title: On submodels and submetamodels with their relation A uniform formalization through inclusion properties

abstract: Model-driven engineering (MDE) recognized software models as first-class objects with their own relationships and operations, up to constitute full structured model spaces. We focus on inclusion capacities through the concepts of submodel and submetamodel which contribute a lot to the structuring effort. Submodels and submetamodels underlie many MDE practices which require their precise characterization for plain control. A typical application is model management as offered by model repositories. On the basis of results on submodel inclusion we stated in a preceding paper, we concentrate on the special form of submodels which are submetamodels and their specific role in model space structuring. Pointing out that relating submodels and submetamodels is two ways, their respective inclusion hierarchies will be systematically characterized and symmetrically compared under the logical relationships of metamodel membership and model well-formedness. As a major result, it will be shown that submodel well-formedness w.r.t submetamodels closely relates to submodel invariance (a property which guarantees transitive structure preservation) applied at both levels. The uniform formalization offers algebraic grounding to better comprehension and control of model spaces which underlie MDE activities. At a much more practical level, reusable technology which takes advantage of established results will be offered.


title: People-Nearby Applications: How Newcomers Move Their Relationships Offline and Develop Social and Cultural Capital

abstract: People-nearby applications (PNAs), such as Tinder and Badoo, help millions of users to make new social connections everyday. However, little is known about how PNAs support offline interactions or what application features are associated with offline encounters. Research suggests that these applications support the development of social capital, but the forms of social capital are unclear. We conducted interviews with 14 active PNA users to address these questions. Our results suggest that while existing PNA features such as filters, profile photos, and chat support online connections, most participants used non-PNA platforms to build mutual trust before meeting offline. In addition, PNA users developed two forms of social (informal and formal) and cultural capital (incorporated and symbolic). We offer insights into how PNAs and non-PNAs intersect to foster feelings of safety and trust prior to offline meetings, and we propose ways for PNAs to support the exchange of cultural and social capital.


title: Relating STDP to BCM

abstract: We demonstrate that the BCM learning rule follows directly from STDP when pre- and postsynaptic neurons fire uncorrelated or weakly correlated Poisson spike trains, and only nearest-neighbor spike interactions are taken into account.


title: Learning Semantic Hierarchies via Word Embeddings

abstract: Semantic hierarchy construction aims to build structures of concepts linked by hypernym‐hyponym (“is-a”) relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words. We identify whether a candidate word pair has hypernym‐hyponym relation by using the word-embedding-based semantic projections between words and their hypernyms. Our result, an F-score of 73.74%, outperforms the state-of-theart methods on a manually labeled test dataset. Moreover, combining our method with a previous manually-built hierarchy extension method can further improve Fscore to 80.29%.


title: A Decentralized Control Method for a Low-Voltage DC Microgrid

abstract: DC microgrids (DC-MGs) are becoming popular as an effective means to integrate various renewable energy resources. Conventionally, the droop control is adopted as a decentralized control strategy for proper power sharing without using any com- munication link. However, the conventional droop control often deteriorates due to the effects of unequal line resistances. In this paper, a control strategy is proposed for a DC-MG to achieve per- fect power sharing considering the effects of line resistances. The DC-MG under study consists of a photovoltaic system, two energy storage systems, a grid-connected converter system, and dc loads. The control strategy of the converters is addressed under various operation modes. To obtain prolonged and reliable operation of the DC-MG, the state of charge of the batteries is also considered in the power sharing strategy. Simulation and experimental results are provided to verify the effectiveness and validity of the proposed method.


title: Automatically tagging email by leveraging other users' folders

abstract: Most email applications devote a significant part of their real estate to organization mechanisms such as folders. Yet, we verified on the Yahoo! Mail service that 70% of email users have never defined a single folder. This implies that one of the most well known email features is underexploited. We propose here to revive the feature by providing a method for generating a lighter form of folders, or tags, benefiting even the most passive users. The method automatically associates, whenever possible, an appropriate semantic tag with a given email. This gives rise to an alternate mechanism for organizing and searching email. We advocate a novel modeling approach that exploits the overall population of users, thereby learning from the wisdom-of-crowds how to categorize messages. Given our massive user base, it is enough to learn from a minority of the users who label certain messages in order to label that kind of messages for the general population. We design a novel cascade classification approach, which copes with the severe scalability and accuracy constraints we are facing. Significant efficiency gains are achieved by working within a low dimensional latent space, and by using a novel hierarchical classifier. Precision level is controlled by separating the task into a two-phase classification process. We performed an extensive empirical study covering three different time periods, over 100 million messages, and thousands of candidate tags per message. The results are encouraging and compare favorably with alternative approaches. Our method successfully tags 72% of incoming email traffic. Performance-wise, the computational overhead, even on surge large traffic, is sufficiently low for our approach to be applicable in production on any large Web mail service.


Entropic Brain-computer Interfaces - Using fNIRS and EEG to Measure Attentional States in a Bayesian Framework.


title: 3D fully convolutional network for vehicle detection in point cloud

abstract: 2D fully convolutional network has been recently successfully applied to the object detection problem on images. In this paper, we extend the fully convolutional network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows significant performance improvement over the previous point cloud based detection approaches.


title: Older Drivers and New In-Vehicle Technologies: Adaptation and Long-Term Effects

abstract: The introduction of new technologies into vehicles has been imposing new forms of interaction, being a challenge to drivers but also to HMI research. The multiplicity of on-board systems in the market has been changing the driving task, being the consequences of such interaction a concern especially to older drivers. Several studies have been conducted to report the natural functional declines of older drivers and the way they cope with additional sources of information and additional tasks in specific moments. However, the evolution of these equipments, their frequent presence in the automotive market and also the increased acceptability and familiarization of older drivers with such technologies, compel researchers to consider other aspects of these interactions: from adaptation to the long term effects of using any in-vehicle technologies.


title: Road reduction filtering for gps-gis navigation

abstract: A novel method of map matching using the Global Positioning System (GPS) has been developed for civilian use, which uses digital mapping data to infer the systematic position errors of less than 100m which result largely from ‘selective availability’ (S/A) imposed by the US military. Selective availability was switched off on the 2nd of May 2000, and is to be replaced with ‘regional denial capabilities in lieu of global degradation’. The system tracks a vehicle on all possible roads (road centre-lines) in a computed error region, then uses a method of rapidly detecting inappropriate road centre-lines from the set of all those possible. This is called the Road Reduction Filter (RRF) algorithm. Point positioning is computed using C/A code pseudorange measurements direct from a GPS receiver. The least squares estimation is performed in the software developed for the experiment described in this paper. Virtual differential GPS (VDGPS) corrections are computed and used from a vehicle's previous positions, thus providing an autonomous alternative to DGPS for in-car navigation and fleet management. Height aiding is used to augment the solution and reduce the number of satellites required for a position solution. Ordnance Survey (OS) digital map data was used for the experiment, i.e. OSCAR 1 m resolution road centre-line geometry and Land Form PANORAMA 1:50,000, 50 m-grid digital terrain model (DTM). Testing of the algorithm is reported and results are analysed. Vehicle positions provided by RRF are compared with the ‘true’ position determined using high precision (cm) GPS carrier phase techniques. It is shown that height aiding using a DTM and the RRF significantly improve the accuracy of position provided by inexpensive single frequency GPS receivers.


title: Robust face presentation attack detection on smartphones : An approach based on variable focus

abstract: Smartphone based facial biometric systems have been well used in many of the security applications starting from simple phone unlocking to secure banking applications. This work presents a new approach of exploring the intrinsic characteristics of the smartphone camera to capture a number of stack images in the depth-of-field. With the set of stack images obtained, we present a new feature-free and classifier-free approach to provide the presentation attack resistant face biometric system. With the entire system implemented on the smartphone, we demonstrate the applicability of the proposed scheme in obtaining a stack of images with varying focus to effectively determine the presentation attacks. We create a new database of 13250 images at different focal length to present a detailed analysis of vulnerability together with the evaluation of proposed scheme. An extensive evaluation of the newly created database comprising of 5 different Presentation Attack Instruments (PAI) has demonstrated an outstanding performance on all 5 PAI through proposed approach. With the set ofcomplementary benefits of proposed approach illustrated in this work, we deduce the robustness towards unseen 2D attacks.


title: Coordination and control of multiple UAVs

abstract: This paper addresses the problems of autonomous task allocation and trajectory planning for a fleet of UAVs. Two methods are compared for solving the optimization that combines task assignment, subjected to UAV capability constraints, and path planning, subjected to dynamics, avoidance and timing constraints. Both sub-problems are non-convex and the two are strongly-coupled. The first method expresses the entire problem as a single mixed-integer linear program (MILP) that can be solved using available software. This method is guaranteed to find the globally-optimal solution to the problem, but is computationally intensive. The second method employs an approximation for rapid computation of the cost of many different trajectories. This enables the assignment and trajectory problems to be decoupled and partially distributed, offering much faster computation. The paper presents several examples to compare the performance and computational results from these two algorithms.


title: Fitting Multiple Connected Ellipses to an Image Silhouette Hierarchically

abstract: In this paper, we seek to fit a model, specified in terms of connected ellipses, to an image silhouette. Some algorithms that have attempted this problem are sensitive to initial guesses and also may converge to a wrong solution when they attempt to minimize the objective function for the entire ellipse structure in one step. We present an algorithm that overcomes these issues. Our first step is to temporarily ignore the connections, and refine the initial guess using unconstrained Expectation-Maximization (EM) for mixture Gaussian densities. Then the ellipses are reconnected linearly. Lastly, we apply the Levenberg-Marquardt algorithm to fine-tune the ellipse shapes to best align with the contour. The fitting is achieved in a hierarchical manner based upon the joints of the model. Experiments show that our algorithm can robustly fit a complex ellipse structure to a corresponding shape for several applications.


