Search results for "format"
showing 10 items of 24643 documents
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression
2019
Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. Thi…
Automatic Integration of Spatial Data into the Semantic Web
2017
International audience
Self-validating bundles for flexible data access control
2016
Modern cloud-based services offer free or low-cost content sharing with significant advantages for the users but also new issues in privacy and security. To protect sensitive contents (i.e., copyrighted, top secret, and personal data) from the unauthorized access, sophisticated access management systems or/and decryption schemes have been proposed, generally based on trusted applications at client side. These applications work also as access controllers, verifying specific permissions and restrictions accessing user’s resources. We propose secure bundles (S-bundles), which encapsulate a behavioral model (provided as bytecode) to define versatile stand-alone access controllers and encoding/d…
On Detection of Network-Based Co-residence Verification Attacks in SDN-Driven Clouds
2017
Modern cloud environments allow users to consume computational and storage resources in the form of virtual machines. Even though machines running on the same cloud server are logically isolated from each other, a malicious customer can create various side channels to obtain sensitive information from co-located machines. In this study, we concentrate on timely detection of intentional co-residence attempts in cloud environments that utilize software-defined networking. SDN enables global visibility of the network state which allows the cloud provider to monitor and extract necessary information from each flow in every virtual network in online mode. We analyze the extracted statistics on d…
Kick Detection and Influx Size Estimation during Offshore Drilling Operations using Deep Learning
2019
An uncontrolled or unobserved influx or kick during drilling has the potential to induce a well blowout, one of the most harmful incidences during drilling both in regards to economic and environmental cost. Since kicks during drilling are serious risks, it is important to improve kick and loss detection performance and capabilities and to develop automatic flux detection methodology. There are clear patterns during a influx incident. However, due to complex processes and sparse instrumentation it is difficult to predict the behaviour of kicks or losses based on sensor data combined with physical models alone. Emerging technologies within Deep Learning are however quite adapt at picking up …
Image-Evoked Affect and its Impact on Eeg-Based Biometrics
2019
Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…
Towards a Security Competence of Software Developers
2020
Software growth has been explosive as people depend heavily on software on daily basis. Software development is a human-intensive effort, and developers' competence in software security is essential for secure software development. In addition, ubiquitous computing provides an added complexity to software security. Studies have treated security competences of software developers as a subsidiary of security engineers' competence instead of software engineers' competence, limiting the full knowledge of the security competences of software developers. This presents a crucial challenge for developers, educators, and users to maintain developers' competences in security. As a first step in pushi…
Finding Software Bugs in Embedded Devices
2021
AbstractThe goal of this chapter is to introduce the reader to the domain of bug discovery in embedded systems which are at the core of the Internet of Things. Embedded software has a number of particularities which makes it slightly different to general purpose software. In particular, embedded devices are more exposed to software attacks but have lower defense levels and are often left unattended. At the same time, analyzing their security is more difficult because they are very “opaque”, while the execution of custom and embedded software is often entangled with the hardware and peripherals. These differences have an impact on our ability to find software bugs in such systems. This chapt…
Escala de percepción ante crisis y pandemias : desarrollo y validación de una escala pedagógica
2021
Objetivos: Abordar la metodología de estudio con respecto a epidemia/pandemia requiere involucrar pensamiento complejo frente a los retos que denota esta situación. El objetivo de este estudio es validar un cuestionario de percepción y creencias dirigido a estudiantes de medicina y enfermería sobre el uso del cine ante la complejidad de un evento pandémico, con el propósito de potenciar las estrategias de enseñanza. Metodología: Se encuestó a 131 estudiantes. La evaluación de la confiabilidad se realizó a través del análisis de consistencia interna. La valoración de los 19 ítems se evaluó mediante análisis de correlación de Pearson y la validez de constructo mediante análisis factorial expl…