Computer Sciences Commons^™

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 4 months, 818 new packages were added to the CRAN package repository. 100 packages were archived and 248 were archived. The following shows the growth of the number of active packages in the CRAN package repository

R Foundation News, Torsten Hothorn

The R Journal

Membership fees and donations received between 2020-09-09 and 2021-01-28.

Rngforgpd: An R Package For Generation Of Univariate And Multivariate Generalized Poisson Data, Hesen Li, Hakan Demirtas, Ruizhe Chen

The R Journal

This article describes the R package RNGforGPD, which is designed for the generation of univariate and multivariate generalized Poisson data. Some illustrative examples are given, the utility and functionality of the package are demonstrated; and its performance is assessed via simulations that are devised around both artificial and real data.

Species Distribution Modeling Using Spatial Point Processes: A Case Study Of Sloth Occurrence In Costa Rica, Paula Moraga

The R Journal

Species distribution models are widely used in ecology for conservation management of species and their environments. This paper demonstrates how to fit a log-Gaussian Cox process model to predict the intensity of sloth occurrence in Costa Rica, and assess the effect of climatic factors on spatial patterns using the R-INLA package. Species occurrence data are retrieved using spocc, and spatial climatic variables are obtained with raster. Spatial data and results are manipulated and visualized by means of several packages such as raster and tmap. This paper provides an accessible illustration of spatial point process modeling that can …

Tulip: A Toolbox For Linear Discriminant Analysis With Penalties, Yuqing Pan, Qing Mai, Xin Zhang

The R Journal

Linear discriminant analysis (LDA) is a powerful tool in building classifiers with easy computation and interpretation. Recent advancements in science technology have led to the popularity of datasets with high dimensions, high orders and complicated structure. Such datasetes motivate the generalization of LDA in various research directions. The R package TULIP integrates several popular high-dimensional LDA-based methods and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification. Functions are included for model fitting, cross validation and prediction. In addition, motivated by datasets with diverse sources of predictors, we further include functions for covariate adjustment. Our package is …

Testing The Equality Of Normal Distributed And Independent Groups’ Means Under Unequal Variances By Doex Package, Mustafa Cavus, Berna Yazıcı

The R Journal

In this paper, we present the doex package contains the tests for equality of normal distributed and independent group means under unequal variances such as Cochran F, Welch-Aspin, Welch, Box, Scott-Smith, Brown-Forsythe, Johansen F, Approximate F, Alexander-Govern, Generalized F, Modified Brown-Forsythe, Permutation F, Adjusted Welch, B2, Parametric Bootstrap, Fiducial Approach, and Alvandi Generalized F-test. Most of these tests are not available in any package. Thus, doex is easy to use for researchers in multidisciplinary studies. In this study, an extensive Monte-Carlo simulation study is conducted to investigate the performance of the the tests for equality of normal distributed group means …

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.12 was released on 28 October, 2020. It is compatible with R 4.0.3 and consists of 1974 software packages, 398 experiment data packages, 968 up-to-date annotation packages, and 28 workflows. Books are a new addition, built regularly from source and therefore fully reproducible; an example is the community-developed Orchestrating Single-Cell Analysis with Bioconductor.

Kuhn-Tucker And Multiple Discrete-Continuous Extreme Value Model Estimation And Simulation In R: The Rmdcev Package, Patrick Lloyd-Smith

The R Journal

This paper introduces the package rmdcev in R for estimation and simulation of KuhnTucker demand models with individual heterogeneity. The models supported by rmdcev are the multiple-discrete continuous extreme value (MDCEV) model and Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. The rmdcev package also implements demand forecasting and welfare calculation for policy simulation. The purpose of this paper is to describe the model estimation and simulation framework and to demonstrate the functionalities of rmdcev using …

A Unified Algorithm For The Non-Convex Penalized Estimation: The Ncpen Package, Dongshin Kim, Sangin Lee, Sunghoon Kwon

