Word sense disambiguation (WSD) is the task to determine the sense of an ambiguous word according to its context. Many existing WSD studies have been using an external knowledge-based unsupervised approach because it ...
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The potential of elec.ric vehicles (EV) to reduce foreign-oil dependence and improve urban air quality has triggered lots of investment by automotive companies recently and mass penetration and market dominance of EVs...
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ISBN:
(纸本)9781538663592;9781538663585
The potential of elec.ric vehicles (EV) to reduce foreign-oil dependence and improve urban air quality has triggered lots of investment by automotive companies recently and mass penetration and market dominance of EVs is imminent. However, EVs need to be charged more frequently than fossil-based vehicles and the charging durations are much longer. This necessitates in advance scheduling and matching depending on the route of the EVs. However, such scheduling and frequent charging may leak sensitive information about the users which may expose their driving patterns, whereabouts, schedules, etc. The situation is compounded with the proliferation of EV chargers such as V2V charging where there can be a lot of privacy exposure if matching of suppliers and EVs is achieved in a centralized manner. To address this issue, in this paper, we propose a privacy-preserving distributed stable matching of EVs with suppliers (i.e., public/private stations, V2V chargers) using preference lists formed by partially homomorphic encryption-based distance calculations while hiding the locations. The simulation results indicate that such a local matching of supplier and demanders can be achieved in a distributed fashion within reasonable computation and convergence times while preserving privacy of users.
This paper describes a method to provide LV four-wire three-phase power converters with the capability of correcting unbalance as an ancillary service to the main role that they play in the distribution system (distri...
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This paper addresses the estimation of the frequency of a sinusoid from compressively sensed measurements. Normally in parameter estimation, measurements are assumed to contain the signal and additive white Gaussian n...
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This paper addresses the estimation of the frequency of a sinusoid from compressively sensed measurements. Normally in parameter estimation, measurements are assumed to contain the signal and additive white Gaussian noise (AWGN). Under the paradigm of compressive sensing (CS), the measurements no longer contain AWGN but correlated noise. Frequency estimation of a sinusoid from measurements obtained through CS using the A WGN assumption will be non-optimal. This paper provides near-optimal frequency estimates for a sinusoid obtained through CS. Estimation of frequency of a sinusoid from CS measurements is cast as a linear least squares problem. A near-optimal solution in closed-form is presented by applying generalized total least squares (GTLS) technique to avoid bias caused by the correlated noise. The accuracy of the closed-form solution is close to the theoretical bound as confirmed by simulations.
Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early s...
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ISBN:
(纸本)9781728102436;9789881476852
Translucency, defined as a jelly-like appearance, is a common clinical feature of basal cell carcinoma, the most common skin cancer. The feature plays an important role in diagnosing basal cell carcinoma in an early stage because the feature can be observed readily in clinical examinations with a high specificity of 93%. Therefore, translucency detection is a critical component of computer aided systems which aim at early detection of basal cell carcinoma. To address this problem, we proposed an automated method for analyzing patches of clinical basal cell carcinoma images using stacked sparse autoencoder (SSAE). SSAE learns high-level features in unsupervised manner and all learned features are fed into a softmax classifier for translucency detection. Across the 4401 patches generated from 32 clinical images, the proposed method achieved a 93% detection accuracy from a five-fold cross-validation. The preliminary result suggested that the proposed method could detect translucency from skin images.
One of major biomedical signals, pain, and its diagnosis has been critical but hard in clinical practice, in particularly for nonverbal patients. However, as we know that neuroimaging methods, such as functional near-...
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One of major biomedical signals, pain, and its diagnosis has been critical but hard in clinical practice, in particularly for nonverbal patients. However, as we know that neuroimaging methods, such as functional near-infrared spectroscopy (fNIRS), have shown some great encouraging assessing neuronal function corresponding to nociception and pain. Specially some research results strongly suggest that neuroimaging, together with supports from machine learning, may be practically used to not only facilitate but also can predict different cognitive tasks over this challenge. The aim of this current research is to expand our previous studies by exploring the classification of fNIRS signals (oxyhaemoglobin) according to temperature level (we define cold and hot) and corresponding pain intensity (say low and high) by means of machine learning models. In order to find out the relations between temperatures and pain intensity, we defined and used the quantitative sensory testing to determine pain threshold and pain tolerance for the cold and heat in all eighteen-healthy people. The classification algorithm is based on a bag-of-words approach, a histogram representation was used in document classification based on the frequencies of extracted words and adapted for time series. Two machine learning algorithms were used separately, namely, K-nearest neighbor (K-NN) and support vector machines (SVM). A comparison between two sets of fNIRS channels was made in our classification task. The results showed that K-NN obtained slightly better results (92.1%) than SVM (91.3%) with all the 24 channels; however, the performances slightly dropped if using only channels from the region of interest with K-NN (91.5%) and SVM (90.8%). These research results encourage potential applications of fNIRS in the development of a physiologically based diagnosis of human pain, including in clinical parties.
Discovering the connectivity patterns of directed networks is a crucial step towards understanding complex systems such as human brains and financial markets. Network inference approaches aim at estimating the hidden ...
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Discovering the connectivity patterns of directed networks is a crucial step towards understanding complex systems such as human brains and financial markets. Network inference approaches aim at estimating the hidden topology given nodal observations. Existing approaches relying on structural equation models (SEMs) require full knowledge of exogenous inputs, which may be unrealistic in certain applications. Recent tensor-based alternatives advocate reformulation of SEMs as a three-way tensor decomposition task that only requires second-order statistics of exogenous inputs for identifying the hidden topology. However, the tensor-based methods are computationally expensive, and is hard to incorporate prior information of the network structure (e.g., sparsity and local smoothness), but prior information is often important for enhancing performance. The present work puts forth a joint diagonalizaition (JD)-based formulation for directed network inference. JD can be viewed as a variant of tensor decomposition, but features more efficient algorithms and can readily incorporate prior information of network topology. New topology identification guarantees that do not rely on knowledge of exogenous inputs are established. Judiciously designed simulations are presented to showcase the effectiveness of the proposed approach.
Wireless Sensor and Actuator Networks (WSANs) is a special category of Wireless Ad- Hoc Networks, that bears all the functionality of Wireless Sensor Networks (WSNs) but they are equipped with actuators capable of per...
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Multi-level-cell (MLC) phase-change memory (PCM) provides higher storage density at the cost of slower reads and writes. Since reads are latency critical, this paper propose a simple and effective bit mapping scheme, ...
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ISBN:
(纸本)9781509033164
Multi-level-cell (MLC) phase-change memory (PCM) provides higher storage density at the cost of slower reads and writes. Since reads are latency critical, this paper propose a simple and effective bit mapping scheme, called Mapping Critical Word to MSBs (MCWM), to address slow reads in MLC. MCWM takes advantage of fast read speed of most-significant-bits (MSBs) of MLC cells and strips a cache line among MLC cells at the bit level. Taking 2- bit MLC as an example, MCWM stores the first half of each cache line at most-significant-bits (MSBs) of MLC cells, and the second half at least-significant-bits (LSBs). This design leverages the observation that most critical words are located within the first half of a cache line. Upon a cache miss, the critical word can be fetched at the same speed as single-level cell (SLC) PCM, thus reducing processor stall time. Experimental results under 4-cores SPEC CPU 2006 workloads show that MCWM can reduce memory read latency by 27.5% and IPC by 13.7% on average, compared with conventional PCM. In addition, MCWM outperforms recently proposed Striped PCM (SPCM) by 12.5% in latency and 6.1% in IPC on average. Additionally, MCWM is complementary to write optimizations. MCWM can reduce read latency of Write Pause by 25% and increase IPC by 11.7% on average.
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