Reviewerly/exHarmony huggingface dataset:
Conditional Probability and Conditional Expectation

One of the most useful concepts in probability theory is that of conditional probability and conditional expectation. The reason is twofold. First, in practice, we are often interested in calculating probabilities and expectations when some partial information is available; hence, the desired probabilities and expectations are conditional ones. Secondly, in calculating a desired probability or expectation it is often extremely useful to first “condition” on some appropriate random variable.
Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia

Abstract In mid-2019 President of the Republic of Indonesia officially decided that the capital city be moved outside of Java. This has caused many responses from the public who responded to this decision. We have seen many of these community responses on social media, especially in Twitter. To see the reality of the response of the Indonesian people requires a study that can draw conclusions from the number of community responses. So from this problem this study was conducted to find the truth of the community response related to the decision to move the Indonesian capital by using the lexicon method. This study also wants to see a comparison of the effect of the stemming process on sentiment analysis. To measure the performance of the Lexicon method, this research will be tested by an expert. Then the results of the experts will be entered into the confusion matrix. From the calculations with the confusion matrix, the results showed that the response of many Indonesian people who agree with the decision to move the Indonesian capital.
Spotlights: Faculty, Alumna, and Courses

Spotlighting SLIS Faculty Dr. Jeff Hirschy, SLIS alum Michelle Finley, and asynchronous elective courses LIS 492 (H002)/LIS 692: Library Marketing &amp; PR and LIS 691: LIS Research - Genealogy Research.
Validation of the Validity of Spectrogram Contour Neural Network - Applied to Inrush Current Classification of Transformer

In this study, in order to effectively analyze the state of the devices using one-dimensional time-series data generated from industrial devices, a new learning method was designed based on Short-Time Fourier Transform (STFT), and its validity was verified. The proposed learning method is a method of extracting contour lines with a preset threshold before applying a 2D spectrogram image to a deep neural network. As such, the contour transformation of the spectrogram image of the frequency distribution is to extract the characteristics of the frequency, so it will be effective in improving the performance of learning. In order to verify the validity of the proposed contour neural network, Learning was conducted as a pre-study, using the time series values of the chaotic system with nonlinear high-dimensional characteristics as a data set, and it was confirmed that the learning rate rapidly increased at a specific contour line.
Hush

Hush is an immersive Virtual Reality experience rooted in the Northern myths of the merpeople, who lured sailors and longing souls to the sea. Like the lost ones of the past, you begin by the seaside. The world around you dissolves as you hear the calling from the sea, and you are submerged in a suggestive underwater world, where the distinction between reality and imagination, man and nature, disappears. Hush is a stand-alone VR piece, but it is also a companion piece to the hybrid documentary film Elsewhere, also directed by Vibeke Bryld and supported by New Danish Screen at the Danish Film Institute, which is based in the same universe as Hush. Elsewhere premiered at CPH:DOX 2021.
A Learning Method for Object Detection from Low Resolution Image

Object recognition the use deep neural networks has been most typically used in real applications. We propose a framework for identifying items in pics of very low decision through collaborative studying of two deep neural networks. It includes photo enhancement network object popularity networks. The picture correction community seeks to decorate images of much lower decision faster and more informative images with the usages of collaborative gaining knowledge of indicatores from object recognition networks. Object popularity networks actively participate in the mastering of photograph enhancement networks, with skilled weights for photographs of excessive resolution. It uses output from photograph enhancement networks as augmented studying recordes to reinforce the overall performance of its identity on a very low decision object. We esablished that the proposed method can improve photograph reconstruction and classification overall performance.
Text Extraction and Recognition in Natural Scene Images using Contourlet Transform and PNN

Of late, the rapid development in the technology and multimedia capability in digital cameras and mobile devices has led to ever increasing number of images or multi-media data to the digital world. Particularly, in natural scene images, the text content provides explicit information to understand the semantics of images. Therefore, a system developed for extracting and recognizing texts accurately from natural scene images, in real-time, has significant relevance to numerous applications such as, assistive technology for people with vision impairment, tourist with language barrier, vehicle number plate detection, street signs, advertisement bill boards, robotics, etc. The extraction of the texts from natural scene images is a formidable task due to large variations in character fonts, styles, sizes, text orientations, presence of complex backgrounds and varying light conditions. The main focus of this research paper is to propose a novel hybrid approach for automatic detection, localization, extraction and recognition of text in natural scene images with cluttered background. Firstly, image regions with text are detected using edge features (GLCM) extracted from Contourlet transformed image and SVM (Support Vector Machine) classifier. Secondly, horizontal projection is applied on text regions for segmenting lines and vertical projection is applied on each text line for segmenting characters. The proposed method for text extraction has produced the precision, recall, F-Score and accuracy of 98.50%, 90.85.62%, 95.00%, and 89.90%, respectively. And, these results prove that the proposed method is efficient. Further, the so extracted characters are processed for recognition using contourlet transform and Probabilistic Neural Network (PNN) classifier. The computed features are moment invariants. Only the English script is considered for the experimentation. The proposed character recognition method has accuracy of 79.07%, which is higher in comparison to accuracy of 75.15% obtained by KNN (K-Nearest Neighbors) classifier
Speech Content Authentication Scheme based on High-Capacity Watermark Embedding

