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41.
A challenge in building pervasive and smart spaces is to learn and recognize human activities of daily living (ADLs). In this paper, we address this problem and argue that in dealing with ADLs, it is beneficial to exploit both their typical duration patterns and inherent hierarchical structures. We exploit efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and apply it to the problem of learning and recognizing ADLs with complex temporal dependencies. The Coxian duration model has several advantages over existing duration parameterization using multinomial or exponential family distributions, including its denseness in the space of nonnegative distributions, low number of parameters, computational efficiency and the existence of closed-form estimation solutions. Further we combine both hierarchical and duration extensions of the hidden Markov model (HMM) to form the novel switching hidden semi-Markov model (SHSMM), and empirically compare its performance with existing models. The model can learn what an occupant normally does during the day from unsegmented training data and then perform online activity classification, segmentation and abnormality detection. Experimental results show that Coxian modeling outperforms a range of baseline models for the task of activity segmentation. We also achieve a recognition accuracy competitive to the current state-of-the-art multinomial duration model, while gaining a significant reduction in computation. Furthermore, cross-validation model selection on the number of phases K in the Coxian indicates that only a small K is required to achieve the optimal performance. Finally, our models are further tested in a more challenging setting in which the tracking is often lost and the activities considerably overlap. With a small amount of labels supplied during training in a partially supervised learning mode, our models are again able to deliver reliable performance, again with a small number of phases, making our proposed framework an attractive choice for activity modeling. 相似文献
42.
Vo Ngoc Phu Vo Thi Ngoc Chau Vo Thi Ngoc Tran 《International Journal of Speech Technology》2017,20(3):487-508
Semantic analysis is very important and very helpful for many researches and many applications for a long time. SVM is a famous algorithm which is used in the researches and applications in many different fields. In this study, we propose a new model using a SVM algorithm with Hadoop Map (M)/Reduce (R) for English document-level emotional classification in the Cloudera parallel network environment. Cloudera is also a distributed system. Our English testing data set has 25,000 English documents, including 12,500 English positive reviews and 12,500 English negative reviews. Our English training data set has 90,000 English sentences, including 45,000 English positive sentences and 45,000 English negative sentences. Our new model is tested on the English testing data set and we achieve 63.7% accuracy of sentiment classification on this English testing data set. 相似文献
43.
Vo Ngoc Phu Vo Thi Ngoc Tran Vo Thi Ngoc Chau Nguyen Duy Dat Khanh Ly Doan Duy 《International Journal of Speech Technology》2017,20(3):593-613
Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results. 相似文献
44.
Tich Phuoc Tran Thi Thanh Sang Nguyen Poshiang Tsai Xiaoying Kong 《Artificial Intelligence Review》2011,35(4):369-382
In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast
growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend
without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning
technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models
are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics
of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy
and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace
Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative
diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy
with relatively small computational complexity compared with other conventional detection methods. 相似文献
45.
Scheidegger C Vo H Koop D Freire J Silva C 《IEEE transactions on visualization and computer graphics》2007,13(6):1560-1567
While there have been advances in visualization systems, particularly in multi-view visualizations and visual exploration, the process of building visualizations remains a major bottleneck in data exploration. We show that provenance metadata collected during the creation of pipelines can be reused to suggest similar content in related visualizations and guide semi-automated changes. We introduce the idea of query-by-example in the context of an ensemble of visualizations, and the use of analogies as first-class operations in a system to guide scalable interactions. We describe an implementation of these techniques in VisTrails, a publicly-available, open-source system. 相似文献
46.
Thi Ngoc Trang Tran Müslüm Atas Alexander Felfernig Martin Stettinger 《Journal of Intelligent Information Systems》2018,50(3):501-526
Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies. 相似文献
47.
48.
49.
Yan Li Thi Thanh Hanh Nguyen Juhui Jin Juho Lim Jiyeon Lee Meizi Piao Il-Kyoon Mok Doman Kim 《Food science and biotechnology》2021,30(4):555
Co-fermentation using yeast (Saccharomyces cerevisiae and Pichia kudriavzevii) and the bacteria (Lactobacillus plantarum) as starters isolated from spontaneous sourdough was conducted for the brewing of glucuronic acid (GlcA)-enriched apple cider. The concentration of GlcA in the apple cider co-fermented for 14 d with commercial S. cerevisiae and L. plantarum was 37.7 ± 1.7 mg/mL while a concentration of 62.8 ± 3.1 mg/mL was recorded for fermentation with P. kudriavzevii and L. plantarum, which was higher than the corresponding single yeast fermentation. The co-fermented apple cider revealed higher 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity of 171.67 ± 0.79 µg trolox equivalents (TE)/mL using P. kudriavzevii and L. plantarum, compared to the control (143.89 ± 7.07 µg TE/mL) just using S. cerevisiae. Thus, the co-fermentation of S. cerevisiae and L. plantarum and P. kudriavzevii and L. plantarum provided a new strategy for the development of GlcA-enriched apple cider with enhanced antioxidant capacity. 相似文献
50.