Reviewerly/exHarmony huggingface dataset:
LibGuides: Remote Meeting Applications: Get Started

This instruction guide is designed to explain the various benefits and features of each software so that you can make an informed decision as to which would be most useful and beneficial to your needs.
An intelligent block matching approach for localisation of copy-move forgery in digital images

Block-based copy-move forgery detection (CMFD) methods work with features from overlapping blocks. As overlapping blocks are involved, thresholds related to similarity and the physical distances are defined to identify the duplicated regions. However, these thresholds are controlled manually in localising the forged regions. In order to overcome this, an intelligent block matching approach for localisation is proposed using colour and texture features (CTF) through firefly algorithm. Investigation of the proposed CTF method is carried out on a standard database, which achieved an average true detection rate of 0.98 and an average false detection rate of 0.07. The proposed CTF method is robust against brightness change, colour reduction, blurring, contrast adjustment attacks, and additive white Gaussian noise. Performance analysis of the CTF method validates its superiority over other existing methods.
Generalization and Stability of GANs: A theory and promise from data augmentation

The instability when training generative adversarial networks (GANs) is a notoriously difficult issue, and the generalization of GANs remains open. In this paper, we will analyze various sources of instability which not only come from the discriminator but also the generator. We then point out that the requirement of Lipschitz continuity on both the discriminator and generator leads to generalization and stability for GANs. As a consequence, this work naturally provides a generalization bound for a large class of existing models and explains the success of recent large-scale generators. Finally, we show why data augmentation can ensure Lipschitz continuity on both the discriminator and generator. This work therefore provides a theoretical basis for a simple way to ensure generalization in GANs, explaining the highly successful use of data augmentation for GANs in practice.
AUGMENTING THE REALITY : Can AR Technology Entice Consumer Engagement? A Quantitative Study

Today, advances in the technological sector spurs invention toward new heights. What can be achieved today was just decades ago science fiction. Recent years, augmented reality has emerged which is ...
On statistical disclosure control technologies for protecting personal data in tabular data sets

The objective of this study is to investigate statistical disclosures and the SDC technologies for protecting personal data in tabular data sets, especially in the context of opening privacy sensitive data sets (as in the case of, e.g., justice domain data sets).The main research questions that will be addressed in this deliverable are:Q1: What are the ways for disclosing personal data when tabular data sets are published?Q2: What are the methods for protecting tabular data sets?Q3: What are the main functionalities of available SDC tools for protecting personal data in tabular data sets and preserving data utility therein?The intention is also to explore those state-of-the-art SDC mechanisms or functionalities that are not yet (widely) integrated in the studied SDC tools.
Image Captioning Algorithm Based on Sufficient Visual Information and Text Information

Most existing attention-based methods on image captioning focus on the current visual information and text information at each step to generate the next word, without considering the coherence between the visual information and the text information itself. We propose sufficient visual information (SVI) module to supplement the existing visual information contained in the network, and propose sufficient text information (STI) module to predict more text Words to supplement the text information contained in the network. Sufficient visual information module embed the attention value from the past two steps into the current attention to adapt to human visual coherence. Sufficient text information module can predict the next three words in one step, and jointly use their probabilities for inference. Finally, this paper combines these two modules to form an image captioning algorithm based on sufficient visual information and text information model (SVITI) to further integrate existing visual information and future text information in the network, thereby improving the image captioning performance of the model. These three methods are used in the classic image captioning algorithm, and have achieved achieve significant performance improvement compared to the latest method on the MS COCO dataset.
Perancangan Busana dengan Konsep Pola Zero Waste

ABSTRAK Along with the development of the era and the increase in the quality of life, the demands of consumers regarding quality and quantity grows. In the fashion industry, this gave a start in the concept of Fast Fashion where clothes are made in an instant based on a trend that is always changing rapidly. In return, fast fashion produces a large amount of waste in the form of pre-consumer waste and aftermath-consumer waste. There is some method that can be done to reduce waste such as implying the 3R system which are Reduce, Reuse, and Recycle. Reduce is the most effective method because it is done by decreasing the waste form the production stage. The reduce method can be done by the fashion industry by using the zero waste concept on the production and the designing stage. This research used the qualitative method of clothing pattern experiments using the zero waste pattern concept. The product made by this research were pattern design using zero waste pattern concept which produced waste less than 15%. Keywords: 3R system, Fast Fashion, Waste, Zero Waste Pattern
Website And Database Implementation For Vehicle Violation Data Check Based On Google Firebase

