Reviewerly/exHarmony huggingface dataset:
EMMA: Danish Natural-Language Processing of Emotion in Text: The new State-of-the-Art in Danish Sentiment Analysis and a Multidimensional Emotional Sentiment Validation Dataset

Sentiment analysis (SA) is the research and development field of computationally analysing emotion in text. One usage example of SA could be to track the sentiment of a company’s mentions on Twitter or to analyse a book’s positivity level. In this paper, we attempt to add to this work in two ways. First, we further develop the current tool Sentida (Lauridsen et al., 2019), which was originally developed to score valence in text. Valence is the amount of positivity in a text, e.g. a review. Our new version has a higher awareness of punctuation and syntax compared to the earlier version and shows significant improvement in classifying valence compared to the previous version in three different validation datasets (p < 0.01). Second, we develop a test dataset which future developers of SA can use called Emma (Emotional Multidimensional Analysis). In Emma, we supplement the dimension valence with a further three emotional dimensions: Intensity, dominance, and utility in a dataset of sentences scored by human coders on these four dimensions. The emotional dimensions are based on cognitive psychology work throughout the last 65 years.With Emma, we present both a more reliable validation dataset and the possibility of further improving the Danish SA field by using the dataset to train a neural network with machine learning for analysing more complex emotions in text. The current standard is the 1-dimensional classification of positivity in text, but with this approach, we allow for a classification in the four dimensions of the Emma dataset that reveals much more complex emotions in texts. To allow others to work with Sentida and Emma, we help update the currently available Sentida optimized for Python and publish Emma on Github.
TEACHING WRITING THROUGH THINK, TALK, WRITE (TTW) TECHNIQUES FORJUNIORHIGH SCHOOL LEVEL

Think-Talk-Write is one of learning strategieswhich purposes to improve students’ understanding ability.This strategy can minimize the students’ difficulties in writing and help the teacher in teaching writing. In other words, Think-Talk-Write can help students construct their own knowledge. So that, the students understandtheconcept better. Students are also able to communicate or discuss their thought with their friends. So, the students can help each other and exchange their ideas. This strategy can help students to understand the material being taught. It is possible thing, if they are already familiar with the material obtained, they will be easy to apply their idea in writing or in this case, students are able to make narrative text. Thus, this researchwas done to see whether Think-Talk-Write techniques gives contribution to improve the students’ motivation in writing narrative text at Junior High Schoollevel. It was found that teaching process ran well. Students were active, enthusiastic and interested in writing. The result of research showed that Think-Talk-Write techniques can improve students’ achievement in writing narrative text.
Occlusion method to obtain saliency maps for CNN

This Bachelor project will study convolutional neural networks created for image classification. Furthermore, it will specifically use an explanatory model for how the network decided a certain classification output. This is to increase the interpretability of the network. However, the completeness of the explanatory model needs to be high for it to be useful. A saliency map of how valuable each image pixel is for the classification will be created, by occluding parts of the image. The MNIST dataset was used, which contains handwritten digits. The main points of research were to study ways to occlude or filter parts of the image. Among the researched topics were the size of the filter, the number of filters and how the filtered pixels should be alternated. The occlusion method to obtain saliency maps was compared with rivalling methods, such as deepLIFT. The conclusion was that the large amount of computational power needed limits the use of occlusion based methods, but the high completeness makes it useful for niche purposes. (Less)
NRGs and the Nature of Action

Good presents an overview of Lessac’s body NRGs (pronounced “energies”) of potency, buoyancy, and radiancy and demonstrates ways to apply them to pursuing an objective. Her exercise, “The Lessac Tactic Circle” incorporates Lessac’s NRGs to inform and enhance a variety of tactics, to galvanize the interactive connection between scene partners, and to reactivate the actor. Instructors will discover specific tools to guide actors to play along the spectrum of the specific physical and vocal behavior possibilities that live within each tactic.
Evaluation of information protection against leakage through the compromising electromagnetic emanations during data exchange via USB interface

The paper deals with the problem of estimating the information security of data exchange through the USB interface with regard to compromising electromagnetic emanations. There is proposed a specialized procedure for detecting and measuring the intensity of informative components of compromising electromagnetic emanations. It is based on the formation of the specialized in-formation packets in the data exchange channel, their transmission being accompanied by the appearance of radio emissions with a linear spectrum. The concentration of power generated by compromising electromagnetic emanations in a set of narrow-band radiations provided by such testing facilitates their detection against the background of other components of compromising electromagnetic emanations having the other sources of origin. Recommendations are given on the choice of testing length in order to maximize the probability of capturing signal samples.
Special Section on the 2019 Symposium on Search-Based Software Engineering

