Reviewerly/exHarmony huggingface dataset:
Designing a conceptual Security Framework for Secure Online Ordering System (SOOS)

The enhancement of internet technology has improved the security of online ordering system through the implementation of secure protocol in e-commerce application. ICT acquisition programmes enhance countries’ capacity to influence and participate in taking decisions which directly or indirectly affect their lives and their total wellbeing of her citizen. This paper therefore looks into the necessary steps to implement security framework for online ordering system (SOOS) in e-commerce application and also this paper investigates security threats and other problems that e-commerce customers and merchant’s faces. Secure Online Ordering System (SOOS) was developed to demonstrate some aspects of security measure that can be applied to minimize the security threats encountered by both customers and merchants. The Secured Electronic Transaction (SET) protocol and cryptography are used in this research to prove clients identity to a server (and vice versa) across an insecure connection. Advanced Encryption Standard (AES) and Message Digest (MD5) hash function are used for Password Based Encryption (PBE) for detecting data corruption and tampering and integrity of data message exchange between client and SET protocol are used to encrypt all the information through the communication channel between client-server, to assure Confidentiality, Integrity, Authentication Content Assurance, Non-Repudiation and other security services. It is therefore recommended among others, that necessary attention should be accorded for designing good and secured shopping cart which must be accompanied with user-friendly shopping cart application.
Word embedding in teaching research writing

In research writing as a scientific basis in education, some mistakes related to choosing appropriate term or definition take place. This research discusses the solving such problem by using word embedding in the Research Writing discipline. This is a word representation form, where one word has a vector and its coordinates. The words with close meaning have similar direction, showing lexical compatibility. To calculate lexical relations, the cosine of the angle between two words’ vectors are considered. Value of highly compatible word combinations is equal to 1. On the other hand, lexically incompatible words should approximately have value -1. To test the system the text of the Constitution of the Republic of Kazakhstan was used. Particularly, words which are not related to meaning of article of the Constitution were inserted, and the system had to identify that inserted words. The system for some words showed high accuracy, however some words showed low accuracy. It is suggested that such factor was because even inserted words were not related in meaning, they could be lexically compatible with their neigh bors. This research is carried out within the framework of the Ministry of Education and Science of Republic of Kazakhstan grant project “Developing and implementing the innovative competency-based model of multilingual IT specialist in the course of national education system modernization”.
LibGuides: Human Resource Management: Web Resources

Library resources guide for the Human Resource Management B.S.B.A. at Saint Joseph's College of Maine
Best Practice in Small School Management Under the Office of the Basic Education Commission, Kamphaengphet Province

The purpose of this research was to study best practices in small school management under the office of the Basic Education Commission. The sample acquired by selecting a specific type was a group of small schools under the Office of the Basic Education Commission, Kamphaengphet Province with the results of the National Basic Education Test (O-NET) in Mathematics and Science were higher than the national average in the academic year 2018, namely the mathematics course with a higher average of 37.50 points and science courses with a higher average of 39.93 points. The data were collected by interviews from school directors, teachers and school performance report, including data analysis by analyzing the content. The results showed that 1) for the management namely (1.1) the personnel aspect: the school directors followed-up and encouraged teachers to develop themselves to create a happy learning environment and build morale at work. In addition, teachers always dedicated their time to students and develop themselves. Students had to read and write fluently (1.2) for the support, there was a distance learning system with a teaching manual as well as an internet system to help study and research (1.3) the management, the goals were set for student development  and  the timetables were arranged for core subjects, secondary subjects and 2) learning management consisted of (2.1) curriculum activities. The teacher analyzed the learners individually , studied indicators and studied the content that had to be taught as well as finding teaching techniques and teaching materials that help learners' interest and fun (2.2) Extracurricular activities, there was an additional teaching after the content was completed by drilling the exam questions that were used for the authentic examination in the past year and took part in the projects with school groups to review together.
LibGuides: Electronic Resources: Library Catalog - What am I searching?

