Automatic summarization of broadcast cricket videos

Summarization of cricket videos is very important because of three reasons: 1) its long duration making manual highlights generation tedious 2) less explored area compared to other sports like soccer 3) huge viewership. We propose a novel summarization scheme for cricket which exploits its contextual semantics. First, we detect the bowling frames based on which the video is temporally segmented into individual deliveries. Then each temporal segment representing a delivery is classified into an interesting or non-interesting segment based on detection of events namely boundaries and wickets. Due to the high frequency of ads and replays in cricket, we have proposed robust algorithms for their removal. Finally, we have proposed a finite state automaton based modeling of the temporal segments to extract key-frames. We have also extended the framework to include text cues and expert choices and also developed a hierarchical summary. We have tested our algorithm on several broadcast cricket videos and obtained good results.

Dynamical analysis of neural networks with time-varying delays using the LMI approach

This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.

Spammers are becoming "smarter" on Twitter

While researchers develop various approaches to detect Twitter spam, spammers thwart their efforts with more complex spamming strategies. The authors' in-depth analysis of more than 570 million tweets revealed three new spamming strategies: coordinated posting behavior, finite-state machine-based spam template, and passive spam.

Continuous finite-time state feedback stabilizers for some nonlinear stochastic systems

This paper is concerned with the problem of finite-time stabilization for some nonlinear stochastic systems. Based on the stochastic Lyapunov theorem on finite-time stability that has been established by the authors in the paper, it is proven that Euler-type stochastic nonlinear systems can be finite-time stabilized via a family of continuous feedback controllers. Using the technique of adding a power integrator, a continuous, global state feedback controller is constructed to stabilize in finite time a large class of two-dimensional lower-triangular stochastic nonlinear systems. Also, for a class of three-dimensional lower-triangular stochastic nonlinear systems, a recursive design scheme of finite-time stabilization is given by developing the technique of adding a power integrator and constructing a continuous feedback controller. Finally, a simulation example is given to illustrate the theoretical results. © 2014 John Wiley & Sons, Ltd.

Gambling and corporate social responsibility (CSR) : re-defining industry and state roles on duty of care, host responsibility and risk management

During the 1990s, states embraced legalised gambling as a means of supplementing state revenue. But gaming machines (EGMs, pokies, VLTs, Slots) have become increasingly controversial in countries such as Australia, Canada and New Zealand, which experienced unprecedented roll-out of gaming machines in casino and community settings; alongside revenue windfalls for both governments and the gambling industry. Governments have recognised that gambling results in a range of social and economic harms and, similar to tobacco and alcohol, have introduced public policies predicated on harm minimisation. Yet despite these, gaming losses have continued to climb in most jurisdictions, along with concerns about gambling-related harms. The first part of this article discusses an emerging debate in Ontario Canada, that draws parallels between host responsibility in alcohol and gambling venues. In Canada, where government owns and operates the gaming industry, this debate prompts important questions on the role of the state, duty of care and regulation ‘in the public interest’ and on CSR, host responsibility and consumer protection. This prompts the question: Do governments owe a duty of care to gamblers?

The article then discusses three domains of accumulating research evidence to inform questions raised in the Ontario debate: evidence that visible behavioural indicators can be used with high confidence to identify problem gamblers on-site in venues as they gamble; new systems using player tracking and loyalty data that can provide management with high precision identification of problem gamblers and associated risk (for protective interventions); and research on technological design features of new generation gaming products in interaction with players, that shows how EGM machines can be the site for monitoring/protecting players. We then canvass some leading international jurisdictions on gambling policy CSR and consumer protection.

In light of this new research, we ask whether the risk of legal liability poses a tipping point for more interventionist public policy responses by both the state and industry. This includes a proactive role for the state in re-regulating the gambling industry/products; instituting new forms of gaming machine product control/protection; and reinforcing corporate social responsibility (CSR) and host responsibility obligations on gambling providers – beyond self-regulatory codes. We argue the ground is shifting, there is new evidence to inform public policy and government regulation and there are new pressures on gambling providers and regulators to avail themselves of the new technology – or risk litigation

Robust finite-time consensus tracking algorithm for multirobot systems

This paper studies the finite-time consensus tracking control for multirobot systems. We prove that finite-time consensus tracking of multiagent systems can be achieved on the terminal sliding-mode surface. Also, we show that the proposed error function can be modified to achieve relative state deviation between agents. These results are then applied to the finite-time consensus tracking control of multirobot systems with input disturbances. Simulation results are presented to validate the analysis.