The existed content authentication schemes based on digital watermark have some shortcomings. In order to solve the problems, a speech content authentication scheme based on high-capacity watermark embedding is proposed, and the high-capacity embedding method is discussed. Firstly, speech signal is framed and segmented, and the samples of each segment are scrambled. Secondly, DCT is performed on the scrambled signal, and low-frequency coefficients are selected as the watermark embedding domain. Lastly, frame number is mapped to a sequence of integers and embedded into the domain based on the embedding method. Theoretical analysis and experimental evaluation results show that the proposed algorithm is inaudible, robust to desynchronous attacks, enhances the embedding capacity, and improves the security of watermark system.
Prediction of Type 1 Diabetes Mellitus using Datamining Techniques

Type1 diabetes is a sickness occurs when your immune system fighting against infection, affects and erode the insulin generating beta cells of the pancreas. In general, when the blood sugar stage increases, the pancreas makes more insulin. Insulin helps to go sugar out of the blood so it can be used for liveliness. Type 1 diabetes occurs due to the immune system which affects cells in the pancreas that make insulin. The pancreas cannot make adequate insulin, so the blood sugar level continues to increase. According to the children history of type 1 diabetes may enhance risk of their life. Type 1 diabetes cannot be cured, but it can be controlled and managed. In this study we use Naive Bayes, linear regression and k-means algorithm for data analysis and prediction. It predicts the diabetes affected children with maximum level of accuracy 96% by using of data mining algorithms.
Editorial acknowledgement

British Journal of Mathematical and Statistical PsychologyVolume 73, Issue 1 p. 184-185 Editorial Acknowledgement Editorial acknowledgement First published: 26 January 2020 https://doi.org/10.1111/bmsp.12200Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume73, Issue1February 2020Pages 184-185 RelatedInformation
Asset Security

In this chapter, you will learn tips in these areas: what assets are, the asset security, protecting assets, privacy, memory and data management, data handling as part of asset security, and the public key infrastructure used to protect assets.
CONTRIBUTORS TO THE ISSUE

Journal of Analytical PsychologyVolume 65, Issue 1 p. 248-251 Contributors to This Issue CONTRIBUTORS TO THE ISSUE First published: 23 January 2020 https://doi.org/10.1111/1468-5922.12559 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume65, Issue1February 2020Pages 248-251 RelatedInformation
New Efficient Locust Based Genetic Classifier for Abdominal Aortic Aneurysms with Digital Image Processing

Magnetic Resonance Imaging (MRI) based Abdominal Aortic Aneurysms (AAA) treatment for Endovascular Aneurysm Repair (EVAR) assess the disease advancement of patients and recognize confusions. Picture preparing is one of the testing and developing medical field. MRI as a hotspot, extracts, distinguishes and classify tainted locale from AAA picture. It is a critical concern yet a troubling and tedious errand performed by radiologists and medical specialist and their experience determine classification precision. Hence it is important to utilize PC supported methods to overcome the above restrictions by utilizing PC supported method. A Locust based hereditary classifier and Gabor wavelet based AAA tumor segmentation and arrangement is proposed in this paper to improve the classification exactness and reduces the acknowledgement complexities of therapeutic picture. The classification performance metrics such as precision, affectability, and explicitness of the proposed strategy is validated for AAA pictures. The accomplished simulated (MATLAB) outcomes of 94.23% of precision, 92.3% of explicitness, and 93.6% of affectability show the enhancement in characterizing ordinary and anomalous tissues of AAA pictures.
A Real-Time Augmented Reality System Using GPU Acceleration

