title: Towards Accurate Predictions of Customer Purchasing Patterns

text: A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%.


title: Discriminative learning of visual words for 3D human pose estimation

text: This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an appearance and position context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.


title: A graph-based recommender system for digital library

text: Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, user-user and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.


title: Real-time people counting system using video camera

text: In this MTech thesis experiments will be tried out on a people counting system in an effort to enhance the accuracy when separating counting groups of people, and nonhuman objects. This system features automatic color equalization, adaptive background subtraction, shadow detection algorithm and Kalman tracking. The aim is to develop a reliable and accurate computer vision alternative to sensor or contact based mechanisms. The problem for many computer vision based systems are making good separation between the background and foreground, and teaching the computers what parts make up a scene. We also want to find features to classify the foreground moving objects, an easy task for a human, but a complex task for a computer. Video has been captured with a birds eye view close to one of the entrances at the school about ten meters above the floor. From this video troublesome parts have been selected to test the changes done to the algorithms and program code.


title: Bicycle dynamics and control: adapted bicycles for education and research

text: In this paper, the dynamics of bicycles is analyzed from the perspective of control. Models of different complexity are presented, starting with simple ones and ending with more realistic models generated from multibody software. Models that capture essential behavior such as self-stabilization as well as models that demonstrate difficulties with rear wheel steering are considered. Experiences using bicycles in control education along with suggestions for fun and thought-provoking experiments with proven student attraction are presented. Finally, bicycles and clinical programs designed for children with disabilities are described.


title: A neostriatal habit learning system in humans.

text: Amnesic patients and nondemented patients with Parkinson's disease were given a probabilistic classification task in which they learned which of two outcomes would occur on each trial, given the particular combination of cues that appeared. Amnesic patients exhibited normal learning of the task but had severely impaired declarative memory for the training episode. In contrast, patients with Parkinson's disease failed to learn the probabilistic classification task, despite having intact memory for the training episode. This double dissociation shows that the limbic-diencephalic regions damaged in amnesia and the neostriatum damaged in Parkinson's disease support separate and parallel learning systems. In humans, the neostriatum (caudate nucleus and putamen) is essential for the gradual, incremental learning of associations that is characteristic of habit learning. The neostriatum is important not just for motor behavior and motor learning but also for acquiring nonmotor dispositions and tendencies that depend on new associations.


title: On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

text: Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.


title: Development of a 6-DOF manipulator actuated with a straight-fiber-type artificial muscle

text: Robots have become an integral part of human life, and the relationship between humans and robots has grown closer. Thus, it is desired that robots have characteristics similar to humans. In this context, we paid attention to an artificial muscle actuator. We used straight-fiber-type artificial muscles, derived from the McKibben type, which have excellent characteristics with respect to the contraction rate and force. We developed a 6-DOF manipulator actuated by a straight fiber artificial muscle. Furthermore, we tried to control the manipulator position by considering its characteristics.


title: From smart to smarter cities: Bridging the dimensions of technology and urban planning

text: This paper discusses the importance of urban form in smart cities. Development of urban form has been a major concern for urban planners, designers and policy makers. Sprawl is one way of urban development which is not considered good for a better living standard. Compact form arose as an opposed idea to urban sprawl. Form based codes (FBCs) is a tool for urban planning and design that attempts to mitigate the problem of urban sprawl, whereas conventional zoning strengthens this. This paper highlights the importance of physical place in smart city and how FBCs attempt to create better physical place than conventional zoning. Our study shows that FBCs can lead to smart growth which can be a solution to bridge technology and urban planning together in a single platform.


title: How to learn a graph from smooth signals

text: We propose a framework that learns the graph structure underlying a set of smooth signals. Given X ∈ Rm×n whose rows reside on the vertices of an unknown graph, we learn the edge weights w ∈ R + under the smoothness assumption that tr ( X>LX ) is small. We show that the problem is a weighted `-1 minimization that leads to naturally sparse solutions. We point out how known graph learning or construction techniques fall within our framework and propose a new model that performs better than the state of the art in many settings. We present efficient, scalable primal-dual based algorithms for both our model and the previous state of the art, and evaluate their performance on artificial and real data.


title: Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation

text: We address the problem of learning a pose-aware, compact embedding that projects images with similar human poses to be placed close-by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet-based rank constraints on real image data. This architecture allows us to learn a robust representation that captures differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose-related tasks, the proposed pose embedding provides a cost-efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose-based image retrieval and action recognition problems.


title: Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning

text: With the ever-increasing urbanization process, systematically modeling people’s activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM. To bridge the gap, we present CrossMap, a novel crossmodal representation learning method that uncovers urban dynamics with massive GTSM data. CrossMap first employs an accelerated mode seeking procedure to detect spatiotemporal hotspots underlying people’s activities. Those detected hotspots not only address spatiotemporal variations, but also largely alleviate the sparsity of the GTSM data. With the detected hotspots, CrossMap then jointly embeds all spatial, temporal, and textual units into the same space using two different strategies: one is reconstructionbased and the other is graph-based. Both strategies capture the correlations among the units by encoding their cooccurrence and neighborhood relationships, and learn lowdimensional representations to preserve such correlations. Our experiments demonstrate that CrossMap not only significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.


title: A 3.4 W Digital-In Class-D Audio Amplifier in 0.14 $\mu $m CMOS

text: In this paper a class-D audio amplifier for mobile applications is presented realized in a 0.14 μm CMOS technology tailored for mobile applications. The amplifier has a simple PDM-based digital interface for audio and control that requires only two pins and enables assembly in 9-bump WL-CSP. The complete audio path is discussed that consists of a Parser, Digital PWM controller, 1-bit DA-converters, analog feedback loop and the Class-D power stage. A reconfigurable gate driver is used that reduces quiescent current consumption and radiated emission.


title: A new paradigm of human gait analysis with Kinect

text: Analysis of human gait helps to find an intrinsic gait signature through which ubiquitous human identification and medical disorder problems can be investigated in a broad spectrum. The gait biometric provides an unobtrusive feature by which video gait data can be captured at a larger distance without prior awareness of the subject. In this paper, a new technique has been addressed to study the human gait analysis with Kinect Xbox device. It ensures us to minimize the segmentation errors with automated background subtraction technique. The closely similar human skeleton model can be generated from background subtracted gait images, altered by covariate conditions, such as change in walking speed and variations in clothing type. The gait signatures are captured from joint angle trajectories of left hip, left knee, right hip and right knee of subject's skeleton model. The experimental verification on Kinect gait data has been compared with our in-house development of sensor based biometric suit, Intelligent Gait Oscillation Detector (IGOD). An endeavor has been taken to investigate whether this sensor based biometric suit can be altered with a Kinect device for the proliferation of robust gait identification system. The Fisher discriminant analysis has been applied on training gait signature to look into the discriminatory power of feature vector. The Naïve Bayesian classifier demonstrates an encouraging classification result with estimation of errors on limited dataset captured by Kinect sensor.


