Opin vísindi
Opin vísindi er varðveislusafn vísindaefnis og doktorsritgerða í opnum aðgangi á vegum íslenskra háskóla og Landsbókasafns Íslands - Háskólabókasafns.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Opinn aðgangur að rannsóknaniðurstöðum er í samræmi við 10. gr. laga nr. 3/2003 um opinberan stuðning við vísindarannsóknir sem og kröfur innlendra og erlendra rannsóknasjóða. Markmiðið með opnum aðgangi er að niðurstöður rannsókna séu aðgengilegar sem flestum óhindrað og án endurgjalds á rafrænu formi. Vistun í varðveislusafninu er varanleg og ætlað að tryggja aðgang að vísindaefni íslenskra háskóla í opnum aðgangi um ókomna tíð. Varðveislusafnið Opin vísindi er tengt við rannsóknagáttina IRIS og rannsóknaniðurstöður í opnum aðgangi sem eru skráðar í IRIS eru um leið vistaðar og gerðar aðgengilegar til framtíðar í varðveislusafninu. Með því að safna þessu efni saman í eitt safn verður aðgangur að því einfaldur og þægilegur fyrir alla sem vilja kynna sér það og geta þannig notið þess öfluga vísindastarfs sem fram fer í háskólum landsins.
Varðveislusafnið er OpenAIRE / OpenAIREplus samhæft og samrýmist kröfum sem gerðar eru um birtingu rannsóknaniðurstaðna úr verkefnum sem styrkt eru úr evrópsku rannsóknaáætlununum FP7 og H2020.
Varðveislusafnið notar opna hugbúnaðinn DSpace.
Nýlega bætt við
An infrastructure for robotic applications as cloud computing services
(2014) Mouradian, Carla; Errounda, Fatima Zahra; Belqasmi, Fatna; Glitho, Roch; Department of Computer Science
Robotic applications are becoming ubiquitous. They are widely used in several areas (e.g., healthcare, disaster management, and manufacturing). However, their provisioning still faces several challenges such as cost and resource usage efficiency. Cloud computing is an emerging paradigm that may aid in tackling these challenges. It has three main facets: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). This paper focuses on the IaaS aspects of robotic applications as cloud computing services. It proposes an architecture that enables cost efficiency through virtualization and dynamic task delegation to robots, including robots that might belong to other clouds. Overlays and RESTful Web services are used as cornerstones. A prototype is built using LEGO Mindstorms NXT as the robotic platform, and JXTA as the overlay middleware. Related work is reviewed, the functional entities and interfaces of the architecture are described, and the prototype architecture is presented along with the implemented scenario.
Continuous Location Statistics Sharing Algorithm with Local Differential Privacy
(Institute of Electrical and Electronics Engineers Inc., 2018-07-02) Zahra, Fatima; Liu, Yan; Abe, Naoki; Liu, Huan; Pu, Calton; Hu, Xiaohua; Ahmed, Nesreen; Qiao, Mu; Song, Yang; Kossmann, Donald; Liu, Bing; Lee, Kisung; Tang, Jiliang; He, Jingrui; Saltz, Jeffrey; Department of Computer Science
Continuous sharing of location statistics produces valuable knowledge to understand important phenomena, such as popular places or pattern behaviors. Most importantly, data should be shared without jeopardizing users' privacy. Differential privacy becomes de-facto technique for private statistical data release. Much work focuses on the centralized setting where users send their original data to a trusted server. Then the server adds controlled noises to generate differentially private statistics. This centralized approach is vulnerable to attacks where an adversary may access the true data by attacking the trusted server. Local differential privacy neutralizes this type of attacks by allowing each user to obfuscate their data before it reaches the server for statistical analysis. In this paper, we propose an algorithm to share location statistics that leverages local differential privacy combined with w-event privacy. Our solution guarantees the user's privacy when continuously releasing statistics over infinite streams. Experimental evaluation on real-life data shows our solution with strong privacy guarantee.