The R Journal

Various R packages have been developed for the non-convex penalized estimation but they can only be applied to the smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP). We develop an R package, entitled ncpen, for the non-convex penalized estimation in order to make data analysts to experience other non-convex penalties. The package ncpen implements a unified algorithm based on the convex concave procedure and modified local quadratic approximation algorithm, which can be applied to a broader range of non-convex penalties, including the SCAD and MCP as special examples. Many user-friendly functionalities such as generalized information criteria, cross-validation …

User-Specified General-To-Specific And Indicator Saturation Methods, Genaro Sucarrat

The R Journal

General-to-Specific (GETS) modelling provides a comprehensive, systematic and cumulative approach to modelling that is ideally suited for conditional forecasting and counterfactual analysis, whereas Indicator Saturation (ISAT) is a powerful and flexible approach to the detection and estimation of structural breaks (e.g. changes in parameters), and to the detection of outliers. To these ends, multi path backwards elimination, single and multiple hypothesis tests on the coefficients, diagnostics tests andgoodness-of-fit measures are combined to produce a parsimonious final model. In many situations a specific model or estimator is needed, a specific set of diagnostics tests may be required, or a specific f …

Editorial, Michael J. Kane

The R Journal

On behalf of the editorial board, I am pleased to present Volume 12 Issue 2 of the R Journal. This is my third and final issue as the Editor-in-Chief. In the last year, we have made some substantial changes to the journal that I believe will continue to increase our capacity to support the growing data science and computational statistics communities, and continue to raise the visibility of the journal. In the last few months we recruited 10 Associate Editors and we are continuing the recruitment process. I’d like to publicly welcome our new Associate Editors, and thank each of …

Openland: Software For Quantitative Analysis And Visualization Of Land Use And Cover Change, Reginal Exavier, Peter Zeilhofer

The R Journal

There is an increasing availability of spatially explicit, freely available land use and cover (LUC) time series worldwide. Because of the enormous amount of data this represents, the continuous updates and improvements in spatial and temporal resolution and category differentiation, as well as increasingly dynamic and complex changes made, manual data extraction and analysis is highly time consuming, and making software tools available to automatize LUC data assessment is becoming imperative. This paper presents a software developed in R, which combines LUC raster time series data and their transitions, calculates state-of-the-art LUC change indicators, and creates spatio-temporal visualizations, all in …

E-Rum2020: How We Turned A Physical Conference Into A Successful Virtual Event, Mariachiara Fortuna, Francesca Vitalini, Mirko Signorelli, Emanuela Furfaro, Federico Marini, Gert Janssenswillen, Riccardo Porreca, Riccardo L. Rossi, Andrea Guzzo, Roberta Sirovich, Andrea Melloncelli, Lorenzo Salvi, Serena Signorelli, Filippo Chiarello

The R Journal

The European R Users Meeting 2020 (e-Rum2020) was a conference that was held virtually in June 2020. Originally, e-Rum2020 had been planned as a physical event to be held in Milano. However, the spread of the COVID-19 pandemic and the declaration of a nationwide lockdown induced the Organizing Committee to fully rethink the event, and to turn it into a live virtual conference. In this article, we describe the challenges that we encountered during the organization of e-Rum2020, and how wereacted to them. In doing so, we aim to provide future conference organizers with useful information on how to organize …

Miwqs: Multiple Imputation Using Weighted Quantile Sum Regression, Paul M. Hargarten, David C. Wheeler

The R Journal

The miWQS package in the Comprehensive R Archive Network (CRAN) utilizes weighted quantile sum regression (WQS) in the multiple imputation (MI) framework. The data analyzed is a set/mixture of continuous and correlated components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, which may differ among the components. This type of data is found in areas such as chemical epidemiological studies, sociology, and genomics. The miWQS package can be run using complete or incomplete data, which may be placed in the first quantile, …

News From The Forwards Taskforce, Heather Turner

The R Journal

Forwards is an R Foundation taskforce working to widen the participation of under represented groups in the R project and in related activities, such as the useR! conference. This report rounds up activities of the taskforce during the second half of 2020.

