Reviewerly/exHarmony huggingface dataset:
Affective Computing for Enhancing Well-Being

chapter Share on Affective Computing for Enhancing Well-Being Authors: Leimin Tian Monash University Monash UniversitySearch about this author , Sharon Oviatt Monash University Monash UniversitySearch about this author , Michal Muszynski Carnegie Mellon University and University of Geneva Carnegie Mellon University and University of GenevaSearch about this author , Brent C. Chamberlain Utah State University Utah State UniversitySearch about this author , Jennifer Healey Adobe Research, San Jose Adobe Research, San JoseSearch about this author , Akane Sano Rice University Rice UniversitySearch about this author Authors Info & Claims Applied Affective ComputingJanuary 2022https://doi.org/10.1145/3502398.3502408Published:07 February 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months5Last 6 weeks1 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
The Relations between Internet using as a Learning Media and the Interest in Learning of Guidance and Counseling Study Program Students

&lt;span lang="EN-US"&gt;This quantitative descriptive research is an correlational, aiming to determine the relationship between the use of the internet as a learning medium and the learning interest of the 2018 students of the Guidance and Counseling study program at Sriwijaya University. The population in this study were 70 students and a sample of 64 students using proportional random sampling technique. The data collection instrument used a questionnaire using a Likert scale. The data analysis used was the product moment correlation. The results of the analysis obtained a correlation coefficient of 0.574 which indicates that the use of the internet as a learning medium with student interest in learning has a relationship with a rather low level of relationship at a significance level of 0.05. So it can be concluded that there is a relationship between the use of the internet as a learning medium and the interest in learning of students from the 2018 batch of the guidance and counseling study program FKIP Sriwijaya University.&lt;/span&gt;
Performance Evaluation of Infrared Image Enhancement Techniques

Infrared (IR) images are widely used in many fields such as medical imaging, object tracking, astronomy and military purposes for securing borders. Infrared images can be captured day or night based on the type of capturing device. The capturing devices use electromagnetic radiation with longer wavelengths. There are several types of IR radiation based on the range of wavelength and corresponding frequency. Due to noising and other artifacts, IR images are not clearly visible. In this paper, we present a complete up-to-date survey on IR imaging enhancement techniques. The survey includes IR radiation types and devices and existing IR datasets. The survey covers spatial enhancement techniques, frequency-domain based enhancement techniques and Deep learning-based techniques
Research on Testability Growth Modeling Method Based on Gompertz Function

Improving the testability level of equipment is of great significance for improving and perfecting the work of testability design. In order to improve the testability level and ensure the effective growth of equipment testability, a testability growth model of equipment based on Gompertz function is proposed, and the Virene algorithm is used to estimate the key parameters in the model, thereby establishing an effective and feasible predictive model. By solving the proposed model, the purpose of predicting the future testability level of equipment is achieved. Experiments show that the proposed model can reasonably predict the future testability level, which provides a strong theoretical basis for the testability design improvement of equipment.
Using OCR to automate document conversion to L<sup>A</sup>T<sub>E</sub>X

The process of transforming a physical document to a digital version leaves loose ends in several portions. There is a lack of solutions that offer end-to-end conversion of hard copies entailing images, graphs, tables, and other details into soft copies. To this end, we attempt to develop a computationally efficient algorithm to convert a document into its digital version through L<sup>A</sup>T<inf>E</inf>X representations of the hard copy. Our research efforts take the problem of using OCR techniques into account for converting an image of a typesetted document into L<sup>A</sup>T<inf>E</inf>X. This work serves as a proof of concept that equation layouts can be learned and individual character recognition is possible with not so sophisticated OCR techniques. The method we created to break the problem down step by step helped modularize and compartmentalize the tasks so that each can focus on the different types of issues that can occur at different levels of granularity.
Addressing Ethical Issues of Affective Computing

chapter Share on Addressing Ethical Issues of Affective Computing Authors: Leimin Tian Monash University Monash UniversitySearch about this author , Sharon Oviatt Monash University Monash UniversitySearch about this author , Michal Muszynski Carnegie Mellon University and University of Geneva Carnegie Mellon University and University of GenevaSearch about this author , Brent C. Chamberlain Utah State University Utah State UniversitySearch about this author , Jennifer Healey Adobe Research, San Jose Adobe Research, San JoseSearch about this author , Akane Sano Rice University Rice UniversitySearch about this author Authors Info & Claims Applied Affective ComputingJanuary 2022https://doi.org/10.1145/3502398.3502410Published:07 February 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months4Last 6 weeks2 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
Graphical Based Authentication Method Combined with City Block Distance for Electronic Payment System