An Analysis of Differential Privacy Research in Location Data
(Institute of Electrical and Electronics Engineers Inc., 2019-05) Errounda, Fatima Zahra; Liu, Yan; Department of Computer Science
Location data is becoming ubiquitous with the spread of smart devices, and social media geo-tagged feeds. However, sharing location data may lead to serious privacy risks that must not be overlooked. Differential privacy is the standard technique that provides strong privacy guarantees regardless of the adversary's side information. Usually, this is achieved by adding noise to the true result of the statistical query extracted from the data. However, a straight forward application of differential privacy to location data is not always possible. The growing interest in designing solutions to achieve differential privacy that take into account the characteristics of location data is evident from the substantial number of works done in this field. This paper briefly reviews research works done in differential privacy targeted toward location data from the data flow perspective, including the collection, aggregation, and mining. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing frameworks, as well as novel approaches, that tackle the characteristics of location data. We identify multiple challenges to the application of differential privacy to location data, such as the calibration of the added noise to assure utility, finding the optimal spatial division to release the location aggregate per region while balancing privacy and utility. We also discuss the future directions concluded from the analysis.
A data annotation architecture for semantic applications in virtualized wireless sensor networks
(Institute of Electrical and Electronics Engineers Inc., 2015-06-29) Khan, Imran; Jafrin, Rifat; Errounda, Fatima Zahra; Glitho, Roch; Crespi, Noel; Morrow, Monique; Polakos, Paul; Badonnel, Remi; Xiao, Jin; Ata, Shingo; De Turck, Filip; Groza, Voicu; dos Santos, Carlos Raniery P.; Department of Computer Science
Wireless Sensor Networks (WSNs) have become very popular and are being used in many application domains (e.g. smart cities, security, gaming and agriculture). Virtualized WSNs allow the same WSN to be shared by multiple applications. Semantic applications are situation-aware and can potentially play a critical role in virtualized WSNs. However, provisioning them in such settings remains a challenge. The key reason is that semantic applications' provisioning mandates data annotation. Unfortunately it is no easy task to annotate data collected in virtualized WSNs. This paper proposes a data annotation architecture for semantic applications in virtualized heterogeneous WSNs. The architecture uses overlays as the cornerstone, and we have built a prototype in the cloud environment using Google App Engine. The early performance measurements are also presented.
A Mobility Forecasting Framework with Vertical Federated Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) Errounda, Fatima Zahra; Liu, Yan; Va Leong, Hong; Sarvestani, Sahra Sedigh; Teranishi, Yuuichi; Cuzzocrea, Alfredo; Kashiwazaki, Hiroki; Towey, Dave; Yang, Ji-Jiang; Shahriar, Hossain; Department of Computer Science
With the prevalence of mobile devices and location-based services, forecasting human mobility has become a critical topic in ubiquitous computing. Existing forecasting approaches usually adopt frameworks with a centralized mobility data holder. However, mobility data typically pertains to independent organizations, introducing two learning challenges. First, since each organization only holds a location domain subset, none can tackle a forecasting model that covers the whole location domain. Second, distributed mobility data compromises the spatio-temporal correlation between locations hindering learning. Hence, reducing the forecasting accuracy. This work proposes a mobility vertical federated forecasting (MVFF) framework that allows the learning process to be jointly conducted over vertically partitioned data belonging to multiple organizations. MVFF enables the forecasting of mobility predictions covering a joint location domain. We evaluate MVFF's performance over two real-world datasets using different spatial and temporal neural network algorithms. Experimental results demonstrate that the two datasets' mean percentage error performance gains are up to 12% and 4% compared to the state-of-the-art, respectively.
Flokkar í Opnum vísindum
Veldu flokk til að skoða.
- University of Iceland
- University of Akureyri
- Bifröst University
- Hólar University College
- IRIS
- Agricultural University of Iceland
- National and University Library of Iceland
- Iceland University of the Arts