Search

Deep neural networks (DNNs) are widely used for image recognition, speech recognition, and other pattern analysis tasks. Despite the success of DNNs, these systems can be exploited by what is termed adversarial examples. An adversarial example, in which a small distortion is added to the input data, can be designed to be misclassified by the DNN while remaining undetected by humans or other systems. Such adversarial examples have been studied mainly in the image domain. Recently, however, studies on adversarial examples have been expanding into the voice domain. For example, when an adversarial example is applied to enemy wiretapping devices (victim classifiers) in a military environment, the enemy device will misinterpret the intended message. In such scenarios, it is necessary that friendly wiretapping devices (protected classifiers) should not be deceived. Therefore, the selective adversarial example concept can be useful in mixed situations, defined as situations in which there is both a classifier to be protected and a classifier to be attacked. In this paper, we propose a selective audio adversarial example with minimum distortion that will be misclassified as the target phrase by a victim classifier but correctly classified as the original phrase by a protected classifier. To generate such examples, a transformation is carried out to minimize the probability of incorrect classification by the protected classifier and that of correct classification by the victim classifier. We conducted experiments targeting the state-of-the-art DeepSpeech voice recognition model using Mozilla Common Voice datasets and the Tensorflow library. They showed that the proposed method can generate a selective audio adversarial example with a 91.67% attack success rate and 85.67% protected classifier accuracy. [ABSTRACT FROM AUTHOR]

Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities through the large number of layers and trainable parameters. In this research, we propose a new architecture of residual inception encoder–decoder neural network (RIED-Net) to learn the nonlinear mapping between the input images and targeting output images. To evaluate the validity of the proposed approach, it is compared with two models from the literature: synthetic CT deep convolutional neural network (sCT-DCNN) and shallow CNN, using both an institutional mammogram dataset from Mayo Clinic Arizona and a public neuroimaging dataset from the Alzheimer's Disease Neuroimaging Initiative. Experimental results show that the proposed RIED-Net outperforms the two models on both datasets significantly in terms of structural similarity index, mean absolute percent error, and peak signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Telemonitoring is the use of electronic devices to remotely monitor patients. Taking the Parkinson’s disease (PD) as an example, the use of at-home testing device (AHTD) enables remote, internet-based measurement of PD vocal symptoms. Translating AHTD measurement into a unified PD rating scale (UPDRS) through predictive analytics enables cost-effective, convenient, and close tracking of PD progression. Building a predictive model between AHTD measurement and UPDRS is not straightforward because PD patients are highly heterogeneous, which requires patient-specific models. Learning a patient-specific model faces the challenge of limited data. Transfer learning (TL) tackles this challenge by leveraging other patients’ information to make up the data shortage when modeling a target patient. Among different TL methods, the category of parameter transfer methods is more appropriate for the telemonitoring application because it transfers patient-specific model parameters but not patients’ data. However, existing parameter transfer methods fall short because not every other patient’s information is helpful and blind transfer causes the problem of negative transfer. To tackle this limitation, we propose a positive TL (PTL) method. We provide an in-depth theoretical study on the risk and condition for negative transfer to happen, which further drive the development of novel PTL algorithms that are robust to negative transfer. We apply PTL to predict UPDRS of 42 PD patients using their AHTD vocal measurement. PTL achieves significantly better accuracy compared with single learning and one-model-fits-all approaches. Note to Practitioners—This paper was motivated by the growing use of telemonitoring devices to facilitate remote, close tracking of disease progression. These devices are usually paired up with predictive analytics to translate the measurement signals into a clinical indicator of disease progression. This is a challenging task because the predictive model needs to be patient specific in order to account for patient heterogeneity. This paper proposes a TL method called PTL that can leverage other patients’ information when building a predictive model for a target patient. The unique feature of PTL is that it can intelligently select which patients to transfer from, and thus preventing negative transfer. PTL is a just-in-time development for the emerging healthcare paradigm of telemonitoring and telemedicine by providing predictive analytics models and algorithms to address the unique challenges and contributing to the use of these remote devices to improve patient care. [ABSTRACT FROM AUTHOR]