User authentication is an essential component in nearly all electronic payment systems. Authentication provides the foundation for a user, legal access control, and user accountability. The most foremost used authentication methods are textual and alphanumeric passwords. However, the alphanumeric password suffers some drawbacks such as easy guessing and hard to remember the password, which can make it vulnerable to attacks such as social engineering and dictionary attacks thereby exposing sensitive information and thus compromising the security of a system. To overcome these challenges associated with an alphanumeric password, we proposed a graphical-based authentication method combined with City block distance to measure the similarity score between the image passwords at the points of registration and login using the KNN algorithm to compute the distance. In this paper, similarity measure was found as an important task for text matching, image processing, and retrieval of images from the database. To achieve optimal performance of the system and make it robust, an experiment was conducted during the login session using the city block distance and other different distance measures that include Euclidean, Cosine similarity, and Jaccard utilizing the KNN algorithm. The experimental results show that the proposed city block distance method has the fastest execution time of 0.0318 ms, minimal matching error of 1.55231, and an acceptable login success rate of 64%, compared to when the graphical-based password is combined with other similarity score distance measures. This paper concludes that the proposed method would be the most reliable authentication for e-payment systems.
Degree Theory and the Sorites Paradox

Degree-theoretical approaches to vagueness attempt to flesh out the idea that properties referred to by vague predicates come in degrees, and that sentences containing such predicates can be true to a degree in between absolute truth and absolute falsity. This many-valued semantics is wedded either to some fuzzy logic, or to a non-truth-functional logic, or even to classical logic. The first part of the chapter is devoted to surveying these different alternatives. Subsequently, we discuss the standard fuzzy approach (SFA) to the Sorites, based on infinite-valued Łukasiewicz logic. The mainstream objections to the SFA are then dispelled from a perspective that views classical logic as an ambiguous logic. Next, we address the status of the Tolerance principle in the SFA. We provide a semantics for vague predicates within Rational Pavelka Logic (RPL), contending that the conditional premisses in a Sorites are ambiguous between a reading as Łukasiewicz conditionals and a reading as 'tolerance conditionals'. In conclusion, we formalise in RPL a purely structural version of the paradox, where no logical constant is involved. We ascribe this paradox to an equivocation over consequence.
Foundations of Bayesian Learning

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Bibliography

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Analysis of Debates and Comments

Opinion documents come in many different forms. So far, we have implicitly assumed that individual documents are independent of each other or have no relationships. In this chapter, we move on to two forms of social media contexts that involve extensive interactions of their participants and are also full of expressions of sentiments and opinions: debates/discussions and comments. However, the key characteristic of the documents in such media forms is that they are not independent of each other, in contrast to stand-alone documents such as reviews and blog posts. The interactive exchanges and discussions among participants make these media forms much richer targets for analysis. Such interactions can be seen as relationships or links both among participants and among posts. Thus, we can not only perform sentiment analysis, as discussed in previous chapters, but also carry out other types of analyses that are characteristic of interactions – for example, grouping people into camps, discovering contentious issues of debates, mining agreement and disagreement expressions, discovering pairwise arguing nature, and so on. Because debates are exchanges of arguments and reasoning among participants who may be engaged in some kind of deliberation to achieve a common goal, it is interesting to study whether each participant in online debate forums gives reasoned arguments with justifiable claims via constructive debates, or whether a participant just exhibits dogmatism and egotistical clashes of ideologies. These tasks are important for many fields of social science, such as political science and communications. Central to these tasks are the sentiments of agreement and disagreement, which are instrumental to these analyses. These additional types of analyses are the focus of this chapter.
Webinar explores virtual donor engagement strategies

Nonprofit Business AdvisorVolume 2022, Issue 390 p. 9-9 Resources Webinar explores virtual donor engagement strategies First published: 09 February 2022 https://doi.org/10.1002/nba.31187Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2022, Issue390March 2022Pages 9-9 RelatedInformation
Linguistic Structure

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Models for Processes in Space or Time

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Maximum Likelihood Pedigree Reconstruction

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Introduction to Cosmochemistry

Relationship between cosmochemistry and geochemistry, historical beginnings of cosmochemistry, tools of cosmochemistry
Linear Time-Invariant Filters

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Revisiting Words

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Pelatihan E-Learning Moodle Berbasis Cloud Untuk Tutor/Guru Pusat Kegiatan Belajar Masyarakat (PKBM)

The Community Learning Activity Center (PKBM) is a non-formal education unit that provides educational services for people in need. Currently PKBM uses face-to-face learning methods in classrooms, so it is not optimal for learning citizens because interaction with tutors can only be done in the PKBM environment. On the other hand, e-learning technology can make it easier for PKBM tutors / teachers in implementing learning because learning can be done anytime and anywhere according to the abilities of the teaching participants. The main objective of this community service activity is to provide training in the use of cloud-based Moodle e-learning for PKBM tutors / teachers. The implementation methods consist of: (1) Needs Analysis; (2) CloudMoodle implementation; (3) Preparation of Training Materials; (4) Data Collection of Prospective Training Participants; (5) Creating PKBM Tutor / Teacher Accounts; and (6) Evaluation and Reporting. The training was attended by 31 participants from 10 PKBM institutions. The training material is delivered interactively with materials including: (i) a general explanation of the Moodle application; (ii) learning material management training; and (iii) Value management training. Based on the results of the evaluation, the training activities were well organized based on the feedback given by the participants in terms of three aspects: (i) the participants' understanding of the training material; (ii) providing training; and (iii) training resource persons.
Continuous Optimization