Study of MANET routing protocols by GloMoSim simulator


Do Your Online Friends Make You Pay ? A Randomized Field Experiment in an Online Music Social Network


Case of spinocerebellar ataxia type 17 (SCA17) associated with only 41 repeats of the TATA-binding protein (TBP) gene.


title: Metric Learning to Rank

abstract: We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of data induced by distance from a query can be optimized against various ranking measures, such as AUC, Precision-at-k, MRR, MAP or NDCG. We demonstrate experimental results on standard classification data sets, and a large-scale online dating recommendation problem.


title: A preliminary study of ecommerce adoption in developing countries

abstract: What factors underlie the adoption dynamics of ecommerce technologies among users in developing countries? Even though the internet promised to be the great equalizer, the nuanced variety of conditions and contingencies that shape user adoption of ecommerce technologies has received little scrutiny. Building on previous research on technology adoption, the paper proposes a global information technology (IT) adoption model. The model includes antecedents of performance expectancy, social influence, and technology opportunism and investigates the crucial influence of facilitating conditions. The proposed model is tested using data from 172 technology users from 37 countries, collected over a 1-year period. The findings suggest that in developing countries, facilitating conditions play a critical moderating role in understanding actual ecommerce adoption, especially when in tandem with technological opportunism. Altogether, the paper offers a preliminary scrutiny of the mechanics of ecommerce adoption in developing countries.


title: Stereo vision and IMU based real-time ego-motion and depth image computation on a handheld device

abstract: Real-time environmental depth perception and ego-motion estimation is essential for all mobile robotic systems. We present a system that computes high quality depth images with 0.5 MPixel resolution using Semi-Global Matching (SGM) and estimates the ego-motion by key frame based visual odometry fused with the data of an inertial measurement unit (IMU). The hardware includes a pair of cameras, a small Intel Core2Duo CPU board, a Spartan 6 FPGA board, an OMAP3530 ARM processor board as well as an IMU. The total weight of the experimental setup is 830 g and is, thus, also feasible for hand-held or flying platforms. Experiments show that the vision system runs at 14.6 Hz with a latency of around 250 ms and produces high quality depth images as well as reliable 6D ego-motion estimates. In the fusion algorithm of visual odometry and IMU data, time delays of the vision system are compensated and a system state estimate is available at the full data rate of the IMU which is important for system control. This paper presents the integration of different techniques into a fast, light weight, real-time system and validates its performance by experiments on real data.


Travel speed prediction using machine learning techniques


title: Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings

abstract: Query-by-example search often uses dynamic time warping (DTW) for comparing queries and proposed matching segments. Recent work has shown that comparing speech segments by representing them as fixed-dimensional vectors --- acoustic word embeddings --- and measuring their vector distance (e.g., cosine distance) can discriminate between words more accurately than DTW-based approaches. We consider an approach to query-by-example search that embeds both the query and database segments according to a neural model, followed by nearest-neighbor search to find the matching segments. Earlier work on embedding-based query-by-example, using template-based acoustic word embeddings, achieved competitive performance. We find that our embeddings, based on recurrent neural networks trained to optimize word discrimination, achieve substantial improvements in performance and run-time efficiency over the previous approaches.


title: Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image

abstract: The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.


title: A mixed-scale dense convolutional neural network for image analysis

abstract: Deep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results in practice, a large number of trainable parameters are often required. Here, we introduce a network architecture based on using dilated convolutions to capture features at different image scales and densely connecting all feature maps with each other. The resulting architecture is able to achieve accurate results with relatively few parameters and consists of a single set of operations, making it easier to implement, train, and apply in practice, and automatically adapts to different problems. We compare results of the proposed network architecture with popular existing architectures for several segmentation problems, showing that the proposed architecture is able to achieve accurate results with fewer parameters, with a reduced risk of overfitting the training data.


title: Microwave-assisted C-C cross-coupling reactions of aryl and heteroaryl halides in water

abstract: The catalytic activity of a benzimidazole-oxime Pd(II)-complex towards Suzuki and Heck C-C cross-coupling reactions of activated and deactivated aryl- and heteroaryl bromides under microwave irradiation as well as thermal heating using water as a green solvent was evaluated. The turnover frequency reached 420,000 under microwave condition.


title: Adaptive Levenberg-Marquardt Algorithm Based Echo State Network for Chaotic Time Series Prediction

abstract: Echo state networks (ESNs) have wide applications in chaotic time series prediction. In the ESN, if the smallest singular value of the reservoir state matrix is infinitesimal, the ill-posed problem might occur during the training process. To overcome this problem, an adaptive Levenberg–Marquardt (LM) algorithm-based echo state network (ALM-ESN) is developed. In the developed ALM-ESN, a new adaptive damping term is introduced into the LM algorithm. The adaptive factor is amended by the trust region technique, furthermore, convergence analysis, and stability analysis are performed. Moreover, to make the inputs fall within the active region of the activation function and improve the learning speed, a weight initialization method using linear algebra is deployed to determine the appropriate input weights and reservoir weights. Simulations demonstrate that the ALM-ESN can overcome the ill-posed problem. Furthermore, it exhibits better performance and robustness for chaotic time series prediction than some other existing methods.


title: Contingent self-esteem and vulnerability to depression: academic contingent self-esteem predicts depressive symptoms in students

abstract: Low self-esteem has been established as a vulnerability factor for depression. In line with recent research, we suggest that a full understanding of the role of self-esteem in depression requires consideration of contingent self-esteem as well. For most people, competence is an important source of self-esteem. Students in particular link their self-esteem to academic competence. To test the hypothesis that academic contingent self-esteem (aCSE) predicts depressive symptoms, two studies were conducted. Preceding the investigation of our hypothesis, the first purpose of Study 1 was to describe the development of aCSE, self-esteem level, and depressive symptoms in adolescence in a sample of German students aged 10–16 (N = 1888) in order to provide a foundation for further analyses. Then, to address the main question, age and gender differences in aCSE, self-esteem level, and depressive symptoms as well as their relations were investigated. The results show that (1) gender differences emerged after the age of 10/11. Girls scored higher on aCSE and depressive symptoms and lower on self-esteem level than did boys, and aCSE and depressive symptoms decreased and self-esteem level increased over time in boys, while the rather disadvantageous pattern in girls remained stable. (2) After controlling for self-esteem level and aCSE, the effects of gender and age × gender interaction on depressive symptoms disappeared, suggesting an influence of aCSE on depressive symptoms. (3) aCSE predicted depressive symptoms over and above self-esteem level. Since the results of Study 1 did not allow for causal conclusions, a longitudinal study (N = 160) was conducted to further investigate the causal role of aCSE. According to the diathesis-stress model, aCSE was expected to serve as a diathesis for developing depressive symptoms in the face of academic stress (daily hassles) during an academic semester at university. The results of Study 2 revealed that aCSE interacted with corresponding hassles to predict increases in depressive symptoms. High levels of academic stress led to increases in depressive symptoms only among students who strongly based their self-esteem on academic competence. Implications for prevention and intervention of depression are discussed.


title: Job Stress in Journalism: Interaction between Organisational Support and Job Demands–Resources Model

abstract: This study aims to analyse the impact of job demands and job resources on job stress among journalists in Jordan. In addition, the moderation effect of organisational support on such relationship is assessed in this research. A questionnaire survey was conducted among journalists working in daily newspapers in Jordan. This study used multiple and hierarchical regression analyses and determined a significant and positive relationship amongst emotional demands, job insecurity, and task significance on job stress. Additionally, organisational support moderated the relationship between task significance and job stress. Results of study revealed that the organisational support moderates the relationship between task significance and job stress. This finding could challenge journalists, newspaper managements and decision-makers in Jordan. When journalists work on sensitive topics and are in conflict areas, they are in need of additional support from newspaper managements to mitigate high job stress and motivate them to produce quality work.


