Addressing Problems with Replicability and Validity of Repository Mining Studies Through a Smart Data Platform
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Analyzing Programming Languages' Energy Consumption: an Empirical Study
Analyzing Programming Languages’ Energy Consumption: An Empirical Study Stefanos Georgiou Maria Kechagia Diomidis Spinellis Athens University of Economics and Delft University of Technology Athens University of Economics and Business Delft, The Netherlands Business Athens, Greece [email protected] Athens, Greece [email protected] [email protected] ABSTRACT increase of energy consumption.1 Recent research conducted by Motivation: Shifting from traditional local servers towards cloud Gelenbe and Caseau [7] and Van Heddeghem et al. [14] indicates a computing and data centers—where different applications are facil- rising trend of the it sector energy requirements. It is expected to itated, implemented, and communicate in different programming reach 15% of the world’s total energy consumption by 2020. languages—implies new challenges in terms of energy usage. Most of the studies, for energy efficiency, have considered energy Goal: In this preliminary study, we aim to identify energy implica- consumption at hardware level. However, there is much of evidence tions of small, independent tasks developed in different program- that software can also alter energy dissipation significantly [2, 5, 6]. 2 3 ming languages; compiled, semi-compiled, and interpreted ones. Therefore, many conference tracks (e.g. greens, eEnergy) have Method: To achieve our purpose, we collected, refined, compared, recognized the energy–efficiency at the software level as an emerg- and analyzed a number of implemented tasks from Rosetta Code, ing research challenge regarding the implementation of modern that is a publicly available Repository for programming chrestomathy. systems. Results: Our analysis shows that among compiled programming Nowadays, more companies are shifting from traditional local languages such as C, C++, Java, and Go offers the highest energy servers and mainframes towards the data centers. -
Rosetta Code: Improv in Any Language
Rosetta Code: Improv in Any Language Piotr Mirowski1, Kory Mathewson1, Boyd Branch1,2 Thomas Winters1,3, Ben Verhoeven1,4, Jenny Elfving1 1Improbotics (https://improbotics.org) 2University of Kent, United Kingdom 3KU Leuven, Dept. of Computer Science; Leuven.AI, Belgium 4ERLNMYR, Belgium Abstract Rosetta Code provides improv theatre performers with artificial intelligence (AI)-based technology to perform shows understandable across many different languages. We combine speech recognition, improv chatbots and language translation tools to enable improvisers to com- municate with each other while being understood—or comically misunderstood—by multilingual audiences. We describe the technology underlying Rosetta Code, detailing the speech recognition, machine translation, text generation and text-to-speech subsystems. We then describe scene structures that feature the system in performances in multilingual shows (9 languages). We provide evaluative feedback from performers, au- Figure 1: Example of a performed Telephone Game. Per- diences, and critics. From this feedback, we draw formers are aligned and one whispers to their partner on analogies between surrealism, absurdism, and multilin- the right a phrase in a foreign language (here, in Swedish), gual AI improv. Rosetta Code creates a new form of language-based absurdist improv. The performance re- which is then repeated to the following performer, until the mains ephemeral and performers of different languages last utterance is voiced into automated speech recognition can express themselves and their culture while accom- and translation to show how information is lost. modating the linguistic diversity of audiences. in which it is performed. Given that improv is based on the Introduction connection between the audience and the performers, watch- Theatre is one of the most important tools we have for shar- ing improv in a foreign language severely limits this link. -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Advanced Data Mining with Weka (Class 5)
