. Image encryption technology can achieve security by converting images into other unrecognizable formats. Many existing visible image encryption methods are not highly visual, which not only causes the leakage of important image information, but also easily causes a waste of resources. In order to solve this problem, this paper proposes a new visual image encryption scheme based on BTC compression. The original image is protected by converting the original image into other formats that ordinary users cannot recognize to achieve the security level required by the user. The experimental results show that the proposed scheme is not only simple and convenient, but also has excellent performance in terms of compression rate and visual distortion. It has been widely used by many researchers.
Vehicle Intelligent Inspection Technology Based on Automatic Multi-functional Follow-up Probe

The distribution network inspection relies on the “Manual Inspection” mode, which has some prominent problems, such as the long time and manpower consumption of the inspection, the long cycle of the inspection, the easy development of defects and the manual error data input. Project team develop vehicle intelligent inspection technology based on automatic multi-functional follow-up probe and design its inspection system whose platform carries automatic multi-functional follow-up probe composed of a set of visible light, infrared, partial discharge detection, at the same time with the automotive computer center based on the technology of Internet of things performing edge calculation, big data analysis, real-time line tracking, real-time data collection and feed-backs, automatic defect report records, and etc. This technology replaces manual inspection with vehicle-mounted inspection and greatly improves the quality and efficiency of distribution network inspection through intelligent research and analysis.
A Cloud-Based Anomaly Detection for IoT Big Data

Security of IoT systems is a growing concern with rising risks and damages due to successful attacks. Breaches are inevitable, attacks have become more sophisticated, and securing critical infrastructure has become a greater challenge. Anomaly detection is an established approach for detecting security attacks, without relying on predefined rules or signatures of potential attacks. However, existing outlier detection techniques require adaptation if they are to be applied in a Big Data cloud context. We describe a novel outlier detection solution, which is currently being used by hundreds of customers with highly variable data scales. We describe our work in adapting this technology to handle IoT on a Big Data cloud setting. Specifically, we focus on efficient outlier analysis and managing large numbers of alerts using automatically controlled alert budgets.
Research and Implementation of Computer Laboratory Network Management Tool Based on Telnet Protocol

Abstract Telnet protocol is a powerful tool to help computer network management. In the era of big data, the combination of computer laboratory, computer teaching and Telnet protocol is a positive trend. This study discusses the basic principles and characteristics of Telnet protocol, the necessity and implementation of computer laboratory network management tools based on Telnet protocol, which provides a boost for the continued development of this trend.
Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector

This paper proposes a streaming anomaly detection algorithm using variational Bayesian non-parametric methods. We extend the use of Dirichlet process mixture models to anomaly detection for online streaming data through the use of streaming variational bayes method and a cohesion function. Using our algorithm, we were able to update model parameters sequentially near real-time, using a fixed amount of computational resources. The algorithm was able to capture the temporal dynamics of the data and enabled good online anomaly detection. We demonstrate the performance, and discuss results, of the algorithm on an industrial datasets with anomalies provided by a local utility.
To Err or Not to Err? : Subtle Interactions Between Parameters for Common Python Library Functions

To Err or Not to Err? : Subtle Interactions Between Parameters for Common Python Library Functions
Securely Computing Clustering Coefficient for Outsourced Dynamic Encrypted Graph Data

Social networks are represented by graphs. Clustering coefficient is a measure of how closely knit the actors are. The higher the clustering coefficient of a node, the more is its importance in the network. When small enterprises, with low storage and computational power, want to outsource their data and computation to a third-party cloud, anonymization alone might not help to protect data privacy. Moreover, fear of data leak and misuse by unauthorized parties force the data owner to encrypt data before outsourcing to the cloud. This makes it difficult to perform queries on the data. It is necessary to design a technique that allows queries to be performed on encrypted outsourced graph data without leaking meaningful information. In this paper, we design a novel graph encryption technique that allows calculating clustering coefficient on the outsourced encrypted graph. The encryption also supports edge and neighborhood queries. To the best of our knowledge, these types of queries have not been possible together before efficiently on encrypted graphs. We show that the designed scheme is secure under chosen-query attack. Moreover, we implement a prototype of the scheme and test on real-life data. The implementation results show that the scheme is practical even for a large database.
Functionally Logical Modeling of the Σπ-Neuron

In this paper, we suggest a circuitry method of the realization of a logical ΣΠ-neuron. We present digital and hybrid schemes of the logical ΣΠ-neuron. Each scheme is divided into two layers, which realize the multiplicative and additive operations, respectively. The schemes are constructed on the basis of digital and analogue elements. The layer performing the multiplicative function is identical in both schemes. The additive layer in the digital scheme is realized by the digital summator. In the hybrid scheme, it is realized by digital-to-analogue converters and the analogue summator of voltages. To realize the threshold function, the hybrid scheme uses a comparator and the digital one uses a digital comparator. The results obtained can be used for the construction of ΣΠ-neural networks.
Lambda-IVR: An Indexing Framework for Video Big Data Retrieval Using Distributed In-memory Computation in the Cloud

Feature indexing for video retrieval poses a significant hurdle for indexing due to three significant challenges. First, there are different types of features in varying nature, such as deep Convolutional Neural Network (CNN) features, handcrafted features, recognized text from the videos, and audio features, etc. Secondly, feature matching for those varying types of features requires different similarity measure methods. And thirdly, considering the Big-Data era the number of features to be indexed is enormous. To address these issues, in this paper, we present a lambda style distributed in-memory scale-out inverted-index based feature indexing framework for video retrieval, which operates as SaaS in the cloud. First, the video features are acquired, decoupled, and the visual features are encoded using an adaptation of an existing feature encoder with improvements. Secondly, the visual encoded features and the textual features are aggregated. Finally, the aggregated features are indexed and readily available for retrieval. Our framework supports incremental updates without the need to re-index the data and can serve enormous concurrent queries. Experimental results show that our framework performs reasonably well in terms of, accuracy, precision, and efficiency.
Machine Learning Based Query Exploration