title: Employee Engagement From a Self-Determination Theory Perspective

text: Macey and Schneider (2008) draw on numerous theories to explain what engagement is and how it is similar to and different from related constructs in the organizational behavior literature. As a result, we now have a better understanding of some of the key ‘‘components’’ of engagement. What appears to be missing, however, is a strong unifying theory to guide research and practice. We believe that such a theory exists in the form of self-determination theory (SDT; Deci & Ryan, 1985; Ryan & Deci, 2000) and its various corollaries, self-concordance theory (SCT; Sheldon & Elliot, 1999), hierarchical theory (Vallerand, 1997), and passion theory (Vallerand et al., 2003). Although Macey and Schneider acknowledged the relevance of SDT and SCT, we believe that much greater use of these theories could be made to justify and extend their conceptual model.


title: Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes

text: The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or "things" to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.


title: Handshaking, gender, personality, and first impressions.

text: Although people's handshakes are thought to reflect their personality and influence our first impressions of them, these relations have seldom been formally investigated. One hundred twelve participants had their hand shaken twice by 4 trained coders (2 men and 2 women) and completed 4 personality measures. The participants' handshakes were stable and consistent across time and coders. There were also gender differences on most of the handshaking characteristics. A firm handshake was related positively to extraversion and emotional expressiveness and negatively to shyness and neuroticism; it was also positively related to openness to experience, but only for women. Finally, handshake characteristics were related to the impressions of the participants formed by the coders. These results demonstrate that personality traits, assessed through self-report, can predict specific behaviors assessed by trained observers. The pattern of relations among openness, gender, handshaking, and first impressions suggests that a firm handshake may be an effective form of self-promotion for women.


title: Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

text:
IMPORTANCE
Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive.

OBJECTIVE
To investigate network dysfunction in MDD through a meta-analysis of rsFC studies.

DATA SOURCES
Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data.

STUDY SELECTION
Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis.

DATA EXTRACTION AND SYNTHESIS
Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network.

RESULTS
Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions.

CONCLUSIONS AND RELEVANCE
Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.


title: Greedy Function Approximation : A Gradient Boosting Machine

text: Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additive expansions based on any tting criterion. Speci c algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classi cation. Special enhancements are derived for the particular case where the individual additive components are decision trees, and tools for interpreting such \TreeBoost" models are presented. Gradient boosting of decision trees produces competitive, highly robust, interpretable procedures for regression and classi cation, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire 1996, and Friedman, Hastie, and Tibshirani 1998 are discussed. 1 Function estimation In the function estimation problem one has a system consisting of a random \output" or \response" variable y and a set of random \input" or \explanatory" variables x = fx1; ; xng. Given a \training" sample fyi;xig N 1 of known (y;x){values, the goal is to nd a function F (x) that maps x to y, such that over the joint distribution of all (y;x){values, the expected value of some speci ed loss function (y; F (x)) is minimized F (x) = argmin F (x) Ey;x (y; F (x)) = argmin F (x) Ex [Ey( (y; F (x)) jx] : (1) Frequently employed loss functions (y; F ) include squared{error (y F ) and absolute error jy F j for y 2 R (regression), and negative binomial log{likelihood, log(1 + e 2yF ), when y 2 f 1; 1g (classi cation). A common procedure is to take F (x) to be a member of a parameterized class of functions F (x;P), where P = fP1; P2; g is a set of parameters. In this paper we focus on \additive" expansions of the form


title: FPGA implementations of fast fourier transforms for real-time signal and image processing

text: Applications based on Fast Fourier Transform (FFT) such as signal and image processing require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. At present, however, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realisation of a High Level framework for the implementation of 1-D and 2-D FFTs for real-time applications. Results show that the parallel implementation of 2-D FFT achieves virtually linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrisable environment based on the developed parallel 2-D FFT architecture is presented as a solution for frequencydomain image filtering application.


title: Priors for people tracking from small training sets

text: We advocate the use of scaled Gaussian process latent variable models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a low-dimensional embedding of the high-dimensional pose data and a density function that both gives higher probability to points close to training data and provides a nonlinear probabilistic mapping from the low-dimensional latent space to the full-dimensional pose space. The SGPLVM is a natural choice when only small amounts of training data are available. We demonstrate our approach with two distinct motions, golfing and walking. We show that the SGPLVM sufficiently constrains the problem such that tracking can be accomplished with straightforward deterministic optimization.


title: Three dual polarized 2.4GHz microstrip patch antennas for active antenna and in-band full duplex applications

text: This paper presents the design, implementation and interport isolation performance evaluation of three dual port, dual polarized 2.4GHz microstrip patch antennas. Input matching and interport isolation (both DC and RF) performance of implemented antennas has been compared by measuring the input return losses (Sii, S22) and interport isolation (S12) respectively at 2.4GHz. Two implemented single layer antennas provide around 40dB RF interport isolation while the multilayer antenna has 60dB isolation between transmit and receive ports at centre frequency with DC isolated ports. The multilayer antenna provides more than 55dB interport isolation for antenna's 10 dB input impedance bandwidth of 50MHz.


title: Evaluating Color Descriptors for Object and Scene Recognition

text: Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.


title: Automatic annotation of human actions in video

text: This paper addresses the problem of automatic temporal annotation of realistic human actions in video using minimal manual supervision. To this end we consider two associated problems: (a) weakly-supervised learning of action models from readily available annotations, and (b) temporal localization of human actions in test videos. To avoid the prohibitive cost of manual annotation for training, we use movie scripts as a means of weak supervision. Scripts, however, provide only implicit, noisy, and imprecise information about the type and location of actions in video. We address this problem with a kernel-based discriminative clustering algorithm that locates actions in the weakly-labeled training data. Using the obtained action samples, we train temporal action detectors and apply them to locate actions in the raw video data. Our experiments demonstrate that the proposed method for weakly-supervised learning of action models leads to significant improvement in action detection. We present detection results for three action classes in four feature length movies with challenging and realistic video data.


title: Semi-supervised hashing for scalable image retrieval

text: Large scale image search has recently attracted considerable attention due to easy availability of huge amounts of data. Several hashing methods have been proposed to allow approximate but highly efficient search. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually given in terms of labeled pairs of images. There exist supervised hashing methods that can handle such semantic similarity but they are prone to overfitting when labeled data is small or noisy. Moreover, these methods are usually very slow to train. In this work, we propose a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeled data. The proposed method can handle both metric as well as semantic similarity. The experimental results on two large datasets (up to one million samples) demonstrate its superior performance over state-of-the-art supervised and unsupervised methods.


title: Investigating Causal Relations by Econometric Models and Cross-spectral Methods

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/econosoc.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.


