Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

Overcoming liability of newness of international new ventures : the role of flexibility

Riding on the trend of globalization, a large number of new ventures have emerged deploying resources in multiple country markets so as to arrive at a competitive advantage. Studies that focus on such international new ventures grew to become a distinct research area: international Entrepreneurship that attracts much research attention but leaves a core issue namely liability of newness unaddressed. About 50 years ago, Stinchcombe (1965) coined this term to explain that most new ventures fail because their founders cannot switch their roles quickly enough to adapt to the changing environment. Although previous empirical studies have examined the entrepreneurial firms from the knowledge based view and organizational learning theory and tried to account for the varied ability of these entrepreneurial firms in switching roles in accordance of circumstances, little or no extant studies employs a Resource-based View (RBV) approach. This study will focus on INVs from emerging economics, trying to examine how INVs overcome liability of newness through “flexibility” to gain good performance during their internationalization. Based on the RBV of the firm, this study will address flexibility in form of a flexible configuration of firm resources consisting of cognitive, structural, and strategic flexibility as the predictors, arguing that these flexibilities would help INVs cope with liability of newness by fostering various dynamic capabilities that have been found to improve INVs’ performance. In addition, this study will focus on those INV firms located in industrial clusters, and examine how an INV\u27s network ties within an industrial cluster moderate the relationships among flexibility and the involved dynamic capabilities. This study collected a sample of 192 Chinese international new ventures, and structural equation modeling was used to test the full model. The findings demonstrate that: (1) all the three dimension of flexibilities have positive impact on international performance; (2) exploratory learning capability and adaptive capability mediate flexibility-international performance relationship while information acquisition capability does not; and (3) an INV’s network ties positively moderates both cognitive flexibility-information acquisition capability relationship and information acquisition capability-exploratory learning capability relationship while negatively moderates information acquisition capability-adaptive capability relationship. On the basis of current findings, implications and future research directions are drawn

EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

The paper aims at detecting on-line cognitive failures in driving by decoding the EEG signals acquired during visual alertness, motor-planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor-planning performed by the driver using the pre-processed parietal signals is classified into four classes: braking, acceleration, steering control and no operation. Cognitive failures in motor-planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the co-pilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time-delay between the onset of motor imagination and the EMG response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor-planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor-execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters

Human Dimensions of the Ecosystem Approach to Fisheries: An Overview of Context, Concepts, Tools and Methods

This document aims to provide a better understanding of the role of the economic, institutional and sociocultural components within the ecosystem approach to fisheries (EAF) process and to examine some potential methods and approaches that may facilitate the adoption of EAF management. It explores both the human context for the ecosystem approach to fisheries and the human dimensions involved in implementing the EAF. For the former, the report provides background material essential to understand prior to embarking on EAF initiatives, including an understanding of key concepts and issues, of the valuation of aquatic ecosystems socially, culturally and economically, and of the many policy, legal, institutional, social and economic considerations relevant to the EAF. With respect to facilitating EAF implementation, the report deals with a series of specific aspects: (1) determining the boundaries, scale and scope of the EAF; (2) assessing the various benefits and costs involved, seen from social, economic, ecological and management perspectives; (3) utilizing appropriate decision-making tools in EAF; (4) creating and/or adopting internal incentives and institutional arrangements to promote, facilitate and fund the adoption of EAF management; and (5) finding suitable external (non-fisheries) approaches for financing EAF implementation

Code smells detection and visualization: A systematic literature review

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Method: Over 83 primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning techniques are used in 35% of the studies. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches several methods are used, such as: city metaphors, 3D visualization techniques. Conclusions: We confirm that the detection of CS is a non trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of CS; increasing the diversity of detected CS and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of CS detection and visualization techniques validation experiments.Comment: submitted to ARC

Resource-constrained re-identification in camera networks

PhDIn multi-camera surveillance, association of people detected in different camera views over time, known as person re-identification, is a fundamental task. Re-identification is a challenging problem because of changes in the appearance of people under varying camera conditions. Existing approaches focus on improving the re-identification accuracy, while no specific effort has yet been put into efficiently utilising the available resources that are normally limited in a camera network, such as storage, computation and communication capabilities. In this thesis, we aim to perform and improve the task of re-identification under constrained resources. More specifically, we reduce the data needed to represent the appearance of an object through a proposed feature selection method and a difference-vector representation method. The proposed feature-selection method considers the computational cost of feature extraction and the cost of storing the feature descriptor jointly with the feature’s re-identification performance to select the most cost-effective and well-performing features. This selection allows us to improve inter-camera re-identification while reducing storage and computation requirements within each camera. The selected features are ranked in the order of effectiveness, which enable a further reduction by dropping the least effective features when application constraints require this conformity. We also reduce the communication overhead in the camera network by transferring only a difference vector, obtained from the extracted features of an object and the reference features within a camera, as an object representation for the association. In order to reduce the number of possible matches per association, we group the objects appearing within a defined time-interval in un-calibrated camera pairs. Such a grouping improves the re-identification, since only those objects that appear within the same time-interval in a camera pair are needed to be associated. For temporal alignment of cameras, we exploit differences between the frame numbers of the detected objects in a camera pair. Finally, in contrast to pairwise camera associations used in literature, we propose a many-to-one camera association method for re-identification, where multiple cameras can be candidates for having generated the previous detections of an object. We obtain camera-invariant matching scores from the scores obtained using the pairwise re-identification approaches. These scores measure the chances of a correct match between the objects detected in a group of cameras. Experimental results on publicly available and in-lab multi-camera image and video datasets show that the proposed methods successfully reduce storage, computation and communication requirements while improving the re-identification rate compared to existing re-identification approaches