9,146 research outputs found
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Land Use and Land Cover Mapping in a Changing World
It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classiďŹcation systems
LASSO â an observatorium for the dynamic selection, analysis and comparison of software
Mining software repositories at the scale of 'big code' (i.e., big data) is a challenging activity. As well as finding a suitable software corpus and making it programmatically accessible through an index or database, researchers and practitioners have to establish an efficient analysis infrastructure and precisely define the metrics and data extraction approaches to be applied. Moreover, for analysis results to be generalisable, these tasks have to be applied at a large enough scale to have statistical significance, and if they are to be repeatable, the artefacts need to be carefully maintained and curated over time. Today, however, a lot of this work is still performed by human beings on a case-by-case basis, with the level of effort involved often having a significant negative impact on the generalisability and repeatability of studies, and thus on their overall scientific value.
The general purpose, 'code mining' repositories and infrastructures that have emerged in recent years represent a significant step forward because they automate many software mining tasks at an ultra-large scale and allow researchers and practitioners to focus on defining the questions they would like to explore at an abstract level. However, they are currently limited to static analysis and data extraction techniques, and thus cannot support (i.e., help automate) any studies which involve the execution of software systems. This includes experimental validations of techniques and tools that hypothesise about the behaviour (i.e., semantics) of software, or data analysis and extraction techniques that aim to measure dynamic properties of software.
In this thesis a platform called LASSO (Large-Scale Software Observatorium) is introduced that overcomes this limitation by automating the collection of dynamic (i.e., execution-based) information about software alongside static information. It features a single, ultra-large scale corpus of executable software systems created by amalgamating existing Open Source software repositories and a dedicated DSL for defining abstract selection and analysis pipelines. Its key innovations are integrated capabilities for searching for selecting software systems based on their exhibited behaviour and an 'arena' that allows their responses to software tests to be compared in a purely data-driven way. We call the platform a 'software observatorium' since it is a place where the behaviour of large numbers of software systems can be observed, analysed and compared
Google search and the mediation of digital health information: a case study on unproven stem cell treatments
Google Search occupies a unique space within broader discussions of direct-to-consumer marketing of stem cell treatments in digital spaces. For patients, researchers, regulators, and the wider public, the search platform influences the who, what, where, and why of stem cell treatment information online. Ubiquitous and opaque, Google Search mediates which users are presented what types of content when these stakeholders engage in online searches around health information. The platform also sways the activities of content producers and the characteristics of the content they produce. For those seeking and studying information on digital health, this platform influence raises difficult questions around risk, authority, intervention, and oversight.
This thesis addresses a critical gap in digital methodologies used in mapping and characterising that influence as part of wider debates around algorithmic accountability within STS and digital health scholarship. By adopting a novel methodological approach to Blackbox auditing and data collection, I provide a unique evidentiary base for the analysis of ads, organic results, and the platform mechanisms of influence on queries related to stem cell treatments. I explore the question: how does Google Search mediate information that people access online about âprovenâ and âunprovenâ stem cell treatments?
Here I show that, in spite of a general ban on advertisements of stem cell treatments, users continue to be presented with content promoting unproven treatments. The types, frequency, and commercial intent of results related to stem cell treatments shifted across user groups including geography and, more troublingly, those impacted by Parkinsonâs Disease and Multiple Sclerosis.
Additionally, I find evidence that the technological structure of Google Search itself enables primary and secondary commercial activities around the mediation and dissemination of health information online. It suggests that Google Searchâs algorithmically-mediated rendering of search results â including both commercial and non-commercial activities - has critical implications for the present and future of digital health studies
Youtube Videos As Learning Media:A Review In EFL Contexts
YouTube has become one of the most visited social media globally. Its saturation in students' lives has triggered experiments and observations on the use of YouTube as learning media. This article discusses the use of YouTube videos to teach English language skills using George's literature review model. The databases for selecting the articles used as the data source in this study were Google scholar and ERIC. The relevant articles were published in reputable international journals and nationally accredited journals in the past ten years. Studies on the use of YouTube videos as a teaching medium indicate that using YouTube videos can improve students' listening, reading, speaking, and writing skills and solve their problems in mastering the skills. Furthermore, YouTube videos have been proven to help students solve their grammar difficulties and increase their vocabulary. YouTube videos are also agreed to have kept the students more interested, motivated, and enthusiastic towards English language learning. These results imply that relevant YouTube videos can provide good learning media for supporting students' learning of English as a Foreign Language
Restorative perception of urban streets: Interpretation using deep learning and MGWR models
Restorative environments help people recover from mental fatigue and negative emotional and physical reactions to stress. Excellent restorative environments in urban streets help people focus and improve their daily behavioral performance, allowing them to regain efficient information processing skills and cognitive levels. High-density urban spaces create obstacles in resident interactions with the natural environment. For urban residents, the restorative function of the urban space is more important than that of the natural environment in the suburbs. An urban street is a spatial carrier used by residents on a daily basis; thus, the urban street has considerable practical value in terms of improving the urban environment to have effective restorative function. Thus, in this study, we explored a method to determine the perceived restorability of urban streets using street view data, deep learning models, and the Ordinary Least Squares (OLS), the multiscale geographically weighted regression (MGWR) model. We performed an empirical study in the Nanshan District of Shenzhen, China. Nanshan District is a typical high-density city area in China with a large population and limited urban resources. Using the street view images of the study area, a deep learning scoring model was developed, the SegNet algorithm was introduced to segment and classify the visual street elements, and a random forest algorithm based on the restorative factor scale was employed to evaluate the restorative perception of urban streets. In this study, spatial heterogeneity could be observed in the restorative perception data, and the MGWR models yielded higher R2 interpretation strength in terms of processing the urban street restorative data compared to the ordinary least squares and geographically weighted regression (GWR) models. The MGWR model is a regression model that uses different bandwidths for different visual street elements, thereby allowing additional detailed observation of the extent and relevance of the impact of different elements on restorative perception. Our research also supports the exploration of the size of areas where heterogeneity exists in space for each visual street element. We believe that our results can help develop informed design guidelines to enhance street restorative and help professionals develop targeted design improvement concepts based on the restorative nature of the urban street
Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience
studying during COVID 19 confinement periods matched the findings in recent research about the
importance of digital accessibility through more e-learning intensive academic experiences. Narrative
and systematic literature reviews enabled providing context in World Health Organizationâs
International Classification of Functioning, Disability and Health, legal and standards framework and
information technology and communication state-of-the art. Assessing Portuguese higher education
institutionsâ web sites alerted to the fact that only outlying institutions implemented near perfect,
accessibility-wise, websites.
Therefore a gap was identified in how accessible the Portuguese higher education websites are, the
needs of all students, including those with disabilities, and even the accessibility minimum legal
requirements for digital products and the services provided by public or publicly funded organizations.
Having identified a problem in society and exploring the scientific base of knowledge for context and
state of the art was a first stage in the Design Science Research methodology, to which followed
development and validation cycles of an Inclusive Intelligent Learning Management System
Framework. The framework blends various Data Science study fields contributions with accessibility
guidelines compliant interface design and content upload accessibility compliance assessment.
Validation was provided by a focus group whose inputs were considered for the version presented in
this dissertation. Not being the purpose of the research to deliver a complete implementation of the
framework and lacking consistent data to put all the modules interacting with each other, the most
relevant modules were tested with open data as proof of concept.
The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute
Scientific Repository and is to be completed with the publication of this thesis and the already started
PhDâs findings in relevant journals and conferences
- âŚ