title: Semantic analysis of soccer video using dynamic Bayesian network

text: Video semantic analysis is formulated based on the low-level image features and the high-level knowledge which is encoded in abstract, nongeometric representations. This paper introduces a semantic analysis system based on Bayesian network (BN) and dynamic Bayesian network (DBN). It is validated in the particular domain of soccer game videos. Based on BN/DBN, it can identify the special events in soccer games such as goal event, corner kick event, penalty kick event, and card event. The video analyzer extracts the low-level evidences, whereas the semantic analyzer uses BN/DBN to interpret the high-level semantics. Different from previous shot-based semantic analysis approaches, the proposed semantic analysis is frame-based for each input frame, it provides the current semantics of the event nodes as well as the hidden nodes. Another contribution is that the BN and DBN are automatically generated by the training process instead of determined by ad hoc. The last contribution is that we introduce a so-called temporal intervening network to improve the accuracy of the semantics output


title: A Flexible Model-Driven Game Development Approach

text: Game developers are facing an increasing demand for new games every year. Game development tools can be of great help, but require highly specialized professionals. Also, just as any software development effort, game development has some challenges. Model-Driven Game Development (MDGD) is suggested as a means to solve some of these challenges, but with a loss in flexibility. We propose a MDGD approach that combines multiple domain-specific languages (DSLs) with design patterns to provide flexibility and allow generated code to be integrated with manual code. After experimentation, we observed that, with the approach, less experienced developers can create games faster and more easily, and the product of code generation can be customized with manually written code, providing flexibility. However, with MDGD, developers become less familiar with the code, making manual codification more difficult.


title: Matchmaking in multi-player on-line games: studying user traces to improve the user experience

text: Designing and implementing a quality matchmaking service for Multiplayer Online Games requires an extensive knowledge of the habits, behaviors and expectations of the players. Gathering and analyzing traces of real games offers insight on these matters, but game server providers are very protective of such data in order to deter possible reuse by the competition and to prevent cheating. We circumvented this issue by gathering public data from a League of Legends server (information over more than 28 million game sessions). In this paper, we present our database which is freely available online, and we detail the analysis and conclusions we draw from this data regarding the expected requirements for the matchmaking service.


title: Knowledge sharing in organisational contexts: a motivation-based perspective

text: Purpose – Facilitating knowledge sharing within organisations is a difficult task: the willingness of individuals to share and integrate their knowledge is one of the central barriers. This paper aims to develop a motivation-based perspective to explore how organisations resolve the social dilemma of knowledge sharing. Design/methodology/approach – The analysis builds on a three-category taxonomy of motivation, adding ‘‘hedonic’’ motivation to the traditional dichotomy of ‘‘extrinsic’’ and ‘‘intrinsic’’ motivation. It uses case studies gleaned from the literature to explore the interactive effects between the different motivators in two different types of knowledge-intensive organisations: professional bureaucracy and operating adhocracy. Findings – Within a professional bureaucracy, the social dilemma of knowledge sharing may be overcome through normative motivation, with provision of hedonic motivation through extrinsic incentives such as training and career progression. In an operating adhocracy where interdependent teamwork is vital, it may be overcome through normative alignment reinforced by intensive socialisation. Extrinsic motivators that align with hedonic motivation may also reinforce the propensity for knowledge sharing. In both organisational types, financial extrinsic incentives do not appear to be relevant on their own, and may ‘‘crowd out’’ other motivators. Research limitations/implications – The cases reported were chosen from the existing literature and, although many were not designed specifically to address motivational issues, suggestive conclusions are drawn. Most of the cases were drawn from organisations rooted in the Anglo-American context and thus care would be needed in generalising the findings to organisations in other contexts. Originality/value – The paper represents the first attempt to apply a three-category taxonomy of motivation to examine knowledge-sharing behaviour in organisations. It highlights the interaction between the different motivators and provides a basis to integrate further the work of social psychologists and socio-economists on incentives and motivation in the context of knowledge sharing.


title: A versatile FMCW Radar System Simulator for Millimeter-Wave Applications

text: For the successful design of low-cost and high-performance radar systems accurate and efficient system simulation is a key requirement. In this paper we present a new versatile simulation environment for frequency-modulated continuous-wave radar systems. Besides common hardware simulation it covers integrated system simulation and concept analysis from signal synthesis to baseband. It includes a flexible scenario generator, accurate noise modeling, and efficiently delivers simulation data for development and testing of signal processing algorithms. A comparison of simulations and measurement results for an integrated 77-GHz radar prototype shows the capabilities of the simulator on two different scenarios.


title: Automatic multilabel classification for Indonesian news articles

text: Problem transformation and algorithm adaptation are the two main approaches in machine learning to solve multilabel classification problem. The purpose of this paper is to investigate both approaches in multilabel classification for Indonesian news articles. Since this classification deals with a large number of features, we also employ some feature selection methods to reduce feature dimension. There are four factors as the focuses of this paper, i.e., feature weighting method, feature selection method, multilabel classification approach, and single-label classification algorithm. These factors will be combined to determine the best combination. The experiments show that the best performer for multilabel classification of Indonesian news articles is the combination of TF-IDF feature weighting method, Symmetrical Uncertainty feature selection method, Calibrated Label Ranking - which belongs to problem transformation approach -, and SVM algorithm. This best combination achieves F-measure of 85.13% in 10-fold cross-validation, but the F-measure decreases to 76.73% in testing because of OOV.


title: Topic Models for Mortality Modeling in Intensive Care Units

text: Mortality prediction is an important problem in the intensive care unit (ICU) because it is helpful for understanding patients’ evolving severity, quality of care, and comparing treatments. Most ICU mortality models primarily consider structured data and physiological waveforms (Le Gall et al., 1993). An important limitation of these structured data approaches is that they miss a lot of vital information captured in providers’ free text notes and reports. In this paper, we propose an approach to mortality prediction that incorporates the information from free text notes using topic modeling.


title: A non-isolated three-port converter for stand-alone renewable power system

text: A novel non-isolated three-port converter (NI-TPC) is proposed interfacing one PV port, one bidirectional battery port and one load port. Single stage power conversion between any two of the three ports is achieved. The topology is derived by decoupling the bidirectional power flow path of the conventional structure into two unidirectional ones. Two of the three ports can be tightly regulated to achieve maximum power harvesting for PV or charge control for battery, and maintain the load voltage constant at the same time, while the third port is left flexible to compensate the power imbalance of the converter. Operation states are analyzed. The multi-regulator competition control strategy is presented to achieve autonomous and smooth state switching when the PV input power fluctuates. The analysis is verified by the experimental results.


title: A Framework for Coordinated Surface Operations Planning at Dallas-Fort Worth International Airport

text: An Integer Programming formulation is developed for optimizing surface operations at Dallas-Fort Worth airport, with the goal of assessing the potential benefits of taxi route planning. The model is based on operations in the eastern half of the airport under the most frequently used configuration. The focus is on operational concepts that optimize taxi routes by utilizing different control points on the airport surface. The benefits of two different concepts for optimizing taxiway operations, namely controlled pushback and taxi reroutes are analyzed, for both current data and a projected data set with approximately twice the traffic density. The analysis estimates that: (1) for current traffic densities, controlled pushback would reduce the average departure taxi time by 17% without altering runway schedule conformance, while the benefits of taxi reroutes would be minimal; and (2) for high-density operations, controlled pushback would reduce the average departure taxi time by 18%, while incorporating taxi reroutes would reduce the average arrival taxi time by 14%. Other benefits analyzed for these control strategies include a decrease in the average time spent in runway crossing queues.