Farmtest: An R Package For Factor-Adjusted Robust Multiple Testing, Koushiki Bose, Jianqing Fan, Yuan Ke, Xiaoou Pan, Wen-Xin Zhou

The R Journal

We provide a publicly available library FarmTest in the R programming system. This library implements a factor-adjusted robust multiple testing principle proposed by Fan et al. (2019) for large-scale simultaneous inference on mean effects. We use a multi-factor model to explicitly capture the dependence among a large pool of variables. Three types of factors are considered: observable, latent, and a mixture of observable and latent factors. The non-factor case, which corresponds to standard multiple mean testing under weak dependence, is also included. The library implements a series of adaptive Huber methods integrated with fast data-driven tuning schemes to estimate model …

Trace: A Differentiable Approach To Line-Level Stroke Recovery For Offline Handwritten Text, Taylor Neil Archibald

Theses and Dissertations

Stroke order and velocity are helpful features in the fields of signature verification, handwriting recognition, and handwriting synthesis. Recovering these features from offline handwritten text is a challenging and well-studied problem. We propose a new model called TRACE (Trajectory Recovery by an Adaptively-trained Convolutional Encoder). TRACE is a differentiable approach using a convolutional recurrent neural network (CRNN) to infer temporal stroke information from long lines of offline handwritten text with many characters. TRACE is perhaps the first system to be trained end-to-end on entire lines of text of arbitrary width and does not require the use of dynamic exemplars. Moreover, …

Generalized Algorithmic Frameworks For Optimizing Distance Calls In Generalized Metric Space Proximity Problems And Methods For Realizing Efficient Signal Reconstruction, Jees Augustine

Computer Science and Engineering Dissertations - Archive

The exponential rise in data, along with its heterogeneity and complexity, helped individuals, businesses, hospitals, enterprises and even governments to thrive on data-driven decision making. However, as the size and complexity of the data surged, challenges in searching for similar objects within databases (proximity search) compounded. As is well known, proximity search is the key and successful method used in Information Retrieval (IR) in vast databases including, Genomics Databases, Image Databases, Video Databases, Text databases, etc. The objective is to retrieve contents from the database, similar to a given object in the database. This dissertation revisits a suite of popular …

Analiza E Efekteve Shëndetsore Gjatë Përdorimit Të Rrjetave Pa Tela Në Kushtet E Jetës Reale, Hashim Sahiti

Theses and Dissertations

Teknologjia Wireless u ideua në 1880 nga Alexander Graham Bell dhe Sumner Tainted kur u zbulua telefoni fotografik. Në ditët e sotme, ka shumë lloje të pajisjeve pa tela që përdoren për të komunikuar. Telefoni celular dhe Rrjeti Wireless janë pjesa e jetës sonë të përditshme në të gjithë botën. Pajisja Wi-Fi lejon shkëmbimin (marrjen dhe dërgimin) e të dhënave përmes pajisjeve pa tela dhe Wi-Fi lëshojnë valë radio. Pajisjet që përdorin Wi-Fi janë kompjuterët, tabletat, celularët, audio player, aparatet fotografikë digjital si dhe pajisjet përcjellëse te kompjuterit si printerët, tastiera, miu e etj. Pajisjet pa tela janë të bazuara …

Vitrina Virtuale E Librit, Genti Sheholli

Theses and Dissertations

Zhvillimi i aplikacioneve moderne në ditët e sotme nuk është vetëm proces i ndërtimit të aplikacionit. Procesi përfshin zhvillimin e shërbimeve në anën e serverit dhe aplikacionit në anën e klientit. Teknologjitë dhe opsionet për të zhvilluar një web aplikacion janë të shumta dhe varësisht se cfarë web aplikacioni duhet të zhvillohet mund të vendosim edhe për teknologjitë në secilin aspekt.

