# On the Energy Footprint of Mobile Testing Frameworks

High energy consumption is a challenging issue that an ever increasing number of mobile applications face today.However, energy consumption is being tested in an ad hoc way, despite being an important non-functional requirement of an application.Such limitation becomes particularly disconcerting during software testing: on the one hand, developers do not really know how to measure energy; on the other hand, there is no knowledge as to what is the energy overhead imposed by the testing framework.In this paper, as we evaluate eight popular mobile UI automation frameworks, we have discovered t

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Fri, 12/14/2018 - 08:21

# Sample Dataset for Testing

10,000 rows of sample data in four formats (CSV, JSON, XML, XLSX) for testing. Fields are as follows: seq, first, last, age, street, city, state, zip, dollar, pick, date, latitude, longitude, birthday, ccnumber, dollar, ccnumber, phone, email, sentence, paragraph

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Wed, 12/12/2018 - 10:10

# One Pass Evaluation and Area-Under-the-Curve Tracking results of the proposed tracker on OTB2015 dataset

The bounding boxes of "Robust Visual Tracking Based on Adaptive Extraction and Enhancement of Correlation Filter" tracker on the whole OTB-2015 dataset.

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Wed, 12/12/2018 - 09:58

# PerTract: Models, Measurements, Analysis

This dataset contains the performance models, simulation and monitoring results, and analysis scripts that we used for our evaluation.

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Wed, 12/12/2018 - 10:05

# A Study of Feature Scattering in the Linux Kernel

Feature code is often scattered across a software system. Scattering is not necessarily bad if used with care, as witnessed by systems with highly scattered features that evolved successfully. Feature scattering, often realized with a pre-processor, circumvents limitations of programming languages and software architectures. Unfortunately, little is known about the principles governing scattering in large and long-living software systems.

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Wed, 12/12/2018 - 10:01

# A Study of Feature Scattering in the Linux Kernel

Feature code is often scattered across a software system. Scattering is not necessarily bad if used with care, as witnessed by systems with highly scattered features that evolved successfully. Feature scattering, often realized with a pre-processor, circumvents limitations of programming languages and software architectures. Unfortunately, little is known about the principles governing scattering in large and long-living software systems.

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Wed, 12/12/2018 - 10:02

# web hosting providers monitoring data

This dataset contains the information about 305 shared web hosting providers, their uptime, response time, number of servers and sites for the period of the previous 3 months (08/14/2018 – 11/11/018).

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Wed, 11/14/2018 - 05:04

# Geomagnetic Field Based Indoor Landmark Classification Using Deep Learning

The Geomagnetic field can be used for classifying different landmark locations inside a big building.

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Thu, 11/08/2018 - 10:34

# Collatz Automata and Compute Residue Class from Reduced Dynamics by Formula

The data set provided source code in C on how to compute Collatz dynamics by automata in terms of residue classes. It also includes algorithms implemented by C codes that can output residue classes by inputting reduced dynamics.  The formular for computing a residue class for a given reduced dynamics is as follows:

Function $Invrs(\cdot)$.

$Invrs: c \rightarrow rs$ takes as input \\

$c=O$ or \\

$c=I^{p_1}O^{q_1}I^{p_2}O^{q_2}...I^{p_n}O^{q_n} \in \{I,O\}^{\geq 2},$ $p_i,q_i\in \mathbb{N}^*, i=1,2,...,n, n \in \mathbb{N}^*$\\

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Thu, 11/08/2018 - 10:34

# Experimental data from: "A Study on the Suitability of Visual Languages for Non-Expert Robot Programmers"

Once the three courses for the three learning scenarios - C# OOP programming, Sphero Edu visual programming and VEDILS authoring tool - were taught, the three student groups were asked to indicate using a scale between one and four - to avoid the selection of neutral options - their perception of the clarity and the interest of the exposition (CL and IT indicators), as well as the time spent studying the course contents (ST indicator).

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Tue, 11/20/2018 - 14:40