title: A silicon vector light sensor for proximity sensing applications

abstract: In this paper, we introduce the 3D design of a vector light sensor for angular proximity detection applications. The sensor structure is based on creating photo sensors on side walls of micro-sized (micron scale) mesa pyramids etched in silicon. On each facet of the mesa pyramid, p-n junctions are formed and used as photodiodes. Unlike planar pixels, this 3D pixel design allows for estimation of the direction of the light incident on the sensor. We present the operating principle, fabrication details and the experimental verification of the device's performance. The sensor's unique design, simple fabrication process, and readout integrated circuit's compatibility adds merits to the current planar designs and could be utilized in angular proximity applications such as multi-focus level imaging or environment mapping for real-time dynamic virtual and augmented visualizations.


title: Distributed intelligence for multi-camera visual surveillance

abstract: Latest advances in hardware technology and state of the art of computer vision and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the different components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed artificial intelligence composed of separate and autonomous modules is justified by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and efficiently built by modular software.


title: Towards large-scale schema and ontology matching

abstract: The purely manual specification of semantic correspondences between schemas is almost infeasible for very large schemas or when many different schemas have to be matched. Hence, solving such large-scale match tasks asks for automatic or semiautomatic schema matching approaches. Large-scale matching needs especially to be supported for XML schemas and different kinds of ontologies due to their increasing use and size, e.g., in e-business and web and life science applications. Unfortunately, correctly and efficiently matching large schemas and ontologies are very challenging, and most previous match systems have only addressed small match tasks. We provide an overview about recently proposed approaches to achieve high match quality or/and high efficiency for large-scale matching. In addition to describing some recent matchers utilizing instance and usage data, we cover approaches on early pruning of the search space, divide and conquer strategies, parallel matching, tuning matcher combinations, the reuse of previous match results, and holistic schema matching. We also provide a brief comparison of selected match tools.


Clustering Based Approach for Customers ’ Classification from Electrical Distribution Systems


title: Unsupervised and semi-supervised learning via ℓ1-norm graph

abstract: In this paper, we propose a novel l 1 -norm graph model to perform unsupervised and semi-supervised learning methods. Instead of minimizing the l 2 -norm of spectral embedding as traditional graph based learning methods, our new graph learning model minimizes the l 1 -norm of spectral embedding with well motivation. The sparsity produced by the l 1 -norm minimization results in the solutions with much clearer cluster structures, which are suitable for both image clustering and classification tasks. We introduce a new efficient iterative algorithm to solve the l 1 -norm of spectral embedding minimization problem, and prove the convergence of the algorithm. More specifically, our algorithm adaptively re-weight the original weights of graph to discover clearer cluster structure. Experimental results on both toy data and real image data sets show the effectiveness and advantages of our proposed method.


title: Virtual cinematography of group scenes using hierarchical lines of actions

abstract: We present an approach to generate shots for 3D computer graphics cinematic sequences from event-based descriptions of scenes of conversations between groups of actors. Our approach creates camera setups using a combination of geometric constraints and aesthetic parameters, while ensuring that the resulting cinematic sequence obeys the heuristics of good cinematography. More specifically, our main contributions are the a method for defining hierarchical lines of action and the identification and use of relevant first principles of cinematography for using these lines of actions. Our approach is more flexible and powerful than those proposed in previous work, mainly because it naturally generalizes to any number of actors in a scene.


title: Chemical composition and insecticidal activity of Cymbopogon citratus essential oil from Cuba and Brazil against housefly

abstract: Essential oil of Cymbopogon citratus collected from Brazil and Cuba was tested to a chemical characterization and then was tested on the post-embryonic development of Musca domestica. The chemical composition analysis by GC-MS of the oils from Brazil/Cuba allowed the identification of 13 and 12 major constituents respectively; nine of them common to both. In the both oils, the main components were the isomers geranial and neral, which together form the compound citral. This corresponds to a total of 97.92%/Brazil and 97.69%/Cuba of the compounds identified. The monoterpene myrcene, observed only in the sample of Cuba, presented a large relative abundance (6.52%). The essential oil of C. citratus (Brazil/Cuba) was dissolved in DMSO and tested at concentrations of 5, 10, 25, 50, 75 and 100% and citral was prepared by mixing 16.8 mg with 960 µL DMSO. Both essential oils and monoterpene citral were applied topically to newly-hatched larvae (1µL/larva). The results showed a lethal concentration (LC50) of 4.25 and 3.24% for the Brazilian and Cuban essential oils, respectively. Mortalities of larval and newly-hatched larvae to adult periods were dose-dependent for the two both oils as for monoterpene citral, reaching 90%. Both essential oils and citral caused morphological changes in adult specimens.


title: TaintScope: A Checksum-Aware Directed Fuzzing Tool for Automatic Software Vulnerability Detection

abstract: Fuzz testing has proven successful in finding security vulnerabilities in large programs. However, traditional fuzz testing tools have a well-known common drawback: they are ineffective if most generated malformed inputs are rejected in the early stage of program running, especially when target programs employ checksum mechanisms to verify the integrity of inputs. In this paper, we present TaintScope, an automatic fuzzing system using dynamic taint analysis and symbolic execution techniques, to tackle the above problem. TaintScope has several novel contributions: 1) TaintScope is the first checksum-aware fuzzing tool to the best of our knowledge. It can identify checksum fields in input instances, accurately locate checksum-based integrity checks by using branch profiling techniques, and bypass such checks via control flow alteration. 2) TaintScope is a directed fuzzing tool working at X86 binary level (on both Linux and Window). Based on fine-grained dynamic taint tracing, TaintScope identifies which bytes in a well-formed input are used in security-sensitive operations (e.g., invoking system/library calls) and then focuses on modifying such bytes. Thus, generated inputs are more likely to trigger potential vulnerabilities. 3) TaintScope is fully automatic, from detecting checksum, directed fuzzing, to repairing crashed samples. It can fix checksum values in generated inputs using combined concrete and symbolic execution techniques. We evaluate TaintScope on a number of large real-world applications. Experimental results show that TaintScope can accurately locate the checksum checks in programs and dramatically improve the effectiveness of fuzz testing. TaintScope has already found 27 previously unknown vulnerabilities in several widely used applications, including Adobe Acrobat, Google Picasa, Microsoft Paint, and ImageMagick. Most of these severe vulnerabilities have been confirmed by Secunia and oCERT, and assigned CVE identifiers (such as CVE-2009-1882, CVE-2009-2688). Corresponding patches from vendors are released or in progress based on our reports.