Advanced Data Mining with Weka Class 5 – Lesson 1 Invoking Python from Weka Peter Reutemann Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz Lesson 5.1: Invoking Python from Weka Class 1 Time series forecasting Lesson 5.1 Invoking Python from Weka Class 2 Data stream mining Lesson 5.2 Building models in Weka and MOA Lesson 5.3 Visualization Class 3 Interfacing to R and other data mining packages Lesson 5.4 Invoking Weka from Python Class 4 Distributed processing with Apache Spark Lesson 5.5 A challenge, and some Groovy Class 5 Scripting Weka in Python Lesson 5.6 Course summary Lesson 5.1: Invoking Python from Weka Scripting Pros script captures preprocessing, modeling, evaluation, etc. write script once, run multiple times easy to create variants to test theories no compilation involved like with Java Cons programming involved need to familiarize yourself with APIs of libraries writing code is slower than clicking in the GUI Invoking Python from Weka Scripting languages Jython - https://docs.python.org/2/tutorial/ - pure-Java implementation of Python 2.7 - runs in JVM - access to all Java libraries on CLASSPATH - only pure-Python libraries can be used Python - invoking Weka from Python 2.7 - access to full Python library ecosystem Groovy (briefly) - http://www.groovy-lang.org/documentation.html - Java-like syntax - runs in JVM - access to all Java libraries on CLASSPATH Invoking Python from Weka Java vs Python Java Output public class Blah { 1: Hello WekaMOOC! public static void main(String[] -
Introduction to Weka
Introduction to Weka Overview What is Weka? Where to find Weka? Command Line Vs GUI Datasets in Weka ARFF Files Classifiers in Weka Filters What is Weka? Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Where to find Weka Weka website (Latest version 3.6): – http://www.cs.waikato.ac.nz/ml/weka/ Weka Manual: − http://transact.dl.sourceforge.net/sourcefor ge/weka/WekaManual-3.6.0.pdf CLI Vs GUI Recommended for in-depth usage Explorer Offers some functionality not Experimenter available via the GUI Knowledge Flow Datasets in Weka Each entry in a dataset is an instance of the java class: − weka.core.Instance Each instance consists of a number of attributes Attributes Nominal: one of a predefined list of values − e.g. red, green, blue Numeric: A real or integer number String: Enclosed in “double quotes” Date Relational ARFF Files The external representation of an Instances class Consists of: − A header: Describes the attribute types − Data section: Comma separated list of data ARFF File Example Dataset name Comment Attributes Target / Class variable Data Values Assignment ARFF Files Credit-g Heart-c Hepatitis Vowel Zoo http://www.cs.auckland.ac.nz/~pat/weka/ ARFF Files Basic statistics and validation by running: − java weka.core.Instances data/soybean.arff Classifiers in Weka Learning algorithms in Weka are derived from the abstract class: − weka.classifiers.Classifier Simple classifier: ZeroR − Just determines the most common class − Or the median (in the case of numeric values) − Tests how well the class can be predicted without considering other attributes − Can be used as a Lower Bound on Performance. -
Mathematica Document
Mathematica Project: Exploratory Data Analysis on ‘Data Scientists’ A big picture view of the state of data scientists and machine learning engineers. ����� ���� ��������� ��� ������ ���� ������ ������ ���� ������/ ������ � ���������� ���� ��� ������ ��� ���������������� �������� ������/ ����� ��������� ��� ���� ���������������� ����� ��������������� ��������� � ������(�������� ���������� ���������) ������ ��������� ����� ������� �������� ����� ������� ��� ������ ����������(���� �������) ��������� ����� ���� ������ ����� (���������� �������) ����������(���������� ������� ���������� ��� ���� ���� �����/ ��� �������������� � ����� ���� �� �������� � ��� ����/���������� ��������������� ������� ������������� ��� ���������� ����� �����(���� �������) ����������� ����� / ����� ��� ������ ��������������� ���������� ����������/�++ ������/������������/����/������ ���� ������� ����� ������� ������� ����������������� ������� ������� ����/����/�������/��/��� ����������(�����/����-�������� ��������) ������������ In this Mathematica project, we will explore the capabilities of Mathematica to better understand the state of data science enthusiasts. The dataset consisting of more than 10,000 rows is obtained from Kaggle, which is a result of ‘Kaggle Survey 2017’. We will explore various capabilities of Mathematica in Data Analysis and Data Visualizations. Further, we will utilize Machine Learning techniques to train models and Classify features with several algorithms, such as Nearest Neighbors, Random Forest. Dataset : https : // www.kaggle.com/kaggle/kaggle -
WEKA: the Waikato Environment for Knowledge Analysis
