In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.
In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.
A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlyi...
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ISBN:
(纸本)9781538656280;9781538656273
A complete emotional expression typically contains a complex temporal course in a natural conversation. Related research on utterance-level, segment-level and multi-level processing lacks understanding of the underlying relation of emotional speech. In this work, a convolutional neural network (CNN) with audio word-based embedding is proposed for emotion modeling. In this study, vector quantization is first applied to convert the low level features of each speech frame into audio words using k-means algorithm. Word2vec is adopted to convert an input speech utterance into the corresponding audio word vector sequence. Finally, the audio word vector sequences of the training emotional speech data with emotion annotation are used to construct the CNN- based emotion model. The NCKU-ES database, containing seven emotion categories: happiness, boredom, anger, anxiety, sadness, surprise and disgust, was collected and five-fold cross validation was used to evaluate the performance of the proposed CNN-based method for speech emotion recognition. Experimental results show that the proposed method achieved an emotion recognition accuracy of 82.34%, improving by 8.7% compared to the Long Short Term Memory (LSTM)- based method, which faced the challenging issue of long input sequence. Comparing with raw features, the audio word-based embedding achieved an improvement of 3.4% for speech emotion recognition.
Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene e...
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Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.
Machine learning and pattern recognition techniques have been successfully applied to algorithmic problems in free groups. In this paper, we seek to extend these techniques to finitely presented non-free groups, with ...
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Titanium dioxide (TiO2) nanoparticulate films was successfully annealed on a glass substrate via alternative hot air treatment (HAT). Interestingly, HAT not only protects the glass substrate deformation but can also r...
Titanium dioxide (TiO2) nanoparticulate films was successfully annealed on a glass substrate via alternative hot air treatment (HAT). Interestingly, HAT not only protects the glass substrate deformation but can also reduce cost and time in the annealing process. The annealed films using HAT at 500 °C for 10 min can be compared with the annealed films in conventional furnace heat treatment (FHT) at 500 °C for 60 min. The results showed the sizes of ∼7.9 nm are obtained after the films annealed with both annealing techniques. The surface roughness of the as-deposited, the annealed films using FHT and HAT were 17.37, 23.74 and 23.26 nm, respectively. The energy band gap of the as- deposited films, the annealed films using FHT and HAT were 3.24 eV, 3.1 and 3.19 eV, respectively. Moreover, the annealed films using FHT and HAT techniques show superhydrophilic with a water contact angle of 3.42° and 2.81°, while the as-deposited films was 8.93°. After aging time testing, superhydrophilicity of the annealed films using HAT is greater than FHT. The result is in good agreement with Ti wt% of the as-deposited, the annealed films using FHT and HAT left on the substrate to 0.15, 0.73 and 0.65 nm after testing simulation for 20 years.
Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders p...
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Smart cities receive great attention today especially in conjunction with ubiquitous computing. People feel the need to access information about their cities anywhere anytime. They wish to be actively involved with lo...
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ISBN:
(纸本)9781538611050
Smart cities receive great attention today especially in conjunction with ubiquitous computing. People feel the need to access information about their cities anywhere anytime. They wish to be actively involved with local government bodies for policy decisions affecting urban lifestyle. Accordingly, this paper describes our research on urban policy management. We analyze urban legislation, more specifically, ordinances or local laws. We categorize ordinances based on smart city characteristics they address. This work deploys data warehousing, XML data management and data mining over categorized ordinances. Interesting findings include relative importance of smart city characteristics considering the focus given by urban agencies. This research helps agencies assess their current ordinance policies with decision support for the future. It also provides urban residents at-a-glance information about their cities and policies with analysis. This work has broader impacts of enhancing smart cities and ubiquitous computing by making useful information widely accessible with suitable inferences.
Group exponentiation is an important operation used in many public-key cryptosystems and, more generally, cryptographic protocols. To expand the applicability of these solutions to computationally weaker devices, it h...
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ISBN:
(纸本)9781450353939
Group exponentiation is an important operation used in many public-key cryptosystems and, more generally, cryptographic protocols. To expand the applicability of these solutions to computationally weaker devices, it has been advocated that this operation is outsourced from a computationally weaker client to a computationally stronger server, possibly implemented in a cloud-based architecture. While preliminary solutions to this problem considered mostly honest servers, or multiple separated servers, some of which honest, solving this problem in the case of a single (logical), possibly malicious, server, has remained open since a formal cryptographic model was introduced in [20]. Several later attempts either failed to achieve privacy or only bounded by a constant the (security) probability that a cheating server convinces a client of an incorrect result. In this paper we solve this problem for a large class of cyclic groups, thus making our solutions applicable to many cryptosystems in the literature that are based on the hardness of the discrete logarithm problem or on related assumptions. Our main protocol satisfies natural correctness, security, privacy and efficiency requirements, where the security probability is exponentially small. In our main protocol, with very limited offline computation and server computation, the client can delegate an exponentiation to an exponent of the same length as a group element by performing an exponentiation to an exponent of short length (i.e., the length of a statistical parameter). We also show an extension protocol that further reduces client computation by a constant factor, while increasing offline computation and server computation by about the same factor.
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