Reviewerly/exHarmony huggingface dataset:
An Attention Arousal Space for Mapping Twitter Data

Not every trending tweet commands your attention! Overflow of social media data will soon require mechanisms to identify the severity of a situation to evaluate if a tweet must be given ample attention. News about bomb blasts, flood, epidemic outbreak affects several sectors and impacts the local economy. Such news is covered by media and government sources which forms meta-information with tweets. Most of the algorithms, both traditional and deep learning can decipher if the opinion is positive, negative, or neutral, they cannot allocate attention to the opinion. Compared to the binary classification of positive and negative sentiments, this article proposes a glove-text-CNN algorithm and attaches meta-information to predict the attention accuracy of sentence-level data. Correlation is being established among the various parameters of a trending hashtag and the most important parameter is being reported. The primary contribution is a modified neural algorithm to define an Attention Arousal Space that is designed to capture five major insights of sentence-level documents by attaching meta-information, to include media score, time decay, retweet, favorite scores and government source to modify original algorithm. The sentiment classification algorithm used for this research is Text-CNN with glove word embeddings, which was trained on 2,000,000 sentiment marked tweets from SemEval datasets. Sentiment classification training accuracy was 98.6% and validation set accuracy was 87.13%. Our experiments show accuracy over Hierarchical Attention Networks (HAN) which proves the effectiveness of this modified algorithm with meta-information for capturing attention.
DIAGNOSIS OF BREAST CANCER USING SEGMENTATION AND CLASSIFICATION TECHNIQUES

Nowadays, Breast cancer or Breast Carcinoma is a harmful disease among women. Also, it is the most curable cancer if it is detected early. There are many methods to improve the accuracy of the diagnosis of breast cancer like Machine Learning. The main aim of this paper is to propose ways to automate the mammogram image processing using different pre-processing and segmentation techniques and then classified using Support Vector Machine (SVM) algorithm. The mini-MIAS dataset is used for processing. The techniques for the segmentation of mammograms are K-means(KM) and Fuzzy C-means Clustering (FCM). A comparison between these two techniques will lead to finding the best algorithm for segmentation. SVM used to classify tumors into benign(non-cancerous) and malignant(cancerous) tumors. The algorithms used to detect cancer will help to increase accuracy. Using different segmentation techniques, the tumor part gets extracted and then identifying the tumor types using the SVM classifier. These algorithms are used to improve the accuracy in predicting breast cancer.
Affective Image Captioning: Extraction and Semantic Arrangement of Image Information with Deep Neural Networks

In recent years, the Internet has become a major source of visual information exchange. Popular social platforms have reported an average of 80 million photo uploads a day. These images, are often accompanied with a user provided text one-liner, called an image caption. Deep Learning techniques have made significant advances towards automatic generation of factual image captions. However, captions generated by humans are much more than mere factual image descriptions. This work takes a step towards enhancing a machine's ability to generate image captions with human-like properties. We name this field as Affective Image Captioning, to differentiate it from the other areas of research focused on generating factual descriptions.To deepen our understanding of human generated captions, we first perform a large-scale Crowd-Sourcing study on a subset of Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M). Three thousand random image-caption pairs were evaluated by native English speakers w.r.t different dimensions like focus, intent, emotion, meaning, and visibility. Our findings indicate three important underlying properties of human captions: subjectivity, sentiment, and variability. Based on these results, we develop Deep Learning models to address each of these dimensions. To address the subjectivity dimension, we propose the Focus-Aspect-Value (FAV) model (along with a new task of aspect-detection) to structure the process of capturing subjectivity. We also introduce a novel dataset, aspects-DB, following this way of modeling. To implement the model, we propose a novel architecture called Tensor Fusion. Our experiments show that Tensor Fusion outperforms the state-of-the-art cross residual networks (XResNet) in aspect-detection.Towards the sentiment dimension, we propose two models:Concept & Syntax Transition Network (CAST) and Show & Tell with Emotions (STEM). The CAST model uses a graphical structure to generate sentiment. The STEM model uses a neural network to inject adjectives into a neutral caption. Achieving a high score of 93% with human evaluation, these models were selected as the top-3 at the ACMMM Grand Challenge 2016.To address the last dimension, variability, we take a generative approach called Generative Adversarial Networks (GAN) along with multimodal fusion. Our modified GAN, with two discriminators, is trained using Reinforcement Learning. We also show that it is possible to control the properties of the generated caption-variations with an external signal. Using sentiment as the external signal, we show that we can easily outperform state-of-the-art sentiment caption models.
INTERNET AND ENGLISH LANGUAGE LEARNING IN THE 4.0 INDUSTRIAL ERA

