Whole body bone scanning technology is a common diagnostic method for bone diseases such as bone metastases and plays an important role in the early diagnosis and treatment of human bone diseases. In this paper, a dee...
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The pervasive presence of IoT sensing devices combined with advances in cloud-based services has made historical- and forecast weather data services widely available for use in smart software systems. The contribution...
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The pervasive presence of IoT sensing devices combined with advances in cloud-based services has made historical- and forecast weather data services widely available for use in smart software systems. The contribution of this paper is the design, performance evaluation, and validation of a software implementation of model-based dynamic fire risk prediction for wooden homes using local weather data. A key feature of the implementation is that the software architecture has been designed to support the use of different underlying cloud-based weather data services, and the integration as a service in third-party smart systems embedding fire risk predictions. The performed evaluation shows that the implementation is efficient, as weather data can be retrieved, preprocessed and fire risk predictions computed within seconds using only in the order of Kb’s of memory; and accurate considering our sets of data, as the fire risks computed by the implementation have been validated against a set of in-situ measurements.
Hidden Vector Encryption (HVE) is a new kind of attribute-based encryption in which a vector is hidden in the ciphertext or linked with the secret key. In ESORICS 2014, Phuong et al. proposed an HVE scheme with consta...
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In recent years, the widespread use of online facial videos has brought increasing attention to remote photoplethys-mography (rPPG) in affective computing. Recovering blood volume pulse (BVP) signals from facial video...
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Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. In this paper, we focus on the safe probabilistic invariance verification problem for discrete-ti...
Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. In this paper, we focus on the safe probabilistic invariance verification problem for discrete-time dynamical systems subject to stochastic disturbances over the infinite time horizon. Our goal is to compute the lower and upper bounds of the liveness probability for a given safe set and set of initial states. This probability represents the likelihood that the system will remain within the safe set for all time. To address this problem, we draw inspiration from stochastic barrier certificates for safety verification and build upon the findings in [21], where an equation was presented for exact probability analysis. We present two sets of optimizations and demonstrate their effectiveness through two examples, using semi-definite programming tools.
In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect ...
In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users’ privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem—key-value set secure aggregation—that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously.
Classical snake robot control leverages mimicking snake-like gaits tuned for specific environments. However, to operate adaptively in unstructured environments, gait generation must be dynamically scheduled. In this w...
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Graph convolutional network has emerged as a focal point in machine learning because of its robust graph processing capability. Most existing graph convolutional network-based approaches are designed for single-view d...
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For inductive power transfer (IPT) systems, the loosely coupled transformer (LCT) is a crucial component. Variations in the air gap can lead to fluctuations in the parameters of the LCT, such as self-inductance and mu...
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Recommender systems that provide recommendations based uniquely on information over users and items may not be very accurate in some situations. Therefore, adding contextual information to recommendations may be a goo...
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