Development of a contact probe incorporating a bragg grating strain sensor for nano coordinate measuring machines

This paper presents a novel optical fibre based micro contact probe system with high sensitivity and repeatability. In this optical fibre probe with a fused spherical tip, a fibre Bragg grating has been utilized as a strain sensor in the probe stem. When the probe tip contacts the surface of the part, a strain will be induced along the probe stem and will produce a Bragg wavelength shift. The contact signal can be issued once the wavelength shift signal is produced and demodulated. With the fibre grating sensor element integrated into the probe directly, the probe system shows a high sensitivity. In this work, the strain distributions along the probe stem with the probe under axial and lateral load are analysed. A simulation of the strain distribution was performed using the finite element package ANSYS 11. Performance tests using a piezoelectric transducer stage with a displacement resolution of 1.5 nm yielded a measurement resolution of 60 nm under axial loading.

A finite-time stability theorem of stochastic nonlinear systems and stabilization designs

This paper focuses on the finite-time stability and stabilization designs of stochastic nonlinear systems. We first present and discuss a definition on the finite-time stability in probability of stochastic nonlinear systems, then we introduce a stochastic Lyapunov theorem on the finite-time stability, which has been established by Yin et al. We also employ this theorem to design a continuous state feedback controller that makes a class of stochastic nonlinear systems to be stable in finite time. An example and a simulation are given to illustrate the theoretical analysis.

Robust finite-horizon Kalman filtering for uncertain discrete-time systems

In this note, we propose a design for a robust finite-horizon Kalman filtering for discrete-time systems suffering from uncertainties in the modeling parameters and uncertainties in the observations process (missing measurements). The system parameter uncertainties are expected in the state, output and white noise covariance matrices. We find the upper-bound on the estimation error covariance and we minimize the proposed upper-bound.

Cumulative restricted Boltzmann machines for ordinal matrix data analysis

Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent opinion profile of citizens around the world, and is competitive against state-of-art collaborative filtering techniques on large-scale public datasets. The model thus has the potential to extend application of RBMs to diverse domains such as recommendation systems, product reviews and expert assessments.

Reduced order functional observers with application to partial state estimation of linear systems with input delay

Reduced order multi-functional observer design for multi-input multi-utput (MIMO) linear time-invariant (LTI) systems with constant delayed inputs is studied. This research is useful in the input estimation of LTI systems with actuator delay, as well as system monitoring and fault detection of these systems. Two approaches for designing an asymptotically stable functional observer for the system are proposed: delay-dependent and delay-free. The delay-dependent observer is infinite-dimensional, while the delay-free structure is finite-dimensional. Moreover, since the delay-free observer does not require any information on the time delay, it is more practical in real applications. However, the delay-dependent observer contains less restrictive assumptions and covers more variety of systems. The proposed observer design schemes are novel, simple to implement, and have improved numerical features compared to some of the other available approaches to design (unknown-input) functional observers. In addition, the proposed observers usually possess lower order than ordinary Luenberger observers, and the design schemes do not need the observability or detectability requirements of the system. The necessary and sufficient conditions of the existence of an asymptoticobserver in each scenario are explored. The extensions of the proposed observers to systems with multiple delayed-inputs are also discussed. Several numerical examples and simulation results are employed to support our theories.

A comprehensive analytical subdomain model and its field solutions for surface-mounted permanent magnet machines

This paper presents a comprehensive analytical subdomain model together with its field solutions for predicting the magnetic field distributions in surface-mounted permanent magnet (PM) machines. The tooth tips and slotting effects during open-circuit, armature reaction, and on-load conditions are considered when deriving the model and developing its solutions. The model derivations and field solutions are extended from a previous model, and can be applied to PM machines with any combinations of slot and pole numbers and any magnetization patterns in the magnets. This model is initially formulated according to Laplace's and Poisson's equations in 2-D polar coordinates by the separation of variables technique in four subdomains, such as magnet, airgap, winding slots, and slot-openings. The field solution of each subdomain is obtained applying the appropriate boundary conditions and interface conditions between every two subdomains, respectively, which can precisely account for the mutual influence between slots. Finite element analysis (FEA) is later deployed to validate the analytical results in a surface-mounted PM machine that has nonoverlapping winding arrangement. For validation purposes, PM machines having 3-slot/2-pole with parallel magnetization and 12-slot/10-pole with either parallel or radial magnetizations are used for comparisons. Computation of global quantities for the motor which include the phase back-EMF and cogging torque is also included. The results indicate that the proposed analytical model can accurately predict the magnetic field distributions in each subdomain and the motor's global quantities, which are in good agreement with those obtained from the FEA.

Robust finite-time tracking control of nonholonomic mobile robots without velocity measurements

The problem of robust finite-time trajectory tracking of nonholonomic mobile robots with unmeasurable velocities is studied. The contributions of the paper are that: first, in the case that the angular velocity of the mobile robot is unmeasurable, a composite controller including the observer-based partial state feedback control and the disturbance feed-forward compensation is designed, which guarantees that the tracking errors converge to zero in finite time. Second, if the linear velocity as well as the angular velocity of mobile robot is unmeasurable, with a stronger constraint, the finite-time trajectory tracking control of nonholonomic mobile robot is also addressed. Finally, the effectiveness of the proposed control laws is demonstrated by simulation.

Stable feature selection with support vector machines

The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods.