A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

Automatic annotation of tennis games: An integration of audio,vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level.     

Implementation of Web‐based argumentation in facilitating elementary school students to learn environmental issues

This research develops a Web‐based argumentation system named the Web‐based Interactive Argumentation System (WIAS). WIAS can provide teachers with the scaffolding for argumentation instruction. Students can propose their statements, collect supporting evidence and share and discuss with peers online. This research adopts a quasi‐experimental design, applying WIAS to the teaching of environmental issues, including mudslides, global warming and nuclear power. Fifty‐seven elementary school fifth graders from two classes participated in this research. With each class as a unit, they were divided into the WIAS group (n = 30) and the traditional argumentation instruction (TAI) group (n = 27). Before research, all students took the pre‐test of the ‘achievement test for environmental issues (ATEI)’ and the ‘environmental literacy scale (ELS).’ Then all students received argumentation training and six classes of argumentation instruction. Students in the WIAS group performed argumentation in the WIAS, while those in the TAI group performed argumentation in a traditional classroom. After the six‐class argumentation instruction, all students took the post‐test of the ATEI and ELS. The results show that students in the WIAS group have significantly better learning effectiveness than those in the TAI group. Students in the WIAS group also exhibited significantly better improvement in their environmental literacy.     

Cure kinetics of neat and graphite-fiber-reinforced epoxy formulations

An investigation was carried out into the cure kinetics of neat and graphite fiber-reinforced epoxy formulation, composed of tetraglycidyl 4,4′-diaminodiphenyl methane (TGDDM) resin and diaminodiphenyl sulfone (DDS) curing agent. Two experimental techniques were employed: isothermal differential scanning calorimetry (IDSC) and dynamic differential scanning calorimetry (DDSC). An autocatalytic mechanism with the overall reaction rate order of 2 was found to describe adequately the cure kinetics, of the neat resin and the composite. All kinetic parameters, including reaction rate constants, activation energies and preexponential factors, were calculated and reported. The presence of graphite fibers in the composite had only a very small initial effect on the kinetics of cure.     

Real-time moustache detection by combining image decolorization and texture detection with applications to facial gender recognition

There are still many challenging problems in facial gender recognition which is mainly due to the complex variances of face appearance. Although there has been tremendous research effort to develop robust gender recognition over the past decade, none has explicitly exploited the domain knowledge of the difference in appearance between male and female. Moustache contributes substantially to the facial appearance difference between male and female and could be a good feature to be incorporated into facial gender recognition. Little work on moustache segmentation has been reported in the literature. In this paper, a novel real-time moustache detection method is proposed which combines face feature extraction, image decolorization and texture detection. Image decolorization, which converts a color image to grayscale, aims to enhance the color contrast while preserving the grayscale. On the other hand, moustache appearance is normally grayscale surrounded by the skin color face tissue. Hence, it is a fast and efficient way to segment the moustache by using the decolorization technology. In order to make the algorithm robust to the variances of illumination and head pose, an adaptive decolorization segmentation has been proposed in which both the segmentation threshold selection and the moustache region following are guided by some special regions defined by their geometric relationship with the salient facial features. Furthermore, a texture-based moustache classifier is developed to compensate the decolorization-based segmentation which could detect the darker skin or shadow around the mouth caused by the small lines or skin thicker from where he/she smiles as moustache. The face is verified as the face containing a moustache only when it satisfies: (1) a larger moustache region can be found by applying the decolorization segmentation; (2) the segmented moustache region is detected as moustache by the texture moustache detector. The experimental results on color FERET database showed that the proposed approach can achieve 89 % moustache face detection rate with 0.1 % false acceptance rate. By incorporating the moustache detector into a facial gender recognition system, the gender recognition accuracy on a large database has been improved from 91 to 93.5 %.     