Now we are in the digital era, or the other term, it is  mostly people around the world including in our country use electronic tool as media four our daily life to cover all our daily needs. It is popular most people named it, the 4.0 Industrial era, it is symbolized by using social media, (social-network) as a  main instrument  of communication both spoken and written. The communication is able to implement  in the society because of the internet connection. The internet connection is able to make the communication conducted to cover any human needs, for examples, the social relations, economic needs, cultures shares, politics discussion, knowledge development, education, and other human life business. This paper particularly investigates the use of internet and its relation to learning English. The writer believes that internet has significant effect for learning English through the media socials that mostly modern society used it in their life particularly the educated people that they are not able separated from the internet exist. K e y word: Internet, English, Language Learning, Industrial Era.
Eye blink to voice format communication for paralyzed patients

The wide growth of technology in the medication field reduces the difficulties of patients to a large extent. Motor Neuron Disease is one such major classes of physical disabilities resulting in disfunction.MND patients are unable to figure like walk and communicate caused by weakness of muscles. The patient has management solely upon his Eye movement. This technique contains strategies like face detection, eye detection, eye pursuit, conversion of blink to voice, Video-Oculogragphy methodology is employed to create communication between patient and caretaker. The image process module incorporates a digital camera and therefore the eye movement-image is captured with OpenCv to get the coordinate of the eyeball. The system permits the patients to speak with caretaker victimization blink pattern converted into voice format The sensible resolution for image process is obtained be Python programming with Open CV
LibGuides: Professional Development for Organizational Effectiveness at Foley Library: Working From Home

This guide was developed by the Organizational Effectiveness Committee to provide ongoing access to professional development opportunities for the Foley Library's staff and faculty members. Resource for distance work.
High-Dimensional Unsupervised Active Learning Method

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the data points as one-dimensional ink drop patterns, in order to summarize the effects of all data points, and then applies a threshold on the resulting vectors. It is based on an ensemble clustering method which performs one-dimensional density partitioning to produce ensemble of clustering solutions. Then, it assigns a unique prime number to the data points that exist in each partition as their labels. Consequently, a combination is performed by multiplying the labels of every data point in order to produce the absolute labels. The data points with identical absolute labels are fallen into the same cluster. The hierarchical property of the algorithm is intended to cluster complex data by zooming in each already formed cluster to find further sub-clusters. The algorithm is verified using several synthetic and real-world datasets. The results show that the proposed method has a promising performance, compared to some well-known high-dimensional data clustering algorithms.
LibGuides: Reference management software: Further help

This guide introduces reference management software and the options available to users of UCL Library Services.
Application Optimisation: Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications

Optimisation of (the configuration and deployment of) distributed cloud applications is a complex problem that requires understanding factors such as infrastructure and application topologies, workload arrival and propagation patterns, and the predictability and variations of user behaviour. This chapter outlines the RECAP approach to application optimisation and presents its framework for joint modelling of applications, workloads, and the propagation of workloads in applications and networks. The interaction of the models and algorithms developed is described and presented along with the tools that build on them. Contributions in modelling, characterisation, and autoscaling of applications, as well as prediction and generation of workloads, are presented and discussed in the context of optimisation of distributed cloud applications operating in complex heterogeneous resource environments.
Viewing the data