Augmented Reality (AR) is the act of overlaying 3D virtual objects into a real-world scene. Using robust computer vision algorithms, it is possible to perform AR using only a single video camera. However, these algorithms are very computationally expensive, and most proposed systems have to sacrifice accuracy for speed. Graphics Processing Units (GPUs), originally designed to power graphics-intensive 3D video games and now commonplace on most gaming PCs, can also be used for general purpose computations. We developed a computer vision-based AR system accelerated by a single GPU, allowing robust feature detection and matching to be performed in every frame. We conducted performance evaluations in both indoor and outdoor environments, with parameters optimized for maximum possible accuracy of recovered poses. Our AR system achieves a stable 10-12 frames per second at 640480 resolution on a laptop.
Supplementary material to &amp;quot;Lignin oxidation products in soil, dripwater and speleothems from four different sites in New Zealand&amp;quot;

Supplement to "Lignin oxidation products in soil, dripwater and speleothems from four different sites in New Zealand"
Installing Kubernetes

In this chapter, we will introduce how to build a Kubernetes cluster both on-premises and in the cloud. We will begin with discussion of the decision process of where to install, on-premises or in the cloud, and what to consider in that process. We will then go through the process of building an on-premises, virtual machine–based Kubernetes cluster using the kubeadm installation method. Then next, we will build a cluster in Azure Kubernetes Service. These clusters will be the foundation for all examples in the remainder of the book.
"Overview of Blockchain Technology: Consensus, Architecture, and Its Future Trends"

The blockchain technology that underpins Bitcoin has lately gained a lot of traction. Blockchain is a decentralized transaction ledger that is immutable. Blockchain applications are emerging in a variety of fields, including financial services, reputation systems, and the Internet of Things (IoT), among others. Nonetheless, concerns with blockchain knowledge, like as security or scalability, have yet to be solved. Bitcoin can only process 7 transactions per second, making it unsuitable for highfrequency trading. Larger blocks, on the other hand, imply more storage capacity and less network transfer. Because fewer individuals desire to own such a large blockchain, this will ultimately lead to centralization. This article provides a comprehensive description of blockchain technology. The researcher first provides an overview of blockchain architecture and then compares many commonly utilized consensus algorithms in different blockchains. A brief summary of technical difficulties and current advances is also included. They also highlight possible future blockchain developments.
A method for reducing information redundancy of digital images for cloud storage

The article is devoted to the topical issue of finding new effective and improving existing widespread compression methods in order to reduce computational complexity and improve the quality of image-renewable image compression images, is important for the introduction of cloud technologies. The article presents a problem To increase the efficiency of cloud storage, it is necessary to determine methods for reducing the information redundancy of digital images by fractal compression of video content, to make recommendations on the possibilities of applying these methods to solve various practical problems. The necessity of storing high-quality video information in new HDTV formats 2k, 4k, 8k in cloud storage to meet the existing needs of users has been substantiated. It is shown that when processing and transmitting high quality video information there is a problem of reducing the redundancy of video data (image compression) provided that the desired image quality is preserved, restored by the user. It has been shown that in cloud storage the emergence of such a problem is historically due to the contradiction between consumer requirements for image quality and the necessary volumes and ways to reduce redundancy of video data, which are transmitted over communication channels and processed in data center servers. The solution to this problem is traditionally rooted in the search for effective technologies for compressing, archiving and compressing video information. An analysis of video compression methods and digital video compression technology has been performed, which reduces the amount of data used to represent the video stream. Approaches to image compression in cloud storage under conditions of preservation or a slight reduction in the amount of data that provide the user with the specified quality of the restored image are shown. Classification of special compression methods without loss and with information loss is provided. Based on the analysis, it is concluded that it is advisable to use special methods of compression with loss of information to store high quality video information in the new formats HDTV 2k, 4k, 8k in cloud storage. The application of video image processing and their encoding and compression on the basis of fractal image compression is substantiated. Recommendations for the implementation of these methods are given.
Math!