title: ZeNA: Zero-Aware Neural Network Accelerator

abstract: Editor’s note: It has been observed that the majority of the kernel weights and input activations in the state-of-the-art convolution neural networks (CNNs) have zero values. This article proposes a CNN hardware accelerator that exploits this property to achieve significant performance and energy improvements. —Mustafa Ozdal, Bilkent University


title: Industrial Experiences of Building a Safety Case in Compliance with ISO 26262

abstract: The ISO 26262 functional safety standard provides appropriate development processes, requirements and safety integrity levels specific for the automotive domain. One crucial requirement consists of the creation of a safety case, a structured argument, which inter-relates evidence and claims, needed to show that safety-critical systems are acceptably safe. The standard is currently not mandatory to be applied to safety critical systems installed in heavy trucks, however, this is likely to be changed by 2016. This paper describes the experience gathered by applying the standard to the Fuel Level Estimation and Display System, a subsystem that together with other subsystems plays a significant role in terms of global system safety for heavy trucks manufactured by Scania. More specifically, exploratory and laborious work related to the creation of a safety case in compliance with ISO 26262 in an inexperienced industrial setting is described, and the paper ends with presenting some lessons learned together with guidelines to facilitate the adoption of ISO 26262.


title: Reconceptualizing the Model of Multiple Dimensions of Identity: The Role of Meaning-Making Capacity in the Construction of Multiple Identities

abstract: We reconceptualize Jones and McEwen's (2000) model of multiple dimensions of identity by incorporating meaning making, based on the results of Abes and Jones's (2004) study of lesbian college students. Narratives of three students who utilize different orders of Kegan's (1994) meaning making (formulaic, transitional, and foundational, as described by Baxter Magolda, 2001) illustrate how meaning-making capacity interacts with the influences of context on the perceptions and salience of students' multiple social identities. Implications for theory, research, and professional practice are discussed.


TASTY: Interactive Entity Linking As-You-Type


title: MEDITATION, RESTORATION, AND THE MANAGEMENT OF MENTAL FATIGUE

abstract: An analysis of the underlying similarities between the Eastern meditation tradition and attention restoration theory (ART) provides a basis for an expanded framework for studying directed attention. The focus of the analysis is the active role the individual can play in the preservation and recovery of the directed attention capacity. Two complementary strategies are presented that can help individuals more effectively manage their attentional resource. One strategy involves avoiding unnecessary costs in terms of expenditure of directed attention. The other involves enhancing the effect of restorative opportunities. Both strategies are hypothesized to be more effective if one gains generic knowledge, self-knowledge, and specific skills. The interplay between a more active form of mental involvement and the more passive approach of meditation appears to have far-reaching ramifications for managing directed attention.


title: Making Models Match: Replicating an Agent-Based Model

abstract: Scientists have increasingly employed computer models in their work. Recent years have seen a proliferation of agent-based models in the natural and social sciences. But with the exception of a few "classic" models, most of these models have never been replicated by anyone but the original developer. As replication is a critical component of the scientific method and a core practice of scientists, we argue herein for an increased practice of replication in the agent-based modeling community, and for widespread discussion of the issues surrounding replication. We begin by clarifying the concept of replication as it applies to ABM. Furthermore we argue that replication may have even greater benefits when applied to computational models than when applied to physical experiments. Replication of computational models affects model verification and validation and fosters shared understanding about modeling decisions. To facilitate replication, we must create standards for both how to replicate models and how to evaluate the replication. In this paper, we present a case study of our own attempt to replicate a classic agent-based model. We begin by describing an agent-based model from political science that was developed by Axelrod and Hammond. We then detail our effort to replicate that model and the challenges that arose in recreating the model and in determining if the replication was successful. We conclude this paper by discussing issues for (1) researchers attempting to replicate models and (2) researchers developing models in order to facilitate the replication of their results.


title: Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer

abstract: In this Review, Dienstmann et al. analyse the complex nature of colorectal cancer and the different subtypes in which this disease can be classified, advocating for a 'multi-molecular' perspective for the development of therapies to treat it.


title: A Smart Haptic Hand-Held Device for Neurosurgical Microdissection

abstract: Microneurosurgery requires dexterity, precision and delicate force application in order to be carried out safely and effectively. Neurosurgeons must apply sufficient force in order to carry out microsurgical procedures effectively but not excessive force such that iatrogenic injury occurs. This paper presents a smart hand-held microsurgical instrument that indicates to the surgeon when a force-threshold has been exceeded by providing vibrotactile feedback. Many existing haptic-feedback systems, particularly master-slave robotic platforms, are large, highly complex, and costly. By comparison, the proposed device is compact, fail-safe and low cost. Two psychophysical user studies were carried out to assess the proposed vibrotactile force-threshold feedback system. A cadaveric pilot study was carried out to evaluate the device in a microdissection task. In all the studies performed, the haptic dissector device has shown to be effective in providing real-time feedback in terms of force application during microsurgical tasks.


title: Improving Estimations in Agile Projects: Issues and Avenues

abstract: From the mid ‘90s on, a number of Agile Methodologies have been proposed, most of them based on the basic values and principles summarised in the 2001 “Agile Manifesto”. These agile methodologies were aimed at small teams with severe project constraints (i.e. small project teams in the same location, the customer as a member of the project team, informal communication, test-driven approach, etc.). Compared to more traditional project methodologies, Agile (or Lightweight) Methodologies are more detailed on Construction and Testing practices, but much less specific about other topics, such as Estimation. Currently, in most Agile Methodologies the experience of the team represents the basis for estimating from the high-level requirements. The application of a Functional Size Measurement Method (FSMM) for estimation purposes raises a number of technical problems in Agile projects (i.e. unstable requirements, iterative SLC, non-functional requirements). A candidate solution is to combine an early sizing method for an agile project with a full FSMM method to be applied later in the SLC, when User Stories (the way XP labels high-level functional requirements) become available and are more stable. The goal of the paper is to identify estimation issues in the most known and adopted agile methodologies, looking at possible improvements at the organiseational level.


title: Wearable Wireless Cardiovascular Monitoring Using Textile-Based Nanosensor and Nanomaterial Systems

abstract: Wearable and ultraportable electronics coupled with pervasive computing are poised to revolutionize healthcare services delivery. The potential cost savings in both treatment, as well as preventive care are the focus of several research efforts across the globe. In this review, we describe the motivations behind wearable solutions to real-time cardiovascular monitoring from a perspective of current healthcare services, as well as from a systems design perspective. We identify areas where emerging research is underway, namely: nanotechnology in textile-based wearable monitors and healthcare solutions targeted towards smart devices, like smartphones and tablets.


title: Design methodology for a high insulation voltage power transmission function for IGBT gate driver

abstract: In this article, a design methodology for DC-DC converters with high insulation capabilities (up to 30kV) is proposed. The objective is to provide a power transmission function for IGBT drivers. To achieve this, a DC-DC full-bridge series-series (FB-SS) resonant converter with a high air gap transformer is selected and studied. This high air gap transformer (loosely coupled transformer (k)) is used for a high galvanic insulation system. Some transformer geometries are analyzed and compared for this specific application field. Therefore, in term of coupling coefficient of transformer (k), the analysis and the proposed investigations prove that pot core based transformers are suitable choices. Simulation results are presented and analyzed and experimental works are briefly described. Finally, the comparison between the simulation and experimental results are illustrated to validate the proposed methodology.


Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery


title: Soil Surface Moisture From EnviSat RA-2: From Modelling Towards Implementation

abstract: This paper presents the current status of ongoing research into the extraction of soil surface moisture data from Radar Altimeter backscatter. One of the motivations for this work is to facilitate comparisons with GRACE data over specific targets such as the Okavango Delta.


title: Research on Combining Scrum with CMMI in Small and Medium Organizations

abstract: Agile method Scrum can effectively resolve numerous problems encountered when Capability Maturity Model Integration(CMMI) is implemented in small and medium software development organizations, but some special needs are hard to be satisfied. According to small and medium organizations' characteristic, the paper analyzes feasibility of combining Scrum and CMMI in depth. It is useful for organizations that build a new project management framework based on both CMMI and Scrum practices.


title: CPS: driving cyber-physical systems to unsafe operating conditions by timing DoS attacks on sensor signals

abstract: DoS attacks on sensor measurements used for industrial control can cause the controller of the process to use stale data. If the DoS attack is not timed properly, the use of stale data by the controller will have limited impact on the process; however, if the attacker is able to launch the DoS attack at the correct time, the use of stale data can cause the controller to drive the system to an unsafe state. Understanding the timing parameters of the physical processes does not only allow an attacker to construct a successful attack but also to maximize its impact (damage to the system). In this paper we use Tennessee Eastman challenge process to study an attacker that has to identify (in realtime) the optimal timing to launch a DoS attack. The choice of time to begin an attack is forward-looking, requiring the attacker to consider each opportunity against the possibility of a better opportunity in the future, and this lends itself to the theory of optimal stopping problems. In particular we study the applicability of the Best Choice Problem (also known as the Secretary Problem), quickest change detection, and statistical process outliers. Our analysis can be used to identify specific sensor measurements that need to be protected, and the time that security or safety teams required to respond to attacks, before they cause major damage.


