SVM for English semantic classification in parallel environment

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.     

A decision tree using ID3 algorithm for English semantic analysis

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.     

Dynamic behaviour of a magnetically actuated floating liquid marble

This paper reports the dynamic behaviour of a magnetically actuated floating liquid marble by analysing the oscillation of the marble. A liquid marble is a liquid droplet coated with hydrophobic powder. Magnetite particles inside the marble make it magnetic. The marble floats on a carrier liquid that contains aqueous glycerol of various concentrations. A permanent magnet located under the carrier liquid drives the floating marble with the initial velocity. Stopping the magnet abruptly causes the marble to oscillate around its final position for a few seconds. The oscillation was recorded and analysed using customised image processing and evaluation software. The damped harmonic motion model was then applied to the data and tested. Subsequently, critical parameters of the system such as the initial displacement, friction correction factor, the apparent frequency and the spring constant were determined and discussed. The simple experimental set-up and convenient theoretical approach allow us to characterise the marble motion under the influence of a magnet with good accuracy.     

BSPNN: boosted subspace probabilistic neural network for email security

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.     

Efficient Multimedia Distribution in Source Constraint Networks

In recent years, the number of peer-to-peer (P2P) applications has increased significantly. One important problem in many P2P applications is how to efficiently disseminate data from a single source to multiple receivers on the Internet. A successful model used for analyzing this problem is a graph consisting of nodes and edges, with a capacity assigned to each edge. In some situations however, it is inconvenient to use this model. To that end, we propose to study the problem of efficient data dissemination in a source constraint network. A source constraint network is modeled as a graph in which, the capacity is associated with a node, rather than an edge. The contributions of this paper include (a) a quantitative data dissemination in any source constraint network, (b) a set of topologies suitable for data dissemination in P2P networks, and (c) an architecture and implementation of a P2P system based on the proposed optimal topologies. We will present the experimental results of our P2P system deployed on PlanetLab nodes demonstrating that our approach achieves near optimal throughput while providing scalability, low delay and bandwidth fairness among peers.     

Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogeneities, and noise.     

Experimental and numerical investigation of the debonding interface between an old concrete and an overlay

The paper focuses on debonding propagation along an interface, notably on the major influence of the interlocking between the two faces of the debonding interface. The aim of the study is to obtain the data necessary for relevant and efficient debonding modelling. The work associates experiment and simulation with the purpose of quantifying the interlocking along the interface. The overlay material investigated was a fibre reinforced mortar (FRM). Direct tension tests of notched FRM specimens were firstly conducted to obtain the tensile strength and the residual normal stress—crack width relationship. Its Young's modulus was determined from compression tests. The substrate-overlay interface was investigated by direct tension tests and flexure tests performed on composite substrate-overlay specimens. The direct tension tests provided the interface tensile strength and the relationship between debonding-opening and residual normal tensile stress. Three point flexural static tests informed on the structural behaviour of the interface. The debonding interface propagation was monitored using a video-microscope with a maximum enlargement of ×175. Using the identified and quantified parameters, modelling of the above mentioned static tests was carried out by the finite elements method using CAST3M code developed in France by CEA (Centre for Atomic Energy). The comparison of modelling and experiment results shows a good coherence and proves the important role of interlocking on the debonding mechanism.     

Design and optimization of electrostatic fluid accelerators

Electrostatic air propulsion is a promising technology with such potential applications as energy-efficient ventilation, air sterilization, cooling of electronics, and dehumidification. The challenges of existing designs include the need to increase air speed, backpressure, energy efficiency, and heat exchange capability. The ultimate goal of this direction of research is to create multi-channel energy efficient ionic pumps. In the described project, a single cell analysis is conducted in this study as a building block of future designs. This paper presents the numerical simulation and experimental results of electrostatic fluid accelerators. This study was conducted for the purpose of optimizing device characteristics through the control of the electric field distribution. Simulations were performed for multiple collector electrode voltage distributions. A method to quantify the change in pump performance between different voltage distributions is presented. The influence of space charge on pump performance is also discussed. A significant improvement of air velocity generated by optimized electrostatic fluid accelerators has been achieved using the proposed approach.     

Scalable Model of On-Wafer Interconnects for High-Speed CMOS ICs

This paper describes the development of an equivalent circuit model of on-wafer interconnects for high-speed CMOS integrated circuits. By strategically cascading two-pi blocks together, the lumped model can characterize the distributed effects. Besides, the elaborately proposed model characterizes the frequency-variant characteristics with frequency-independent components. Thus, the model can be easily plugged into commercial computer-aided design tools. By adopting a newly invented optimization algorithm, namely, particle swarm optimization (PSO), the model parameters are extracted and formulated as empirical expressions. Therein, with each set of the geometrical parameters, the interconnect behaviors can be accurately predicted. The accuracy of the model is validated by comparisons with the on-wafer measurements up to 30 GHz. Moreover, the scalability of the proposed model is also discussed     

Multi-Level Composition for Software Federations

Reusability is the holly grail of software engineers. But reusability requires powerful composition mechanisms since the pieces to compose have no reason to match perfectly. Unfortunately, the composition mechanisms available today, mostly method call and component assembly are rather primitive. This paper shows what is the current state of the art in software composition, showing that there is little composition flexibility at code level, even using workflow approaches.The approach presented here claims that composition requires reasoning at different levels of abstraction. Workflow support is first decoupled from real tools, using abstract tool modeling. Contracts have been included to increase the independence between process and tools. Then, we have introduced conceptual abstractions linked by contract to real tools. Finally, we show that it is possible to define composition at the abstract level.The resulting system shows very high adaptability capabilities. Experience shows, however, that to be practical, this approach requires adapted framework and specialized tools. This paper shows the experience gained in implementing many versions of such a framework. The current system is currently in industrial use.