Last encounter routing (LER) is an excellent routing paradigm that exploits distributed mobility diffusion to achieve both moving object tracking and packet networking services in dynamic mobile networks. From our observations, we discover that LER can be easily extended to a mobile DHT protocol that introduces excellent performance to high-speed mobility environments. This is of particular interest when higher level of mobility and membership dynamics go hand in hand. In our simple but powerful DHT paradigm, a data publish/look-up process consists of a sequence of spatial motion tracking of the rendezvous node that is responsible for the data resource. Thus, we name the protocol MX-DHT (Motion-MiX-DHT). As opposed to existing topology based DHT schemes, MX-DHT does not require additional management of logical or overlying look-up topologies, except for the one-hop encounter records of logical metadata carried by mobile nodes. Therefore, in high-speed mobility and dynamic membership environments, MX-DHT achieves a significant reduction in the communication costs of the publish/look-up and join/leave operations as compared to existing mobile DHT schemes. An extensive set of experiments showed that MX-DHT is a cost-effective solution to providing a content centric networking service in different types of networks with dynamic mobility and membership changes. [ABSTRACT FROM AUTHOR]

Recently, heterogeneous networks with small cells have been widely used to increase the capacity of mobile systems. In such environments, accurate estimation of the mean cell sojourn time is critical for evaluating the performance of the network and its applications. It is especially important to analyze the cell sojourn times of mobile users as they reside in different network tiers: either macro-cell-only or small-cell-covered areas. But because small cells are deployed in an irregular manner, it is difficult to derive the analytical mean cell sojourn time in a macro-cell-only area. In this letter, we propose a novel approach to resolve this difficulty. We developed a simple but effective trick that approximates the heterogeneous network to a discrete grid so that it becomes tractable, making it possible to derive the analytical mean sojourn time in the macro-cell-only area. Simulation results confirm that the proposed method has excellent accuracy for general random walk mobility models including random waypoint, Brownian motion, tailored Brownian motion, and truncated Levy walk. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an efficient multihop broadcast protocol for asynchronous duty-cycled wireless sensor networks (EMBA) where each node independently wakes up according to its own schedule. EMBA adopts two techniques of the forwarder's guidance and the overhearing of broadcast messages and ACKs. A node transmits broadcast messages with guidance to neighbor nodes. The guidance presents how the node forwards the broadcast message to neighbor nodes by using unicast transmissions. This technique significantly reduces redundant transmissions and collisions. The overhearing of broadcast messages and ACKs helps to reduce the number of transmissions, thus it minimizes the active time of nodes. We implement EMBA and conventional protocols of ADB and RI-MAC broadcast in ns-2 simulator to compare their performance. The simulation results show that EMBA outperforms ADB and RI-MAC broadcast in both sparse and dense networks. EMBA achieves lower message cost than the conventional protocols and significantly improves the energy efficiency in terms of both duty cycle and energy consumption. [ABSTRACT FROM PUBLISHER]

Presents information on a submesh allocation strategy for mesh-connected multiprocessor systems. Details on massively parallel processor (MPP) computer architecture; What is the goal of submesh allocation; Previous strategies used for mesh-connected multiprocessor systems;Advantages of submesh allocation.

Two major goals of queue management are flow fairness and queue-length stability However, most prior works dealt with these goals independently. In this paper, we show that both goals can be effectively achieved at the same time. We propose a novel scheme that realizes flow fairness and queue-length stability. In the proposed scheme, high-bandwidth flows are identified via a multilevel caching technique. Then, we calculate the base drop probability for resolving congestion with a stable queue, and apply it to individual flows differently depending on their sending rates. Via extensive simulations, we show that the proposed scheme effectively realizes flow fairness between unresponsive and TCP flows, and among heterogeneous TCP flows, while maintaining a stable queue. [ABSTRACT FROM AUTHOR]

This letter proposes a novel medium reservation MAC protocol that reduces both end-to-end delay and energy consumption for wireless sensor networks. MRMAC reduces end-to-end delay by informing the intended receiver of NPAT (next packet arrival time) and MRI (medium reservation information) to reserve the medium in advance. Through this medium reservation phase, following packets can be delivered with reduced delay in multihop streaming phase. A simulation shows that the result of the proposed protocol outperforms previous works. [ABSTRACT FROM PUBLISHER]