title: Complex regulation and diverse functions of alpha-klotho.

abstract: It has been understood that the α-klotho gene, first identified as an aging-related gene, is actually necessary for regulating mineral homeostasis in vertebrates. All vertebrates, including humans, actively maintain calcium and phosphate ions in bones, circulating blood, and cerebrospinal fluid. Therefore, disruptions in homeostasis cause osteoporosis, ectopic calcification, and epilepsy. Vitamin D and parathyroid hormone are well-known hormones for maintaining calcium and phosphate balance. Synthesis of vitamin D, secretion of parathyroid hormone, and absorption of calcium and phosphate ions are all finely controlled by α-Klotho-dependent machineries. However, the precise molecular mechanisms and functions of α-Klotho are still unclear. In this article, we present an overview of recent progress in α-klotho research, with a main focus on molecular aspects.


title: Rapid-turnaround characterization methods for MRAM development

abstract: Magnetic random access memory (MRAM) technology, based on the use of magnetic tunnel junctions (MTJs), holds the promise of improving on the capabilities of existing charge-based memories by offering the combination of nonvolatility, speed, and density in a single technology. In this paper we review rapid-turnaround methods which have been developed or applied in new ways to characterize MRAM chips at various stages during processing, with particular emphasis on the MTJs. The methods include current-in-plane tunneling (CIPT), Kerr magnetometry, vibrating sample magnetometry (VSM), and conducting atomic force microscopy (CAFM). Use of the methods has enabled rapid learning with respect to the materials used for the MTJs, as well as tuning of the MTJ geometry in terms of size and shape and of the patterning methods employed. Examples of the use of each of the methods are presented along with interpretation of the data via critical operating parameters.


title: Clustering in sensor networks

abstract: Wireless sensor networks (WSNs) have recently gained the attention of researchers in many challenging aspects. The most important challenge in these networks is energy conservation. One of the most popular solutions in making WSNs energy-efficient is to cluster the networks. In clustering, the nodes are divided into some clusters and then some nodes, called cluster-heads, are selected to be the head of each cluster. In a typical clustered WSN, the regular nodes sense the field and send their data to the cluster-head, then, after gathering and aggregating the data, the cluster-head transmits them to the base station. Clustering the nodes in WSNs has many benefits, including scalability, energy-efficiency, and reducing routing delay. In this paper we present a state-of-the-art and comprehensive survey on clustering approaches. We first begin with the objectives of clustering, clustering characteristics, and then present a classification on the clustering algorithms in WSNs. Some of the clustering objectives considered in this paper include scalability, fault-tolerance, data aggregation/fusion, increased connectivity, load balancing, and collision avoidance. Then, we survey the proposed approaches in the past few years in a classified manner and compare them based on different metrics such as mobility, cluster count, cluster size, and algorithm complexity.


title: A compact super wideband monopole antenna

abstract: A novel compact Mickey-mouse shaped monopole antenna with super wideband (SWB) performance is presented. The proposed antenna consists of a radiating patch with a pair of ears and a top-corner rounded ground plane with a one-step staircase slot. In comparison to the conventional circular monopole structure, this configuration can achieve a much wider impedance bandwidth: in excess of 50:1 (from 2 to 100 GHz) for a return loss greater than 10 dB. In addition to the impedance, the radiation pattern and gain of the proposed antenna are also presented and discussed. This SWB prototype is implemented and the measured results are in good agreement with the simulated ones.


INTERACTION OF INFORMATION IN WORD RECOGNITION 1


title: Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

abstract: This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: (1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; (2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; (3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.


title: Parametric study of LWR spent fuel dissolution kinetics

abstract: Abstract Spent fuel dissolution rates have been measured using a flowthrough system where the uranium concentrations remained well below solubility limits, thus allowing unambiguous reaction rates to be determined. The study was based on a statistically designed test matrix that covered four water chemistry variables: temperature (25 to 75°C), pH (8 to 10), carbonate/bicarbonate concentration (2 × 10 −4 M to 2 × 10 −2 M), and oxygen fugacity (O 2 partial pressure 0.002 to 0.2 atm). Results obtained to date and reported here encompass the first three, all at 0.2 atm O 2 partial pressure. The dissolution rate was found to be moderately dependent on temperature but only weakly dependent on pH and carbonate/bicarbonate concentration.


title: Topic Aware Neural Response Generation

abstract: We consider incorporating topic information into the sequence-to-sequence framework to generate informative and interesting responses for chatbots. To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The model utilizes topics to simulate prior knowledge of human that guides them to form informative and interesting responses in conversation, and leverages the topic information in generation by a joint attention mechanism and a biased generation probability. The joint attention mechanism summarizes the hidden vectors of an input message as context vectors by message attention, synthesizes topic vectors by topic attention from the topic words of the message obtained from a pre-trained LDA model, and let these vectors jointly affect the generation of words in decoding. To increase the possibility of topic words appearing in responses, the model modifies the generation probability of topic words by adding an extra probability item to bias the overall distribution. Empirical study on both automatic evaluation metrics and human annotations shows that TA-Seq2Seq can generate more informative and interesting responses, and significantly outperform the-state-of-the-art response generation models.


title: Mira: Liveness in iPad Controllers for Max/MSP

abstract: Mira is an iPad app for mirroring Max patchers in real time with minimal configuration. The Mira iPad app discovers open Max patchers automatically using the Bonjour protocol, connects to them over WiFi and negotiates a description of the Max patcher. As objects change position and appearance, Mira makes sure that the interface on the iPad stays up to date. Mira eliminates the need for an explicit mapping step between the interface and the system being controlled. The user is never asked to input an IP address, nor to configure the mapping between interface objects on the iPad and those in the Max patcher. So the prototyping composer is free to rapidly configure and reconfigure the interface.


title: Location-Based Mobile Games for Spatial Knowledge Acquisition

abstract: This is a pre-print of a paper from Workshop on Cognitive Engineering for Mobile GIS 2011. http://psumobile.org/?q=content/workshop-cognitive-engineering-mobile-gis-2011


title: B vitamin polymorphisms and behavior: Evidence of associations with neurodevelopment, depression, schizophrenia, bipolar disorder and cognitive decline

abstract: Abstract The B vitamins folic acid, vitamin B12 and B6 are essential for neuronal function, and severe deficiencies have been linked to increased risk of neurodevelopmental disorders, psychiatric disease and dementia. Polymorphisms of genes involved in B vitamin absorption, metabolism and function, such as methylene tetrahydrofolate reductase (MTHFR), cystathionine β synthase (CβS), transcobalamin 2 receptor (TCN2) and methionine synthase reductase (MTRR), have also been linked to increased incidence of psychiatric and cognitive disorders. However, the effects of these polymorphisms are often quite small and many studies failed to show any meaningful or consistent associations. This review discusses previous findings from clinical studies and highlights gaps in knowledge. Future studies assessing B vitamin-associated polymorphisms must take into account not just traditional demographics, but subjects’ overall diet, relevant biomarkers of nutritional status and also analyze related genetic factors that may exacerbate behavioral effects or nutritional status.


title: Towards using Reservoir Computing Networks for noise-robust image recognition

abstract: Reservoir Computing Network (RCN) is a special type of the single layer recurrent neural networks, in which the input and the recurrent connections are randomly generated and only the output weights are trained. Besides the ability to process temporal information, the key points of RCN are easy training and robustness against noise. Recently, we introduced a simple strategy to tune the parameters of RCN resulted in an effective and noise-robust RCN-based model for speech recognition. The aim of this work is to extend that study to the field of image processing. In particular, we investigate the potential of RCNs in achieving a competitive performance on the well-known MNIST dataset by following the aforementioned parameter optimizing strategy. Moreover, we achieve good noise robust recognition by utilizing such a network to denoise images and supplying them to a recognizer that is solely trained on clean images. The conducted experiments demonstrate that the proposed RCN-based handwritten digit recognizer achieves an error rate of 0.81 percent on the clean test data of the MNIST benchmark and that the proposed RCN-based denoiser can effectively reduce the error rate on the various types of noise.


title: Investigating gated recurrent networks for speech synthesis

abstract: Recently, recurrent neural networks (RNNs) as powerful sequence models have re-emerged as a potential acoustic model for statistical parametric speech synthesis (SPSS). The long short-term memory (LSTM) architecture is particularly attractive because it addresses the vanishing gradient problem in standard RNNs, making them easier to train. Although recent studies have demonstrated that LSTMs can achieve significantly better performance on SPSS than deep feed-forward neural networks, little is known about why. Here we attempt to answer two questions: a) why do LSTMs work well as a sequence model for SPSS; b) which component (e.g., input gate, output gate, forget gate) is most important. We present a visual analysis alongside a series of experiments, resulting in a proposal for a simplified architecture. The simplified architecture has significantly fewer parameters than an LSTM, thus reducing generation complexity considerably without degrading quality.