title: Unsupervised Learning of Sequence Representations by Autoencoders

text: Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples. In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error. Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies. Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, which results in a higher-quality representation. The learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information. Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style). One promising application of the ISA model is that it can be readily used in the semi-supervised learning scenario, in which a large amount of unlabeled data is leveraged to extract high-quality sequence representations and thus to improve the performance of the subsequent supervised learning tasks on limited labeled data.


title: A practical path loss model for indoor WiFi positioning enhancement

text: Positioning within a local area refers to technology whereby each node is self-aware of its position. Based on empirical study, this paper proposes an enhancement to the path loss model in the indoor environment for improved accuracy in the relationship between distance and received signal strength. We further demonstrate the potential of our model for the WiFi positioning system, where the mean errors in the distance estimation are 2.3 m and 2.9 m for line of sight and non line of sight environments, respectively.


title: xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs

text: In this paper we show how attackers can covertly leak data (e.g., encryption keys, passwords and files) from highly secure or air-gapped networks via the row of status LEDs that exists in networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device ('side-channel'), intentionally controlling the status LEDs to carry any type of data ('covert-channel') has never studied before. A malicious code is executed on the LAN switch or router, allowing full control of the status LEDs. Sensitive data can be encoded and modulated over the blinking of the LEDs. The generated signals can then be recorded by various types of remote cameras and optical sensors. We provide the technical background on the internal architecture of switches and routers (at both the hardware and software level) which enables this type of attack. We also present amplitude and frequency based modulation and encoding schemas, along with a simple transmission protocol. We implement a prototype of an exfiltration malware and discuss its design and implementation. We evaluate this method with a few routers and different types of LEDs. In addition, we tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and also discuss different detection and prevention countermeasures. Our experiment shows that sensitive data can be covertly leaked via the status LEDs of switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per LED. Keywords— ezfiltration; air-gap; network; optical; covertchannel (key words)


title: Steganalysis of QIM Steganography in Low-Bit-Rate Speech Signals

text: Steganalysis of the quantization index modulation QIM steganography in a low-bit-rate encoded speech stream is conducted in this research. According to the speech generation theory and the phoneme distribution properties in language, we first point out that the correlation characteristics of split vector quantization VQ codewords of linear predictive coding filter coefficients are changed after the QIM steganography. Based on this observation, we construct a model called the Quantization codeword correlation network QCCN based on split VQ codeword from adjacent speech frames. Furthermore, the QCCN model is pruned to yield a stronger correlation network. After quantifying the correlation characteristics of vertices in the pruned correlation network, we obtain feature vectors that are sensitive to steganalysis. Finally, we build a high-performance detector using the support vector machine SVM classifier. It is shown by experimental results that the proposed QCCN steganalysis method can effectively detect the QIM steganography in encoded speech stream when it is applied to low-bit-rate speech codec such as G.723.1 and G.729.


title: Relation Extraction : A Survey

text: With the advent of the Internet, large amount of digital text is generated everyday in the form of news articles, research publications, blogs, question answering forums and social media. It is important to develop techniques for extracting information automatically from these documents, as lot of important information is hidden within them. This extracted information can be used to improve access and management of knowledge hidden in large text corpora. Several applications such as Question Answering, Information Retrieval would benefit from this information. Entities like persons and organizations, form the most basic unit of the information. Occurrences of entities in a sentence are often linked through well-defined relations; e.g., occurrences of person and organization in a sentence may be linked through relations such as employed at. The task of Relation Extraction (RE) is to identify such relations automatically. In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques. We also cover the paradigms of Open Information Extraction (OIE) and Distant Supervision. Finally, we describe some of the recent trends in the RE techniques and possible future research directions. This survey would be useful for three kinds of readers i) Newcomers in the field who want to quickly learn about RE; ii) Researchers who want to know how the various RE techniques evolved over time and what are possible future research directions and iii) Practitioners who just need to know which RE technique works best in various settings.


title: The Treatment of Missing Values and its Effect on Classifier Accuracy

text: The presence of missing values in a dataset can affect the performance of a classifier constructed using that dataset as a training sample. Several methods have been proposed to treat missing data and the one used most frequently deletes instances containing at least one missing value of a feature. In this paper we carry out experiments with twelve datasets to evaluate the effect on the misclassification error rate of four methods for dealing with missing values: the case deletion method, mean imputation, median imputation, and the KNN imputation procedure. The classifiers considered were the Linear Discriminant Analysis (LDA) and the KNN classifier. The first one is a parametric classifier whereas the second one is a non parametric classifier.


title: Designing Media Architecture: Tools and Approaches for Addressing the Main Design Challenges

text: Media Architecture is reaching a level of maturity at which we can identify tools and approaches for addressing the main challenges for HCI practitioners working in this field. While previous influential contributions within Media Architecture have identified challenges for designers and offered case studies of specific approaches, here, we (1) provide guidance on how to tackle the domain-specific challenges of Media Architecture design -- pertaining to the interface, integration, content, context, process, prototyping, and evaluation -- on the basis of the development of numerous installations over the course of seven years, and thorough studies of related work, and (2) present five categories of tools and approaches -- software tools, projection, 3D models, hardware prototyping, and evaluation tools -- developed to address these challenges in practice, exemplified through six concrete examples from real-life cases.


title: More is Less: On the End-to-End Security of Group Chats in Signal, WhatsApp, and Threema

text: Secure instant messaging is utilized in two variants: one-to-one communication and group communication. While the first variant has received much attention lately (Frosch et al., EuroS Cohn-Gordon et al., EuroS Kobeissi et al., EuroS&P17), little is known about the cryptographic mechanisms and security guarantees of secure group communication in instant messaging. To approach an investigation of group instant messaging protocols, we first provide a comprehensive and realistic security model. This model combines security and reliability goals from various related literature to capture relevant properties for communication in dynamic groups. Thereby the definitions consider their satisfiability with respect to the instant delivery of messages. To show its applicability, we analyze three widely used real-world protocols: Signal, WhatsApp, and Threema. By applying our model, we reveal several shortcomings with respect to the security definition. Therefore we propose generic countermeasures to enhance the protocols regarding the required security and reliability goals. Our systematic analysis reveals that (1) the communications' integrity – represented by the integrity of all exchanged messages – and (2) the groups' closeness – represented by the members' ability of managing the group – are not end-to-end protected. We additionally show that strong security properties, such as Future Secrecy which is a core part of the one-to-one communication in the Signal protocol, do not hold for its group communication.


title: Existential meaning's role in the enhancement of hope and prevention of depressive symptoms.

text: The authors confirmed that existential meaning has a unique relationship with and can prospectively predict levels of hope and depressive symptoms within a population of college students. Baseline measures of explicit meaning (i.e., an individual's self-reported experience of a sense of coherence and purpose in life) and implicit meaning (i.e., an individual's self-reported embodiment of the factors that are normatively viewed as comprising a meaningful life) explained significant amounts of variance in hope and depressive symptoms 2 months later beyond the variance explained by baseline levels of hope/depression, neuroticism, conscientiousness, agreeableness, openness to experience, extraversion, and social desirability. The authors discuss implications of these findings for the field of mental health treatment and suggest ways of influencing individuals' experience of existential meaning.


