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Sentiment analysis, also known as opinion mining, seeks to figure out points of view from documents. Sentiment classification is a specific task of sentiment analysis that divides documents into positive and negative sentiment polarities according to the attitudes expressed. Feature extraction is a significant part of sentiment classification. Traditional feature extraction methods mine statistical...
In this paper, we consider the problem of multiparty deep learning (MDL), wherein autonomous data owners jointly train accurate deep neural network models without sharing their private data. We design, implement, and evaluate ∝MDL, a new MDL paradigm built upon three primitives: asynchronous optimization, lightweight homomorphic encryption, and threshold secret sharing. Compared with prior work, ∝MDL...
A temperature prediction model based on Self-adaption Particle Swarm Optimization (SAPSO) and Extreme Learning Machine (ELM) is proposed in this paper. The nano-iron powder decomposing furnace temperature prediction model is established based on ELM. ELM, a neural network, is developed rapidly in recent years, but it requires a lot of hidden layer neurons to achieve ideal prediction accuracy. In order...
The huge amount of text documents has made the manual organization of text data a tedious task. Automatic text classification helps to easily handle the large number of documents by organising them automatically into predefined classes. The effectiveness and efficiency of automatic text classification largely depends on the way text documents are represented. A text document is usually viewed as a...
Lyrics are an important part of songs. Lyrics recognition is the basis of retrieving songs and recognizing the content of songs, which is of great value. At present, the research of speech recognition has made great progresses. But there are still difficulties in recognition of lyrics in songs with accompaniment. Related research is generally lacking, especially for Chinese lyrics in songs with accompaniment,...
With the organic food market on the rise, organic food fraud has become an issue to consumers, producers and the market. Traditional methods of food quality determination are time consuming and require expert laboratory analysis. Recent studies based on spectroscopic analysis have shown its potential effectiveness in non-destructive food analysis. This paper explores the use of low cost Near Infrared...
Microblogging websites such as twitter and Sina Weibo have attracted many users to share their experiences and express their opinions on a variety of topics. Sentiment classification of microblogging texts is of great significance in analyzing users' opinion on products, persons and hot topics. However, conventional bag-of-words-based sentiment classification methods may meet some problems in processing...
Goal: A brain-computer interface (BCI) provides a way to translate the motion intentions of human using brain signals such as electroencephalogram (EEG) into control commands. EEG signals are highly subject specific and non-stationary. One of the most challenging tasks is to classify motion intentions since the recorded EEG signals have inherent non-stationarities which are due to changes in the signal...
Upper limb rehabilitation robot is used to assist in completing rehabilitation training for patients with upper limb disorder. It is inevitable that the control system is probably disrupted by the patients in the process of rehabilitation training so that the movement of the rehabilitation robot is not smooth. This problem is not conductive to rehabilitation. In view of the contour tracking method...
Research on the influence of specific hydrological environment change on current velocity and water exchange has a long history in oceanography; however, the majority of previous work has been based on traditional ocean models such as POM and FVCOM. This paper presents a stable joint method that combines a support vector machine with a hydrological model to predict current velocity in different hydrological...
Traffic lights recognition is important to make intelligent vehicles safe. Most of existing means to detect and recognize traffic lights focus on color, size and shape of traffic lights, which are great affected by weather and illumination conditions. In this work, we utilize deep learning and SVM classifiers to recognize traffic lights for varying illumination conditions. More specifically, a PCA...
Recent years have witnessed a series of occupy protest events all over the world. Detecting and monitoring these events is an important and challenging task in social science research and also can provide reference for government's emergency management. Existing methods mainly solve this problem by document clustering techniques. This paper proposes a novel graph-based occupy protest event detection...
In this paper, we present a new filtering method based on the empirical mode decomposition (EMD) for classification of motor imagery (MI) electroencephalogram (EEG) signals for enhancing brain-computer interface (BCI). The EMD method decomposes EEG signals into a set of intrinsic mode functions (IMFs). These IMFs can be considered narrow-band, amplitude and frequency modulated (AM-FM) signals. The...
In this paper an improvement over our previous work is proposed to handle short-medium range surveillance videos. The features of histogram of oriented social force (HOSF) are the primitive building blocks to capture the interactions among people. To reduce the correlation among data, whitening procedure is applied on features. We use Bag-of-Feature (BoF) to pool HOSF in a given frame. Since our goal...
Many real world classification problems lack of a large number of labeled data for learning an effective classifier. Active learning methods seek to address this problem by reducing the number of labeled instances needed to build an effective classifier. Most current active learning methods, however, are myopic, i.e. select one single unlabelled sample to label at a time. Obviously, such a strategy...
A novel and efficient human action recognition method utilizing spatio-temporal interest point detector and 3D speed up robust features (3D SURF) descriptor is proposed. The spatio-temporal interest points are detected using two separate linear filters. Then 3D SURF descriptor is presented and demonstrated in detail to represent the local region around interest point. The experimental results on KTH...
In this paper a simple and effective crowd behavior normality method is proposed. We use the histogram of oriented social force (HOSF) as the feature vector to encode the observed events of a surveillance video. A dictionary of codewords is trained to include typical HOSFs. To detect whether an event is normal is accomplished by comparing how similar to the closest codeword via z-value. The proposed...
According to the current circumstances, the cultivating and training of the newest armored equipments maintenance support personnel is difficult to meet army's demand all the time, and can't meet the needs of equipments development either, we researched the maintenance training simulation system which is basic to half-practicality and virtual reality technology in chief. We made the overall design...
Hand gesture recognition aims to recognize the meaningful expressions of hand motion. It is widely used in information visualization, robotics, sign language understanding, medicine and healthcare. Some methods have been proposed for hand gesture recognition. But no single algorithm can handle all kinds of situations, because of the complex environment. In this study, we propose a hybrid method for...
Memristors have been rediscovered recently and then gained increasing attentions. Their unique properties, such as high density, nonvolatility, and recording historic behavior of current (or voltage) profile, have inspired the creation of memristor-based neuromorphic computing architecture. Rather than the existing crossbar-based neuron network designs, we focus on memristor-based synapse and the...
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