title: Mindfulness training increases cooperative decision making in economic exchanges: Evidence from fMRI

abstract: Emotions have been shown to exert influences on decision making during economic exchanges. Here we investigate the underlying neural mechanisms of a training regimen which is hypothesized to promote emotional awareness, specifically mindfulness training (MT). We test the hypothesis that MT increases cooperative economic decision making using fMRI in a randomized longitudinal design involving 8 weeks of either MT or active control training (CT). We find that MT results in an increased willingness to cooperate indexed by higher acceptance rates to unfair monetary offers in the Ultimatum Game. While controlling for acceptance rates of monetary offers between intervention groups, subjects in the MT and CT groups show differential brain activation patterns. Specifically, a subset of more cooperative MT subjects displays increased activation in the septal region, an area linked to social attachment, which may drive the increased willingness to express cooperative behavior in the MT cohort. Furthermore, MT resulted in attenuated activity in anterior insula compared with the CT group in response to unfair monetary offers post-training, which may suggest that MT enables greater ability to effectively regulate the anterior insula and thereby promotes social cooperation. Finally, functional connectivity analyses show a coupling between the septal region and posterior insula in the MT group, suggesting an integration of interoceptive inputs. Together, these results highlight that MT may be employed in contexts where emotional regulation is required to promote social cooperation.


title: Image Forensics Based on Transfer Learning and Convolutional Neural Network

abstract: There have been a growing number of interests in using the convolutional neural network(CNN) in image forensics, where some excellent methods have been proposed. Training the randomly initialized model from scratch needs a big amount of training data and computational time. To solve this issue, we present a new method of training an image forensic model using prior knowledge transferred from the existing steganalysis model. We also find out that CNN models tend to show poor performance when tested on a different database. With knowledge transfer, we are able to easily train an excellent model for a new database with a small amount of training data from the new database. Performance of our models are evaluated on Bossbase and BOW by detecting five forensic types, including median filtering, resampling, JPEG compression, contrast enhancement and additive Gaussian noise. Through a series of experiments, we demonstrate that our proposed method is very effective in two scenario mentioned above, and our method based on transfer learning can greatly accelerate the convergence of CNN model. The results of these experiments show that our proposed method can detect five different manipulations with an average accuracy of 97.36%.


title: Augmented Efficient BackProp for backpropagation learning in deep autoassociative neural networks

abstract: We introduce Augmented Efficient BackProp as a strategy for applying the backpropagation algorithm to deep autoencoders, i.e., autoassociators with many hidden layers, without relying on a weight initialization using restricted Boltzmann machines. This training method is an extension of Efficient BackProp, first proposed by LeCun et al. [1], and is benchmarked on three different types of application datasets.


title: Single Image 3D without a Single 3D Image

abstract: Do we really need 3D labels in order to learn how to predict 3D? In this paper, we show that one can learn a mapping from appearance to 3D properties without ever seeing a single explicit 3D label. Rather than use explicit supervision, we use the regularity of indoor scenes to learn the mapping in a completely unsupervised manner. We demonstrate this on both a standard 3D scene understanding dataset as well as Internet images for which 3D is unavailable, precluding supervised learning. Despite never seeing a 3D label, our method produces competitive results.


title: Meta-analysis of correlations among usability measures

abstract: Understanding the relation between usability measures seems crucial to deepen our conception of usability and to select the right measures for usability studies. We present a meta-analysis of correlations among usability measures calculated from the raw data of 73 studies. Correlations are generally low: effectiveness measures (e.g., errors) and efficiency measures (e.g., time) have a correlation of .247 ± .059 (Pearson's product-moment correlation with 95% confidence interval), efficiency and satisfaction (e.g., preference) one of .196 ± .064, and effectiveness and satisfaction one of .164 ± .062. Changes in task complexity do not influence these correlations, but use of more complex measures attenuates them. Standard questionnaires for measuring satisfaction appear more reliable than homegrown ones. Measures of users' perceptions of phenomena are generally not correlated with objective measures of the phenomena. Implications for how to measure usability are drawn and common models of usability are criticized.


title: Identifying unique devices through wireless fingerprinting

abstract: We propose a new fingerprinting technique that differentiates between unique devices over a Wireless Local Area Network (WLAN) simply through the timing analysis of 802.11 probe request frames. Our technique can be applied to spoof detection, network reconnaissance, and implementation of access control against masquerading attacks. Experimental results indicate that our technique is consistent and accurate in differentiating between unique devices. In contrast with existing wireless fingerprinting techniques, our technique is passive, non-invasive and does not require the co-operation of fingerprintee hosts.


title: 3D unsharp masking for scene coherent enhancement

abstract: We present a new approach for enhancing local scene contrast by unsharp masking over arbitrary surfaces under any form of illumination. Our adaptation of a well-known 2D technique to 3D interactive scenarios is designed to aid viewers in tasks like understanding complex or detailed geometric models, medical visualization and navigation in virtual environments. Our holistic approach enhances the depiction of various visual cues, including gradients from surface shading, surface reflectance, shadows, and highlights, to ease estimation of viewpoint, lighting conditions, shapes of objects and their world-space organization. Motivated by recent perceptual findings on 3D aspects of the Cornsweet illusion, we create scene coherent enhancements by treating cues in terms of their 3D context; doing so has a stronger effect than approaches that operate in a 2D image context and also achieves temporal coherence. We validate our unsharp masking in 3D with psychophysical experiments showing that the enhanced images are perceived to have better contrast and are preferred over unenhanced originals. Our operator runs at real-time rates on a GPU and the effect is easily controlled interactively within the rendering pipeline.


title: Optimal real-time bidding for display advertising

abstract: In this paper we study bid optimisation for real-time bidding (RTB) based display advertising. RTB allows advertisers to bid on a display ad impression in real time when it is being generated. It goes beyond contextual advertising by motivating the bidding focused on user data and it is different from the sponsored search auction where the bid price is associated with keywords. For the demand side, a fundamental technical challenge is to automate the bidding process based on the budget, the campaign objective and various information gathered in runtime and in history. In this paper, the programmatic bidding is cast as a functional optimisation problem. Under certain dependency assumptions, we derive simple bidding functions that can be calculated in real time; our finding shows that the optimal bid has a non-linear relationship with the impression level evaluation such as the click-through rate and the conversion rate, which are estimated in real time from the impression level features. This is different from previous work that is mainly focused on a linear bidding function. Our mathematical derivation suggests that optimal bidding strategies should try to bid more impressions rather than focus on a small set of high valued impressions because according to the current RTB market data, compared to the higher evaluated impressions, the lower evaluated ones are more cost effective and the chances of winning them are relatively higher. Aside from the theoretical insights, offline experiments on a real dataset and online experiments on a production RTB system verify the effectiveness of our proposed optimal bidding strategies and the functional optimisation framework.


title: Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

abstract: Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.


title: Convergence Analysis of Quantized Primal-Dual Algorithms in Network Utility Maximization Problems

abstract: This paper investigates the asymptotic and non-asymptotic behavior of the quantized primal dual algorithm in network utility maximization problems, in which a group of agents maximize the sum of their individual concave objective functions under linear constraints. In the asymptotic scenario, we use the information theoretic notion of differential entropy power to establish universal lower bounds on the exponential convergence rates of joint primal dual, primal and dual variables under optimum achieving quantization schemes. These results provide trade offs between the speed of exponential convergence, the agents objective functions, the communication bit rates, and the number of agents and constraints. In the non-asymptotic scenario, we obtain lower bounds on the mean square distance of joint primal dual, primal and dual variables from the optimal solution for any finite time instance. These bounds hold regardless of the quantization scheme used.


title: Pipsqueak: Lean Lambdas with Large Libraries

abstract: Microservices are usually fast to deploy because each microservice is small, and thus each can be installed and started quickly. Unfortunately, lean microservices that depend on large libraries will start slowly and harm elasticity. In this paper, we explore the challenges of lean microservices that rely on large libraries in the context of Python packages and the OpenLambda serverless computing platform. We analyze the package types and compressibility of libraries distributed via the Python Package Index and propose PipBench, a new tool for evaluating package support. We also propose Pipsqueak, a package-aware compute platform based on OpenLambda.


title: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

abstract: We revisit the problem of visual depth estimation in the context of autonomous vehicles. Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving. We argue that the challenges of removing this gap are significant, owing to fundamental limitations of monocular vision. As a result, we focus our efforts on depth estimation by stereo. We propose a novel semi-supervised learning approach to training a deep stereo neural network, along with a novel architecture containing a machine-learned argmax layer and a custom runtime (that will be shared publicly) that enables a smaller version of our stereo DNN to run on an embedded GPU. Competitive results are shown on the KITTI 2015 stereo dataset. We also evaluate the recent progress of stereo algorithms by measuring the impact upon accuracy of various design criteria.