title: Distributed and parallel time series feature extraction for industrial big data applications

abstract: The all-relevant problem of feature selection is the identification of all strongly and weakly relevant attributes. This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously. Here, we are proposing an efficient, scalable feature extraction algorithm for time series, which filters the available features in an early stage of the machine learning pipeline with respect to their significance for the classification or regression task, while controlling the expected percentage of selected but irrelevant features. The proposed algorithm combines established feature extraction methods with a feature importance filter. It has a low computational complexity, allows to start on a problem with only limited domain knowledge available, can be trivially parallelized, is highly scalable and based on well studied non-parametric hypothesis tests. We benchmark our proposed algorithm on all binary classification problems of the UCR time series classification archive as well as time series from a production line optimization project and simulated stochastic processes with underlying qualitative change of dynamics.


title: A harmonic suppression circularly polarized patch antenna for an RF ambient energy harvesting system

abstract: This paper presented a new design of harmonic suppression microstrip patch antenna with the circular polarization properties at frequency 900MHz for an RF ambient energy harvesting system. The purpose of harmonic suppression antenna is to eliminate the using of a harmonic filter circuit in filtering the unwanted signal at the harmonic frequencies in a rectenna system. By introducing a truncating to corner of the rectangular microstrip patch antenna to achieved the required axial ratio, an antenna with circular polarization property is presented. The dimension of patch is reduced by applying partially ground plane. Then an implementation of a defected ground structure (DGS) with a circular shape slot, the unwanted radiation at harmonic frequencies are successfully suppressed. The patch antenna is designed on an FR4 substrate with er=4.7 and a thickness of 1.6 mm has -20dB return loss at 900 MHz and suppress the second and third harmonic up to -2dB. The proposed circularly polarized harmonic suppression antenna is then fabricated and measured for the verification of the design.


title: Static Testing of Balance Function of Human Body

abstract: Aging population has been a global problem in 21 century.The balance function of human body is an important index to evaluate the aged healthy.Many aged diseases can lead to losing balance such as dizziness.The balance function of the aged plays an important role in estimating the danger factors,establishing precationary and healing measurement,and diagnosing disease.The testing system of gravity center of human body can judge the balance function through the variety of gravity center.


title: An ADAMTS17 splice donor site mutation in dogs with primary lens luxation.

abstract: PURPOSE ::: To identify the genetic cause of isolated canine ectopia lentis, a well-characterized veterinary disease commonly referred to as primary lens luxation (PLL) and to compare the canine disease with a newly described human Weill-Marchesani syndrome (WMS)-like disease of similar genetic etiology. ::: ::: ::: METHODS ::: Genomewide association analysis and fine mapping by homozygosity were used to identify the chromosomal segment harboring the PLL locus. The resequencing of a regional candidate gene was used to discover a mutation in a splice donor site predicted to cause exon skipping. Exon skipping was confirmed by reverse transcription-polymerase chain reaction amplification of RNA isolated from PLL-affected eyes and from skin fibroblast cultures from PLL-affected dogs. An allelic discrimination assay was used to genotype individual dogs at the splice donor site mutation. ::: ::: ::: RESULTS ::: The PLL locus was mapped to a 664-kb region of canine chromosome 3 containing regional candidate gene ADAMTS17. Resequencing ADAMTS17 revealed a GT-->AT splice-donor-site mutation at the 5' end of intron 10. The predicted exon 10 skipping and resultant frame shift were confirmed with RNA derived from PLL-affected dogs. The ADAMTS17 mutation was significantly associated with clinical PLL in three different dog breeds. ::: ::: ::: CONCLUSIONS ::: A truncating mutation in canine ADAMTS17 causes PLL, a well-characterized veterinary disease, which can now be compared to a recently described rare WMS-like disease caused by truncating mutations of the human ADAMTS17 ortholog.


Inquisitive semantics and pragmatics


title: Hilbert space embeddings and metrics on probability measures

abstract: A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing, and independence testing. This embedding represents any probability measure as a mean element in a reproducing kernel Hilbert space (RKHS). A pseudometric on the space of probability measures can be defined as the distance between distribution embeddings: we denote this as $\gamma_k$, indexed by the kernel function $k$ that defines the inner product in the RKHS. We present three theoretical properties of $\gamma_k$. First, we consider the question of determining the conditions on the kernel $k$ for which $\gamma_k$ is a metric: such $k$ are denoted {\em characteristic kernels}. Unlike pseudometrics, a metric is zero only when two distributions coincide, thus ensuring the RKHS embedding maps all distributions uniquely (i.e., the embedding is injective). While previously published conditions may apply only in restricted circumstances (e.g. on compact domains), and are difficult to check, our conditions are straightforward and intuitive: bounded continuous strictly positive definite kernels are characteristic. Alternatively, if a bounded continuous kernel is translation-invariant on $\bb{R}^d$, then it is characteristic if and only if the support of its Fourier transform is the entire $\bb{R}^d$. Second, we show that there exist distinct distributions that are arbitrarily close in $\gamma_k$. Third, to understand the nature of the topology induced by $\gamma_k$, we relate $\gamma_k$ to other popular metrics on probability measures, and present conditions on the kernel $k$ under which $\gamma_k$ metrizes the weak topology.