title: Why software fails [software failure]

text: Most IT experts agree that software failures occur far more often than they should despite the fact that, for the most part, they are predictable and avoidable. It is unfortunate that most organizations don't see preventing failure as an urgent matter, even though that view risks harming the organization and maybe even destroying it. Because software failure has tremendous implications for business and society, it is important to understand why this attitude persists.


title: Transfer learning for class imbalance problems with inadequate data

text: A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data are not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting-based instance transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.


title: Player Type Models: Towards Empirical Validation

text: Player type models -- such as the BrainHex model -- are popular approaches for personalizing digital games towards individual preferences of players. Although several player type models have been developed and are currently used in game design projects, there is still a lack of data on their validity. To close this research gap we currently investigate the psychometric properties (factor structure, reliability, stability) and predictive validity (if player type scores can predict player experience) of the player type model BrainHex in an ongoing project. Results of two online studies (n1=592, n2=243) show that the psychometric properties of the BrainHex model could be improved. We suggest to improve the according questionnaire and sketch how the predictive validity could be investigated in future studies.


title: Scalable Metadata Management Techniques for Ultra-Large Distributed Storage Systems - A Systematic Review

text: The provisioning of an efficient ultra-large scalable distributed storage system for expanding cloud applications has been a challenging job for researchers in academia and industry. In such an ultra-large-scale storage system, data are distributed on multiple storage nodes for performance, scalability, and availability. The access to this distributed data is through its metadata, maintained by multiple metadata servers. The metadata carries information about the physical address of data and access privileges. The efficiency of a storage system highly depends on effective metadata management. This research presents an extensive systematic literature analysis of metadata management techniques in storage systems. This research work will help researchers to find the significance of metadata management and important parameters of metadata management techniques for storage systems. Methodical examination of metadata management techniques developed by various industry and research groups is described. The different metadata distribution techniques lead to various taxonomies. Furthermore, the article investigates techniques based on distribution structures and key parameters of metadata management. It also presents strengths and weaknesses of individual existing techniques that will help researchers to select the most appropriate technique for specific applications. Finally, it discusses existing challenges and significant research directions in metadata management for researchers.


title: An Integral Model to Provide Reactive and Proactive Services in an Academic CSIRT Based on Business Intelligence

text: Cyber-attacks have increased in severity and complexity. That requires, that the CERT/CSIRT research and develops new security tools. Therefore, our study focuses on the design of an integral model based on Business Intelligence (BI), which provides reactive and proactive services in a CSIRT, in order to alert and reduce any suspicious or malicious activity on information systems and data networks. To achieve this purpose, a solution has been assembled, that generates information stores, being compiled from a continuous network transmission of several internal and external sources of an organization. However, it contemplates a data warehouse, which is focused like a correlator of logs, being formed by the information of feeds with diverse formats. Furthermore, it analyzed attack detection and port scanning, obtained from sensors such as Snort and Passive Vulnerability Scanner, which are stored in a database, where the logs have been generated by the systems. With such inputs, we designed and implemented BI systems using the phases of the Ralph Kimball methodology, ETL and OLAP processes. In addition, a software application has been implemented using the SCRUM methodology, which allowed to link the obtained logs to the BI system for visualization in dynamic dashboards, with the purpose of generating early alerts and constructing complex queries using the user interface through objects structures. The results demonstrate, that this solution has generated early warnings based on the level of criticality and level of sensitivity of malware and vulnerabilities as well as monitoring efficiency, increasing the level of security of member institutions.


title: Extensive Analysis and Prediction of Optimal Inventory levels in supply chain management based on Particle Swarm Optimization Algorithm

text: Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain. The dynamic nature of the excess stock level and shortage level from one period to another is a serious issue. In addition, consideration of multiple products and more supply chain members leads to very complex inventory management process. Moreover, the supply chain cost increases because of the influence of lead times for supplying the stocks as well as the raw materials. A better optimization methodology would consider all these factors in the prediction of the optimal stock levels to be maintained in order to minimize the total supply chain cost. Here, we are proposing an optimization methodology that utilizes the Particle Swarm Optimization algorithm, one of the best optimization algorithms, to overcome the impasse in maintaining the optimal stock levels at each member of the supply chain.


title: CIO and Business Executive Leadership Approaches to Establishing Company-wide Information Orientation

text: In the digital world, business executives have a heightened awareness of the strategic importance of information and information management to their companies’ value creation. This presents both leadership opportunities and challenges for CIOs. To prevent the CIO position from being marginalized and to enhance CIOs’ contribution to business value creation, they must move beyond being competent IT utility managers and play an active role in helping their companies build a strong information usage culture. The purpose of this article is to provide a better understanding of the leadership approaches that CIOs and business executives can adopt to improve their companies’ information orientation. Based on our findings from four case studies, we have created a four-quadrant leadership-positioning framework. This framework is constructed from the CIO’s perspective and indicates that a CIO may act as a leader, a follower or a nonplayer in developing the company’s information orientation to achieve its strategic focus. The article concludes with guidelines that CIOs can use to help position their leadership challenges in introducing or sustaining their companies’ information orientation initiatives and recommends specific leadership approaches depending on CIOs’ particular situations.


title: Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis

text: Classification of patient samples is a crucial aspect of cancer diagnosis. DNA hybridization arrays simultaneously measure the expression levels of thousands of genes and it has been suggested that gene expression may provide the additional information needed to improve cancer classification and diagnosis. This paper presents methods for analyzing gene expression data to classify cancer types. Machine learning techniques, such as Bayesian networks, neural trees, and radial basis function (RBF) networks, are used for the analysis of CAMDA Data Set 2. These techniques have their own properties including the ability of finding important genes for cancer classification, revealing relationships among genes, and classifying cancer. This paper reports on comparative evaluation of the experimental results of these methods.


title: SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

text: Indoor localization typically relies on measuring a collection of RF signals, such as Received Signal Strength (RSS) from WiFi, in conjunction with spatial maps of signal fingerprints. A new technology for localization could arise with the use of 4G LTE telephony small cells, with limited range but with rich signal strength information, namely Reference Signal Received Power (RSRP). In this paper, we propose to combine an ensemble of available sources of RF signals to build multi-modal signal maps that can be used for localization or for network deployment optimization. We primarily rely on Simultaneous Localization and Mapping (SLAM), which provides a solution to the challenge of building a map of observations without knowing the location of the observer. SLAM has recently been extended to incorporate signal strength from WiFi in the so-called WiFi-SLAM. In parallel to WiFi-SLAM, other localization algorithms have been developed that exploit the inertial motion sensors and a known map of either WiFi RSS or of magnetic field magnitude. In our study, we use all the measurements that can be acquired by an off-the-shelf smartphone and crowd-source the data collection from several experimenters walking freely through a building, collecting time-stamped WiFi and Bluetooth RSS, 4G LTE RSRP, magnetic field magnitude, GPS reference points when outdoors, Near-Field Communication (NFC) readings at specific landmarks and pedestrian dead reckoning based on inertial data. We resolve the location of all the users using a modified version of Graph-SLAM optimization of the users poses with a collection of absolute location and pairwise constraints that incorporates multi-modal signal similarity. We demonstrate that we can recover the user positions and thus simultaneously generate dense signal maps for each WiFi access point and 4G LTE small cell, “from the pocket”. Finally, we demonstrate the localization performance using selected single modalities, such as only WiFi and the WiFi signal maps that we generated.