Automatic inpainting by removing fence-like structures in RGBD images

Recent inpainting techniques usually require human interactions which are labor intensive and dependent on the user experiences. In this paper, we introduce an automatic inpainting technique to remove undesired fence-like structures from images. Specifically, the proposed technique works on the RGBD images which have recently become cheaper and easier to obtain using the Microsoft Kinect. The basic idea is to segment and remove the undesired fence-like structures by using both depth and color information, and then adapt an existing inpainting algorithm to fill the holes resulting from the structure removal. We found that it is difficult to achieve a satisfactory segmentation of such structures by only using the depth channel. In this paper, we use the depth information to help identify a set of foreground and background strokes, with which we apply a graph-cut algorithm on the color channels to obtain a more accurate segmentation for inpainting. We demonstrate the effectiveness of the proposed technique by experiments on a set of Kinect images.     

Self-Repairing Algorithm of Lattice-Type Self-Reconfigurable Modular Robots

Self-reconfigurable modular robots consist of many identical modules. By changing the connections among modules, the configuration of the robot can be transformed into other configurations. For the self-reconfigurable modular robot, one of its main functions is its self-repairing ability. First, the module of the lattice-type self-reconfigurable robot is presented. It is composed of a central cube and six rotary arms. On each rotary arm the docking mechanism is designed to show the self-repairing ability. Second, the basic motion of the self-reconfigurable robot is described to change the positions of the module. The state matrix and the location matrix are proposed to describe the connection states. Third, a self-repairing algorithm based on the positions of the faulty modules is presented. The algorithm applies the Breadth-First-Search method and the Depth-First-Search method to find a locomotion path by which the faulty module is ejected and replaced by a spare module. At last, a simulation on the fourth-order lattice-type self-reconfigurable robot consisting of 729 modules shows the feasibility and effectiveness of this self-repairing algorithm in three dimensions.     

Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time

The article investigates the discrete-time controller for the longitudinal dynamics of the hypersonic flight vehicle with throttle setting constraint. Based on functional decomposition, the dynamics can be decomposed into the altitude subsystem and the velocity subsystem. Furthermore, the discrete model could be derived using the Euler expansion. For the velocity subsystem, the controller is proposed by estimating the system uncertainty and unknown control gain separately with neural networks. The auxiliary error signal is designed to compensate the effect of throttle setting constraint. For the altitude subsystem, the desired control input is approximated by neural network while the error feedback is synthesized for the design. The singularity problem is avoided. Stability analysis proves that the errors of all the signals in the system are uniformly ultimately bounded. Simulation results show the effectiveness of the proposed controller.     

An efficient algorithm for incrementally mining frequent closed itemsets

The purpose of mining frequent itemsets is to identify the items in groups that always appear together and exceed the user-specified threshold of a transaction database. However, numerous frequent itemsets may exist in a transaction database, hindering decision making. Recently, the mining of frequent closed itemsets has become a major research issue because sets of frequent closed itemsets are condensed yet complete representations of frequent itemsets. Therefore, all frequent itemsets can be derived from a group of frequent closed itemsets. Nonetheless, the number of transactions in a transaction database can increase rapidly in a short time period, and a number of the transactions may be outdated. Thus, frequent closed itemsets may be changed with the addition of new transactions or the deletion of old transactions from the transaction database. Updating previously closed itemsets when transactions are added or removed from the transaction database is challenging. This study proposes an efficient algorithm for incrementally mining frequent closed itemsets without scanning the original database. The proposed algorithm updates closed itemsets by performing several operations on the previously closed itemsets and added/deleted transactions without searching the previously closed itemsets. The experimental results show that the proposed algorithm significantly outperforms previous methods, which require a substantial length of time to search previously closed itemsets.     

Stabilizing bit-rates in networked control systems with decentralized event-triggered communication

In a decentralized event-triggered networked control system (NCS), an agent samples and transmits its local state information to the controller when some local event occurs. Such event-triggered NCSs were expected to be more efficient than traditional periodically sampled system in terms of communication channel usage. This paper studies the stability of decentralized event-triggered NCS in the presence of quantization and delays. We point out some potential issues in decentralized event-triggered design and propose an alternative decentralized event with a linear-affine threshold, which avoids infinitely fast data transmission. Conditions on quantizer and communication channel are derived, which, when satisfied, can guarantee stability of the resulting NCS. Based on these conditions, finite stabilizing bit-rates are provided.