WEKA: The Waikato Environment for Knowledge Analysis Stephen R. Garner Department of Computer Science, University of Waikato, Hamilton. ABSTRACT WEKA is a workbench designed to aid in the application of machine learning technology to real world data sets, in particular, data sets from New Zealand’s agricultural sector. In order to do this a range of machine learning techniques are presented to the user in such a way as to hide the idiosyncrasies of input and output formats, as well as allow an exploratory approach in applying the technology. The system presented is a component based one that also has application in machine learning research and education. 1. Introduction The WEKA machine learning workbench has grown out of the need to be able to apply machine learning to real world data sets in a way that promotes a “what if?…” or exploratory approach. Each machine learning algorithm implementation requires the data to be present in its own format, and has its own way of specifying parameters and output. The WEKA system was designed to bring a range of machine learning techniques or schemes under a common interface so that they may be easily applied to this data in a consistent method. This interface should be flexible enough to encourage the addition of new schemes, and simple enough that users need only concern themselves with the selection of features in the data for analysis and what the output means, rather than how to use a machine learning scheme. The WEKA system has been used over the past year to work with a variety of agricultural data sets in order to try to answer a range of questions. -
Artificial Intelligence for the Prediction of Exhaust Back Pressure Effect On
applied sciences Article Artificial Intelligence for the Prediction of Exhaust Back Pressure Effect on the Performance of Diesel Engines Vlad Fernoaga 1 , Venetia Sandu 2,* and Titus Balan 1 1 Faculty of Electrical Engineering and Computer Science, Transilvania University, Brasov 500036, Romania; [email protected] (V.F.); [email protected] (T.B.) 2 Faculty of Mechanical Engineering, Transilvania University, Brasov 500036, Romania * Correspondence: [email protected]; Tel.: +40-268-474761 Received: 11 September 2020; Accepted: 15 October 2020; Published: 21 October 2020 Featured Application: This paper presents solutions for smart devices with embedded artificial intelligence dedicated to vehicular applications. Virtual sensors based on neural networks or regressors provide real-time predictions on diesel engine power loss caused by increased exhaust back pressure. Abstract: The actual trade-off among engine emissions and performance requires detailed investigations into exhaust system configurations. Correlations among engine data acquired by sensors are susceptible to artificial intelligence (AI)-driven performance assessment. The influence of exhaust back pressure (EBP) on engine performance, mainly on effective power, was investigated on a turbocharged diesel engine tested on an instrumented dynamometric test-bench. The EBP was externally applied at steady state operation modes defined by speed and load. A complete dataset was collected to supply the statistical analysis and machine learning phases—the training and testing of all the AI solutions developed in order to predict the effective power. By extending the cloud-/edge-computing model with the cloud AI/edge AI paradigm, comprehensive research was conducted on the algorithms and software frameworks most suited to vehicular smart devices. -
Statistical Software
Statistical Software A. Grant Schissler1;2;∗ Hung Nguyen1;3 Tin Nguyen1;3 Juli Petereit1;4 Vincent Gardeux5 Keywords: statistical software, open source, Big Data, visualization, parallel computing, machine learning, Bayesian modeling Abstract Abstract: This article discusses selected statistical software, aiming to help readers find the right tool for their needs. We categorize software into three classes: Statisti- cal Packages, Analysis Packages with Statistical Libraries, and General Programming Languages with Statistical Libraries. Statistical and analysis packages often provide interactive, easy-to-use workflows while general programming languages are built for speed and optimization. We emphasize each software's defining characteristics and discuss trends in popularity. The concluding sections muse on the future of statistical software including the impact of Big Data and the advantages of open-source languages. This article discusses selected statistical software, aiming to help readers find the right tool for their needs (not provide an exhaustive list). Also, we acknowledge our experiences bias the discussion towards software employed in scholarly work. Throughout, we emphasize the software's capacity to analyze large, complex data sets (\Big Data"). The concluding sections muse on the future of statistical software. To aid in the discussion, we classify software into three groups: (1) Statistical Packages, (2) Analysis Packages with Statistical Libraries, and (3) General Programming Languages with Statistical Libraries. This structure -
MLJ: a Julia Package for Composable Machine Learning