Chapter 9 sees us return to Jupyter to use its features for data visualization and easy user interaction. We will look at how to use our asynchronous functions with widgets in Jupyter notebooks as well as advanced use of iterators and ways of implementing complex data types.
Library Research Guides: Data Visualization: Additional Resources

This guide is meant to be a basic introduction to data visualization. Anyone is free to use it!
MobilePose: Real-Time 3D Hand Pose Estimation from a Single RGB Image

Estimating 3D hand poses from RGB images is a challenging task. In this work we construct efficient neural networks to regress sparse 3D skeletons consisting of 21 keypoints in the hand. Additionally heatmaps are regressed to locate the keypoints in 2D. The networks created can be divided into three parts: feature extraction, heatmap regression and 3D pose regression. To obtain the 3D coordinates relative to the camera we introduce a method based on the best projection given the predictions. Main focus has been investigating network structures proven to be efficient in other computer vision task: EfficientNet, EfficientDet and MobileNetV2. A weighted bi-directional feature pyramid network, BiFPN, inspired by EfficientDet was added to MobileNetV2. This resulted in a new proposed network structure, MobilePose. The size of a network is affected by the input image resolution. Decreasing the resolution resulted in lower inference time. Images with the size 112 × 112 was used to achieve real-time performance. However the best accuracy was obtained with 224 × 224 images, the highest resolution tested. EfficientDet and MobilePose performed best and similar in terms of accuracy on the FreiHAND dataset. Comparing inference time on a Samsung S10 mobile device MobilePose is preferred. MobilePose was improved by adding complexity to the network. This resulted in achieving the highest accuracy in total with an average keypoint error of 1.4 cm, assuming the depth of a root keypoint is know, and 5.0 cm with calculated root depth. (Less)
Literature Review Of Authentication Layer For Public Cloud Computing: A Meta-Analysis

Cloud computing is a rapidly growing technology due to its highly flexible uses and applications. It also has other features such as simplicity, quick data access and reduced data storage costs. Consequently, it has been widely used by many organizations. This widespread use of cloud computing among organizations causes many security issues. Moreover, cloud computing layers are likely to be jeopardized by many security risks such as privileged user access, data location, data segregation, and data recovery. This paper aims to prepare an ample debate of a literature review-based studies that provided important insights to researchers in the scope of security cloud computing. The researcher applied a relevant set of keywords. These keywords are limited to the title, abstract and keywords search archives published between 2010 and June 2018. The database search returned a total of 308 publications. In addition, we conducted backward-forward searches from the reference lists of relevant, quality previous works on the security framework in public cloud computing studies. Then, the researcher filtered the publications to only full text access articles that were written in English only. Finally, this study obtained a many publication. The findings of this paper address many important points such as in this study is recommended to apply behavior recognition with password for improving authentication layer performance in cloud computing. This study finds most of current studies neglected the present of human factor in password-based authentication, and learnability in password-based authentication is highly weak. Despite this, very few studies have adopted the behavior recognition with password in public cloud.
A Correlation Thresholding Algorithm for Learning Factor Analysis Models

Author(s): Kim, Dale | Advisor(s): Zhou, Qing | Abstract: We consider the problem of learning the structure of the factor analysis model. The traditional method of Exploratory Factor Analysis (EFA), despite its widespread application, is often criticized for its ad-hoc use of rotation criteria for learning solutions. Additionally, more recently developed penalized EFA methods partially address these issues, but remain computationally intense. We propose a fast correlation thresholding algorithm, that is theoretically motivated by graph theory, to simultaneously learn the structure of a factor analysis model for an unknown number of factors. We derive the conditions for structural identifiability and parameter uniqueness, as well as show asymptotic consistency for our algorithm. Finally, we present a simulation study and real data example to test and demonstrate its performance.
From Dew Over Cloud Towards the Rainbow