title: Simultaneous localization and mapping: part I

text: This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method. While there are still many practical issues to overcome, especially in more complex outdoor environments, the general SLAM method is now a well understood and established part of robotics. Another part of the tutorial summarized more recent works in addressing some of the remaining issues in SLAM, including computation, feature representation, and data association


title: A solution to the simultaneous localization and map building (SLAM) problem

text: The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1], [2], [3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including sub-optimal map-building algorithms and map management.


title: Walk detection and step counting on unconstrained smartphones

text:
Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use.
 This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.


title: Analyzing features for activity recognition

text: Human activity is one of the most important ingredients of context information. In wearable computing scenarios, activities such as walking, standing and sitting can be inferred from data provided by body-worn acceleration sensors. In such settings, most approaches use a single set of features, regardless of which activity to be recognized. In this paper we show that recognition rates can be improved by careful selection of individual features for each activity. We present a systematic analysis of features computed from a real-world data set and show how the choice of feature and the window length over which the feature is computed affects the recognition rates for different activities. Finally, we give a recommendation of suitable features and window lengths for a set of common activities.


title: On Managing Very Large Sensor-Network Data Using Bigtable

text: Recent advances and innovations in smart sensor technologies, energy storage, data communications, and distributed computing paradigms are enabling technological breakthroughs in very large sensor networks. There is an emerging surge of next-generation sensor-rich computers in consumer mobile devices as well as tailor-made field platforms wirelessly connected to the Internet. Billions of such sensor computers are posing both challenges and opportunities in relation to scalable and reliable management of the peta- and exa-scale time series being generated over time. This paper presents a Cloud-computing approach to this issue based on the two well-known data storage and processing paradigms: Bigtable and MapReduce.


title: A Bayesian Nonparametric Approach for Multi-label Classification

text: Many real-world applications require multi-label classification where multiple target labels are assigned to each instance. In multi-label classification, there exist the intrinsic correlations between the labels and features. These correlations are beneficial for multi-label classification task since they reflect the coexistence of the input and output spaces that can be exploited for prediction. Traditional classification methods have attempted to reveal these correlations in different ways. However, existing methods demand expensive computation complexity for finding such correlation structures. Furthermore, these approaches can not identify the suitable number of label-feature correlation patterns. In this paper, we propose a Bayesian nonparametric (BNP) framework for multi-label classification that can automatically learn and exploit the unknown number of multi-label correlation. We utilize the recent techniques in stochastic inference to derive the cheap (but efficient) posterior inference algorithm for the model. In addition, our model can naturally exploit the useful information from missing label samples. Furthermore, we extend the model to update parameters in an online fashion that highlights the flexibility of our model against the existing approaches. We compare our method with the state-of-the-art multi-label classification algorithms on real-world datasets using both complete and missing label settings. Our model achieves better classification accuracy while our running time is consistently much faster than the baselines in an order of magnitude.


title: Complexity leadership theory : An interactive perspective on leading in complex adaptive systems

text: leadership theory: An interactive perspective on leading in complex adaptive systems" (2006). Management Department Faculty Publications. Paper 8. Traditional, hierarchical views of leadership are less and less useful given the complexities of our modern world. Leadership theory must transition to new perspectives that account for the complex adaptive needs of organizations. In this paper, we propose that leadership (as opposed to leaders) can be seen as a complex dynamic process that emerges in the interactive " spaces between " people and ideas. That is, leadership is a dynamic that transcends the capabilities of individuals alone; it is the product of interaction, tension, and exchange rules governing changes in perceptions and understanding. We label this a dynamic of adaptive leadership, and we show how this dynamic provides important insights about the nature of leadership and its outcomes in organizational fields. We define a leadership event as a perceived segment of action whose meaning is created by the interactions of actors involved in producing it, and we present a set of innovative methods for capturing and analyzing these contextually driven processes. We provide theoretical and practical implications of these ideas for organizational behavior and organization and management theory.


title: Practical Option Pricing with Support Vector Regression and MART by

text: Support Vector Regression and MART by Ian I-En Choo Stanford University


title: Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations

text: Evidence for the etiology of autism spectrum disorders (ASDs) has consistently pointed to a strong genetic component complicated by substantial locus heterogeneity. We sequenced the exomes of 20 individuals with sporadic ASD (cases) and their parents, reasoning that these families would be enriched for de novo mutations of major effect. We identified 21 de novo mutations, 11 of which were protein altering. Protein-altering mutations were significantly enriched for changes at highly conserved residues. We identified potentially causative de novo events in 4 out of 20 probands, particularly among more severely affected individuals, in FOXP1, GRIN2B, SCN1A and LAMC3. In the FOXP1 mutation carrier, we also observed a rare inherited CNTNAP2 missense variant, and we provide functional support for a multi-hit model for disease risk. Our results show that trio-based exome sequencing is a powerful approach for identifying new candidate genes for ASDs and suggest that de novo mutations may contribute substantially to the genetic etiology of ASDs.


title: GhostVLAD for set-based face recognition

text: The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes ghost clusters, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network’s ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.


title: Similarity Breeds Proximity: Pattern Similarity within and across Contexts Is Related to Later Mnemonic Judgments of Temporal Proximity

text: Experiences unfold over time, but little is known about the mechanisms that support the formation of coherent episodic memories for temporally extended events. Recent work in animals has provided evidence for signals in hippocampus that could link events across temporal gaps; however, it is unknown whether and how such signals might be related to later memory for temporal information in humans. We measured patterns of fMRI BOLD activity as people encoded items that were separated in time and manipulated the presence of shared or distinct context across items. We found that hippocampal pattern similarity in the BOLD response across trials predicted later temporal memory decisions when context changed. By contrast, pattern similarity in lateral occipital cortex was related to memory only when context remained stable. These data provide evidence in humans that representational stability in hippocampus across time may be a mechanism for temporal memory organization.


title: Conspicuous Consumption and Household Indebtedness

text: Using a novel, large dataset of consumer transactions in Singapore, we study how conspicuous consumption affects household indebtedness. The coexistence of private housing (condominiums) and subsidized public housing (HDB) allows us to identify conspicuous consumers. Conditional on the same income and other socioeconomic characteristics, those who choose to reside in condominiums—considered a status good—are likely to be more conspicuous than their counterparts living in HDB units. We consistently find that condominium residents spend considerably more (by up to 44%) on conspicuous goods but not differently on inconspicuous goods. Compared with their matched HDB counterparts, more conspicuous consumers have 13% more credit card debt and 151% more delinquent credit card debt. Furthermore, the association between conspicuous consumption and credit card debt is concentrated among younger, male, single individuals. These results suggest that status-seeking-induced conspicuous consumption is an important determinant of household indebtedness.