This is an information guide about the Electronic Resources at NOVA Libraries. For more information, contact LTS@nvcc.edu
Giovan Giorgio Trissino tra traduzione dantesca e percezione della poesia

Giovan Giorgio Trissino between Dantean translation and his notion of poetry · This essay proposes some remarks on the reception of Dante’s poetic theory in Giovan Giorgio Trissino works. In particular, after a first part devoted to the study of some places of De la volgare eloquentia, the contribution deals with the perception of poetical forms in the Sixteenth Century, focusing attention on the relationship between sonnet et ballad in Trissinian Poetica.
Comparative Analysis of Regularization Techniques in Artificial Neural Networks

Regularization is one of the important elements that affects the neural networks. In this paper we are going to demonstrate working of the ANN model on a dataset obtained from kaggle i.e. ECG heart records. To improve the accuracy of the model we will be trying some of the regularization techniques such as Ridge, Lasso and Dropout. We will be demonstrating on how each of the regularization techniques work and then implement them on the model to compare the accuracy of the model with different regularization techniques.
LibGuides: Alma Training: Fulfillment

Alma training, documentation and resources for staff. Fulfillment is the process by which patrons borrow and return physical resources, or access electronic or digital resources, from the institution.
Scene Representations for View Synthesis with Deep Learning

Author(s): Srinivasan, Pratul | Advisor(s): Ng, Ren; Ramamoorthi, Ravi | Abstract: In this dissertation, we investigate the question of how 3D scenes should be represented, such that the representation can be effectively estimated from standard photographs and can then be used to synthesize images of the same scene from novel unobserved viewpoints. Recovering photorealistic scene representations from images has been a longstanding goal of computer vision and graphics, and has typically been addressed using representations from standard computer graphics pipelines, such as triangle meshes, which are not particularly amenable to end-to-end optimization for maximizing the fidelity of rendered images. Instead, we advocate for the use of scene representations that are specifically well-suited to being used in differentiable deep learning pipelines. We explore the efficacy of various representations for view synthesis tasks including synthesizing local views around a single input image, extrapolating views around a pair of nearby input images, and interpolating novel views from a set of unstructured images. We present scene representations that succeed at the aforementioned tasks, which share two common properties: they represent scenes as volumes and that they avoid the poor scaling properties of regularly-sampled voxel grids by using compressed or parameter-efficient volume representations.
An Investigation Into Predict-and-Optimize Machine Learning

Predict-and-Optimize (PnO) is a relatively new machine learning paradigm that has attracted recent interest: it concerns the prediction of parameters that determine the value of solutions to an optimization problem, such that the optimizer ends up picking a good solution. Training estimators with standard loss functions like mean squared error and cross-entropy provides no guarantees that their predictions aid the optimizer in this aim. A number of approaches have been suggested over the past few years on how to most effectively tackle the PnO-setting, such as Smart Predict, then Optimize (SPO) and the Quadratic Programming Task Loss (QPTL). We investigate an experiment of the paper that introduced QPTL, and find that an estimator used as baseline approach was set back by two factors: a class imbalance, and a training duration that was too short. However, QPTL still outperforms the baseline approach. We consider the use of the Gumbel-Softmax Straight-Through Estimator for SPO and QPTL when training neural networks on a multi-class classification dataset (MNIST) in a PnO-setting. We compare the results for SPO and QPTL for different output activation functions (linear output, sigmoid output, gumbel-softmax output) when predicting the objective parameters in 0-1 unweighted Knapsack problems and Bipartite Matching problems using this dataset. We find that neural networks trained via SPO with a linear output tend to show best performance, and that neural networks trained via QPTL are relatively unaffected by the output activation function of choice. Finally we find that PnO approaches, SPO in particular, can see large performance increases by constructing a large number of optimisation problems from a small pool of training data.
On The Flexophotovoltaic Effect In Semiconductor P-N-Structures

In this work, the first experimentally discovered effect of flexophotovoltaics (FPV) in silicon p-n-structures under the influence of local mechanical stress on the frontal surface is theoretically substantiated.. The regularities of the manifestation of the FPV effect are determined depending on the magnitude of the local pressure force and the intensity of photoexcitation.. Statistical processing of the experimental data by the least squares method was carried out and a new empirical formula was obtained for the experimentally determined dependence of the short circuit photocurrent of a silicon structure on the local mechanical stress.
LibGuides: Applying to Graduate School: Find a Program

A guide on UIUC Library resources and tools to help guide recent graduates through the application process.
Sentiment Analysis Techniques on Food Reviews Using Machine Learning

Review or opinion is a text which expresses the user's thought and response to the product or service he/she has availed or purchased. Processing this input and getting to know whether these sentiments are positive, negative, or neutral is called sentiment analysis. The reviews are then used by data analysts to perform evaluations about the product/service. We classify these sentiments in three principal types positive, negative, and neutral. Amazon Food reviews dataset would be used to train the classifier. Zomato, being the most popular food delivery site and restaurant aggregator provides information, menus, and user reviews of restaurants. Python language is used to conduct research on a carefully chosen data and apply a classification algorithm on it.
Financial Crises and Innovation