title: A 14-Item Mediterranean Diet Assessment Tool and Obesity Indexes among High-Risk Subjects: The PREDIMED Trial

abstract: Objective: Independently of total caloric intake, a better quality of the diet (for example, conformity to the Mediterranean diet) is associated with lower obesity risk. It is unclear whether a brief dietary assessment tool, instead of full-length comprehensive methods, can also capture this association. In addition to reduced costs, a brief tool has the interesting advantage of allowing immediate feedback to participants in interventional studies. Another relevant question is which individual items of such a brief tool are responsible for this association. We examined these associations using a 14-item tool of adherence to the Mediterranean diet as exposure and body mass index, waist circumference and waist-to-height ratio (WHtR) as outcomes.


title: Using cloud computing to support higher education in networks and systems

abstract: Virtual machine (VM) technology is widely used in higher education to support pedagogy and research. However, without a standard platform for managing VM technology, VM-based infrastructure is likely to be acquired and deployed in an ad-hoc manner. The virtual computing lab (VCL) system is an open source cloud computing platform developed specifically to support higher education. The main goal of VCL is to make available dedicated, custom compute environments to users. We built a small VCL cloud and trialed its use to support several networking courses offered in the School of Computing at Clemson University during the 2013/2014 academic year. In this paper, we summarise the trial and provide results and conclusions. We found that well provisioned PCs might support up to ten VMs however when subject to peak loads, the system is not able to support accurate and reproducible network experiments.


title: "May I talk to you? : -) " - facial animation from text

abstract: We introduce a facial animation system that produces real-time animation sequences including speech synchronization and non-verbal speech-related facial expressions from plain text input. A state-of-the-art text-to-speech synthesis component performs linguistic analysis of the text input and creates a speech signal from phonetic and intonation information. The phonetic transcription is additionally used to drive a speech synchronization method for the physically based facial animation. Further high-level information from the linguistic analysis such as different types of accents or pauses as well as the type of the sentence is used to generate non-verbal speech-related facial expressions such as movement of head, eyes, and eyebrows or voluntary eye blinks. Moreover, emotions are translated into XML markup that triggers emotional facial expressions.


title: A new wavelet-based denoising algorithm for high-frequency financial data mining

abstract: Denoising analysis imposes new challenge for mining high-frequency financial data due to its irregularities and roughness. Inefficient decomposition of the systematic pattern (the trend) and noises of high-frequency data will lead to erroneous conclusion as the irregularities and roughness of the data make the application of traditional methods difficult. In this paper, we propose the local linear scaling approximation (in short, LLSA) algorithm, a new nonlinear filtering algorithm based on the linear maximal overlap discrete wavelet transform (MODWT) to decompose the systematic pattern and noises. We show several unique properties of this brand-new algorithm, that are, the local linearity, computational complexity, and consistency. We conduct a simulation study to confirm these properties we have analytically shown and compare the performance of LLSA with MODWT. We then apply our new algorithm with the real high-frequency data from German equity market to investigate its implementation in forecasting. We show the superior performance of LLSA and conclude that it can be applied with flexible settings and suitable for high-frequency data mining.


title: Advanced Image Classification Using Wavelets and Convolutional Neural Networks

abstract: Image classification is a vital technology many people in all arenas of human life utilize. It is pervasive in every facet of the social, economic, and corporate spheres of influence, worldwide. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep learning algorithms. This paper uses Convolutional Neural Networks (CNN) to classify handwritten digits in the MNIST database, and scenes in the CIFAR-10 database. Our proposed method preprocesses the data in the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By separating the image into different subbands, important feature learning occurs over varying low to high frequencies. The fusion of the learned low and high frequency features, and processing the combined feature mapping results in an increase in the detection accuracy. Comparing the proposed methods to spatial domain CNN and Stacked Denoising Autoencoder (SDA), experimental findings reveal a substantial increase in accuracy.


title: The user-subjective approach to personal information management systems

abstract: In this article we suggest a user-subjective approach to Personal Information Management (PIM) system design. This approach advocates that PIM systems relate to the subjective value-added attributes that the user gives to the data stored in the PIM system. These attributes should facilitate system use: help the user find the information item again, recall it when needed, and use it effectively in the next interaction with the item. Driven from the user-subjective approach are three generic principles which are described and discussed: (a) The subjective classification principle, stating that all information items related to the same subjective topic should be classified together regardless of their technological format; (b) The subjective importance principle, proposing that the subjective importance of information should determine its degree of visual salience and accessibility; and (c) The subjective context principle, suggesting that information should be retrieved and viewed by the user in the same context in which it was previously used. We claim that these principles are only sporadically implemented in operating systems currently available on personal computers, and demonstrate alternatives for interface design.


title: Review of: WordNet: An Electronic Lexical Database

abstract: Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and implementation of the WordNet lexical database and searching software, Randee I. Tengi. Part 2: automated discovery of WordNet relations, Marti A. Hearst representing verb alterations in WordNet, Karen T. Kohl et al the formalization of WordNet by methods of relational concept analysis, Uta E. Priss. Part 3 Applications of WordNet: building semantic concordances, Shari Landes et al performance and confidence in a semantic annotation task, Christiane Fellbaum et al WordNet and class-based probabilities, Philip Resnik combining local context and WordNet similarity for word sense identification, Claudia Leacock and Martin Chodorow using WordNet for text retrieval, Ellen M. Voorhees lexical chains as representations of context for the detection and correction of malapropisms, Graeme Hirst and David St-Onge temporal indexing through lexical chaining, Reem Al-Halimi and Rick Kazman COLOR-X - using knowledge from WordNet for conceptual modelling, J.F.M. Burg and R.P. van de Riet knowledge processing on an extended WordNet, Sanda M. Harabagiu and Dan I Moldovan appendix - obtaining and using WordNet.


title: Sensor-Based Activity Recognition

abstract: Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.


title: Novelty Paper Recommendation Using Citation Authority Diffusion

abstract: Survey of academic literature or papers should be considered with both relevance and importance of references. Authors cite related references by considering integrity and novelty. However, the state-of-art publicly academic search engines and services can only recommend related papers of a certain topic. It shows to manually evaluate the novelty of the recommended papers is necessary. In this paper, we propose a citation-network-based methodology, namely Citation Authority Diffusion (CAD), to rapidly mine the limited key papers of a topic, and measure the novelty on literature survey. A defined Authority Matrix ($AM$) is used to standardize duplication rate of authors and to describe the authority relation between the citing and the cited papers. Based on $AM$, our $CAD$ methodology leverages the Belief Propagation to diffuse the authority among the citation network. Therefore, CAD transforms the converged citation network to a novelty paper list to researchers. The experimental results show CAD can mine more novelty papers by using real-world cases.


title: Content Embedding Regularized Matrix Factorization for Recommender Systems

abstract: In recommender systems, numerous efforts have been made on utilizing textual information in matrix factorization to alleviate the problem of data sparsity. Recently, some of the works have explored neural networks to go for an in-depth understanding of textual item content, and further generate more accurate item latent models. These works achieve impressive effectiveness on performing recommendations. Nevertheless, there remains an open issue as how to effectively exploit description documents of both users and items in matrix factorization. In this paper, we proposed content embedding regularized matrix factorization (CERMF) to address this issue. CERMF adopts convolution neural networks to simultaneously generate the independent embedding representations for the users and the items. Then, dual embeddings are used to regularize the generation of latent models for users and items. Experimental results are proved that exploiting content information of both users and items dramatically improves the accuracy of recommendations as compared with the state-of-the-art recommendation models.


title: Group swimming and aquatic exercise programme for children with autism spectrum disorders: A pilot study

abstract: Objective: To evaluate the effectiveness of a 14-week aquatic exercise programme for children with autism spectrum disorders (ASD).Design: Non-randomized control trial.Methods: Twelve children participated in this pilot study with seven participants in the aquatic exercise group and five in the control group. The programme was held twice per week for 40 minutes per session. Swimming skills, cardiorespiratory endurance, muscular endurance, mobility skills and participant and parent satisfaction were measured before and after the intervention.Results: No significant between-group changes were found. Within-group improvements for swimming skills were found for the intervention group. Programme attendance was high. Parents and children were very satisfied with the programme activities and instructors.Conclusions: This pilot programme was feasible and showed potential for improving swimming ability in children with ASD. Exercise intensity was low for some participants, most likely contributing to a lack of sig...


# jazz : Automatic Music Genre Detection


title: Self-Commissioning of Inverter Dead-Time Compensation by Multiple Linear Regression Based on a Physical Model

abstract: Dead-time and switch voltage drops represent the most important sources of distortion of the (average) output voltage in pulsewidth modulation inverters. Their effect is a function of the parameters of the drive system and of the operating conditions and is often intolerable in many drives applications, thus requiring a proper compensation strategy. Many techniques are implemented in industrial drives and reported in the literature, even very recently. Differently from standard approaches, the proposed methodology is based on a detailed physical model of the power converter (including output capacitance), described by a small set of parameters. A novel self-commissioning identification procedure is introduced, adopting multiple linear regression. The technique is tested on a commercial drive in comparison with state-of-the-art techniques. In addition, back electromotive force estimation improvements in a permanent-magnet synchronous motor sensorless drive system are shown to provide additional validation of the method.


title: C-RNN-GAN: Continuous recurrent neural networks with adversarial training

abstract: Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.


title: Safe and feasible motion generation for autonomous driving via constrained policy net

abstract: Policy networks have great potential to learn sophisticated driving policy under complicated interaction between human drivers. However, it is hard for policy networks to satisfy safety and feasibility constraints, which is not a challenging task for conventional motion generation methods, such as optimization-based approach. In this paper, we propose Constrained Policy Net (CPN), which can learn safe and feasible driving policy from arbitrary inequality-constrained optimization-based expert planners. Instead of supervised learning with L 2 norm as the loss, we incorporate the domain knowledge of the expert planner directly into the training loss of the policy net by applying barrier functions to the safety and feasibility constraints of the optimization problem. An exemplar scenario with obstacles on both sides is used to implement the proposed CPN. Test results demonstrate that the policy net can learn to generate motions near boundaries of safety and feasibility constraints to achieve high driving quality as the baseline optimization while the constraints are satisfied.


title: Recovering Role-based Access Control Security Models from Dynamic Web Applications

abstract: Security of dynamic web applications is a serious issue. While Model Driven Architecture (MDA) techniques can be used to generate applications with given access control security properties, analysis of existing web applications is more problematic. In this paper we present a model transformation technique to automatically construct a role-based access control (RBAC) security model of dynamic web applications from previously recovered structural and behavioral models. The SecureUML model generated by this technique can be used to check for security properties of the original application. We demonstrate our approach by constructing an RBAC security model of PhpBB, a popular internet bulletin board system.


title: Multi-class Open Set Recognition Using Probability of Inclusion

abstract: The perceived success of recent visual recognition approaches has largely been derived from their performance on classification tasks, where all possible classes are known at training time. But what about open set problems, where unknown classes appear at test time? Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under an assumption of incomplete class knowledge. In this paper, we formulate the problem as one of modeling positive training data at the decision boundary, where we can invoke the statistical extreme value theory. A new algorithm called the P I -SVM is introduced for estimating the unnormalized posterior probability of class inclusion.


title: Exploring romantic relationships on social networking sites using the self-expansion model

abstract: Several hypotheses were derived from the self-expansion model (Aron & Aron, 1986) concerning romantic relationships and social networking sites (SNSs). A sample of 276 participants responded to questions about their relationship history and SNS uses and a subset of those (N=149) responded to additional questions about a current romantic partner. Results suggest that past self-expansion leaves a residue shown by more interests. This finding was moderated by overall Facebook use. Particular Facebook behaviors such as tagging one's partner in status updates, appearing together in photographs, and listing similar interests on profiles are indicative of self-expansion processes typically found in romantic relationships.


title: Effect of a Nutritional Intervention in Athlete’s Body Composition, Eating Behaviour and Nutritional Knowledge: A Comparison between Adults and Adolescents

abstract: The objective of the present study is to evaluate and compare the effect of a nutritional intervention between adolescent and adult. In a before and after quasi-experimental clinical study, 32 athletes (21 adults, age range 20–32 years; 11 adolescents, age range: 12–19 years) participated in a nutritional counselling consisting of four consultations separated by an interval of 45 to 60 days. The athlete’s eating behaviour, body composition and nutrition knowledge were evaluated at the beginning and at the end of the protocol. Both groups increased lean body mass and nutritional knowledge. Adolescents increased their mid-arm muscle circumference and improved meal frequency, and daily water intake. Athletes of both groups improved their ingestion of vegetables and fruits and decreased the ingestion of sweets and oils. Adolescents showed a higher prevalence of individuals that remained within or approached to the recommendations of sweets. This is the first study to evaluate and compare the effect of a nutritional intervention between adolescent and adult athletes body composition, eating behaviour and nutritional knowledge. The nutritional counselling has been effective in promoting beneficial changes on the athlete’s eating behaviour, nutritional knowledge and body composition, however, some healthy changes were only experienced by adolescents, especially in the frequency of meals and the intake of sweets.


title: Small antenna embedded in a wrist-watch for application in telemedicine

abstract: In this paper, the design and radiation performance of a miniature antenna embedded in small devices with a wrist-watch form factor, operating in both MICS (Medical Implant Communication Service) (402.0-405.0 MHz) and Bluetooth (2.4-2.483 GHz) bands, are presented. The radiating element, based on a spiral shape Planar Inverted-F Antenna (PIFA), was fabricated and tested. Simulated and experimental results of the structure are compared and discussed. As this antenna is dedicated to be embedded in a watch, the influence of its interaction with the other parts of the device such as batteries, transceiver, and the LCD is investigated. The influence of the wrist of the user on the antenna performance is also considered.


title: The Role and Impact of Animals with Pediatric Patients.

abstract: Animal-facilitated therapy (AFT), more specifically known as animal-assisted therapy (AAT) or "pet therapy," has had an increased presence in the literature with a surge of recent research methodologies exploring this complementary alternative medicine (CAM) intervention. However, limited studies have been conducted in the pediatric population, with many articles anecdotal in nature. A literature review included primary data sources PubMed, PsychINFO, Medline, and CINAHL, and yielded positive and beneficial outcomes to be gained through AAT in the pediatric population. Primary outcome variables of decreased anxiety and pain are the most commonly reported results. Further research studies are indicated to include the effects of AFT with children with different diseases and diagnoses. Exploration of other psychosocial and physical variables, such as self-esteem, would be useful. Interdisciplinary strategies are needed to develop interventions to help reduce patient symptoms and treatment-associated stress, as well as to facilitate healing and wellness beyond traditional medical treatment plans. Complementary therapies are of continued interest to the health care community, especially for pediatric nurses. Effective use of animals to facilitate conversation, lead discussion, or break communication barriers has been demonstrated through both research and anecdotal reports.


title: BBDS: Blockchain-Based Data Sharing for Electronic Medical Records in Cloud Environments

abstract: Disseminating medical data beyond the protected cloud of institutions poses severe risks to patients’ privacy, as breaches push them to the point where they abstain from full disclosure of their condition. This situation negatively impacts the patient, scientific research, and all stakeholders. To address this challenge, we propose a blockchain-based data sharing framework that sufficiently addresses the access control challenges associated with sensitive data stored in the cloud using immutability and built-in autonomy properties of the blockchain. Our system is based on a permissioned blockchain which allows access to only invited, and hence verified users. As a result of this design, further accountability is guaranteed as all users are already known and a log of their actions is kept by the blockchain. The system permits users to request data from the shared pool after their identities and cryptographic keys are verified. The evidence from the system evaluation shows that our scheme is lightweight, scalable, and efficient.


title: Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming

abstract: Streaming data processing has been gaining attention due to its application into a wide range of scenarios. To serve the booming demands of streaming data processing, many computation engines have been developed. However, there is still a lack of real-world benchmarks that would be helpful when choosing the most appropriate platform for serving real-time streaming needs. In order to address this problem, we developed a streaming benchmark for three representative computation engines: Flink, Storm and Spark Streaming. Instead of testing speed-of-light event processing, we construct a full data pipeline using Kafka and Redis in order to more closely mimic the real-world production scenarios. Based on our experiments, we provide a performance comparison of the three data engines in terms of 99th percentile latency and throughput for various configurations.


title: Learning of Coordination Policies for Robotic Swarms

abstract: Inspired by biological swarms, robotic swarms are envisioned to solve real-world problems that are difficult for individual agents. Biological swarms can achieve collective intelligence based on local interactions and simple rules; however, designing effective distributed policies for large-scale robotic swarms to achieve a global objective can be challenging. Although it is often possible to design an optimal centralized strategy for smaller numbers of agents, those methods can fail as the number of agents increases. Motivated by the growing success of machine learning, we develop a deep learning approach that learns distributed coordination policies from centralized policies. In contrast to traditional distributed control approaches, which are usually based on human-designed policies for relatively simple tasks, this learning-based approach can be adapted to more difficult tasks. We demonstrate the efficacy of our proposed approach on two different tasks, the well-known rendezvous problem and a more difficult particle assignment problem. For the latter, no known distributed policy exists. From extensive simulations, it is shown that the performance of the learned coordination policies is comparable to the centralized policies, surpassing state-of-the-art distributed policies. Thereby, our proposed approach provides a promising alternative for real-world coordination problems that would be otherwise computationally expensive to solve or intangible to explore.