This article is dedicated to the Philosophy of Computing, where Computing is taken in its wide sense of “constructed/programmed machine action”. As in modern times computing equipment ubiquitously penetrates all aspects of human and natural environments, suddenly Computer Science has to deal with, and consequently be responsible for, an almost inexhaustible area of natural and human life—individual, social, political and economic subsistence, and cultivation (adaptation/change) of natural environments. Therefore, there are three main fields it has to deal with: the Machine, the Human and the Nature. So there are also three major aspects from which the development of the Future Ecosystem, presently extremely influenced by Computer Science, has to be seen and done: the Technological, the Humanistic and the Naturalistic. As archetypal symbols, colours are used throughout our history. A symbolic “division” of a future Nature–Human–Machine Ecosystem, or of the major symbolic areas of Computer Science, is given through the vision of a Rainbow: Infrared—Energy, Red—Hardware, Orange—Creativity, Yellow—Appropriateness, Green—Nature, Blue—Communication, Indigo—Cooperation, Violet—Interference, Ultraviolet—Visions.
현실 세계에서의 로봇 파지 작업을 위한 정책/가치 심층 강화학습 플랫폼 개발

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.
SOCIAL MEDIA AND YOUTH: CHALLENGES AHEAD

India is passing through a transitional period in its history, from a feudal agricultural society into a modern industrial society. The 21st Century is witnessing constant changes in the global market. Media too is undergoing constant changes under the effects of globalisation. The effects of globalisation are bringing rapid developments in technology and communication through Social Media which plays a vital role in changing the mindset and lifestyle of the people. Although all levels of generation have come to embrace the changes, social network has brought about, teenagers and young adults are also victims and are the most fanatic users of these sites. Hence, an attempt is made in this research to investigate the influence of social media on youth.  As a case study, a sample survey was conducted through a structured questionnaire among the college students. Analytical methodology was adopted to examine the impact of social media among women students.
Why the public is torn over the contact-tracing app and how the government can maximize uptake

Drawing on a qualitative study consisting of five focus groups, Simon Williams, Christopher J Armitage, Tova Tampe and Kimberly Dienes find that people are currently torn over whether or not they will use the contract-tracing app when it is available. They discuss the main concerns that emerged from the research and offer some key recommendations for ensuring that there will be sufficient uptake.
THE INFLUENCE STUDENT ATTENDANCE MANAGEMENT SYSTEM ON ACADEMIC PERFORMANCE

Student Attendance Management System is designed to manage the student’s attendance record. This project is about to study the influence of student attendance management system in order to verify their attendance on. The foremost reason this system is being proposed is to overcome the current manual attendance process that is happening in all Universiti. The cutting-edge technology world has led to the idea for transforming the manual attendance by provide faster, accurate, and efficient system. With attendance system, it can eliminate some problems such as buddy signing, loss of attendance sheet, and control student skip class rate. That is how Student Attendance Management System is being developed. The purpose of constructing this system is to increase the efficiency and performance of the attendance process besides minimizing the overall time, work and paper used. The target user that will be using this system is student and lecturer. The major functions that Student Attendance Management System performing is add and manipulate attendance record, automated calculation on number of absent and percentage present of the student based on subject and flexibility to generate complete attendance documents (attendance list, attendance report, warning letter) any time in need.
Career Maximization, Employee Engagement and Certain Cultural Variables as Antecedents of Employee Creativity: A Study using Structural Equation Modelling

Employee creativity is one of the main cognitive factors of organizational performance;which could lead success, and sustainability of the firm. There are many antecedents of creativity.  The present study was conducted to identify the impact of career maximization, employee engagement and the cultural dimensions of collectivism and uncertainty avoidance on employee creativity of Saudi employees.  Data collected from 389 employees of varying demographics was analyzed using Structural Equation Modelling.  The results showed that uncertainty avoidance was against the hypothesized relationship between employee engagement and creativity.  It was found that career maximization is significantly related to employee engagement.  Collectivism was found to be related to employee engagement and creativity.  Similarly, employee engagement was found to significantly related to creativity.  The study assumes significance, as Saudi Arabia has unique culture, and no earlier studies have been conducted to examine the relationship between the variables identified in the present study.  It is expected that the findings of the study will motivate further researches in this interesting area.