With the continuous advances in technology, we can observe an increase in the rate at which we acquire new information. Compared with the previous decades, in today’s world, we have access to a volume of data on the order of zettabytes. Therefore, scientists find it more challenging to interact with structured databases and implicitly to write relevant and concise SQL queries that will uncover relevant results. Starting from an initial query, given as input by the user, we will further generate the so-called negated query. The tuples resulted from the execution of this query will be the ones that are undoubtedly unwanted by the user. There are multiple possible ways of building the negation relative to the number of initial conditions. With the help of a machine learning algorithm, that uses the labeled dataset obtained from the positive and negative sets, we can rewrite a different and improved query. This will contain all the initial positive tuples but also some new, very similar ones. The initial search could be expanded from an intuitively written query to a more inclusive one that will select similar tuples with less intuitive feature values. Through this method the user could potentially highlight less important feature correlations and even uncover hidden patterns in the dataset. Most importantly this could be achieved without any knowledge of the logic behind the actual features.
Research on two-stage Feature Mining Fusion Method based on Genetic Algorithm

In the actual fusion of two-stage feature data, reconstruction errors are easy to occur in the process of feature region reconstruction in different stages, resulting in too few final fusible mining indicators. To solve this problem, this paper designs a two-stage feature mining fusion method based on genetic algorithm. Based on the two-stage mining features, the two-stage feature areas are divided, and the feature area reconstruction project is controlled by setting the background template of the feature area. Then the genetic algorithm is used to construct the mining fusion mapping relationship, and different fusion levels are set to complete the research on the feature mining fusion method. Set up a two-stage feature experiment platform, mine feature data, take the feature data obtained as the experimental object, and use two traditional fusion methods to conduct comparative experiments with this article. The results show that the method in this paper can integrate the most index values, which proves that the method is more effective.
An Improvement in Dense Field Copy-Move Image Forgery Detection

The advancements in image editing software besides enhanced multi-media storage and transmission systems have increased the number of counterfeit images being shared. These can have serious consequences; therefore, it becomes important to detect tampering. Copy-move image forgery being a simple yet, quite elusive way, is one of the most frequently used forgery techniques. In this type of forgery, a small region of the source image is copied and pasted somewhere else within that image. Many algorithms have been proposed to detect copy-move attack but those are computationally complex making them unfit for real-time systems. Also, accuracy in detection systems still needs to be improved to rely on these. In this paper, an effort has been made to enhance the accuracy of already existing five dense field detection techniques by experimental analysis of the minimum size of duplication and linear fitting error used in the post-processing stage. After the former analysis, a combination of morphological operations dilation and closing, and sharpening technique unsharp masking, has been proposed in the pre-processing stage for the improvement of results. The results have been tested and compared for accuracy, precision, recall, and F1 score by using the CoMoFoD dataset for each technique.
Improving Identification of Wind Tunnel Systems Using Clustering-Based Ensemble Outlier Detection Model

This paper proposes an outlier ensemble based on the clustering technique to improve the identification accuracy of wind tunnel systems, which are very critical pieces of test equipment in the field of aerospace. The use of clustering has two objectives in our outlier ensemble, that is, generating diverse training subsets and improving the robustness of base detectors. By analyzing the data characteristics of wind tunnel systems, we propose a hybrid criterion to determine the most appropriate clustering algorithm and the corresponding clustering number. This criterion is constituted by a qualitative and a quantitative criterion. The qualitative criterion is implemented first to eliminate several candidates from alternative clustering algorithms. Moreover, the quantitative criterion is used to determine the ultimate algorithm. In addition, a robust base detector is also developed with the assistance of the selected clustering algorithm. Finally, we verify the proposed detection model in two ways. In an offline application, the model is verified through two system identification models. In an online application, it is verified only through the performance of outlier detection.
Introduction to Machine Learning

In this chapter, we learn the general theory of machine learning. We shall take a look at examples of what learning is, what is the meaning of “machines learned,” and what relative entropy is. We will learn how to handle data in probability theory, and describe “generalization” and its importance in learning.
The Test of Covariation Functions of Cylindrical and Circular Images

Nowadays, image processing problems are becoming increasingly important due to development of the aerospace Earth monitoring systems, radio and sonar systems, medical devices for early disease diagnosis etc. But the most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations, images are set on a cylinder (for example, images of pipelines, blood vessels, parts during turning) or on a circle (for example, images of the facies (thin film) of dried biological fluid, an eye, cut of a tree trunk). The peculiarity of the domain for specifying such images requires its consideration in their models and processing algorithms. In the present paper, autoregressive models of cylindrical and circular images are considered, and expressions of the correlation function depending on the autoregression parameters are given. The spiral scan of a cylindrical image can be considered as a quasiperiodic process due to the correlation of image rows. To represent inhomogeneous images with random heterogeneity, «doubly stochastic» models are used in which one or more controlling images control the parameters of the resulting image. Given the resulting image, it is possible to estimate parameters of the model of controlling images. But it is not enough to identify hidden images completely. It is necessary to investigate the covariation function of given image. Does it match the hypothetical one? The test for covariation functions of cylindrical and circular images is proposed with investigation its power relative to parameters of image model.