title: A survey of thresholding techniques

abstract: Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.


title: Guidelines are only half of the story: accessibility problems encountered by blind users on the web

abstract: This paper describes an empirical study of the problems encountered by 32 blind users on the Web. Task-based user evaluations were undertaken on 16 websites, yielding 1383 instances of user problems. The results showed that only 50.4% of the problems encountered by users were covered by Success Criteria in the Web Content Accessibility Guidelines 2.0 (WCAG 2.0). For user problems that were covered by WCAG 2.0, 16.7% of websites implemented techniques recommended in WCAG 2.0 but the techniques did not solve the problems. These results show that few developers are implementing the current version of WCAG, and even when the guidelines are implemented on websites there is little indication that people with disabilities will encounter fewer problems. The paper closes by discussing the implications of this study for future research and practice. In particular, it discusses the need to move away from a problem-based approach towards a design principle approach for web accessibility.


title: Refining the Evaluation of Treatment Foster Care

abstract: Treatment foster care (TFC) is a promising alternative intervention for children and adolescents at risk for placement in residential care settings. An examination of the processes and outcomes of TFC is complicated and leads to the conclusions that: (a) TFC is delivered with such variability that conclusions about its effectiveness are difficult to draw; (b) many variables in the child's ecology that potentially confound the effects of the intervention remain unexamined; and (c) service impacts have been defined narrowly. This article synthesizes the state of knowledge in these three domains and discusses how an ecosystemic framework can advance and refine knowledge building in this field of practice.


title: Models of visuospatial and verbal memory across the adult life span

abstract: The authors investigated the distinctiveness and interrelationships among visuospatial and verbal memory processes in short-term, working, and long-term memories in 345 adults. Beginning in the 20s, a continuous, regular decline occurs for processing-intensive tasks (e.g., speed of processing, working memory, and long-term memory), whereas verbal knowledge increases across the life span. There is little differentiation in the cognitive architecture of memory across the life span. Visuospatial and verbal working memory are distinct but highly interrelated systems with domain-specific short-term memory subsystems. In contrast to recent neuroimaging data, there is little evidence for dedifferentiation of function at the behavioral level in old compared with young adults. The authors conclude that efforts to connect behavioral and brain data yield a more complete understanding of the aging mind. The present study is a life span approach to understanding visuospatial and verbal working memory and its relationship to long-term memory. It is well-documented that measures of overall cognitive resource such as speed of processing and working memory capacity mediate virtually all age-related variance on higher order cognitive tasks, including long-term memory tasks (Hultsch, Hertzog, & Dixon, 1990; Kliegl, Mayr, & Krampe, 1994; Park et al., 1996; Salthouse, 1996). Thus far, the primary debate among researchers on aging has focused on understanding the interrelationships among basic indicators of cognitive resources (e.g., sensory function, speed of processing, working memory capacity) in explaining age-related decline on more complex cognitive tasks (Baltes & Lindenberger, 1997; Lindenberger & Baltes, 1994; Park et al., 1996; Salthouse, 1996). The present study builds on this long history of work on aging and cognitive resources by addressing the possibility that working memory resources are specialized by content (visuospatial and verbal) and that this specialization may change with age. Moreover, we examined the relationship of content-based working memory tasks to long-term memory for visuospatial and verbal information, with a particular focus on the role of working memory. We used findings from the neuroimaging and behavioral literature to develop neurobiologically plausible structural equation models to understand the relationship of domain-specific working memory to domain-specific long-term memory. We adopt a traditional view of working memory, conceptualizing it as a pool of mental energy or cognitive resources


title: 3D Convolutional Neural Networks for Classification of Functional Connectomes

abstract: Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer’s disease, and stroke. While a growing number of studies have demonstrated the promise of machine learning algorithms for rs-fMRI based clinical or behavioral prediction, most prior models have been limited in their capacity to exploit the richness of the data. For example, classification techniques applied to rs-fMRI often rely on region-based summary statistics and/or linear models. In this work, we propose a novel volumetric Convolutional Neural Network (CNN) framework that takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. We showcase our approach on a challenging large-scale dataset (ABIDE, with \(N>2,000\)) and report state-of-the-art accuracy results on rs-fMRI-based discrimination of autism patients and healthy controls.


title: Identifying beneficial task relations for multi-task learning in deep neural networks

abstract: Multi-task learning (MTL) in deep neural networks for NLP has recently received increasing interest due to some compelling benefits, including its potential to efficiently regularize models and to reduce the need for labeled data. While it has brought significant improvements in a number of NLP tasks, mixed results have been reported, and little is known about the conditions under which MTL leads to gains in NLP. This paper sheds light on the specific task relations that can lead to gains from MTL models over single-task setups.


title: Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks

abstract: Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and relations in text and large-scale knowledge bases (KBs). Neelakantan et al. (2015) use RNNs to compose the distributed semantics of multi-hop paths in KBs; however for multiple reasons, the approach lacks accuracy and practicality. This paper proposes three significant modeling advances: (1) we learn to jointly reason about relations, entities, and entity-types; (2) we use neural attention modeling to incorporate multiple paths; (3) we learn to share strength in a single RNN that represents logical composition across all relations. On a largescale Freebase+ClueWeb prediction task, we achieve 25% error reduction, and a 53% error reduction on sparse relations due to shared strength. On chains of reasoning in WordNet we reduce error in mean quantile by 84% versus previous state-of-the-art. The code and data are available at this https URL


title: Learning from Imbalanced Data

abstract: With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.


