Reviewerly/exHarmony huggingface dataset:
Introduction

This book started after teaching an intensive course on algorithms to working programmers in Kyiv, in spring 2016. It took more than 3 years to complete, and, meanwhile, I also did three iterations of the course. Its aim is to systematically explain how to write efficient programs and, also, the approaches and tools for determining why the program isn’t efficient enough. In the process, it will teach you some Lisp and show in action the technics of algorithmic development. And, even if you won’t program in Lisp afterward, you’ll still be able to utilize the same approaches and tools or be inclined to ask why they aren’t available in your language of choice from its authors. :)
ADR Brief

Alternatives to the High Cost of LitigationVolume 37, Issue 11 p. 170-173 ADR Brief ADR Brief Daniel Bornstein, Daniel BornsteinSearch for more papers by this author Daniel Bornstein, Daniel BornsteinSearch for more papers by this author First published: 27 November 2019 https://doi.org/10.1002/alt.21821Read 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 Volume37, Issue11December 2019Pages 170-173 RelatedInformation
Crypto-Criminals

In this chapter, the author discusses the world of crypto-crime. Beginning with an account of the arrest of Alexander Vinnik, the Russian accused of running a money-laundering cryptocurrency exchange, the chapter goes on to describe the ways in which blockchain has allowed dramatic levels of criminality to spread throughout the industry.
Quantifying Inference Learning Model to Explore Twitter User Emotions

Increasing social media used by different peoples express their opinions and feelings in the form sentences and text messages. So that extracting the information from message i.e which consists different issues in text and identifying anxiety depression of individuals and measuring well-being or mood of a community. This is because of its significance in a wide scope of fields, for example, business and governmental issues. Individuals express assessments about explicit themes or elements with various qualities and powers, where these estimations are firmly identified with their own sentiments and feelings. Various techniques and lexical assets have been proposed to break down feeling from normal language writings, tending to various assessment measurements. In this article, we propose a novel inference methodology for quantifying and inferring the Twitters users’ conclusion grouping utilizing distinctive notion measurements as meta-level highlights. We consolidate angles, for example, assessment quality, feeling and extremity markers, created by existing estimation investigation strategies and assets. Our exploration demonstrates that the mix of assumption measurements gives critical improvement in Twitter feeling characterization errands, for example, extremity and subjectivity.
Track 1 What's It All About? Integrating the Web into the Serials Chain

12Serialists need to be prepared to do more than just “surf the Web” to find interesting sites. In this workshop, participants learn basic HTML coding and create a professional or departmental home page with links to Web sites useful to serialists. Techniques covered include basic HTML tagging, creating hot links, including images in Web pages, and using colors and other design tools. An introduction to HTML editing software and procedures for converting documents from other formats to HTML was also covered. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Advanced Machine Learning Technique to Handle Filtering Unwanted Messages in Online Social Networks

Social networks are increased in present days because of communication between different users like Face book, Google and Twitter. Major fundamental issue behind online social networks is control user’s messages in-front of sharing rumor related messages and posts unwanted messages. It is still main challenge to define user’s share other user’s details in social network communication. In this paper, Greedy Heuristic based Advanced Short Text Classifier (GHASTC) used to classify filtering with rumor related classification for multi user’s interaction in social networks. This hybrid approach gives direct control to users to control unwanted data posted on own space. The proposed approach works with rule based filtering system, which consists a customized filtering for unwanted content in online social networks. The experimental results show efficient filtering results with comparison of traditional techniques.
An Invincible Rudimentary Architecture for Data Security in Cloud Environment Using Multi Cloud

Cloud is a distributed access of shared pool of resources that can provide the efficient service on demand basics to cloud user by the service providers. The important functions of cloud computing are storage and computation. The cloud user can use the cloud functions without any computing resources. Because of the popularity of cloud computing, providing security to the stored data is a major concern. In the literature, there are various works which have been proposed to provide the efficient security to the stored data in cloud environment. This paper provides the details of need of security in cloud environment, various categories of security threats, mechanisms, cloud architecture, multi-cloud and various security algorithms in detailed manner. The main attention of this paper is to point out the works on security in cloud computing, show the proposed architecute of multi cloud instead of single cloud and the way for the future work for the efficient design of cloud secure service system.
Author Index Volume 34 (2020)

International Journal of Pattern Recognition and Artificial IntelligenceVol. 34, No. 14, 2099001 (2020) Free AccessAuthor Index Volume 34 (2020)https://doi.org/10.1142/S0218001420990013Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract FiguresReferencesRelatedDetails Recommended Vol. 34, No. 14 Metrics History PDF download
Medical Analytics in the Presence of Human-Error. An Exploration of EMR Data Quality using MIMIC-III. (Preprint)

