Reviewerly/exHarmony huggingface dataset:
Gray Wolf Optimization and Least Square Estimatation As A New Learning Algorithm For Interval Type-II ANFIS

Gray Wolfe Optimization (GWO) is one of the meta-heuristic method and it is a popular technique in Many engineering and economic applications. GWO and Least Square Estimatation (LSE) are used to optimize the antecedents and consequents parameters of interval type-2 ANFIS respectively. We are checking the new learning algorithm by using the interval type-2 ANFIS in prediction of Mackey-Glass time series and the results were very encouraging compared to other algorithms.
Pragmatic Analysis Of Lucy Virtual Learning

The aims of this research are: 1) To find out sometypes ofpragmatic deixis used by Lucy in virtual learning English; 2) To find out the dominant pragmatic of deixis used by lucy in virtual learning English. This research investigated how dopragmatic deixis used by Lucy virtual learning English on YouTube with the help of transcript by the video of Lucy in Virtual learning English which may help the researcher to find the type of deixis of Lucy. This research is descriptive research. Type of this research is qualitative design. This research used primary data that is transcript of 5 videos of Lucy in virtual learning English that analyzed in this research, the secondary data is taken from journal that related with this research. The result of this research are: researcher found totally From the number of 45 extract, the researcher found 484 were person deixis, spatial deixes were 74 and temporal were 14 in, in the table those above describe that there are several data in section in frequency and percentage of deixis. The most dominant of Lucy used is 1st singular that is 205 time.
LibGuides: LSC 596- School Library Media Seminar : AASL Standards and Evidence

This professional portfolio highlights my work for the culminating seminar and practicum for k-12 Library Media certification in Rhode Island.
Deconstructing 1-Local Expanders.

A 1-local 2d-regular \(2^n\)-vertex graph is represented by d bijections over \(\{0,1\}^n\) such that each bit in the output of each bijection is a function of a single bit in the input. An explicit construction of 1-local expanders was presented by Viola and Wigderson (ECCC, TR16-129, 2016), and the goal of the current work is to de-construct it; that is, make its underlying ideas more transparent.
Hybrid clay-based materials for the treatment of pesticide-contaminated water

The International Conference on Advanced Materials, Nanosciences and Applications (ICAMANA 2019), Oujda (Morocco), 10th-13th december 2019
Structured Paragraph Embeddings of Financial Earnings Calls

Financial earnings calls contain rich information about the quarterly performance and future projections of public companies. Such information is highly relevant to developing trading strategies and understanding economic trends. However, due to the unstructured nature of call transcripts important signals can be difficult to extract. In this preliminary work, we propose a novel paragraph embedding method that leverages the structure inherent in the Q&A format of earnings calls. We show that the proposed method improves classification performance over more general methods and provides a useful measure of similarity between paragraphs.
A VR/AR Interface Design based on Unaligned Hand Position and Gaze Direction

In conventional natural user interface schemes that use hand gestures without holding a sensor device, users usually need to hold up their hands to align them to the gaze direction where the (virtual) object is displayed. Also, users need to move their hands in the designated space for quite a long time, which may cause fatigue and decrease the performance of gesture recognition. Hence, we propose a new interface scheme that alleviates this problem by freeing the hand position from the gaze direction such that users can even put their hands on a desk. For this, we need to have more robust hand detection and an effective gesture recognition scheme, because the hands can be placed anywhere in the space, and hand motion is not calibrated to the virtual space. For robust hand detection, we propose an algorithm based on a new hand pose model, which is implemented using a wide-angle camera. For efficient gaze calibration and tracking, we use a three-dimensional eyeball model from our previous work. Because the gaze and hand motion/positions are not aligned, we designed a finite state machine for robust gesture flow. Based on this scheme, we defined six interactive interfaces for the target object: click, double click, drag and drop, zoom in, zoom out, and return home. More complicated interactions can be implemented based on these six basic interfaces. The proposed interface scheme is implemented on a handheld mobile device, which is shown to work robustly for a wide range of hand positions.
A general approach to progressive intelligence