title: Wireless Network Virtualization: A Survey, Some Research Issues and Challenges

text: Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.


title: Unfolding the role of protein misfolding in neurodegenerative diseases

text: Recent evidence indicates that diverse neurodegenerative diseases might have a common cause and pathological mechanism — the misfolding, aggregation and accumulation of proteins in the brain, resulting in neuronal apoptosis. Studies from different disciplines strongly support this hypothesis and indicate that a common therapy for these devastating disorders might be possible. The aim of this article is to review the literature on the molecular mechanism of protein misfolding and aggregation, its role in neurodegeneration and the potential targets for therapeutic intervention in neurodegenerative diseases. Many questions still need to be answered and future research in this field will result in exciting new discoveries that might impact other areas of biology.


title: Vehicle Number Plate Detection System for Indian Vehicles

text: An exponential increase in number of vehicles necessitates the use of automated systems to maintain vehicle information. The information is highly required for both management of traffic as well as reduction of crime. Number plate recognition is an effective way for automatic vehicle identification. Some of the existing algorithms based on the principle of learning takes a lot of time and expertise before delivering satisfactory results but even then lacks in accuracy. In the proposed algorithm an efficient method for recognition for Indian vehicle number plates has been devised. The algorithm aims at addressing the problems of scaling and recognition of position of characters with a good accuracy rate of 98.07%.


title: Generalized Thompson Sampling for Contextual Bandits

text: Thompson Sampling, one of the oldest heuristics for solving multi-armed bandits, has recently been shown to demonstrate state-of-the-art performance. The em pirical success has led to great interests in theoretical understanding of this heuristic. In this pap er, we approach this problem in a way very different from existing efforts. In particular, motiv ated by the connection between Thompson Sampling and exponentiated updates, we propose a new family of algorithms called Generalized Thompson Sampling in the expert-learning framework, which includes Thompson Sampling as a special case. Similar to most expert-learning algorithms, Generalized Thompson Sampling uses a loss function to adjust the experts’ weights. General regr et bounds are derived, which are also instantiated to two important loss functions: square loss a nd logarithmic loss. In contrast to existing bounds, our results apply to quite general contextual bandi ts. More importantly, they quantify the effect of the “prior” distribution on the regret bounds.


title: Analysis of users and non-users of smartphone applications

text: Purpose: Smartphones facilitate the potential adoption of new mobile applications. The purpose of this research is to study users and non-users of three selected mobile applications, and find out what really drives the intention to use these applications across users and non-users. Design/methodology/approach: The authors measured actual usage of mobile applications in a panel study of 579 Finnish smartphone users, using in-device measurements as an objective way to identify users and non-users. A web-based survey was used in collecting data to test an extended TAM model in explaining intention to use. Findings: Perceived technological barriers negatively affect behavioural control, reflecting people’s assessment of themselves being capable of using the services without trouble. Behavioural control is directly linked to perceived usefulness (except for games) and perceived enjoyment, as hypothesized. Perceived enjoyment and usefulness were generically found to explain intention to use applications for both users and for non-users. Research limitations/implications: With regards to the impact of social norms, the study finds that further research needs to be done in exploring its impact more thoroughly. The dataset of the research, consisting purely of male-dominated, young smartphone users, make the generalization of results difficult. Practical implications: There are differences regarding what drives the usage of different kinds of mobile applications. In this study, map applications and mobile Internet, are driven by more utilitarian motivations, whereas games are more hedonic. It is also clear that not everybody are using applications facilitated by smartphones, and therefore the presented approach of studying users and non-users separately provides a new approach to analyze adoption on a practical level. Originality/value: This research proves that models like TAM should not treat mobile services as a generic concept, but instead to specifically address individual mobile services. The research also demonstrates the unique value of combining objective usage measurements (reflecting actual behaviour) with traditional survey data in more comprehensively modelling service adoption. 2009 Elsevier Ltd. All rights reserved.


title: Retransmission steganography and its detection

text: The paper presents a new steganographic method called RSTEG (retransmission steganography), which is intended for a broad class of protocols that utilises retransmission mechanisms. The main innovation of RSTEG is to not acknowledge a successfully received packet in order to intentionally invoke retransmission. The retransmitted packet carries a steganogram instead of user data in the payload field. RSTEG is presented in the broad context of network steganography, and the utilisation of RSTEG for TCP (transmission control protocol) retransmission mechanisms is described in detail. Simulation results are also presented with the main aim of measuring and comparing the steganographic bandwidth of the proposed method for different TCP retransmission mechanisms, as well as to determine the influence of RSTEG on the network retransmission level.


title: Verification of an Implementation of Tomasulo's Algorithm by Compositional Model Checking

text: An implementation of an out-of-order processing unit based on Tomasulo’s algorithm is formally verified using compositional model checking techniques. This demonstrates that finite-state methods can be applied to such algorithms, without recourse to higher-order proof systems. The paper introduces a novel compositional system that supports cyclic environment reasoning and multiple environment abstractions per signal. A proof of Tomasulo’s algorithm is outlined, based on refinement maps, and relying on the novel features of the compositional system. This proof is fully verified by the SMV verifier, using symmetry to reduce the number of assertions that must be verified.


title: Many-to-Many Geographically-Embedded Flow Visualisation: An Evaluation

text: Showing flows of people and resources between multiple geographic locations is a challenging visualisation problem. We conducted two quantitative user studies to evaluate different visual representations for such dense many-to-many flows. In our first study we compared a bundled node-link flow map representation and OD Maps [37] with a new visualisation we call MapTrix. Like OD Maps, MapTrix overcomes the clutter associated with a traditional flow map while providing geographic embedding that is missing in standard OD matrix representations. We found that OD Maps and MapTrix had similar performance while bundled node-link flow map representations did not scale at all well. Our second study compared participant performance with OD Maps and MapTrix on larger data sets. Again performance was remarkably similar.


title: Estimating Smart City sensors data generation

text: Nowadays, Smart Cities are positioned as one of the most challenging and important research topics, highlighting major changes in people's lifestyle. Technologies such as smart energy, smart transportation or smart health are being designed to improve citizen's quality of life. Smart Cities leverage the deployment of a network of devices - sensors and mobile devices-, all connected through different means and/or technologies, according to their network availability and capacities, setting a novel framework feeding end-users with an innovative set of smart services. Aligned to this objective, a typical Smart City architecture is organized into layers, including a sensing layer (generates data), a network layer (moves the data), a middleware layer (manages all collected data and makes it ready for usage) and an application layer (provides the smart services benefiting from this data). In this paper a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. Finally, we discuss some solutions to both reduce the data transmission and improve the data management.


title: Supervised Extraction of Diagnosis Codes from EMRs: Role of Feature Selection, Data Selection, and Probabilistic Thresholding

text: Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.


