464,886 research outputs found
An Exploratory Study of Forces and Frictions affecting Large-Scale Model-Driven Development
In this paper, we investigate model-driven engineering, reporting on an
exploratory case-study conducted at a large automotive company. The study
consisted of interviews with 20 engineers and managers working in different
roles. We found that, in the context of a large organization, contextual forces
dominate the cognitive issues of using model-driven technology. The four forces
we identified that are likely independent of the particular abstractions chosen
as the basis of software development are the need for diffing in software
product lines, the needs for problem-specific languages and types, the need for
live modeling in exploratory activities, and the need for point-to-point
traceability between artifacts. We also identified triggers of accidental
complexity, which we refer to as points of friction introduced by languages and
tools. Examples of the friction points identified are insufficient support for
model diffing, point-to-point traceability, and model changes at runtime.Comment: To appear in proceedings of MODELS 2012, LNCS Springe
Systemizers are better code-breakers: self-reported systemizing predicts code-breaking performance in expert hackers and naĂŻve participants
Studies on hacking have typically focused on motivational aspects and general personality traits of the individuals who engage in hacking; little systematic research has been conducted on predispositions that may be associated not only with the choice to pursue a hacking career but also with performance in either naïve or expert populations. Here, we test the hypotheses that two traits that are typically enhanced in autism spectrum disorders—attention to detail and systemizing—may be positively related to both the choice of pursuing a career in information security and skilled performance in a prototypical hacking task (i.e., crypto-analysis or code-breaking). A group of naïve participants and of ethical hackers completed the Autism Spectrum Quotient, including an attention to detail scale, and the Systemizing Quotient (Baron-Cohen et al., 2001, 2003). They were also tested with behavioral tasks involving code-breaking and a control task involving security X-ray image interpretation. Hackers reported significantly higher systemizing and attention to detail than non-hackers. We found a positive relation between self-reported systemizing (but not attention to detail) and code-breaking skills in both hackers and non-hackers, whereas attention to detail (but not systemizing) was related with performance in the X-ray screening task in both groups, as previously reported with naïve participants (Rusconi et al., 2015). We discuss the theoretical and translational implications of our findings
Cracking the Code on Stem: A People Strategy for Nevada\u27s Economy
Nevada has in place a plausible economic diversification strategy—and it’s beginning to work. Now, the state and its regions need to craft a people strategy. Specifically, the state needs to boost the number of Nevadans who possess at least some postsecondary training in the fields of science, technology, engineering, or math—the so-called “STEM” disciplines (to which some leaders add arts and design to make it “STEAM”).
The moment is urgent—and only heightened by the projected worker needs of Tesla Motors’ planned “gigafactory” for lithium-ion batteries in Storey County.
Even before the recent Tesla commitment, a number of the more high-tech industry sectors targeted by the state’s new economic diversification strategy had begun to deliver significant growth. Most notable in fast-growing sectors like Business IT Ecosystems (as defined by the Governor’s Office for Economic Development) and large sectors like Health and Medical Services, this growth has begun to increase the demand in Nevada for workers with at least a modicum of postsecondary training in one or more STE M discipline.
However, there is a problem. Even though many available opportunities require no more than the right community college certificate, insufficient numbers of Nevadans have pursued even a little STEM training. As a result, too few Nevadans are ready to participate in the state’s emerging STEM economy. The upshot: Without concerted action to prepare more Nevadans for jobs in STEM-intensive fields, skills shortages could limit growth in the state’s most promising target industries and Nevadans could miss out on employment that offers superior paths to opportunity and advancement.
Which is the challenge this report addresses: Aimed at focusing the state at a critical moment, this analysis speaks to Nevada’s STEM challenge by providing a new assessment of Nevada’s STEM economy and labor market as well as a review of actions that leaders throughout the state—whether in the public, private, civic, or philanthropic sectors—can take to develop a workforce capable of supporting continued growth through economic diversification
Simulating city growth by using the cellular automata algorithm
The objective of this thesis is to develop and implement a Cellular Automata
(CA) algorithm to simulate urban growth process. It attempts to satisfy the
need to predict the future shape of a city, the way land uses sprawl in the
surroundings of that city and its population. Salonica city in Greece is
selected as a case study to simulate its urban growth. Cellular automaton
(CA) based models are increasingly used to investigate cities and urban
systems. Sprawling cities may be considered as complex adaptive systems,
and this warrants use of methodology that can accommodate the space-time
dynamics of many interacting entities. Automata tools are well-suited for
representation of such systems. By means of illustrating this point, the
development of a model for simulating the sprawl of land uses such as
commercial and residential and calculating the population who will reside in
the city is discussed
Spartan Daily, October 3, 1983
Volume 81, Issue 24https://scholarworks.sjsu.edu/spartandaily/7073/thumbnail.jp
An evaluation framework to drive future evolution of a research prototype
The Open Source Component Artefact Repository (OSCAR) requires
evaluation to confirm its suitability as a development environment
for distributed software engineers. The evaluation will take note of
several factors including usability of OSCAR as a stand-alone system,
scalability and maintainability of the system and novel features not
provided by existing artefact management systems. Additionally, the
evaluation design attempts to address some of the omissions (due to
time constraints) from the industrial partner evaluations.
This evaluation is intended to be a prelude to the evaluation of the
awareness support being added to OSCAR; thus establishing a baseline
to which the effects of awareness support may be compared
The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
Third party apps that work on top of personal cloud services such as Google
Drive and Dropbox, require access to the user's data in order to provide some
functionality. Through detailed analysis of a hundred popular Google Drive apps
from Google's Chrome store, we discover that the existing permission model is
quite often misused: around two thirds of analyzed apps are over-privileged,
i.e., they access more data than is needed for them to function. In this work,
we analyze three different permission models that aim to discourage users from
installing over-privileged apps. In experiments with 210 real users, we
discover that the most successful permission model is our novel ensemble method
that we call Far-reaching Insights. Far-reaching Insights inform the users
about the data-driven insights that apps can make about them (e.g., their
topics of interest, collaboration and activity patterns etc.) Thus, they seek
to bridge the gap between what third parties can actually know about users and
users perception of their privacy leakage. The efficacy of Far-reaching
Insights in bridging this gap is demonstrated by our results, as Far-reaching
Insights prove to be, on average, twice as effective as the current model in
discouraging users from installing over-privileged apps. In an effort for
promoting general privacy awareness, we deploy a publicly available privacy
oriented app store that uses Far-reaching Insights. Based on the knowledge
extracted from data of the store's users (over 115 gigabytes of Google Drive
data from 1440 users with 662 installed apps), we also delineate the ecosystem
for third-party cloud apps from the standpoint of developers and cloud
providers. Finally, we present several general recommendations that can guide
other future works in the area of privacy for the cloud
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