<sec> <title>BACKGROUND</title> Public Electronic Medical Records (EMR) datasets are a goldmine for vendors and researchers seeking to develop analytics designed to assist caregivers in monitoring, diagnosis, and treatment of patients. Both complex machine-learning-based tools, which require copious amounts of data to train, and a simple trend graph presented in a patient-centered dashboard, are sensitive to noise. </sec> <sec> <title>OBJECTIVE</title> We aim to systematically explore data errors in MIMIC-III as a representative of secondary use datasets and the impact of these errors on downstream analytics. </sec> <sec> <title>METHODS</title> We discuss the unique challenge of accounting for the specific patient's medical condition and personal characteristics such as age, weight, gender, and others, in identifying data errors when only a few measurements of each patient are available. To do so, we examine the prevalence and manifestations of errors in one of the most popular public medical research databases - MIMIC-III. We then evaluate how these errors impact visual analytics, score-based sepsis analytics SOFA and qSOFA, and a machine-learning-based sepsis predictor. </sec> <sec> <title>RESULTS</title> We find a variety of error patterns in MIMIC-III and highlight effective methods to find them. All analytics are found to be sensitive to sporadic error. Visual analytics are severely impacted, limiting their usefulness in the presence of error. qSOFA and SOFA suffer a score change of +1 (of 3) and +2.3-4 (of 15). The sepsis predictor suffers from a 0.01-0.3 score change compared to a median score of 0.08. </sec> <sec> <title>CONCLUSIONS</title> The use of statistical methods to detect data errors is limited to high-throughput scenarios and large data aggregations. There is a dearth of medical guidelines and error-detection practices to support rule-based systems, required to keep analytics safe and trustworthy in low-volume scenarios. Analytics developers should test their software’s sensitivity to error on public datasets. The medical informatics community should improve support for medical data-quality endeavors by creating guidelines for plausible values and analytics robustness to error and collecting real-world dirty datasets which contain errors as they appear in normal EMR use. </sec>
Inheritance

The single table inheritance is the default JPA strategy. Conforming to this strategy, all the classes in an inheritance hierarchy are represented via a single table in the database.
Chameleon

chameleon change the color of skin according to the surrounding environment for hiding the body, can not only avoid predators, and also confuse their prey. I use against neural network, I input a lot of images of chameleon various parts to the machine learning algorithm, the neural network generated the color-changing chameleon skin based on these pictures. Artificial-intelligence one day can be like a chameleon hide himself with pixel camouflage? Work is divided into two versions, image and image + interaction: in the interactive version, the color of the chameleon can change color according to the color of the catch by the electron trap.
NODE-INDEPENDENT METHOD FOR GASTROENTEROLOGICAL SIGNAL PROCESSING BASED ON CUBIC SPLINES

This paper discusses a local cubic spline function built independently of node points using basic functions. the size of the calculations required to find the parameters to be determined during the construction of the spline function does not depend on the number of node points. Local-based splines are used to build such spline functions. Restoration of the gastroenterological signal was performed on the basis of the spline-function model discussed in the article. The result of a cubic spline-function error independent of the node points was compared with the result of the Lagrange classical polynomial error (Table 2).
The Salesperson with a Speech Impediment: An Objective Research and Analysis on the Importance of Clarity, Structure and Logic of Arguments

There is no sales document as crucial to winning business deals as the business proposal document. We noticed that business proposals try their best to be persuasive, and yet, they do not have the impact they hope to create. In order to understand why we conducted detailed research and found out that customers simply refuse to read some proposals. There are three reasons for this: Logic, Clarity, and Aesthetics. Our research showed that the lack of logic or the presence of logical fallacies account was the reason most often quoted by customers (63% of the times) for not wanting to read the business proposal, followed by clarity, at 29% and aesthetics at 9%. To write this research paper, we’ve focused primarily on understanding logical fallacies, categorizing them and identifying which of them are the most identifiable and which of them are most frequent. We also recommend a systematic way to avoid logical fallacies in proposals.
P 099 - Does instrumented gait analyis add clinical value in children with severe intelectual disabilities ?