title: A Comparative Study of Feature Extraction Algorithms in Customer Reviews

text: The paper systematically compares two feature extraction algorithms to mine product features commented on in customer reviews. The first approach [17] identifies candidate features by applying a set of POS patterns and pruning the candidate set based on the log likelihood ratio test. The second approach [11] applies association rule mining for identifying frequent features and a heuristic based on the presence of sentiment terms for identifying infrequent features. We evaluate the performance of the algorithms on five product specific document collections regarding consumer electronic devices. We perform an analysis of errors and discuss advantages and limitations of the algorithms.


title: Culture care theory, research, and practice.

text: Today nurses are facing a world in which they are almost forced to use transculturally-based nursing theories and practices in order to care for people of diverse cultures. The author, who in the mid-50s pioneered the development of the first transcultural nursing theory with a care focus, discusses the relevance, assumptions, and predictions of the culture care theory along with the ethnonursing research method. The author contends that transcultural nursing findings are gradually transforming nursing practice and are providing a new paradigm shift from traditional medical and unicultural practice to multiculturally congruent and specific care modalities. A few research findings are presented to show the importance of being attentive to cultural care diversities and universalities as the major tenets of the theory. In addition, some major contributions of the theory are cited along with major challenges for the immediate future.


title: Decision making, the P3, and the locus coeruleus-norepinephrine system.

text: Psychologists and neuroscientists have had a long-standing interest in the P3, a prominent component of the event-related brain potential. This review aims to integrate knowledge regarding the neural basis of the P3 and to elucidate its functional role in information processing. The authors review evidence suggesting that the P3 reflects phasic activity of the neuromodulatory locus coeruleus-norepinephrine (LC-NE) system. They discuss the P3 literature in the light of empirical findings and a recent theory regarding the information-processing function of the LC-NE phasic response. The theoretical framework emerging from this research synthesis suggests that the P3 reflects the response of the LC-NE system to the outcome of internal decision-making processes and the consequent effects of noradrenergic potentiation of information processing.


title: OBFS: A File System for Object-Based Storage Devices

text: The object-based storage model, in which files are made up of one or more data objects stored on self-contained Object-Based Storage Devices (OSDs), is emerging as an architecture for distributed storage systems. The workload presented to the OSDs will be quite different from that of generalpurpose file systems, yet many distributed file systems employ general-purpose file systems as their underlying file system. We present OBFS, a small and highly efficient file system designed for use in OSDs. Our experiments show that our user-level implementation of OBFS outperforms Linux Ext2 and Ext3 by a factor of two or three, and while OBFS is 1/25 the size of XFS, it provides only slightly lower read performance and 10%–40% higher write performance.


title: Are effective teachers like good parents? Teaching styles and student adjustment in early adolescence.

text: This study examined the utility of parent socialization models for understanding teachers' influence on student adjustment in middle school. Teachers were assessed with respect to their modeling of motivation and to Baumrind's parenting dimensions of control, maturity demands, democratic communication, and nurturance. Student adjustment was defined in terms of their social and academic goals and interest in class, classroom behavior, and academic performance. Based on information from 452 sixth graders from two suburban middle schools, results of multiple regressions indicated that the five teaching dimensions explained significant amounts of variance in student motivation, social behavior, and achievement. High expectations (maturity demands) was a consistent positive predictor of students' goals and interests, and negative feedback (lack of nurturance) was the most consistent negative predictor of academic performance and social behavior. The role of motivation in mediating relations between teaching dimensions and social behavior and academic achievement also was examined; evidence for mediation was not found. Relations of teaching dimensions to student outcomes were the same for African American and European American students, and for boys and girls. The implications of parent socialization models for understanding effective teaching are discussed.


title: Compact second harmonic-suppressed bandstop and bandpass filters using open stubs

text: Integration of bandstop filters with the bandstop or bandpass filter are presented in this paper. By replacing the series quarter-wavelength connecting lines of conventional open-stub bandpass/bandstop filters with the equivalent T-shaped lines, one could have compact open-stub bandstop/bandpass filters with second harmonic suppression. Transmission-line model calculation is used to derive the design equations of the equivalent T-shaped lines. Experiments have also been done to validate the design concept. Compared with the conventional open-stub bandpass/bandstop filters, over 30-dB improvement of the second harmonic suppression and 28.6% size reduction are achieved


title: The Blockchain as a Software Connector

text: Blockchain is an emerging technology for decentralized and transactional data sharing across a large network of untrusted participants. It enables new forms of distributed software architectures, where components can find agreements on their shared states without trusting a central integration point or any particular participating components. Considering the blockchain as a software connector helps make explicitly important architectural considerations on the resulting performance and quality attributes (for example, security, privacy, scalability and sustainability) of the system. Based on our experience in several projects using blockchain, in this paper we provide rationales to support the architectural decision on whether to employ a decentralized blockchain as opposed to other software solutions, like traditional shared data storage. Additionally, we explore specific implications of using the blockchain as a software connector including design trade-offs regarding quality attributes.


title: Conceptual modeling for ETL processes

text: Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the problem of the definition of ETL activities and provide formal foundations for their conceptual representation. The proposed conceptual model is (a) customized for the tracing of inter-attribute relationships and the respective ETL activities in the early stages of a data warehouse project; (b) enriched with a 'palette' of a set of frequently used ETL activities, like the assignment of surrogate keys, the check for null values, etc; and (c) constructed in a customizable and extensible manner, so that the designer can enrich it with his own re-occurring patterns for ETL activities.


title: Beyond data warehousing: what's next in business intelligence?

text: During the last ten years the approach to business management has deeply changed, and companies have understood the importance of enforcing achievement of the goals defined by their strategy through metrics-driven management. The DW process, though supporting bottom-up extraction of information from data, fails in top-down enforcing the company strategy. A new approach to BI, called Business Performance Management (BPM), is emerging from this framework: it includes DW but it also requires a reactive component capable of monitoring the time-critical operational processes to allow tactical and operational decision-makers to tune their actions according to the company strategy. The aim of this paper is to encourage the research community to acknowledge the coming of a second era in BI, to propose a general architecture for BPM, and to lay the premises for investigating the most challenging of the related issues.


title: A UML Based Approach for Modeling ETL Processes in Data Warehouses

text: Data warehouses (DWs) are complex computer systems whose main goal is to facilitate the decision making process of knowledge workers. ETL (Extraction-Transformation-Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into DWs. ETL processes are a key component of DWs because incorrect or misleading data will produce wrong business decisions, and therefore, a correct design of these processes at early stages of a DW project is absolutely necessary to improve data quality. However, not much research has dealt with the modeling of ETL processes. In this paper, we present our approach, based on the Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of these ETL processes together with the conceptual schema of the target DW in an integrated manner. We provide the necessary mechanisms for an easy and quick specification of the common operations defined in these ETL processes such as, the integration of different data sources, the transformation between source and target attributes, the generation of surrogate keys and so on. Moreover, our approach allows the designer a comprehensive tracking and documentation of entire ETL processes, which enormously facilitates the maintenance of these processes. Another advantage of our proposal is the use of the UML (standardization, ease-of-use and functionality) and the seamless integration of the design of the ETL processes with the DW conceptual schema. Finally, we show how to use our integrated approach by using a well-known modeling tool such as Rational Rose.