Qëllimi i këtij web aplikacioni është hulmtimi në teknologjit të kohës që kanë marrë hov të zhvillimit në aplikacionet moderne dhe hulumtim në zhvillimin e aplikacionit me struktura moderne.

Ky web aplikacion është një sintezë e proceseve, procedurave dhe …

Analizimi Dhe Zhvillimi I Sistemit Për Komunikim Në Telemjekësi Duke Përdorur Teknologjinë Webrtc, Fitim Mehmeti

Theses and Dissertations

Sistemet softuerike për komunikim në përgjithësi e në veçanti telemedicina që për qëllim ka ofrimin e kujdesit shëndetsor në distancë janë duke u bërë aset i rëndësishëm në shtetet e zhvilluara. Kufizimet në kohë, vendndodhja, kushtet atmosferike, tash së fundmi edhe pandemia COVID-19 janë ndër arsyet kryesore për zhvillimin e sistemeve të tilla. Ky punim është përqëndruar në hulumtimin, analizimin dhe krahasimin e sistemeve për komunikim online, mënyra se si funksionojnë këto sisteme, gjithashtu është fokusuar në zhvillimin e një aplikacioni për komunikim me video për pacientët dhe mjekët duke përdorur teknologjinë webRTC, ky projekt do të zhvillohet në bazë …

Ndikimi I Ueb Aplikacionit Dhe Aplikacionit Për Telefonë Në Menaxhimin Dhe Shperndarjen E Informacionit Në Ditët E Sotme, Jetmir Feka

Theses and Dissertations

Në kohën tonë teknologjia po ecën me hapa shumë të shpejtë, sidomos në fushën e pajisjeve elektronike si: kompjuterët, tabletat, telefonat e mençur etj. Numri i përdoruesve të internetit dhe pajisjeve elektronike është shumë i madh dhe është në rritje të vazhdueshme. Qëllimi i këtij hulumtimi është identifikimi efektiv i strategjisë për zhvillimin e sistemit të menaxhimeve të portaleve online, si dhe efikasitetin në shpërndarjen e tyre. Një kompanie i nevojitet një sistem për menaxhim të lajmeve i cili është një element kryesor në zhvillimin e kompanisë. Shumica e kompanive mediatike sot kanë krijuar ueb-sajte online në mënyrë që punën …

Ndërtimi I Një "To-Do" Aplikacioni Me Teknologjinë Flutter, Emnolina Brahimi

Theses and Dissertations

Aplikacionet për telefon mobil viteve të fundit kanë filluar të zëvendësojnë shumë veprimtari që në të shkuarën i kemi bërë në mënyrë primitive te themi. Nevoja për të kursyer sa më shumë kohë, energji dhe para ka bërë që të krijohen aplikacione të ndryshme, inovative dhe mbi të gjitha aplikacione që ofrojnë zgjidhje të problemeve.

Smartphone-ët por edhe shumë pajisje të tjera elektronike si tabletet dhe smartwatch janë bërë aq të nevojshme sa që pothuajse secili person i rritur mund të posedoj një të tillë.

Duke parë kërkesën dhe nevojën në tregun tonë për zgjidhje të problemeve përmes aplikacioneve, si …

Implementimi I Devops Strategjisë Në Kompani Të Vogla Dhe Të Mesme, Diellza Shabani

Theses and Dissertations

DevOps është një kornizë konceptuale për riintegrimin e zhvillimit dhe funksionimit të Sistemeve të Informacionit. Unë kam kryer një studim për strategjitë e ndryshme të implementimit të DevOps në kompani të ndryshme. Unë vërejta se DevOps nuk është studiuar në mënyrë adekuate në literaturën shkencore. Ekzistojnë relativisht pak kërkime në dispozicion në DevOps dhe studimet shpesh janë me cilësi të ulët. Unë gjithashtu vërejta që DevOps mbështetet nga një kulturë e bashkëpunimit, automatizimi, matje, shkëmbimi i informacionit dhe përdorimi i shërbimit në internet. DevOps përfiton nga SI dhe zhvillimi i operacioneve. Ajo gjithashtu ka efekte pozitive në zhvillimin e shërbimit …