Financial crises are accompanied by permanent drops in economic growth and output. Technological progress and innovation are important drivers of economic growth. This paper studies how financial crises affect innovative activities. Using cross-country panel data on patenting at the industry-level, we identify a financial channel whereby disruptions in financial markets impact patenting activity. Specifically, we find that patenting decreases more following banking crises for industries that are more dependent on external finance. This financial channel is not at play during currency crises, sovereign debt crises, or recessions more generally, suggesting that disruption in banking activity matters for investment in innovative activities. The effect on patenting is economically large and long-lasting, resulting in less patenting, in terms of both total quantity and quality, for 10 years or longer after a banking crisis. The average patent quality, however, does not appear to decline. We show the results are not likely to be driven by reverse causality or omitted variables. These findings provide a link between banking crises and the observed patterns of lower long-term growth. Liquidity support in the aftermath of banking crises appears to help reduce the effects through the financial channel over the short term.
Japanese Hiragana Handwriting Pattern Recognition Using Template Matching Correlation Method

Hiragana is one of the traditional Japanese letters used to translate native Japanese words. The introduction of an object requires a learning process, which is obtained through the characteristic in the form of unique features on similar objects, but manually it is quite difficult to distinguish these letters. This writing explains the discussion system to differentiate between hiragana letters starting from preprocess namely grayscale and threshold, then segmenting and normalization, while image classification uses the Template Matching Correlation method. The results of tests carried out assessing the test rate of around 76% using the Matching Template Correlation method. While the remaining 14% indicates that the object identified does not match the intended results.
MOBILE APPLICATIONS TO IMPROVE DIDACTICS IN TIMES OF COVID, IN TECHNICAL HIGH SCHOOLS, FROM THE MUNICIPAL SCHOOL QUEVEDO, 2020

UNIANDES extension in Quevedo, through the department of outreach with the community, in the Systems career, incorporates its eighth semester students, to provide computer solutions in times of pandemic, in the Quevedo Municipal Educational Unit, when building mobile applications, in order to improve teaching processes between teachers and students in virtual mode. The applications were used and the results were satisfactory, with constant feedback and formative evaluations as a priority, in different subjects. In the construction of them, the teacher contributes with his didactic experience, the contents and evaluations. Of the five students of UNIANDES, they build a mobile application for five subjects, with all the suggestions of each teacher, the same ones that were used by 150 students, who in 90% of the proposed educational objectives were met. It is intended that for subsequent projects, academic units of the curriculum will be increased by subjects, if possible, for the six part-time periods of the school year.
Integrating aspect-aware interactive attention and emotional position-aware for multi-aspect sentiment analysis.

Aspect-level Sentiment Analysis is a fine-grained sentiment analysis task, which aims to infer the corresponding sentiment polarity with different aspects in an opinion sentence. Attention-based neural networks have proven to be effective in extracting aspect terms, but the prior models are based on context-dependent. Moreover, the prior works only attend aspect terms to detect the sentiment word and cannot consider the sentiment words that might be influenced by domain-specific knowledge. In this work, we proposed a novel integrating Aspect-aware Interactive Attention and Emotional Position-aware module for multi-aspect sentiment analysis (abbreviated to AIAEP) where the aspect-aware interactive attention is utilized to extract aspect terms, and it fuses the domain-specific information of an aspect and context and learns their relationship representations by global context and local context attention mechanisms. Specifically, in the sentiment lexicon, the syntactic parse is used to increase the prior domain knowledge. Then we propose a novel position-aware fusion scheme to compose aspect-sentiment pairs. It combines absolute distance and relative distance from aspect terms and sentiment words, which can improve the accuracy of polarity classification. Extensive experimental results on SemEval2014 task4 restaurant and AIChallenge2018 datasets demonstrate that AIAEP can outperform state-of-the-art approaches, and it is very effective for aspect-level sentiment analysis.
Velocity models for routine earthquake monitoring at volcanoes: from deterministic to Bayesian

&amp;lt;p&amp;gt;Earthquake location is a primary function of volcano observatories worldwide and the resulting catalogs of seismicity are integral to interpretations and forecasts of volcanic activity.&amp;amp;#160; Ensuring earthquake location accuracy is therefore of critical importance.&amp;amp;#160; However, accurate earthquake locations require accurate velocity models, which are not always available.&amp;amp;#160; In addition, difficulties involved in applying traditional velocity modeling methods often mean that earthquake locations are computed at volcanoes using velocity models not specific to the local volcano.&amp;amp;#160; &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Traditional linearized methods that jointly invert for earthquake locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, which are then iteratively updated to minimize the data misfit.&amp;amp;#160; However, these deterministic methods are susceptible to local minima and divergence, issues exacerbated by sparse seismic networks and/or poor data quality common at volcanoes.&amp;amp;#160; In cases where independent prior constraints on local velocity structure are not available, these methods may result in systematic errors in velocity models and hypocenters, especially if the full range of possible starting values is not explored.&amp;amp;#160; Furthermore, such solutions depend on subjective choices for model regularization and parameterization.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In contrast, Bayesian methods promise to avoid all these pitfalls.&amp;amp;#160; Although these methods traditionally have been difficult to implement due to additional computational burdens, the increasing use and availability of High-Performance Computing resources mean widespread application of these methods is no longer prohibitively expensive.&amp;amp;#160; In this presentation, we apply a Bayesian, hierarchical, trans-dimensional Markov chain Monte Carlo method to jointly solve for hypocentral parameters, 1D velocity structure, and station corrections using data from monitoring networks of varying quality at several volcanoes in the U.S. and South America.&amp;amp;#160; We compare the results with those from a more traditional deterministic approach and show that the resulting velocity models produce more accurate earthquake locations.&amp;amp;#160; Finally, we chart a path forward for more widespread adoption of the Bayesian approach, which may improve catalogs of volcanic seismicity at observatories worldwide.&amp;amp;#160;&amp;lt;/p&amp;gt;
Design and Implementation of Distributed Web Crawler for Drug Website Search using Hefty based Enhanced Bandwidth Algorithms

The development of an expert system based search tool needs a superficial structure to satisfy the requirements of current web scale. Search engines and the internet crawler are used to mine the required information from the web and surf the internet in an efficient fashion. Distributed crawler is one of the types of a web crawler, which is a dispersed computation method. In this paper, we design and implement the concept of Efficient Distributed Web Crawler using enhanced bandwidth and hefty algorithms. Mostly Web Crawler doesn’t have any distributed cluster performance system and any implemented algorithm. In this paper, a novel Hefty Algorithm and enhanced bandwidth algorithm are combined for a better-distributed crawling system. The hefty algorithm is implemented to provide strong and efficient surfing results while applying on the drug web search. We also concentrate on the efficiency of the proposed distributed web crawler by implementing Enhanced Bandwidth algorithm.
Engineering practices and engineering education: prolegomena to the model of the future

A theoretical and methodological analysis of the content characteristics of engineering education correlated with the challenges of the fourth industrial revolution (Industry 4.0) showed that trends of the third technological revolution dominate in the current university engineers' training model. One of the effective tools for the formation of a university model for training an engineer of the future could be the ideas of using educational social engineering problems based on elements of socio-humanitarian expertise of engineering design decisions. An optimally designed model of an engineer of the future will improve the quality of training and, ultimately, avoid the negative (fatal) consequences of insufficiently substantiated engineering design decisions.
Percepción estudiantil del impacto de las prácticas comunitarias sobre la formación académica

This article aims to determine the advantages and challenges of community practices in virtual environments through the linkage program of the Universidad Internacional del Ecuador - UIDE, Guayaquil. The project consisted of creating multidisciplinary teams of 3 students whose goal was to hold virtual workshops for 3 entrepreneurial beneficiaries in general, the same ones that were identified and invited by students to connect from any virtual tool. The methodology applied in this study was mixed: qualitative and quantitative. For the qualitative part, two in-depth interviews were conducted; while for the quantitative part a survey was conducted with filter variables and 5-dimensional Likert scales, the same ones ranging from totally disagreed to totally agreed. The design used was non-experimental. The sample included 2 teachers and 101 students who were part of this program. The most relevant results in this study indicated that 56% believe that internet access is a problem, as well as 70% believe that linkage program represents an important contribution to the development of skills in community business.
Implementation of Naive Bayesian Data Mining Algorithm on Deceased Registration Data

The use of data mining algorithm in different domains like marketing, finance, retail, cyber security, fraud detection, medical science, etc… is well known. In recent times, the data mining is also implemented for e-governance data. Normally, e-governance data are only used for Online Transaction Processing System (OLTP). However, one can use data mining algorithm to uncover hidden and new trends from e-governance data. In this paper, Naive Bayes data mining algorithm is applied on Deceased Registration data to find hidden trends.