title: Single Network Panoptic Segmentation for Street Scene Understanding

abstract: In this work, we propose a single deep neural network for panoptic segmentation, for which the goal is to provide each individual pixel of an input image with a class label, as in semantic segmentation, as well as a unique identifier for specific objects in an image, following instance segmentation. Our network makes joint semantic and instance segmentation predictions and combines these to form an output in the panoptic format. This has two main benefits: firstly, the entire panoptic prediction is made in one pass, reducing the required computation time and resources; secondly, by learning the tasks jointly, information is shared between the two tasks, thereby improving performance. Our network is evaluated on two street scene datasets: Cityscapes and Mapillary Vistas. By leveraging information exchange and improving the merging heuristics, we increase the performance of the single network, and achieve a score of 23.9 on the Panoptic Quality (PQ) metric on Mapillary Vistas validation, with an input resolution of 640 × 900 pixels. On Cityscapes validation, our method achieves a PQ score of 45.9 with an input resolution of 512 × 1024 pixels. Moreover, our method decreases the prediction time by a factor of 2 with respect to separate networks.


title: XML Matchers: approaches and challenges

abstract: Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.


title: Context-Based Speech Recognition Error Detection And Correction

abstract: In this paper we present preliminary results of a novel unsupervised approach for high-precision detection and correction of errors in the output of automatic speech recognition systems. We model the likely contexts of all words in an ASR system vocabulary by performing a lexical co-occurrence analysis using a large corpus of output from the speech system. We then identify regions in the data that contain likely contexts for a given query word. Finally, we detect words or sequences of words in the contextual regions that are unlikely to appear in the context and that are phonetically similar to the query word. Initial experiments indicate that this technique can produce high-precision targeted detection and correction of misrecognized query words.


title: Leveraging the Serverless Architecture for Securing Linux Containers

abstract: Linux containers present a lightweight solution to package applications into images and instantiate them in isolated environments. Such images may include vulnerabilities that can be exploited at runtime. A vulnerability scanning service can detect these vulnerabilities by periodically scanning the containers and their images for potential threats. When a threat is detected, an event may be generated to (1) quarantine or terminate the compromised container(s) and optionally (2) remedy the vulnerability by rebuilding a secure image. We believe that such event-driven process is a great fit to be implemented in a serverless architecture. In this paper we explore the design of an automated threat mitigation architecture based on OpenWhisk and Kubernetes.


title: Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulk

abstract: While 3D object detection and pose estimation has been studied for a long time, its evaluation is not yet completely satisfactory. Indeed, existing datasets typically consist in numerous acquisitions of only a few scenes because of the tediousness of pose annotation, and existing evaluation protocols cannot handle properly objects with symmetries. This work aims at addressing those two points. We first present automatic techniques to produce fully annotated RGBD data of many object instances in arbitrary poses, with which we produce a dataset of thousands of independent scenes of bulk parts composed of both real and synthetic images. We then propose a consistent evaluation methodology suitable for any rigid object, regardless of its symmetries. We illustrate it with two reference object detection and pose estimation methods on different objects, and show that incorporating symmetry considerations into pose estimation methods themselves can lead to significant performance gains. The proposed dataset is available at http://rbregier.github.io/dataset2017.


title: Remembering the real me: Nostalgia offers a window to the intrinsic self.

abstract: Seven studies reveal that nostalgia, a sentimental affection for the past, offers a window to the intrinsic self-concept�who people think they truly are. In Study 1, state nostalgia was associated with higher authenticity and lower extrinsic self-focus (concern with meeting extrinsic value standards). In Study 2, experimentally primed nostalgia increased perceived authenticity of the past self, which in turn predicted reduced current extrinsic self-focus. Study 3 showed that nostalgia increased the accessibility of the intrinsic self-concept but not the everyday self-concept. Study 4 provided evidence for a moderator suggested by our theoretical analysis: Recalling a nostalgic event increased felt nostalgia and positive affect, but this effect was attenuated if participants were prompted to recognize external factors controlling their behavior during that event. Next we treated nostalgia as an outcome variable and a moderator to test whether nostalgia is triggered by, and buffers against, threats to the intrinsic self. Using a mediation approach, Study 5 showed that participants primed to feel blocked in intrinsic self-expression responded with increased nostalgia. In Study 6, intrinsic self-threat reduced intrinsic self-expression and subjective well-being for participants who were not given an opportunity to respond with nostalgia but not for participants who were allowed to reflect on a nostalgic memory. In line with the experimental findings, correlational data from Study 7 indicated that dispositional nostalgia positively predicted intrinsic self-expression and well-being. Understanding nostalgia as a window to the intrinsic self points to new directions for research on nostalgia�s antecedents, moderators, and consequences for well-being. (PsycINFO Database Record (c) 2015 APA, all rights reserved)


title: Secure lightweight context-driven data logging for bodyworn sensing devices

abstract: Rapid advancement in wearable technology has unlocked a tremendous potential of its applications in the medical domain. Among the challenges in making the technology more useful for medical purposes is the lack of confidence in the data thus generated and communicated. Incentives have led to attacks on such systems. We propose a novel lightweight scheme to securely log the data from bodyworn sensing devices by utilizing neighboring devices as witnesses who store the fingerprints of data in Bloom filters to be later used for forensics. Medical data from each sensor is stored at various locations of the system in chronological epoch-level blocks chained together, similar to the blockchain. Besides secure logging, the scheme offers to secure other contextual information such as localization and timestamping. We prove the effectiveness of the scheme through experimental results. We define performance parameters of our scheme and quantify their cost benefit trade-offs through simulation.