In biological learning, data are used to improve performance not only on the current task, but also on previously encountered and as yet unencountered tasks. In contrast, classical machine learning starts from a blank slate, or tabula rasa, using data only for the single task at hand. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called catastrophic forgetting). Many recent approaches for continual or lifelong learning have attempted to maintain performance given new tasks. But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data. We propose omnidirectional transfer learning algorithms, which includes two special cases of interest: decision forests and deep networks. Our key insight is the development of the omni-voter layer, which ensembles representations learned independently on all tasks to jointly decide how to proceed on any given new data point, thereby improving performance on both past and future tasks. Our algorithms demonstrate omnidirectional transfer in a variety of simulated and real data scenarios, including tabular data, image data, spoken data, and adversarial tasks. Moreover, they do so with quasilinear space and time complexity.
LibGuides: Publishing Responsibly: Introduction

How to publish your research papers responsibly, including information on Open Access (OA), reseearch assessment, etc.
Robust and Scalable Conversational AI

Even conversational systems have attracted a lot of attention recently, the current systems sometimes fail due to the errors from different components. This keynote talk presents following research directions for improving robustness and scalability of conversational systems: 1) we first focus on learning language embeddings specifically for spoken scenarios such as more noisy inputs during inference, and 2) secondly we extend the dialogue systems to access not only structured but unstructured knowledge and propose a novel learning framework for natural language understanding and generation on top of duality towards better scalability. The enhanced robustness and scalability shows the great potential of guiding future research directions.
Delgado Community College Libraries: Cloud Computing: Home

This guide presents resources on the topic of cloud computing available from Delgado Community College Libraries and some websites.
Leveraging CDA 230 to Counter Online Extremism

Current events make it plain that social media platforms have become vectors for the global spread of extremism, including the most virulent forms of racial and religious hatred. As offline violence with demonstrable links to online extremism escalates, regulators have made it clear that they expect the world’s largest social media platforms to more actively police harmful online speech, including that of terrorist organizations and organized hate groups. Among US tech companies, Facebook has been the most receptive to the idea of increased government regulation. In an unusual op-ed in The Washington Post, Mark Zuckerberg explicitly asked Congress to tell him what to do about hate speech and terrorist propaganda on his services. Such regulation would be a significant departure from past US policy concerning online speech. Since the early days of the internet, US-based online services have benefited from a policy that gives them broad discretion to set and enforce their own guidelines defining acceptable (and unacceptable) user speech. That policy, codified in section 230 of the Communications Decency Act (CDA), was adopted in large part to help foster the internet’s growth as a diverse forum for civic (and civil) discourse.In recent years, section 230 has come under fire from all directions. Some critics believe its grant of broad discretion allows social media platforms to be too permissive about their users’ speech. Others believe it lets them be too restrictive. At a moment of great uncertainty for the future of section 230, this article explains the positive role it can play in platforms’ efforts to take greater responsibility for regulating hate speech and extremist content. I argue that the scope of immunity in Section 230 needn’t be narrowed, but the statute could be productively amended to better safeguard free speech as the world’s largest social media platforms turn to automated tools to comply with new, speech-restrictive European regulations.
Novel Texture Feature for Content Based Image Retrieval

Content-Based Image Retrieval (CBIR) is an image retrieval technique that fetches relevant pictures from a huge image database based on the content feature of a given query image. It has many applications such as satellite image indexing, development of recommendation system in many applications and medical image processing. Feature extraction, indexing and similar image retrieval are few major steps in the development of CBIR system for any applications. Extraction of relevant feature that effectively represents the image is a challenging task. Therefore, the performance of a CBIR system completely depends upon the ability of the feature sets used to represent the image contents. In addition the dimensionality of the feature set used also affect the retrieval performance in terms of response time. The objective of this work is to analyze the retrieval performance of few recently proposed texture features and propose a novel feature extraction technique that has better retrieval efficiency while addressing the issues of dimensionality. In this paper we implemented and analyze the retrieval performance of three recently proposed texture features and also proposed a novel method for texture feature extraction for the design of CBIR system. The evaluation is performed using five popular publicly available benchmark data sets with varied complexities. The data sets include real-life images, texture images, facial images, etc. Extensive experiments are performed to tune the parameter that affects the retrieval performance for each texture feature considered for evaluation. The performance of the proposed method is compared with other methods in terms of average precession and average recall percentage. The performance of the proposed method is found to be best among all irrespective of the dataset used.
A Análise Cultural e o Estudo de Tendências na Comunicação e Gestão de Marcas: Estudo de caso publicitário em contexto semiótico

The present work presents connections in conceptual and methodological terms in the context of cultural analysis, considering approaches and concepts of textual analysis, trend studies and cultural branding. The case study chosen to test these links is the promotional video of the advertising campaign titled Gucci and Beyond for the  Gucci  Autumn/Winter 2017-18 collection. This was clearly inspired by the science fiction series  Star Trek , among other elements within fantasy and science fiction. The strong symbolic construction in these narratives make this a particularly interesting case to study the connections between culture and brands. The objective is to underline a methodological articulation capable of not only identifying the web of symbolic narratives present in brand communication, but also the cultural elements that constitute them and the sociocultural trends that are behind them.
Applicability of augmented reality in education

We live in a world full of fast evolving technology. One of the newer types of technology is augmented reality. The basic function of augmented reality is its ability to link reality with virtual elements and present them in a reality environment. The combination needs to offer a certain level of interactivity in real time. Lastly, added virtual objects need to seamlessly blend with a real environment in 3-D. Diploma thesis provides an introduction of augmented reality, its possibilities for education and its potencial problems. We explored the usability of augmented reality for multimedia learning, using Mayer's principles for guidelines. We also wrote a preparation for one of the learning topics of computer science class where students use augmented reality for the purpose of multimedia learning.
Malicious SHA-3

In this paper, we investigate Keccak — the cryptographic hash function adopted as the SHA-3 standard. We propose a malicious variant of the function, where new round constants are introduced. We show that for such a variant, collision and preimage attacks are possible. We also identify a class of w eak keys for malicious Keccak working in the MAC mode. Ideas presented in the paper were verified by implementing the attacks on the function with the 128-bit hash. Additionally, we show how the idea of malicious Keccak could be used in differential fault analysis against real Keccak working in the keyed mode such as the authenticated encryption mode.
DeepEncyclolink: A Cross-Encyclopedia, Cross-language Article-Linking System Based on Deep Learning

DeepEncyclolink is a web-based system for linking pairs of corresponding articles from different encyclopedias in different languages. The core technology of DeepEncyclolink is based on paragraph embeddings calculated by a long-short-term-memory network with attention. Compared to our previous feature-based machine learning model, DeepEncyclolink has made great strides in coverage and accuracy. DeepEncyclolink offers an easy-to-use user interface and is supported by a powerful service backend for retrieving and selecting equivalent articles between English Wikipedia and Chinese Baidu Encyclopedia. DeepEncyclolink breaks the barriers of different encyclopedias and different languages, making it easy for users to access knowledge written in various languages.
The Study On The Influence Of Employee Involvement On Employee Performance

Nowadays organizations operate in a highly competitive environment and needs a different approach to manage employees. Various studies have shown that employee involvement is a critical component of human resources management. The level of employee involvement in their job is a measure of the success of individual performance on their jobs and overall organization performance. This research investigated the correlation between employee involvement and job performance to the organization. The objective of the study was to determine the effect of employee involvement on job performance in the divisional secretariat of kaluwanchikudy, Batticaloa, Sri Lanka. The study adopted a descriptive survey research design. The population for the study included all the employees of one divisional secretariat who were 194 in total. Simple random sampling was used to select 100 respondents from the organization. Data was collected using structured questionnaires and analyzed using both descriptive and inferential statistics. The study findings revealed that employee involvement and employee performance were at higher level. Hence there is a strong positive relationship between Employee involvement variables and Employee Performance.  Thus it can be concluded that enabling employees participate and be involved in matters that affect their jobs increases job performance.
LibGuides: Guide to Staying Home: Entertainment

This guide includes resources provided or recommended by the Kraemer Family Library for making the most of UCCS's remote learning period.
Statistical Pattern Recognition

Over the past two decades, statistical methods have played a prominent role in the development of pattern recognition techniques. The statistical decision theory and related fields have been a forum where significant theoretical advances and innovations have taken place. This chapter presents a brief and elementary account of some important topics in the statistical theory of pattern recognition. Statistical methods provide the proper framework for studying pattern recognition problems when the pattern-generating mechanism can be faithfully represented by a statistical model, and the goal of the recognition is to decide whether or not a given pattern belongs to some prespecified class of patterns. People merely have some vague idea about the underlying statistical distributions, together with a number of design samples. It is therefore necessary to give the pattern-recognition system, the capability of learning the unknown information from the data available.
LibGuides: General Information Management: Specialized Databases