Road Map Generation And Feature Extraction Algorithms From Gps Trajectories And Trajectories Data Warehousing, Tariq Alsahfi

Computer Science and Engineering Dissertations - Archive

Advanced technologies in location acquisition allow us to track the movement of moving objects (people, planes, vehicles, animals, ships, ..) in geographical space. These technologies generate a vast amount of trajectory data (TD). Several applica- tions in different fields can utilize such trajectory data, for example, traffic control management, social behavior analysis, wildlife migrations and movements, ship tra- jectories, shoppers behavior in a mall, facial nerve trajectory, location-based services (LBS) and many others. Fortunately, there are now many trajectory data sets avail- able that collected from moving objects such as cars with enabled GPS devices. Two main challenges arise when …

Adaptive Graph Convolutional Neural Network And Its Biomedical Applications, Ruoyu Li

Computer Science and Engineering Dissertations - Archive

As the rise of graph neural networks, many deep learning frameworks have been extended to graph-structured data. The research in many diverse regimes have been tremendously reshaped, especially in areas like medical image understanding. When input data reach the scale of whole slides images (WSIs), the modeling becomes more challenging and we have to balance the trade-off between performance and efficiency. Furthermore, the theory of existing graph convolution has its own constraints which prevent learning robust graph representation on data that has diverse topological structure and are infeasible for graph sampling or coarsening. To tackle the problems we introduced a …

Jointly Optimizing Sensing Pipelines For Multimodal Mixed Reality Interaction, Ramesh Darshana Rathnayake Kanatta Gamage, Ashen De Silva, Dasun Puwakdandawa, Lakmal Meegahapola, Archan Misra, Indika Perera

Research Collection School Of Computing and Information Systems

Natural human interactions for Mixed Reality Applications are overwhelmingly multimodal: humans communicate intent and instructions via a combination of visual, aural and gestural cues. However, supporting low-latency and accurate comprehension of such multimodal instructions (MMI), on resource-constrained wearable devices, remains an open challenge, especially as the state-of-the-art comprehension techniques for each individual modality increasingly utilize complex Deep Neural Network models. We demonstrate the possibility of overcoming the core limitation of latency–vs.–accuracy tradeoff by exploiting cross-modal dependencies–i.e., by compensating for the inferior performance of one model with an increased accuracy of more complex model of a different modality. We present a …

Co-Embedding Attributed Networks With External Knowledge, Pei-Chi Lo, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Attributed network embedding aims to learn representations of nodes and their attributes in a low-dimensional space that preserves their semantics. The existing embedding models, however, consider node connectivity and node attributes only while ignoring external knowledge that can enhance node representations for downstream applications. In this paper, we propose a set of new VAE-based embedding models called External Knowledge-Aware Co-Embedding Attributed Network (ECAN) Embeddings to incorporate associations among attributes from relevant external knowledge. Such external knowledge can be extracted from text corpus and knowledge graphs. We use multi-VAE structures to model the attribute associations. To cope with joint encoding of …

A Hybrid Approach For Detecting Prerequisite Relations In Multi-Modal Food Recipes, Liangming Pan, Jingjing Chen, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Modeling the structure of culinary recipes is the core of recipe representation learning. Current approaches mostly focus on extracting the workflow graph from recipes based on text descriptions. Process images, which constitute an important part of cooking recipes, has rarely been investigated in recipe structure modeling. We study this recipe structure problem from a multi-modal learning perspective, by proposing a prerequisite tree to represent recipes with cooking images at a step-level granularity. We propose a simple-yet-effective two-stage framework to automatically construct the prerequisite tree for a recipe by (1) utilizing a trained classifier to detect pairwise prerequisite relations that fuses …

Learning To Dispatch For Job Shop Scheduling Via Deep Reinforcement Learning, Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu Chi

Research Collection School Of Computing and Information Systems

Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw …