15,096 research outputs found
Adaptive data synchronization algorithm for IoT-oriented low-power wide-area networks
The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications
Prevention of information harvesting in a cloud services environment
We consider a cloud data storage involving three entities, the cloud customer, the cloud business centre which provides services, and the cloud data storage centre. Data stored in the data storage centre comes from a variety of customers and some of these customers may compete with each other in the market place or may own data which comprises confidential information about their own clients. Cloud staff have access to data in the data storage centre which could be used to steal identities or to compromise cloud customers. In this paper, we provide an efficient method of data storage which prevents staff from accessing data which can be abused as described above. We also suggest a method of securing access to data which requires more than one staff member to access it at any given time. This ensures that, in case of a dispute, a staff member always has a witness to the fact that she accessed data
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
Computer vision is experiencing an AI renaissance, in which machine learning
models are expediting important breakthroughs in academic research and
commercial applications. Effectively training these models, however, is not
trivial due in part to hyperparameters: user-configured values that control a
model's ability to learn from data. Existing hyperparameter optimization
methods are highly parallel but make no effort to balance the search across
heterogeneous hardware or to prioritize searching high-impact spaces. In this
paper, we introduce a framework for massively Scalable Hardware-Aware
Distributed Hyperparameter Optimization (SHADHO). Our framework calculates the
relative complexity of each search space and monitors performance on the
learning task over all trials. These metrics are then used as heuristics to
assign hyperparameters to distributed workers based on their hardware. We first
demonstrate that our framework achieves double the throughput of a standard
distributed hyperparameter optimization framework by optimizing SVM for MNIST
using 150 distributed workers. We then conduct model search with SHADHO over
the course of one week using 74 GPUs across two compute clusters to optimize
U-Net for a cell segmentation task, discovering 515 models that achieve a lower
validation loss than standard U-Net.Comment: 10 pages, 6 figure
Health Access Broker: Secure, Patient-Controlled Management of Personal Health Records in the Cloud
Secure and privacy-preserving management of Personal Health Records (PHRs)
has proved to be a major challenge in modern healthcare. Current solutions
generally do not offer patients a choice in where the data is actually stored
and also rely on at least one fully trusted element that patients must also
trust with their data. In this work, we present the Health Access Broker (HAB),
a patient-controlled service for secure PHR sharing that (a) does not impose a
specific storage location (uniquely for a PHR system), and (b) does not assume
any of its components to be fully secure against adversarial threats. Instead,
HAB introduces a novel auditing and intrusion-detection mechanism where its
workflow is securely logged and continuously inspected to provide auditability
of data access and quickly detect any intrusions.Comment: Copy of the paper accepted at 13th International Conference on
Computational Intelligence in Security for Information Systems (CISIS
Endoscopic imaging of quantum gases through a fiber bundle
We use a coherent fiber bundle to demonstrate the endoscopic absorption
imaging of quantum gases. We show that the fiber bundle introduces spurious
noise in the picture mainly due to the strong core-to-core coupling. By direct
comparison with free-space pictures, we observe that there is a maximum column
density that can be reliably measured using our fiber bundle, and we derive a
simple criterion to estimate it. We demonstrate that taking care of not
exceeding such maximum, we can retrieve exact quantitative information about
the atomic system, making this technique appealing for systems requiring
isolation form the environment
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