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Introduction

Chapter 1 provides a general introduction to the book, outlining the need for police investigators to engage in online identity assumption and justifying a linguistic contribution that can be made in terms of the variety of ways in which linguists and forensic linguists have approached questions of language and identity. We set out the context of online sexual abuse, in particular the abuse of children.
Image Compression using Different Vector Quantization Algorithms and Its Comparison

Image compression techniques are presented in this paper which can be used for storage and transmission of digital lossy images. It is mostly important in both multimedia and medical field to store huge database and data transfer. Medical images are used for diagnosis purposes. So, vector quantization is a novel method for lossy image compression that includes codebook design, encoding and decoding stages. Here, we have applied different lossy compression techniques like VQ-LBG (Vector quantization- Linde, Buzo and Gray algorithm), DWT-MSVQ (Discrete wavelet transform-Multistage Vector quantization), FCM (Fuzzy c-means clustering) and GIFP-FCM (Generalized improved fuzzy partitions-FCM) methods on different medical images to measure the qualities of compression. GIFP-FCM is an extension of classical FCM and IFP-FCM (Improved fuzzy partitions FCM) algorithm with a purpose to reward hard membership degree. The presentation is assessed based on the effectiveness of grouping output. In this method, a new objective function is reformulated and minimized so that there is a smooth transition from fuzzy to crisp mode. It is fast, easy to implement and has rapid convergence. Thus, the obtained results show that GIFP-FCM algorithm gives better PSNR performance, high CR (compression ratio), less MSE (Mean square error) and less distortion as compared to other above used methods indicating better image compression.
Introduction and Motivation

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Performance modelling of multi-tier cloud applications using Simpy

Todays cloud deployed applications are mostly multi-tiered. Usually, the first tier consists of an Application Service Providers' (ASPs) web servers, the second tier has application servers and the third tier contains database servers. Tiered architectures are often difficult to evaluate in terms of performance. Existing performance models are very effective in finding the mean performance measures. However, metrics such as response-time percentiles are of greater importance to the end-users since it is more desirable to reduce the variability of a system’s response time, rather than minimizing the mean response time. In this work, a multi-tier application is modeled as an open queuing network of 3-tiers and the response-time percentiles are estimated using discrete event simulation. Here, we assume that each tier is replicated into a number of copies and each copy runs on a separate Virtual Machine (VM). Although simulation models are computationally more expensive as compared to analytical models, they are much more general. The simulation model of this work can be used as decision support for ASPs in order to determine the optimal configuration of VMs for a given workload such that a required response-time percentile is within a given threshold. In this work, Simpy, a discrete event simulation framework, has been used. The results show that as the number of VMs are increased in a 3-tier open queueing network, the overall system performance (i.e. percentiles and mean response times) does not necessarily become better. The results further show that different system configurations containing the same number of VMs, yield different performance depending on the replication level in different tiers.
Fundamentals

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Minimax Lower Bounds

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Mining Intent

Before performing an action, we almost always have the intent to perform the action first. In many cases, we also talk about or write about our intents. Although the concept of intent has been investigated in philosophy and psychology, researchers in these fields are usually not concerned with the language used to state intent or how to infer intent from written language computationally, which is our objective in this chapter. Studying intent computationally is just beginning, and our understanding of the problem remains limited.
Statistics of Linear Stochastic Differential Equations

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Future Direction

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Statistical estimation of the ergodicity of the series of the average monthly runoff of the Desna River and its correlation characteristics

Summary The work’s aim is to study the average monthly water discharge of the Desna River as an ergodic periodically correlated random process. Due that, the methods of the theory of random functions are applied. The assessment of the statistical characteristics (periodic functions of mathematical expectation, dispersion and correlation functions) was carried out according to the data of hydrological observations on the Desna River (Chernigov) for 1920–2015. The formed time series with a discreteness step of one month consists of 1152 indicators of average monthly water flow rates. According to estimates of mathematical expectation and dispersion with a correlation period of twelve months, we obtained a model of ergodic random process. According to the correlation matrix of the average monthly water discharge, the ordinates of the function for each month of the year are obtained. It is concluded that in the water regime of the Desna River the highest runoff occurs in April-May - the passage of spring floods. For all other months - low water. Water flow variability is highest in March-May. In addition, for April and May, unlike the low-flow months, there is no relationship between the water runoff of these months and the other months of the year.