Context: Cross-project defect prediction applies to the scenarios that the target projects are new projects. Most of the previous studies tried to utilize the training data from other projects (i.e., the source projects). However, metrics used by practitioners to measure the extracted program modules from different projects may not be the same, and performing heterogeneous defect prediction (HDP) is challenging.Objective: Researchers have proposed many novel HDP methods with promising performance until now. Recently, unsupervised defect prediction (UDP) methods have received more attention and show competitive performance. However, to our best knowledge, whether HDP methods can perform significantly better than UDP methods has not yet been thoroughly investigated.Method: In this article, we perform a comparative study to have a holistic look at this issue. Specifically, we compare five HDP methods with four UDP methods on 34 projects in five groups under the same experimental setup from three different perspectives: non-effort-aware performance indicators (NPIs), effort-aware performance indicators (EPIs) and diversity analysis on identifying defective modules.Result: We have the following findings: (1) HDP methods do not perform significantly better than some of UDP methods in terms of two NPIs and four EPIs. (2) According to two satisfactory criteria recommended by previous studies, the satisfactory ratio of existing HDP methods is pessimistic. (3) The diversity of prediction for defective modules across HDP vs. UDP methods is more than that within HDP methods or UDP methods.Conclusion: The above findings implicate there is still a long way for the HDP issue to go. Given this, we present some observations about the road ahead for HDP.
In-Memory Computation for Real-Time Face Recognition

In the modern era, a facial recognition-based authentication system is quite demanding. Facial recognition systems are widely used in many disciplines like branches of biometric verification, video-based surveillance systems, interaction between human and computers, access control systems and network security. The traditional face recognition systems work well for detecting and recognizing faces but when it comes to real-time operation, the system usually faces a problem of drop in frame rate with an increase in the number of faces which might affect the performance of the whole system. Due to the dropping frame rate, we might miss some frames during the operation which in turn makes our whole system unreliable to use in a real-time environment. In this paper, we propose a real-time face recognition system that uses in-memory computation to keep frame rate constant during the entire process even with an increase in the number of faces and hence makes the system consistent and precise during its operation.
Barriers Which Impede the Implementation of an Effective Deep Web Data Extraction in VBPS

Semantic block detection is an approach to deal with mining of data from Web pages and Web applications. Conventional strategies cannot perform better, as the new Web site configuration develops with new technologies. Extraction of Web information from the full Web page will be the intensive assignment to recover the substantial info because they are Web site programming language subordinate. A “layer tree” is developed to replace various levels of irregularities between the DOM tree portrayal and the visual format of the Web page. There are various limitations and barriers arise for the different scenarios and to find out and resolve the issues accordingly with the degree of complexity of the problem. As the flow of information has been upgraded and updated on the Web pages exponentially on the daily bases and to find out the correct information in this bulk of data is the real task. VBPS helps in finding the deep data among the huge Web pages but as the technology has been updated dynamically this approach has faced with some limitations. In this paper, we are discussing about these limitations and its comparison with the other techniques in this field.
Social Media Risk Management : and the impact on organization IT security

The purpose of this study was to investigate and try to describe how social media risk management is performed and what impact social media risk management could have on organizations IT security.  ...
Prediction of Portuguese Performance Based on Particle Swarm Neural Network Algorithm

Aiming at the shortcomings of current prediction model of Portuguese performance, such as lowaccuracy and slow speed, a performance prediction model of Portuguese on the basis of the particle swarmoptimization neural net-work is proposed. First of all, a large number of Portuguese performance data arecollected and preprocessed, then the neural network is used to study the teaching samples of Portuguese performance, and particle swarm optimization algorithm is used to select the threshold value, weight value andother parameters of the neural network, so as to establish the optimal prediction model of Portuguese performance, in the end, the effectiveness and superiority of the model are compared and tested with the performance data of Portuguese. The results show that this model can improve the accuracy and reliability ofthe prediction results, and provide valuable information for Portuguese teaching.
Reflecting and adjusting in large-scale Agile software development : A case study

Background. Agile software development has seen increased use in large-scale projects in recent times. Many larger corporations transition from using a traditional plan-driven approach for developi ...
A Deep Learning Based Breast Cancer Detection Algorithm with Feature Fusion Techniques