Different factors are forcing a change in the logistics market, most notably e-commerce and manufacturing of custom-made products. This fact is highly linked with warehouse management, where exploitation costs increase with the new value-added tasks required in logistics. In this work, we present a detection system, based on 2D pattern recognition, to estimate the pose of pallets placed in open areas in non-fixed positions. The detection method is part of a novel automation solution designed to retrofit manual operated forklifts, adding a new autonomous working mode, obtaining a highly flexible pallet handling system that could be applied in shared spaces with humans.
Data Transformation

Once a dataset has been filtered and shaped (as covered in the previous chapter), it probably still needs a good few modifications to make it ready for consumption. Many of these modifications are, at their heart, a series of fairly simple yet necessary techniques that you apply to make the data cleaner and more standardized. I have chosen to group these approaches under the heading data transformation.
New hybrid framework to detect phishing web pages, based on rules and variant selection of features

Phishing phenomena are increasing day after day due to its simplicity to use; it is enough for hackers to clone legitimate website and send it by e-mail to victims to access it within the use of social engineering techniques to lure them and gain their confidence. Hackers use the lack of knowledge of regular users when surfing on the internet and understanding the role of uniform resource locator (URL) of a web page. This fact let hackers create malicious forms of URLs, like very long ones or containing some suspicious characters. Despite cloning web pages, hackers can inject malicious codes into this web page for nefarious uses, so detecting or preventing this kind of web pages is the objective of this paper. We present a new hybrid framework to identify phishing web pages based on different ways and methodologies for features extraction techniques using only the URL as the main entry and without having any visual experience before, also we use hybrid analysis to be complete and accurate.
Role of Skeletonization Techniques for Recognition of Odia Off-Line Characters

Many character recognition methods use a thinning stage which facilitates shape analysis and stroke identification. There are several thinning algorithms reported in the literature of OCR. Odia, one of the Indian languages, has structurally different scripts than other Indian languages. Unlike in other Indian scripts, there has not been any special effort by OCR researchers to develop recognition techniques of this unique structural character set. In this paper, some major thinning algorithms are examined with Odia character set and show that these algorithms exhibit some sort of deficiencies when applied to Odia characters and the vital features of the character is not retained in the process. We propose a new parallel thinning technique that preserves all important features of the script.
Improving Missing Value for Distributed Database using SMOTE and Random Forest Technique

This research presents the development and improves the missing values from the database distributed in various database centers by balancing the data with the Synthetic Minority Over-sampling Technique. By increasing the amount of data and using the random forest to create a decision tree to help improve the missing value for distributed databases. The results showed that the evaluation of the model efficiency using the 10-fold cross validation method was higher than the percentage split at 65%, with data increasing to 400%, equal to 10,600 records in total. The 10-fold cross validation method gives the following performance values: 98.87%, 98.70%, 98.70%, and 98.76% for the precision, recall, F-measure, and accuracy, respectively. It can be said that the model created is a method that can help improve missing value for the distributed database.
Intents

This chapter covers Android IPC intents. Intents are one of the core inter-process communication (IPC) mechanisms in Android, allowing for applications to communicate (e.g., send data or initiate an action) with other Android components (including applications) even if the recipient is not currently running.
Virtual cluster optimisation for MapReduce-like applications

Infrastructure-as-a-service clouds are becoming ubiquitous for provisioning virtual machines on demand. Cloud service providers expect to use the least resources to deliver the best services. As users frequently request virtual machines to build virtual clusters and run MapReduce-like jobs for big data processing, cloud service providers intend to optimise the virtual cluster to minimise network latency and subsequently reduce data movement cost. In this paper, we focus on the virtual machine placement issue for provisioning virtual clusters with minimum network latency in clouds. We define the distance as the latency between virtual machines and use it to measure the affinity of a virtual cluster. Such metric of distance indicates the considerations of virtual machine placement and the topology of physical nodes in clouds. Then, we formulate our problem as the classical shortest distance problem and solve it by building an integer programming model. A greedy virtual machine placement algorithm is designed to get a compact virtual cluster. Furthermore, an improved heuristic algorithm is also presented for achieving a global resource optimisation. The simulation results verify our algorithms and the experiment results validate the improvement achieved by our approaches.
Explicit time discretization

In this chapter, we consider the same space semi-discrete problem as in the previous chapter, but we now discretize it in time using an explicit scheme. We first discuss generic properties of explicit Runge–Kutta schemes (ERK). Then we analyze the explicit Euler scheme, second-order two-stage ERK schemes, and third-order three-stage ERK schemes. The key advantage of explicit schemes over implicit schemes is that the linear algebra at each time step is greatly simplified since one has to invert only the mass matrix. However, the stability of ERK schemes requires that the time step be limited by a CFL-like condition.
Error Handling