MLJ: A Julia package for composable machine learning Anthony D. Blaom1, 2, 3, Franz Kiraly3, 4, Thibaut Lienart3, Yiannis Simillides7, Diego Arenas6, and Sebastian J. Vollmer3, 5 1 University of Auckland, New Zealand 2 New Zealand eScience Infrastructure, New Zealand 3 Alan Turing Institute, London, United Kingdom 4 University College London, United Kingdom 5 University of Warwick, United Kingdom 6 University of St Andrews, St Andrews, United Kingdom 7 Imperial College London, United Kingdom DOI: 10.21105/joss.02704 Software • Review Introduction • Repository • Archive Statistical modeling, and the building of complex modeling pipelines, is a cornerstone of modern data science. Most experienced data scientists rely on high-level open source modeling Editor: Yuan Tang toolboxes - such as sckit-learn (Buitinck et al., 2013; Pedregosa et al., 2011) (Python); Weka (Holmes et al., 1994) (Java); mlr (Bischl et al., 2016) and caret (Kuhn, 2008) (R) - for quick Reviewers: blueprinting, testing, and creation of deployment-ready models. They do this by providing a • @degleris1 common interface to atomic components, from an ever-growing model zoo, and by providing • @henrykironde the means to incorporate these into complex work-flows. Practitioners are wanting to build increasingly sophisticated composite models, as exemplified in the strategies of top contestants Submitted: 21 July 2020 in machine learning competitions such as Kaggle. Published: 07 November 2020 MLJ (Machine Learning in Julia) (A. Blaom, 2020b) is a toolbox written in Julia that pro- License Authors of papers retain vides a common interface and meta-algorithms for selecting, tuning, evaluating, composing copyright and release the work and comparing machine model implementations written in Julia and other languages. -
Snapshots of Open Source Project Management Software
International Journal of Economics, Commerce and Management United Kingdom ISSN 2348 0386 Vol. VIII, Issue 10, Oct 2020 http://ijecm.co.uk/ SNAPSHOTS OF OPEN SOURCE PROJECT MANAGEMENT SOFTWARE Balaji Janamanchi Associate Professor of Management Division of International Business and Technology Studies A.R. Sanchez Jr. School of Business, Texas A & M International University Laredo, Texas, United States of America [email protected] Abstract This study attempts to present snapshots of the features and usefulness of Open Source Software (OSS) for Project Management (PM). The objectives include understanding the PM- specific features such as budgeting project planning, project tracking, time tracking, collaboration, task management, resource management or portfolio management, file sharing and reporting, as well as OSS features viz., license type, programming language, OS version available, review and rating in impacting the number of downloads, and other such usage metrics. This study seeks to understand the availability and accessibility of Open Source Project Management software on the well-known large repository of open source software resources, viz., SourceForge. Limiting the search to “Project Management” as the key words, data for the top fifty OS applications ranked by the downloads is obtained and analyzed. Useful classification is developed to assist all stakeholders to understand the state of open source project management (OSPM) software on the SourceForge forum. Some updates in the ranking and popularity of software since -
Using Tcl to Curate Openstreetmap Kevin B
Using Tcl to curate OpenStreetMap Kevin B. Kenny 5 November 2019 The massive OpenStreetMap project, which aims to crowd-source a detailed map of the entire Earth, occasionally benefits from the import of public-domain data, usually from one or another government. Tcl, used with only a handful of extensions to orchestrate a large suite of external tools, has proven to be a valuable framework in carrying out the complex tasks involved in such an import. This paper presents a sample workflow of several such imports and how Tcl enables it. mapped. In some cases, the only acceptable approach is to Introduction avoid importing the colliding object altogether. OpenStreetMap (https://www.openstreetmap.org/) is an This paper discusses some case studies in using Tcl scripts ambitious project to use crowdsourcing, or the open-source to manage the task of data import, including data format model of development, to map the entire world in detail. In conversion, managing of the relatively easy data integrity effect, OpenStreetMap aims to be to the atlas what issues such as topological inconsistency, identifying objects Wikipedia is to the encyclopaedia. for conflation, and applying the changes. In many ways, it Project contributors (who call themselves, “mappers,” in gets back to the roots of Tcl. There is no ‘programming in preference to any more formal term like “surveyors”) use the large’ to be done here. The scripts are no more than a tools that work with established programming interfaces to few hundred lines, and all the intensive calculation and data edit a database with a radically simple structure and map management is done in an existing ecosystem of tools.