title: An adaptive generalized interpolation material point method for simulating elastoplastic materials

abstract: We present an adaptive Generalized Interpolation Material Point (GIMP) method for simulating elastoplastic materials. Our approach allows adaptive refining and coarsening of different regions of the material, leading to an efficient MPM solver that concentrates most of the computation resources in specific regions of interest. We propose a C1 continuous adaptive basis function that satisfies the partition of unity property and remains non-negative throughout the computational domain. We develop a practical strategy for particle-grid transfers that leverages the recently introduced SPGrid data structure for storing sparse multi-layered grids. We demonstrate the robustness and efficiency of our method on the simulation of various elastic and plastic materials. We also compare key kernel components to uniform grid MPM solvers to highlight performance benefits of our method.


title: Guided deep network for depth map super-resolution: How much can color help?

abstract: Since the quality of depth maps produced by Time-of-Flight (TOF) cameras is low, color-guided recovery methods have been proposed to increase spatial resolution and suppress unwanted noise. Despite successful applications of deep neural networks in color image super-resolution (SR), their potential for depth map SR is largely unknown. In this paper, we present a deep neural network architecture to learn the end-to-end mapping between low-resolution and high-resolution depth maps. Furthermore, we introduce a novel color-guided deep Fully Convolutional Network (FCN) and propose to jointly learn two nonlinear mapping functions (color-to-depth and LR-to-HR) in the presence of noise. Experimental results on several benchmark data sets show that our method outperforms several existing state-of-the-art depth SR algorithms. Moreover, this work attempts to partially shed some light onto the fundamental question in color-guided depth recovery — how much can color help in depth SR?


Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data


title: Growing Echo-State Network With Multiple Subreservoirs

abstract: An echo-state network (ESN) is an effective alternative to gradient methods for training recurrent neural network. However, it is difficult to determine the structure (mainly the reservoir) of the ESN to match with the given application. In this paper, a growing ESN (GESN) is proposed to design the size and topology of the reservoir automatically. First, the GESN makes use of the block matrix theory to add hidden units to the existing reservoir group by group, which leads to a GESN with multiple subreservoirs. Second, every subreservoir weight matrix in the GESN is created with a predefined singular value spectrum, which ensures the echo-sate property of the ESN without posterior scaling of the weights. Third, during the growth of the network, the output weights of the GESN are updated in an incremental way. Moreover, the convergence of the GESN is proved. Finally, the GESN is tested on some artificial and real-world time-series benchmarks. Simulation results show that the proposed GESN has better prediction performance and faster leaning speed than some ESNs with fixed sizes and topologies.


title: Structured Learning of Human Interactions in TV Shows

abstract: The objective of this work is recognition and spatiotemporal localization of two-person interactions in video. Our approach is person-centric. As a first stage we track all upper bodies and heads in a video using a tracking-by-detection approach that combines detections with KLT tracking and clique partitioning, together with occlusion detection, to yield robust person tracks. We develop local descriptors of activity based on the head orientation (estimated using a set of pose-specific classifiers) and the local spatiotemporal region around them, together with global descriptors that encode the relative positions of people as a function of interaction type. Learning and inference on the model uses a structured output SVM which combines the local and global descriptors in a principled manner. Inference using the model yields information about which pairs of people are interacting, their interaction class, and their head orientation (which is also treated as a variable, enabling mistakes in the classifier to be corrected using global context). We show that inference can be carried out with polynomial complexity in the number of people, and describe an efficient algorithm for this. The method is evaluated on a new dataset comprising 300 video clips acquired from 23 different TV shows and on the benchmark UT--Interaction dataset.


title: Multiple Description Coding: Compression Meets the Network

abstract: This article focuses on the compressed representations of pictures. The representation does not affect how many bits get from the Web server to the laptop, but it determines the usefulness of the bits that arrive. Many different representations are possible, and there is more involved in their choice than merely selecting a compression ratio. The techniques presented represent a single information source with several chunks of data ("descriptions") so that the source can be approximated from any subset of the chunks. By allowing image reconstruction to continue even after a packet is lost, this type of representation can prevent a Web browser from becoming dormant.


title: Anomaly Detection in Video Surveillance via Gaussian Process

abstract: In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It esta...


title: Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

abstract: How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems. Though previous work along this research line has shown some promising results, most existing methods either fail to significantly compress a well-trained deep network or require a heavy retraining process for the pruned deep network to re-boost its prediction performance. In this paper, we propose a new layer-wise pruning method for deep neural networks. In our proposed method, parameters of each individual layer are pruned independently based on second order derivatives of a layer-wise error function with respect to the corresponding parameters. We prove that the final prediction performance drop after pruning is bounded by a linear combination of the reconstructed errors caused at each layer. Therefore, there is a guarantee that one only needs to perform a light retraining process on the pruned network to resume its original prediction performance. We conduct extensive experiments on benchmark datasets to demonstrate the effectiveness of our pruning method compared with several state-of-the-art baseline methods.


Classifying heart sounds using peak location for segmentation and feature construction


title: Cluster-head election using fuzzy logic for wireless sensor networks

abstract: Wireless sensor networks (WSNs) present a new generation of real-time embedded systems with limited computation, energy and memory resources that are being used in a wide variety of applications where traditional networking infrastructure is practically infeasible. Appropriate cluster-head node election can drastically reduce the energy consumption and enhance the lifetime of the network. In this paper, a fuzzy logic approach to cluster-head election is proposed based on three descriptors-energy, concentration and centrality. Simulation shows that depending upon network configuration, a substantial increase in network lifetime can be accomplished as compared to probabilistically selecting the nodes as cluster-heads using only local information.


title: Privacy-Preserving Multivariate Statistical Analysis: Linear Regression and Classification

abstract: Multivariate statistical analysis is an important data analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither party is willing to disclose its private data to the other party or any third party. The current statistical analysis techniques cannot be used directly to support this kind of computation because they require all parties to send the necessary data to a central place. In this paper, We define two Secure 2-party multivariate statistical analysis problems: Secure 2-party Multivariate Linear Regression problem and Secure 2-party Multivariate Classification problem. We have developed a practical security model, based on which we have developed a number of building blocks for solving these two problems.


title: A Permanent Magnetic Actuator for 126 kV Vacuum Circuit Breakers

abstract: Permanent magnetic actuators (PMAs) have been widely used in medium-voltage vacuum circuit breakers (VCBs) due to their high reliability and controllability. However, a conventional bistable PMA cannot be adopted in power equipment for the transmission voltage level such as 126 kV VCBs directly because of its low-velocity characteristics. The objective of this paper is to propose a multimagnetic circuit PMA to satisfy the high-velocity requirements for 126 kV VCBs. The novel PMA is a bistable and axisymmetric structure, including holding and driving components whose magnetic circuits are separated. Air gaps are specifically designed in the magnetic circuits to improve the velocity performance of the novel PMA. Both static and dynamic characteristics of the novel PMA model have been calculated through finite-element software coupled with multi-body dynamics software. Furthermore, a prototype of the PMA has been developed according to the calculation results. The experimental results on the prototype have shown that the velocity characteristics of the PMA are able to meet the requirements of a 126 kV VCB and agree well with the simulation results.


title: Modelling and PID controller design for a quadrotor unmanned air vehicle

abstract: This paper presents the modelling of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as the quadrotor aircraft. The paper presents a new model design method for the flight control of an autonomous quad rotor. The paper describes the controller architecture for the quadrotor as well. The dynamic model of the quad-rotor, which is an under actuated aircraft with fixed four pitch angle rotors, will be described. The Modeling of a quadrotor vehicle is not an easy task because of its complex structure. The aim is to develop a model of the vehicle as realistic as possible. The model is used to design a stable and accurate controller. This paper explains the developments of a PID (proportionalintegral-derivative) control method to obtain stability in flying the Quad-rotor flying object. The model has four input forces which are basically the thrust provided by each propeller connected to each rotor with fixed angle. Forward (backward) motion is maintained by increasing (decreasing) speed of front (rear) rotor speed while decreasing (increasing) rear (front) rotor speed simultaneously which means changing the pitch angle. Left and right motion is accomplished by changing roll angle by the same way. The front and rear motors rotate counter-clockwise while other motors rotate clockwise so that the yaw command is derived by increasing (decreasing) counter-clockwise motors speed while decreasing (increasing) clockwise motor speeds.


title: Diagnostic criteria for complicated grief disorder.

abstract: Objective: Some prolonged and turbulent grief reactions include symptoms that differ from the DSM-IV criteria for major depressive disorder. The authors investigated a new diagnosis that would include these symptoms. Method: They developed observer-based definitions of 30 symptoms noted clinically in previous longitudinal interviews of bereaved persons and then designed a plan to investigate whether any combination of these would serve as criteria for a possible new diagnosis of complicated grief disorder. Using a structured diagnostic interview, they assessed 70 subjects whose spouses had died. Latent class model analyses and signal detection procedures were used to calibrate the data against global clinical ratings and self-report measures of grief-specific distress. Results: Complicated grief disorder was found to be characterized by a smaller set of the assessed symptoms. Subjects selected by an algorithm for these symptom patterns did not significantly overlap with subjects who received a diagnosis of major depressive disorder. Conclusions: A new diagnosis of complicated grief disorder may be indicated. Its criteria would include the current experience (more than a year after a loss) ofintense intrusive thoughts, pangs of severe emotion, distressing yearnings, feeling excessively alone and empty, excessively avoiding tasks reminiscent of the deceased, unusual sleep disturbances, and maladaptive levels of loss of interest in personal activities.


title: Deep multi-task learning with low level tasks supervised at lower layers

abstract: In all previous work on deep multi-task learning we are aware of, all task supervisions are on the same (outermost) layer. We present a multi-task learning architecture with deep bi-directional RNNs, where different tasks supervision can happen at different layers. We present experiments in syntactic chunking and CCG supertagging, coupled with the additional task of POS-tagging. We show that it is consistently better to have POS supervision at the innermost rather than the outermost layer. We argue that this is because “lowlevel” tasks are better kept at the lower layers, enabling the higher-level tasks to make use of the shared representation of the lower-level tasks. Finally, we also show how this architecture can be used for domain adaptation.


title: Attaining Customer Loyalty! The Role of Consumer Attitude and Consumer Behavior

abstract: All over the world every business and profit earning firm want to make their consumer loyal. There are many factors responsible for this customer loyalty but two of them are prominent. This research study is focused on that how customer satisfaction and customer retention contribute towards customer loyalty. For analysis part of this study, Universities students of Peshawar Region were targeted. A sample of 120 were selected from three universities of Peshawar. These universities were Preston University, Sarhad University and City University of Science and Information technology. Analysis was conducted with the help of SPSS 19. Results of the study shows that customer loyalty is more dependent upon Customer satisfaction in comparison of customer retention. Customer perceived value and customer perceived quality are the major factors which contribute for the customer loyalty of Universities students for mobile handsets.


title: Characteristics of Double Exponentially Tapered Slot Antenna (DETSA) Conformed in the Longitudinal Direction Around a Cylinder

abstract: The characteristics of a double exponentially tapered slot antenna (DETSA) as a function of the radius that the DETSA is conformed to in the longitudinal direction is presented. It is shown through measurements and simulations that the radiation pattern of the conformed antenna rotates in the direction through which the antenna is curved, and that diffraction affects the radiation pattern if the radius of curvature is too small or the frequency too high. The gain of the antenna degrades by only 1 dB if the radius of curvature is large and more than 2 dB for smaller radii. The main effect due to curving the antenna is an increased cross polarization in the E-plane


title: The Relationship between Test Anxiety and Academic Achievement

abstract: The major aim of this research study was to explore the relationship between test anxiety and academic achievement of students at the post graduate level. A sample of 414 students was randomly selected from seven different science departments in a public sector university in Lahore, Pakistan. Data were collected by using the Test Anxiety Inventory (TAI) developed by Spielberger. Pearson correlation, multivariate statistics and regression analyses were run for data analysis. It was found that a significant negative relationship exists between test anxiety scores and students’ achievement scores. Results showed that a cognitive factor (worry) contributes more in test anxiety than affective factors (emotional). Therefore, it is concluded that test anxiety is one of the factors which are responsible for students’ underachievement and low performance but it can be managed by appropriate training of students in dealing with factors causing test anxiety.


title: Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles

abstract: BackgroundThere are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape (http://cytoscape.org/) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged ‘network evolution’ found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results.ResultsOrthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface.ConclusionOrthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.


title: Honeypot detection in advanced botnet attacks

abstract: Botnets have become one of the major attacks in the internet today due to their illicit profitable financial gain. Meanwhile, honeypots have been successfully deployed in many computer security defence systems. Since honeypots set up by security defenders can attract botnet compromises and become spies in exposing botnet membership and botnet attacker behaviours, they are widely used by security defenders in botnet defence. Therefore, attackers constructing and maintaining botnets will be forced to find ways to avoid honeypot traps. In this paper, we present a hardware and software independent honeypot detection methodology based on the following assumption: security professionals deploying honeypots have a liability constraint such that they cannot allow their honeypots to participate in real attacks that could cause damage to others, while attackers do not need to follow this constraint. Attackers could detect honeypots in their botnets by checking whether compromised machines in a botnet can successfully send out unmodified malicious traffic. Based on this basic detection principle, we present honeypot detection techniques to be used in both centralised botnets and Peer-to-Peer (P2P) structured botnets. Experiments show that current standard honeypots and honeynet programs are vulnerable to the proposed honeypot detection techniques. At the end, we discuss some guidelines for defending against general honeypot-aware attacks.


title: Comprehensive Experimental Analyses of Automotive Attack Surfaces

abstract: Modern automobiles are pervasively computerized, and hence potentially vulnerable to attack. However, while previous research has shown that the internal networks within some modern cars are insecure, the associated threat model--requiring prior physical access--has justifiably been viewed as unrealistic. Thus, it remains an open question if automobiles can also be susceptible to remote compromise. Our work seeks to put this question to rest by systematically analyzing the external attack surface of a modern automobile. We discover that remote exploitation is feasible via a broad range of attack vectors (including mechanics tools, CD players, Bluetooth and cellular radio), and further, that wireless communications channels allow long distance vehicle control, location tracking, in-cabin audio exfiltration and theft. Finally, we discuss the structural characteristics of the automotive ecosystem that give rise to such problems and highlight the practical challenges in mitigating them.


title: INVESTIGATING THE STATE-OF-THE-ART IN ENTERPRISE ARCHITECTURE MANAGEMENT METHODS IN LITERATURE AND PRACTICE

abstract: In recent years, enterprise architecture (EA) management has emerged to one of the major challenges for enterprises. When looking for guidance in this field companies can choose from a variety of EA management approaches, which have been developed by scientists, practitioners, and governmental organizations. However, these approaches differ significantly in a number of characteristics and especially when it comes to methods for the EA management function. There is neither a common understanding of the scope and content of the main activities an EA management function consists of nor has a commonly accepted reference method been developed.


title: A performance evaluation of Docker-based MQTT server implementation on Internet of Things device

abstract: The paper presents an analysis of the implementation of the MQTT server on the Raspberry Pi device. The standard configuration of the server (without virtualization layer) is compared to the configuration based on a Docker container and the principles of microservices. The test system has been evaluated over the performance of the MQTT server.


title: A Wideband Analog-Controlled Variable-Gain Amplifier With dB-Linear Characteristic for High-Frequency Applications

abstract: A higher frequency, over 2 GHz, is suggested for current 4G or 5G wideband applications. By adopting a unique gain control method, an analog-controlled variable-gain amplifier (VGA) with an accurate dB-linear characteristic is presented. The designed VGA not only features large bandwidth, but also has accurate gain adjustment with a relatively wide control voltage range. The VGA has a measured gain range of 24 dB, of which 17.3 dB is dB-linear with less than $\pm$ 0.3-dB gain error. The $-$ 3-dB bandwidth is relatively constant from 2 to 2.2 GHz for the entire dB-linear range. An output 1-dB compression point of 1.8 dBm and a noise figure of 24 dB are measured. Due to the simple structure, the current consumption of the VGA core is only 2.9 mA from a 1.2-V supply, and the size is only ${\hbox{225}}\ \mu{\hbox{m}}\times {\hbox{45}}\ \mu{\hbox{m}}$ , excluding pads. Moreover, the robustness of the designed VGA is verified by means of Monte Carlo simulation.


title: A fast ellipse/circle detector using geometric symmetry

abstract: Through the use of a global geometric symmetry, a fast ellipse/circle detector is proposed in this paper. Based on the geometric symmetry, the proposed method first locates candidates of ellipse and circle centers. In the meantime, according to these candidate centers, all feature points in an input image are grouped into several subimages. Then, for each subimage, by using geometric symmetry again, all ellipses and circles are obtained. The method significantly reduces the time required to evaluate all possible parameters without using edge direction information. Experimental results are given to show the correctness and effectiveness of the proposed method.


title: Video Object Segmentation using Tracked Object Proposals

abstract: We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking. We are motivated by the fact that the objects semantic category tends not to change throughout the video while its appearance and location can vary considerably. In order to capture the specific object appearance independent of its category, for each video we train a fully convolutional network using augmentations of the given annotated frame. We refine the appearance segmentation mask with the bounding boxes provided either by a semantic object detection network, when applicable, or by a previous frame prediction. By introducing a temporal continuity constraint on the detected boxes, we are able to improve the object segmentation mask of the appearance network and achieve competitive results on the DAVIS datasets.