The libvirt error functions are designed to give more detailed information about what caused a failure in the case that a normal libvirt function or method returns an error. An important thing to note about Python libvirt error reporting is that errors are stored on a per-thread basis and not per connection.
Measuring the Performance of a Classifier

This chapter looks at the use of true and false positive and negative classifications as a better way of measuring the performance of a classifier than predictive accuracy alone. Other performance measures can be derived from these four basic ones, including true positive rate (or hit rate), false positive rate (or false alarm rate), precision, accuracy and F1 score. The values of true positive rate and false positive rate are often represented diagrammatically by a ROC graph. Joining the points on a ROC graph to form a ROC curve can often give insight into the best way of tuning a classifier. A Euclidean distance measure of the difference between a given classifier and the performance of a hypothetical perfect classifier is described.
Sentiment Analysis and Summarization of Social Media Content using Topic Modeling

Explosion of Web 2.0 had made different social media platforms like Facebook, Twitter, Blogs, etc a data hub for the task of Data Mining. Sentiment Analysis or Opinion mining is an automated process of understanding an opinion expressed by customers. By using Data mining techniques, sentiment analysis helps in determining the polarity (Positive, Negative &amp; Neutral) of views expressed by the end user. Nowadays there are terabytes of data available related to any topic then it can be advertising, politics and Survey Companies, etc. CSAT (Customer Satisfaction) is the key factor for this survey companies. In this paper, we used topic modeling by incorporating a LDA algorithm for finding the topics related to social media. We have used datasets of 900 records for analysis. By analysis, we found three important topics from Survey/Response dataset, which are Customers, Agents &amp; Product/Services. Results depict the CSAT score according to Positive, Negative and Neutral response. We used topic modeling which is a statistical modeling technique. Topic modeling is a technique for categorization of text documents into different topics. This approach helps in better summarization of data according to the topic identification and depiction of polarity classification of sentiments expressed.
Synonym Multi-keyword Search Over Encrypted Data Using Hierarchical Bloom Filters Index

Search on encrypted data refers to the capability to identify and retrieve a set of objects from an encrypted collection that suit the query without decrypting the data. Users probably search not only exact or fuzzy keyword due to their lack of data content. Therefore, they might be search the same meaning of stored word but different in structure. So, this chapter presents synonym multi-keyword search over encrypted data with secure index represented by hierarchical bloom filters structure. The hierarchical index structure improves the search process, and it can be efficiently maintained and constructed. Extensive analysis acquired through controlled experiments and observations on selected data show that the proposed scheme is efficient, accurate and secure.
Telecom Narrow Bandwidth Two-photon Source with High Fidelity Polarization Entanglement

We demonstrate two-photon sources for long-distance quantum communication. The highest spectral brightness 3.94×105 pairs/(s MHz mW), the narrowest linewidth in telecom of 1.06 MHz and entanglement fidelity of 91.8% are shown.
Decision-making strategy for detecting authenticated recommendations and identification of valuable customers in online shopping sites

The online shopping sites help customers not only choose various products but also place their reviews on those products. Their choices and opinions are often beneficial to other customers who want to buy the product. In this paper, we propose a method to identify only the best recommenders and the best buyers to validate the authenticity of the opinions and they are denoted as the valuable customers. The number of customers being influenced by the recommender develops a link or connection, this leads to a chain of connectivity. This concept has been justified by applying graph theory notion. Since the online sites are dynamic in nature, hence to keep the record set updated with valuable customers' information decision tree learning technique has been implemented. The effectiveness of the method is tested by applying it to a data set obtained from Amazon.
Building a VR motion simulator.

CFD and used to conduct a quantitative user evaluation (n = 31) with both types of VR displays. The findings of this study reveal that VR immersion significantly improves the accuracy of simulated stent placement in complex arterial geometries, relative to traditional desktops with no immersion (p < 0.05). Higher accuracy is noted by the use of semi-immersive VR display, which offers higher display fidelity as compared to the fully immersive VR display (p < 0.05). Interestingly, CFD data mapped on to arterial geometries strongly influences the location of stent placement. This finding is demonstrated by the lack of significant accuracy deviation between the two immersive displays when CFD data is shown. This study provides compelling evidence that a CFD-based VR system rendered on semi-immersive displays can enable more accurate and efficient stent placement.
Web Development

I use my Mac for web development all day long, and Terminal is one of many applications I use all the time.
Dualism loop and string

the holographic space-time interval leads to the concept of dualism between loops and strings.