Transportation is one of the most important aspect in human daily activity that used almost every day. In Indonesia, the growth of vehicles numbers each year has always increased in line with population growth increase as well. As the growth of vehicles number making space on the street diminish, many vehicle users commit violation and seize pedestrian rights. In order to overcome these violations, a tool is needed to discipline vehicle users to comply with regulation. Amans is a solution for the problem, it is a mini Internet of Things (IoT) device that record vehicle plates every time it violates the regulations. This paper demonstrates the data processing obtained from Amans device which is the vehicle plate number, this data stored in Firebase Realtime Database. Furthermore, the database is accessible for the vehicle user as they are able to access it through a website. The aim of this paper is to demonstrates the creating of fully functional system that able to store data to the database in less than two seconds. Keywords: Vehicle, Database, Website, Firebase, Python
Home: Collection Development Policy: Selection of Library Materials

This guide outlines the Ike Skelton Combined Arms Research Library's Collection Development Policy.
Plate Motorcycle Detection Using Raspberry Pi 3

A motorized vehicle, whether it’s a motorbike or a car, is a means of trans- portation that is very important for humans today. , Amans is a mini IoT that regulates the limits for motorized vehicles Amans is a technology based on Raspberry pi 3 that can support a city to become a smart city. Amans uses the Deep Neural Network (DNN) system as its algorithm so that Amans can detect and copy letters and numbers on a motorized vehicle plate Amans can detect motorized vehicle plates accurately using either the camera in real time or image media. The level of accuracy of Amans is quite good at 76.6% of the trial success rate. Keywords: Raspberry pi, DNN, Vehicle, Pedestrian, IoT, Safely, sidewalk
Ciência da Informação e Web Semântica: uma relação efetiva ou apócrifa?

The Web is the privileged place for seeking information. The potential of the so-called semantic web to enhance retrieval of relevant information seems essential. In Information Science (IS), this idea was developed from the Otlet’s “analogic semantic web”, justifying the connection between the Semantic Web (SW) and the field of IS. We aimed to exam and analyze the presence of subjects related to SW in the output of IS master and PhD courses in Portugal and Brazil (2005-2015). Specifically, we aimed to: identify IS courses, master's dissertations (MD) and PhD thesis (PD) associated to SW; identify the points of confluence between the IS and the SW in these works; characterize the academic profile of these works’ authors and advisors. Based on a qualitative approach, we performed a literature review and an empirical study focused on content analysis of the retrieved works (RCAAP and OASISbr). As results, we have: verified 4 unique records in RCAAP and 40 in OASISbr; identified 4 Master’s and 3 PhD courses (Portugal) and 16 masters and 9 PhD courses (Brazil); retrieved no MD/PD in Portugal, 16 MD and 5 PD in Brazil; identified 3 confluence points: a) mutual contribution (62%), b) WS as an interdisciplinary theme (29%) and c) complementary knowledge (9%); identified 11 works (52%) were the respective researchers have training in IS and Computer Science (CS). The study points to the WS as a field of investigation in IS, although essentially theoretical, insofar as practical interdisciplinary works in CI/CS are incipient and not representative.
Libraries: Editing your Journals Website Page on OJS: Creating your Static Page

This guide will walk you through the process of making your journal's home page welcoming and accessible. The guide includes tips around adding a logo and adding an image to the sidebar of your journal's website.
Hear Audio From NASA's Perseverance As It Travels Through Deep Space

A microphone aboard NASA's Mars 2020 Perseverance rover has recorded the sounds of the spacecraft as it hurtles through interplanetary space.
Identifying functions for smartphone basedapplications in volunteer emergency response

Emergency response organisations struggle with resource constraints and thereby faces challenges in providing high-quality public services. Utilising voluntary first responders is one way to addres ...
PERANAN MODEL PEMBELAJARAN VALUE CLARIFICATION TECHNIQUE BERBANTUAN MEDIA AUDIO VISUAL PADA PEMBELAJARAN IPS ERA MILENIAL

To fulfill education in the current millenial generation, educators always make interesting innovations that can make students’ interests increase. The development of teaching concepts by applying learning principles, models and interactive media to social studies learning continues. The application of social values in the Value Clarification Technique (VCT) learning model through the help of audio visual media into social studies lessons, students can instiil social values individually as well as learning experiences for real life provision. In addition, students not only have cognitive aspects, but also have adventages in the affective aspects which are certainly needed in the millenial era.
Generating synthetic brain MR images using a hybrid combination of Noise-to-Image and Image-to-Image GANs

Generative Adversarial Networks (GANs) have attracted much attention because of their ability to learn high-dimensional, realistic data distributions. In the field of medical imaging, they can be u ...
Strategies for effective utilization of training data for machine translation

Machine Translation (MT) systems are developed with the aim to automatically translate text from one language (the source language) to another language (the target language). The vast majority of present-day MT systems are data-driven, that is, they learn how to translate between the two languages of interest by modelling the probabilities of translation patterns from large amounts of parallel corpora. For many language pairs, the training data is compiled from multiple sources that often come from different domains and can have varying degrees of translation equivalence. One limitation of standard training mechanisms is that they assume training data to be homogeneous with respect to domain and quality. As a result, these training mechanisms may learn noisy or undesirable translation patterns. This thesis explores training strategies that can refine the quality of the translation model and minimize the effect of training data variations. The majority of the strategies proposed in this thesis are based on the idea of iterative knowledge transfer, in which the quality of a model is refined through predictions of other models that are trained in earlier steps. The main idea underlying the solutions proposed in this thesis is that knowledge gathered by the models during initial training steps can be refined and used for solving varied problems such as domain adaptation and better utilization of noisy training data. Based on this idea, this thesis proposes solutions to four different problems. First, this thesis demonstrates that the quality of models used in phrase-based machine translation can be improved by re-estimating them from their own predictions on the training data. Second, this thesis investigates the problem of low performance of neural MT models when used to translate sentences from domains that are underrepresented in the training data. This thesis proposes a knowledge transfer strategy for training neural MT models with stable performance across multiple domains. Third, we address the problem of the negative effect of low-quality training data on the performance of neural MT systems. Our research demonstrates that the performance degradation due to noise in the training data can be reduced using knowledge transfer from a strong model trained on small high-quality data. Fourth, this thesis discusses the limitations of single architectures for neural MT. We propose a combination of two different neural network architectures to effectively model important features of the source sentences.
Hindi Visual Genome 1.1

Data----Hindi Visual Genome 1.1 is an updated version of Hindi Visual Genome 1.0. The update concerns primarily the text part of Hindi Visual Genome, fixing translation issues reported during 2019 multimodal task. In the image part, only one segment and thus one image were removed from the dataset.Hindi Visual Genome 1.1 serves in WAT 2020 Multi-Modal Machine Translation Task.Hindi Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account.The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.A third test set is called ``challenge test set'' consists of 1.4K segments and it was released for WAT2019 multi-modal task. The challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.Dataset Formats--------------The multimodal dataset contains both text and images.The text parts of the dataset (train and test sets) are in simpletab-delimited plain text files.All the text files have seven columns as follows:Column1 - image_idColumn2 - XColumn3 - YColumn4 - WidthColumn5 - HeightColumn6 - English TextColumn7 - Hindi TextThe image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.Data Statistics----------------The statistics of the current release is given below.Parallel Corpus Statistics---------------------------Dataset Segments English Words Hindi Words------- --------------------------------------Train 28930 143164 145448Dev 998 4922 4978Test 1595 7853 7852Challenge Test 1400 8186 8639------- --------------------------------------Total 32923 164125 166917The word counts are approximate, prior to tokenization.Citation--------If you use this corpus, please cite the following paper:@article{hindi-visual-genome:2019, title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}}, author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan}, journal={Computaci{\'o}n y Sistemas}, volume={23}, number={4}, pages={1499--1505}, year={2019}}
Understanding the Mechanics of SPIGOT: Surrogate Gradients for Latent Structure Learning

Latent structure models are a powerful tool for modeling language data: they can mitigate the error propagation and annotation bottleneck in pipeline systems, while simultaneously uncovering linguistic insights about the data.One challenge with end-to-end training of these models is the argmax operation, which has null gradient.In this paper, we focus on surrogate gradients, a popular strategy to deal with this problem.We explore latent structure learning through the angle of pulling back the downstream learning objective.In this paradigm, we discover a principled motivation for both the straight-through estimator (STE) as well as the recently-proposed SPIGOT-a variant of STE for structured models.Our perspective leads to new algorithms in the same family.We empirically compare the known and the novel pulled-back estimators against the popular alternatives, yielding new insight for practitioners and revealing intriguing failure cases.
Victims of Online Prostitution Involving Indonesian Actresses in Victimology Perspective

The prostitution modus operandi is placed on the ease, speed and novelty of information to facilitate the information transaction. Internet access which spreads widely and individually has attracted perpetrator to commit obscene transaction. The access speed provided by internet enables perpetrators in real time to negotiate their potential service users and persons who will be offered to provide obscene services. It doesn’t mention to the information novelty which can be changed, deleted and even made confidential has became incentive for perpetrator to commit the act. The online prostitution or cyber prostitution basically has same form with prostitution as stipulated in Article 296 of Penal Code, the difference is only found in the use of information technology and the scope of act. But still, the focus mainly on victim remains absent from online prostitution.
Obstacle Avoidance for an Autonomous Robot Car using Deep Learning

The focus of this study was deep learning. A small, autonomous robot car was used for obstacle avoidance experiments. The robot car used a camera for taking images of its surroundings. A convolutio ...
Sentiment Analysis of Tweets using SVM and Maximum Entropy

: Sentiment analysis is the computational learning of masses' opinions. Sentiment analysis will classify the text in a sentence or document to find out the opinions expressed in the sentence or document, which can be positive or negative. This scheme discusses the sentiment of someone on Twitter towards public figures. The data used is in the form of tweet data with the keywords #NEP or National Education Policy. Tweets obtained are then processed by doing text preprocessing and then classified using the support vector machine algorithm with maximum entropy. The performance of this algorithm uses and obtained a precision of 73.33%, the accuracy of 77.47%, recall of 87.76%, and f-measure of 79.66% respectively.
Help with Data Management for the Novice and Experienced Alike

With the powerful analyses and resources they enable, digital humanities tools have captivated researchers from many different fields who want to use them to study science. Digital tools, as well as funding agencies, research communities, and academic administrators, require researchers to think carefully about how they conceptualize, manage, and store data, and about what they plan to do with that data once a given project is over. The difficulties of developing strategies to address these problems can prevent new researchers from sticking with digital tools and flummox even experienced researchers. To help overcome the data hurdle, we present five principles to help researchers, novice and experienced alike, conceptualize and plan for their data. We illustrate the use of those principles with two digital projects from the history of science, the Embryo Project and the Marine Biological Laboratory History Project, and their associated HPS Repository for data. The principles apply beyond the digital realm, so those who collect and manage data by more traditional means will also find them useful.
Organizational And Administrative Aspects Of Russian Education Modernization

The study of organizational and administrative foundations of Russian education modernization is the topical issue of higher education pedagogy. The article deals with the organizational and substantive changes of the pedagogical education; they are innovative in the frame of education modernization. The subject of Russian education modernization is considered in terms of the innovation principle and revealed through introduction of new technologies. The authors describe distant learning technologies, interactive forms, projects methods and methods that allow to simulate real pedagogical situations when studying at the University, as well as innovative educational programs that have appeared due to labor market requirements. The conclusion states that in order to achieve the goals and tasks, it is necessary to revise the content of general science, general professional and special training of future teachers, based on the unity, interrelation and traditional and innovative training methods that depend on each other. The revision should be based on modeling and research activities.
Regulating the Algorithmic Society : The Regulation of Online Platforms’ Use of Algorithms and How the Use Affects Society

Regulating the Algorithmic Society : The Regulation of Online Platforms’ Use of Algorithms and How the Use Affects Society
LibGuides: Special Circumstances During the COVID-19 Crisis: Remote Resources And Guidance For Users: Remote Resources on the World Wide Web

The Lightner Library's operations have changed due to the COVID-19 crisis. Learn more about the changes, as well as the many virtual services we are offering, including reference help and interlibrary loan.
STANDARDS AND NON-STANDARD SOLUTIONS IN TACTICAL AND STRATEGIC TASKS

The paper provides an analysis of the standard and non-standard solutions in the field of education. Tactical and strategic objectives of the use of the State Educational Standard of Russia (GOS) as a reference level of education necessary for society in a certain historical period of time are outlined. The formation of didactic units based on the requirements of the new educational standard can be carried out in two ways: the preservation of a set of disciplines based on the accordance to the standard; a complete update of the discipline structures.
Contemporary Online Banking Fraud in Norway : A case study

Banks are currently battling rising of fraudulent activities as it damages their relationship with their customers. Online banking fraud is decreasing trust and confidence among the customers and d ...