Kinematic Analysis and Dimensional Synthesis of Exechon Parallel Kinematic Machine for Large Volume Machining

A parallel kinematic machine (PKM) topology can only give its best performance when its geometrical parameters are optimized. In this paper, dimensional synthesis of a newly developed PKM is presented for the first time. An optimization method is developed with the objective to maximize both workspace volume and global dexterity of the PKM. Results show that the method can effectively identify design parameter changes under different weighted objectives. The PKM with optimized dimensions has a large workspace to footprint ratio and a large well-conditioned workspace, hence justifies its suitability for large volume machining.

Parallels between machine and brain decoding

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies. © 2012 Springer-Verlag.

Starbog: The Garden Virtuoso and the Turing Machine

This article will discuss a recent ensemble composition entitled Starbog which was toured and broadcast in Britain in 2006 . The composition of Starbog focused on developing working methods which combined computer-based techniques (using OpenMusic) with more subconscious means of generating musical ideas. The challenge in achieving this was as much aesthetic/philosophical as it was technical and the present article is intending as a ‘sounding’ which focuses on the influence OpenMusic has had on the composer’s music, rather than documenting the nature of the often simple application of algorithms.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

Shame, intimacy and self-definition: An assessment of the emotional foundation and intimate relationship consequences of an introjective personality orientation.

The current study sought to elaborate and test a theoretical proposition that introjective personality functioning, which has been implicated in various psychological difficulties (e.g., self-critical depression, obsessive-compulsive disorder), has an emotional foundation in the self-conscious emotion of shame and is supported by dissociation. Moreover, introjective functioning was predicted to be associated with reduced interpersonal intimacy. To test the model, a Web-based survey design using path analysis was used. Three hundred and fifteen university students were assessed with measures of self-conscious emotions (i.e., shame, guilt, and embarrassment), introjective (self-definition) and anaclitic (relational) personality style, pathological dissociation, and interpersonal intimacy. Introjective personality was found to be associated with increased shame and reduced interpersonal intimacy. However, the path between pathological dissociation and introjective functioning was not significant. The results are discussed with reference to the moderating influence of introjective functioning between shame and reduced interpersonal intimacy.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.