Cancer has been portrayed as a heterogeneous disease comprising of a wide range of subtypes. The early diagnosis of a cancer type is very important to determine the course of medical treatment required by the patient. The significance of classification cancerous cells into benign or malignant has driven many research studies, in the biomedical and the bioinformatics field. In the past years researchers have been encouraged to use different machine learning (ML) techniques for cancer detection, as well as prediction of survivability and recurrence. Machine learning with image processing can be used to distinguish key highlights from complex datasets and uncover their significance. The predictive models talks about here depend on different administered ML strategies and on various input features and data samples. We have used genetic algorithm and feature fusion to make the algorithm more efficient. The hybrid algorithm to detect the type of breast cancer (benign or malignant) and selection of features which are more relevant for prediction. We have made a comparative study to find out the best algorithm of the above, for prediction of cancer type. With a high level of accuracy, any of these methods can be used to predict the type of breast cancer of any particular patient.
Perancangan Digital Kuisioner Kepuasan Mahasiswa Menggunakan Metode Waterfall

Satisfaction is the level of consumer feelings obtained after consumers enjoy something. Student satisfaction is the level of difference between what students expect about employee service, lecturer competencies supported by facilities and infrastructure with the actual situation and conditions given by the university. Students are consumers or customers of a college so the concept of student satisfaction can be equated with customer satisfaction, namely the level of one's feelings after comparing performance or results which he felt compared to his expectations. To improve the quality of service quality both academically and non-academically, STIKOM PGRI Banyuwangi is committed to making a digital-based student satisfaction questionnaire system. This system is designed web-based using PHP and MySQL using the waterfall method. With the existence of this system, it is expected that it can be seen the percentage level of student satisfaction with the service and availability of facilities and infrastructure at STIKOM PGRI Banyuwangi.
Subject Guides: Welcome to Special Collections: Activities and Events

A guide to visiting the Special Collections department at the University of Iowa Libraries.
Super-resolution processing using luminance dispersion in local area

Based on super-resolution processing, reconstruction refers to the technique of transforming several low-resolution images into a high-resolution image as a general tendency. The fractal super-resolution method is the most effective technique for obtaining a high-resolution image from a single low-resolution image. This technology performs expansion by partitioning the original image into block groups in the local region, which are called range blocks, and block groups in the global region, which are called domain blocks. Further, it replaces a range block with the domain block that resembles it the most. In this study, blocks containing image edges are subjected to fractal super-resolution processing using luminance variance, and areas with gradual changes in density are expanded by interpolation.
Monitoring of temporal seismic velocity changes in the North Anatolian Fault zone using data derived scattering properties

&amp;lt;p&amp;gt;Monitoring of temporal seismic velocity changes can provide us with information on the mechanical state of the Earth&amp;amp;#8217;s crust due to processes of stress build-up and release.&amp;lt;span&amp;gt;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In current work, we use the Dense Array of North Anatolia [1], which has been continuously recording from May 2012 until October 2013, to analyse the spatio-temporal variations of seismic velocity changes in the North Anatolian Fault zone (NAF). We compute daily ambient-noise cross-correlation functions for all 63 three-component stations in the frequency band between 0.1 &amp;amp;#8211; 1 Hz.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To retrieve spatial distribution of seismic velocity changes in such an inhomogeneous fault zone, we go beyond the simple linear travel-time shifts approximation and homogeneous sensitivity kernel. We therefore invert for the travel-time shifts at different lag-times. Furthermore, we use sensitivity kernels for media with inhomogeneous scattering properties. The scattering properties for the sensitivity kernels are derived from the data: a scattering mean free path inside the fault zone (northern strand of NAF) of &amp;amp;#8764;&amp;amp;#160;10 km and &amp;amp;#8764; 150 km outside the fault zone, the attenuation coefficient inside and outside the fault zone are 80 and 100 respectively.&amp;lt;span&amp;gt;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[1] DANA. Dense array for north anatolia. International Federation of Digital Seismograph Networks doi:10.7914/SN/YH2012, 2012.&amp;lt;/p&amp;gt;
Mind Mapping in Teaching Writing

The objective of this research is to know whether the students’ ability in writing descriptive text using mind mapping in Madrasah Aliyah Negeri 1 Banggai can improve or not and to know how mind mapping improve the students’ ability of writing. This research was quasi experimental research by using non-equivalent design. The population of this research were all of the tenth grade students’ in Madrasah Aliyah Negeri 1 Banggai. The sample were X science 1 and X science 2 and consist of 35 and 34 students. The instrument of this research was written test. The kinds of test were pre-test and post-test. The mean of pre-test score in experimental class is 41 and the mean of post-test in experimental class is 83. The mean of pre-test score in controlled class is 37.35 and the mean of post-test in controlled class is 47.65. From the calculation of tcounted was higher than ttable, the result of tcounted is 18.80 meanwhile ttable in the degree of significance 0.05 is 1.66792. If the tcounted&gt; ttable, it means that Ha is accepted. It shows that the comparison of tcounted and ttable is 18.80 &gt; 1.66792. In the other words the alternative hypothesis is proven. It assumed that mind mapping technique can improve the students’ ability of writing descriptive text.Keywords: Writing Ability; Descriptive Text; Mind Mapping.
Predicting variability of horizontal surface solar irradiance using machine learning

Renewable energies play an increasingly important role for energy production in Europe. Unlike coal or gas powerplants, solar energy production is highly variable in space and time. This is due to the strong variability of cloudsand their influence on the surface solar irradiance. Especially in regions with large contribution from photovoltaicpower production, the intermittent energy feed-in to the power grid can be a risk for grid stability. Therefore goodforecasts of temporal and spatial variability of surface irradiance are necessary to be able to properly regulate thepower supply.
Research Guides: Altmetric and Altmetric Explorer for Institutions: Alternative Metrics Overview and Activity (Adapted from Research Impact Challenge)

This guide provides an introduction and overview of the Altmetric and Altmetric Explorer for Institutions tools licensed by the U-M Library. This page comes from the Research Impact Challenge
Design and Development of Platform System for English Assisted Instruction Based on ASP

The platform is designed to solve the problem that teachers and students cannot work and communicate in a unified digital learning platform. The purpose of teaching assistant platform system is to establish a unified and convenient digital platform for teachers and students. The online English teaching system is a B/S structure of the system, its release needs the support of WEB server, and database system to facilitate the storage of system, data query, modify, delete, update the information system, also needs a simple, convenient programming tools that can interact with the database. In view of the above requirements, this system chooses IIS as development environment 5.0SQL Server2000 and ASP. The paper presents design and development of platform system for English assisted instruction based on ASP.
UofL Libraries: Web Design: Home

A guide to web design tools and techniques from the Web Management Team at the University of Louisville Libraries.
Distantly Supervised Question Parsing

The emergence of structured databases for Question Answering (QA) systems has led to developing methods, in which the problem of learning the correct answer efficiently is based on a linking task between the constituents of the question and the corresponding entries in the database. As a result, parsing the questions in order to determine their main elements, which are required for answer retrieval, becomes crucial. However, most datasets for QA systems lack gold annotations for parsing, i.e., labels are only available in the form of (question, formal-query, answer). In this paper, we propose a distantly supervised learning framework based on reinforcement learning to learn the mentions of entities and relations in questions. We leverage the provided formal queries to characterize delayed rewards for optimizing a policy gradient objective for the parsing model. An empirical evaluation of our approach shows a significant improvement in the performance of entity and relation linking compared to the state of the art. We also demonstrate that a more accurate parsing component enhances the overall performance of QA systems.
LibGuides: LibrarySkills@UCL: Referencing and managing information: Independent research

A guide to skills and support for referencing and managing information sources and research data.
HaiQ: Synthesis of Software Design Spaces with Structural and Probabilistic Guarantees.

Formal methods used to validate software designs, like Alloy, OCL, and B, are powerful tools to analyze complex structures (e.g., architectures, object-relational mappings) captured as sets of relational constraints. However, their applicability is limited when software is subject to uncertainty (derived, e.g., from lack of control over third-party components, interaction with physical elements). In contrast, quantitative verification has emerged as a powerful way of providing quantitative guarantees about the performance, cost, and reliability of systems operating under uncertainty. However, quantitative verification methods do not retain thefl exibility of relational modeling in describing structures, forcing engineers to trade structural exploration for analytic capabilities that concern probabilistic and other quantitative guarantees. This paper contributes a method (HaiQ) that enhances structural modeling/synthesis with quantitative guarantees in the style provided by quantitative verification. It includes a language for describing structure and (stochastic) behavior of systems, and a temporal logic that allows checking probability and reward-based properties over sets of feasible design alternatives implicitly described by the relational constraints in a HaiQ model. We report the results of applying a prototype tool in two domains, on which we show the feasibility of synthesizing structural designs that optimize probabilistic and other quantitative guarantees.
Entailment is Undecidable for Symbolic Heap Separation Logic Formulæ with Non-Established Inductive Rules

Entailment is undecidable in general for Separation (SL) Logic formulæ with inductive definitions, but it has been shown to be decidable [1] if the inductive rules satisfy three conditions, namely progress, connectivity and establishment. We show that entailment is undecidable if the latter condition is dropped, thus drawing a much clearer frontier for (un)decidability.