While there are tons of things that you can do with math in programming and in Java, we are going to cover the essentials here, starting with basic arithmetic operators.
Smart Glass for Visual Impaired People

To defeat the voyaging trouble for the outwardly debilitated individuals, this endeavor presents an ETA (Electronic Travel Aids)- canny controlling device in the condition of a few eyeglasses for provide these people guidance gainfully and safely. Unique in relation to existing works, a novel Convolution Neural Network(CNN) based deterrent keeping away from calculation is proposed, which uses Google's pre-prepared datasets of different classes to take care of the issues of identifying little impediments, and straight forward obstructions, for example bicycle. For absolutely visually impaired individuals, three sorts of voice guidelines to educate the bearing where they can proceed. For deaf and dumb people we integrate two servo motors to insist them through touch. The prototype consists of pair of servo motors and camera in the eye glass and its effectiveness and precision were tried by an ongoing snag. The test result demonstrates that the savvy controlling glass is effective in accuracy than any other traditional algorithms. Thus it serves as a user friendly device by its simplistic design.
Fake User Detection in Twitter using Random forest algorithm with Python

Millions of users are engaged with social networking sites around the world. Social sites like twitter, Facebook have a large impact on rare unwanted consequences caused in our regular life in user’s interactions. In order to disperse a large amount of inappropriate and harmful data protruding social networking sites are made as a target platform for the spammers. Twitter is main example that has become one of the important platforms for unreasonable amount of spam in all the tomes for fake users to tweet and promote websites or services that crates a major effect for legitimate users and also it disturbs resource consumption. By resulting the opening for unusual and harmful information there is an increase of fake identities that expands invalid data. Research on current online social networks (OSN) is quite natural for detection of fake users on twitter. In this paper using random forest classifier and ROC curve to detect fake users.
Machine Learning

In earlier chapters, we discussed aspects of computer architecture and how to efficiently program and deploy software. Thus far, we’ve been successful getting computers to carry out what they have been programmed to accomplish. Beyond traditional programming, questions arise about whether or not computers can mimic humans in terms of intelligence and learning. In science fiction literature, there are many stories of machines taking over the world. Is this possible? Until relatively recently, these fictions have been given little credence because there are fundamental differences between how human intelligence and computing machines work. Machines act as obedient servants – working as they are explicitly programmed to accomplish a well-defined task. They did not learn and improve or develop intelligence. And that’s where machine learning comes to play. Some of the most succinct descriptions of machine learning are from Stanford and McKinsey & Co. As per Stanford, “Machine learning is the science of getting computers to act without being explicitly programmed.” And, as per McKinsey & Co, “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”
Chapter 2 Blockchain: implications for accounting and audit practice

With the advent of blockchain technology (BT), the global business is undergoing a major transformation. This technology originated primarily to support Bitcoin cryptocurrency as a way of providing support for conducting reliable transactions with instantaneous verification. The immutable and supposedly tamper-proof nature of the records, which are the byproducts of blockchain, provide trust between the parties involved in a transaction. Using BT, individuals are able to virtually do business, enter contracts and exchange goods and services with authentication. In addition, this technology establishes a permanent audit trail. In this chapter, we discuss business applications of BT in terms of recording, valuing and verifying transactions. We also explore the ramifications of blockchain for the audit of corporate financial reports and associated accounting challenges, especially those involving cryptocurrencies.
In the News

Welcome to the January 2020 issue of C&amp;RL News. A lot of attention gets paid to communicating library activities and value across campus, but internal communication can be just as essential for success. Starting out this month’s issue, Kelly Delevan and Natalie LoRusso discuss “Library outreach as a model for staff inreach” at Syracuse University.
Coordination control and obstacle avoidance for a team of mobile robots in unknown environment

This paper presents a dual-level control structure for controlling a mobile robot and/or a team of mobile robots to navigate through an unknown (static or dynamic) environment. The higher-level controller operates in cooperation with robot's state estimation and mapping algorithm, extended Kalman filter - simultaneous localisation and mapping (EKF-SLAM), and the lower-level controller (PID) controls the motion of the robot when it encounters an obstacle and manoeuvres the robot around the obstacle/s. The higher-level controller jumps in as soon as the robot is out of the obstacle range and moves the robot to the goal. The obstacle avoidance technique involves a novel approach to calculate the rebound angle. The experimental results show that the proposed dual-level control structure can be effectively used to manoeuvre a mobile robot or a team of mobile robots to navigate through a dynamic environment.
Observational Methods

This chapter assesses observational methods. What distinguishes observational methods, and particularly participant observation (PO), from other data-gathering methods is, among others, that it privileges direct observation over the use of instruments. This implies that the observation is likely to be holistic, in the sense of involving the five senses, rather than only one of the senses (typically sight). PO also privileges observation in real time over the retrospective observation of traces left by past activities (texts, pictures, filmed sequences). It allows one to observe discourses, practices, interactions, and context together rather than separately. It focuses on first-hand observation, as opposed to observation based on secondary sources (involving a mediator or third person). PO is sometimes accused of harbouring a naive belief in the scientific virtue of ‘gut feeling’.