title: CHARM++: a portable concurrent object oriented system based on C++

abstract: We describe Charm++, an object oriented portable parallel programming language based on C++. Its design philosophy, implementation, sample applications and their performance on various parallel machines are described. Charm++ is an explicitly parallel language consisting of C++ with a few extensions. It provides a clear separation between sequential and parallel objects. The execution model of Charm++ is message driven, thus helping one write programs that are latency-tolerant. The language supports multiple inheritance, dynamic binding, overloading, strong typing, and reuse for parallel objects, all of which are more difficult problems in a parallel context. Charm++ provides specific modes for sharing information between parallel objects. It is based on the Charm parallel programming system, and its runtime system implementation reuses most of the runtime system for Charm.


title: Load-Adaptive Control Algorithm of Half-Bridge Series Resonant Inverter for Domestic Induction Heating

abstract: Domestic induction cookers operation is based on a resonant inverter which supplies medium-frequency currents (20-100 kHz) to an inductor, which heats up the pan. The variable load that is inherent to this application requires the use of a reliable and load-adaptive control algorithm. In addition, a wide output power range is required to get a satisfactory user performance. In this paper, a control algorithm to cover the variety of loads and the output power range is proposed. The main design criteria are efficiency, power balance, acoustic noise, flicker emissions, and user performance. As a result of the analysis, frequency limit and power level limit algorithms are proposed based on square wave and pulse density modulations. These have been implemented in a field-programmable gate array, including output power feedback and mains-voltage zero-cross-detection circuitry. An experimental verification has been performed using a commercial induction heating inverter. This provides a convenient experimental test bench to analyze the viability of the proposed algorithm.


title: Improving Exploratory Search Experience through Hierarchical Knowledge Graphs

abstract: In information retrieval and information visualization, hierarchies are a common tool to structure information into topics or facets, and network visualizations such as knowledge graphs link related concepts within a domain. In this paper, we explore a multi-layer extension to knowledge graphs, hierarchical knowledge graphs (HKGs), that combines hierarchical and network visualizations into a unified data representation. Through interaction logs, we show that HKGs preserve the benefits of single-layer knowledge graphs at conveying domain knowledge while incorporating the sense-making advantages of hierarchies for knowledge seeking tasks. Specially, this paper describes our algorithm to construct these visualizations, analyzes interaction logs to quantitatively demonstrate performance parity with networks and performance advantages over hierarchies, and synthesizes data from interaction logs, inter- views, and thinkalouds on a testbed data set to demonstrate the utility of the unified hierarchy+network structure in our HKGs.


title: Closed loop speed control of chopper fed DC motor for industrial drive application

abstract: In this paper a comparative transient and robustness analysis of closed loop speed control employing different linear controllers for the same dc motor using 4 quadrant chopper is investigated. The controller configurations can be broadly classified under (i) Integer order PID controller (ii) linear state space observer and (iii) fractional order PID controller. All of them exhibit superior performances, the first one is the conventional controller used in industries but the later two are modern controllers that have rich potential for industry use and have large advantage over the conventional controllers. The closed loop control of chopper fed DC motor is shown with the 1st quadrant operation of chopper circuit.


title: The boomRoom: mid-air direct interaction with virtual sound sources

abstract: In this paper we present a system that allows to "touch", grab and manipulate sounds in mid-air. Further, arbitrary objects can seem to emit sound. We use spatial sound reproduction for sound rendering and computer vision for tracking. Using our approach, sounds can be heard from anywhere in the room and always appear to originate from the same (possibly moving) position, regardless of the listener's position. We demonstrate that direct "touch" interaction with sound is an interesting alternative to indirect interaction mediated through controllers or visual interfaces. We show that sound localization is surprisingly accurate (11.5 cm), even in the presence of distractors. We propose to leverage the ventriloquist effect to further increase localization accuracy. Finally, we demonstrate how affordances of real objects can create synergies of auditory and visual feedback. As an application of the system, we built a spatial music mixing room.


title: A Cognitive Framework for the Analysis of Game Play: Tasks, Schemas and Attention Theory 1

abstract: A fully developed nomology for the study of games requires the development of explanatory theoretical constructs associated with validating observational techniques. Drawing from cognition sciences, a framework is proposed based upon the integration of schema theory with attention theory. Cognitive task analysis provides a foundation for preliminary schema descriptions, which can then be elaborated according to more detailed models of cognitive and attentional processes. The resulting theory provides a rich explanatory framework for the cognitive processes underlying game play, as well as detailed hypotheses for the hierarchical structure of pleasures and rewards motivating players. Game engagement is accounted for as a process of schema selection or development, while immersion is explained in terms of schema execution. This framework is being developed not only to explain the substructures of game play, but also to provide schema models that may inform game design processes and provide detailed criteria for the design of patterns of game features for entertainment, pedagogical and therapeutic purposes.


title: D3D: Distilled 3D Networks for Video Action Recognition

abstract: State-of-the-art methods for video action recognition commonly use an ensemble of two networks: the spatial stream, which takes RGB frames as input, and the temporal stream, which takes optical flow as input. In recent work, both of these streams consist of 3D Convolutional Neural Networks, which apply spatiotemporal filters to the video clip before performing classification. Conceptually, the temporal filters should allow the spatial stream to learn motion representations, making the temporal stream redundant. However, we still see significant benefits in action recognition performance by including an entirely separate temporal stream, indicating that the spatial stream is "missing" some of the signal captured by the temporal stream. In this work, we first investigate whether motion representations are indeed missing in the spatial stream of 3D CNNs. Second, we demonstrate that these motion representations can be improved by distillation, by tuning the spatial stream to predict the outputs of the temporal stream, effectively combining both models into a single stream. Finally, we show that our Distilled 3D Network (D3D) achieves performance on par with two-stream approaches, using only a single model and with no need to compute optical flow.


title: Outline extraction of motorway from helicopter image sequence

abstract: Behavior analysis of each driver during congestion is still a problem in microscopic traffic analysis. To solve this problem, image sequences of traffic data are collected by helicopter. After detection and tracking of the vehicles, detailed data for microscopic traffic analysis result. To increase speed and reliability of vehicle detection and tracking, road detection is suggested. In this way, the search area for vehicle detection is limited to road boundaries while other moving objects such as vegetation or pedestrians are omitted. Road detection from aerial helicopter images is done by a line detection method based on edge detection and morphology operations. Unwanted lines and other errors introduced during the line detection are removed by a threshold method. These thresholds are based on gray value and connected component labeling. After this improvement process, some parts of road lines are still missing, due to physical obstacles and the previous operations. With a Hough transform method line fragments are connected to each other in order to form elongated road lines. The image sequence data are taken by a Basler A101f digital camera installed on a helicopter. Results show that most road lines are correctly detected and connected but still some minor parts of the lines are not completely connected. Results can be improved by using a better technique for local maxima detection of the parameter space. The parameter space obviously shows number and positions of line that are completely correct are calculated.


title: Noisy subspace clustering via matching pursuits

abstract: Sparsity-based subspace clustering algorithms have attracted significant attention thanks to their excellent performance in practical applications. A prominent example is the sparse subspace clustering (SSC) algorithm by Elhamifar and Vidal, which performs spectral clustering based on an adjacency matrix obtained by sparsely representing each data point in terms of all the other data points via the Lasso. When the number of data points is large or the dimension of the ambient space is high, the computational complexity of SSC quickly becomes prohibitive. Dyer et al. observed that SSC-orthogonal matching pursuit (OMP) obtained by replacing the Lasso by the greedy OMP algorithm results in significantly lower computational complexity, while often yielding comparable performance. The central goal of this paper is an analytical performance characterization of SSC-OMP for noisy data. Moreover, we introduce and analyze the SSC-matching pursuit (MP) algorithm, which employs MP in lieu of OMP. Both SSC-OMP and SSC-MP are proven to succeed even when the subspaces intersect and when the data points are contaminated by severe noise. The clustering conditions we obtain for SSC-OMP and SSC-MP are similar to those for SSC and for the thresholding-based subspace clustering (TSC) algorithm due to Heckel and Bolcskei. Analytical results in combination with numerical results indicate that both SSC-OMP and SSC-MP with a data-dependent stopping criterion automatically detect the dimensions of the subspaces underlying the data. Experiments on synthetic and on real data show that SSC-MP often matches or exceeds the performance of the computationally more expensive SSC-OMP algorithm. Moreover, SSC-MP compares very favorably to SSC, TSC, and the nearest subspace neighbor algorithm, both in terms of clustering performance and running time. In addition, we find that, in contrast to SSC-OMP, the performance of SSC-MP is very robust with respect to the choice of parameters in the stopping criteria.