This guide is for the NCU specialization General Information Management. It contains resources available in the NCU Library.
ORGANIZATION OF LEISURE TIME FOR FUTURE OFFICERS: THE PSYCHOLOGICAL PROGRAM

The organized leisure is an integral part of the moral and psychological support for the preparation and use of the Armed Forces. The purpose of this work was to streamline the program promoting cadets’ leisure and leisure activities, by taking into account their psychological characteristics. Special attention was paid to cadets’ recreational activities, namely their developmental exercises during free time. The study object: training of future officers. The study theme: psychological features of the organized leisure, and used philosophical and psychological methods for it. The study sample: cadets of the Military Academy (Odessa), aged of 17-25 years. The total number was 120 people. Used methods: Rokeach Value Survey, observation, expert evaluation, Kelly’s Original Role Construct Repertory Test, Cattell’s Personality Factor Questionnaire. The method of factor analysis: principal component with varimax rotation and Kaiser normalization. The obtained data were interpreted and five factors were identified. The first factor&nbsp;was the most pronounced; it was called “mind and conscience when using free time”. The criteria were given for different levels of leisure based on the following indicators:&nbsp; initiative, focus on creativity and self-improvement, support of others, volitional component and satisfaction. The differences in cadets’ semantic constructs depending on the organization levels of their free time were examined. Cadets’ free time must be used to built-up resources necessary for resistance to crisis phenomena and stress factors. The obtained data proved effectiveness of the proposed program. Conclusion: The free time and leisure activities should be organised on the basis of a special program that takes account an individual’s basic professional orientation and his/her personal beliefs. &nbsp
End-to-End Machine Learning Frameworks for Medicine: Data Imputation, Model Interpretation and Synthetic Data Generation

Author(s): Yoon, Jinsung | Advisor(s): van der Schaar, Mihaela | Abstract: Tremendous successes in machine learning have been achieved in a variety of applications such as image classification and language translation via supervised learning frameworks. Recently, with the rapid increase of electronic health records (EHR), machine learning researchers got immense opportunities to adopt the successful supervised learning frameworks to diverse clinical applications. To properly employ machine learning frameworks for medicine, we need to handle the special properties of the EHR and clinical applications: (1) extensive missing data, (2) model interpretation, (3) privacy of the data. This dissertation addresses those specialties to construct end-to-end machine learning frameworks for clinical decision support. We focus on the following three problems: (1) how to deal with incomplete data (data imputation), (2) how to explain the decisions of the trained model (model interpretation), (3) how to generate synthetic data for better sharing private clinical data (synthetic data generation). To appropriately handle those problems, we propose novel machine learning algorithms for both static and longitudinal settings. For data imputation, we propose modified Generative Adversarial Networks and Recurrent Neural Networks to accurately impute the missing values and return the complete data for applying state-of-the-art supervised learning models. For model interpretation, we utilize the actor-critic framework to estimate feature importance of the trained model's decision in an instance level. We expand this algorithm to active sensing framework that recommends which observations should we measure and when. For synthetic data generation, we extend well-known Generative Adversarial Network frameworks from static setting to longitudinal setting, and propose a novel differentially private synthetic data generation framework.To demonstrate the utilities of the proposed models, we evaluate those models on various real-world medical datasets including cohorts in the intensive care units, wards, and primary care hospitals. We show that the proposed algorithms consistently outperform state-of-the-art for handling missing data, understanding the trained model, and generating private synthetic data that are critical for building end-to-end machine learning frameworks for medicine.
LibGuides: Wegner Library Interlibrary Loan-ILLiad: Tracking Requests

A how-to guide for creating, navigating and placing a request with Wegner's Interlibrary Loan System, ILLiad.
Research on Intelligent Identification Method of Power Equipment Based on Deep Learning

After investigation and analysis, this paper studies deep learning to solve the automatic analysis and identification of massive unstructured media data in the power system. In terms of feature extraction, based on the Alexnet model, two independent CNN models are proposed to extract the characteristics of power equipment. In terms of recognition algorithm, the advantages of traditional machine learning methods are combined with the advantages of random forests. Intelligent identification algorithm for power equipment combined with CNN.