title: Clustering-based undersampling in class-imbalanced data

abstract: Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.


title: Heterogeneous Networks With Power-Domain NOMA: Coverage, Throughput, and Power Allocation Analysis

abstract: In a heterogeneous cellular network (HetNet), consider that a base station in the HetNet is able to simultaneously schedule and serve $K$ users in the downlink by performing the power-domain non-orthogonal multiple access (NOMA) scheme. This paper aims at the preliminary study on the downlink coverage and throughput performances of the HetNet with the \textit{non-cooperative} and the (proposed) \textit{cooperative} NOMA schemes. First, the coverage probability and link throughput of $K$ users in each cell are studied and their accurate expressions are derived for the non-cooperative NOMA scheme in which no BSs are coordinated to jointly transmit the NOMA signals for a particular user. We show that the coverage and link throughput can be largely reduced if transmit power allocations among the $K$ users do not satisfy the constraint derived. Next, we analyze the coverage and link throughput of $K$ users for the cooperative NOMA scheme in which the void BSs without users are coordinated to enhance the farthest NOMA user in a cell. The derived accurate results show that cooperative NOMA can significantly improve the coverage and link throughput of all users. Finally, we show that there exist optimal power allocation schemes that maximize the average cell coverage and throughput under some derived power allocation constraints and numerical results validate our analytical findings.


title: Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition

abstract: Dialect identification (DID) is a special case of general language identification (LID), but a more challenging problem due to the linguistic similarity between dialects. In this paper, we propose an end-to-end DID system and a Siamese neural network to extract language embeddings. We use both acoustic and linguistic features for the DID task on the Arabic dialectal speech dataset: Multi-Genre Broadcast 3 (MGB-3). The end-to-end DID system was trained using three kinds of acoustic features: Mel-Frequency Cepstral Coefficients (MFCCs), log Mel-scale Filter Bank energies (FBANK) and spectrogram energies. We also investigated a dataset augmentation approach to achieve robust performance with limited data resources. Our linguistic feature research focused on learning similarities and dissimilarities between dialects using the Siamese network, so that we can reduce feature dimensionality as well as improve DID performance. The best system using a single feature set achieves 73% accuracy, while a fusion system using multiple features yields 78% on the MGB-3 dialect test set consisting of 5 dialects. The experimental results indicate that FBANK features achieve slightly better results than MFCCs. Dataset augmentation via speed perturbation appears to add significant robustness to the system. Although the Siamese network with language embeddings did not achieve as good a result as the end-to-end DID system, the two approaches had good synergy when combined together in a fused system.


title: Real-time recording and classification of eye movements in an immersive virtual environment

abstract: Despite the growing popularity of virtual reality environments, few laboratories are equipped to investigate eye movements within these environments. This primer is intended to reduce the time and effort required to incorporate eye-tracking equipment into a virtual reality environment. We discuss issues related to the initial startup and provide algorithms necessary for basic analysis. Algorithms are provided for the calculation of gaze angle within a virtual world using a monocular eye-tracker in a three-dimensional environment. In addition, we provide algorithms for the calculation of the angular distance between the gaze and a relevant virtual object and for the identification of fixations, saccades, and pursuit eye movements. Finally, we provide tools that temporally synchronize gaze data and the visual stimulus and enable real-time assembly of a video-based record of the experiment using the Quicktime MOV format, available at http://sourceforge.net/p/utdvrlibraries/. This record contains the visual stimulus, the gaze cursor, and associated numerical data and can be used for data exportation, visual inspection, and validation of calculated gaze movements.


title: CryptVMI: Encrypted Virtual Machine Introspection in the Cloud

abstract: Virtualization techniques are the key in both public and private cloud computing environments. In such environments, multiple virtual instances are running on the same physical machine. The logical isolation between systems makes security assurance weaker than physically isolated systems. Thus, Virtual Machine Introspection techniques become essential to prevent the virtual system from being vulnerable to attacks. However, this technique breaks down the borders of the segregation between multiple tenants, which should be avoided in a public cloud computing environment. In this paper, we focus on building an encrypted Virtual Machine Introspection system, CryptVMI, to address the above concern, especially in a public cloud system. Our approach maintains a query handler on the management node to handle encrypted queries from user clients. We pass the query to the corresponding compute node that holds the virtual instance queried. The introspection application deployed on the compute node processes the query and acquires the encrypted results from the virtual instance for the user. This work shows our design and preliminary implementation of this system.


ioCane: A Smart-Phone and Sensor-Augmented Mobility Aid for the Blind


title: Entity-relationship and object-oriented data modeling—an experimental comparison of design quality

abstract: Abstract We compare EER and OO data models from the point of view of design quality. Quality is measured in terms of (a) correctness of the conceptual schemas being designed, (b) time to complete the design task, and (c) designers' preferences of the models. Result of an experimental comparison of the two models reveal that the EER model surpasses the OO model for designing unary and ternary relationships, it takes less time to design EER schemas, and the EER model is preferred by designers. We conclude that even if the objective is to implement an OO database schema, the recommended procedure is to: (1) create an EER conceptual scheme, (2) map it to an OO schema, and augment the target schema with behavioral constructs that are unique to the OO approach.


title: RGBD-fusion: Real-time high precision depth recovery

abstract: The popularity of low-cost RGB-D scanners is increasing on a daily basis. Nevertheless, existing scanners often cannot capture subtle details in the environment. We present a novel method to enhance the depth map by fusing the intensity and depth information to create more detailed range profiles. The lighting model we use can handle natural scene illumination. It is integrated in a shape from shading like technique to improve the visual fidelity of the reconstructed object. Unlike previous efforts in this domain, the detailed geometry is calculated directly, without the need to explicitly find and integrate surface normals. In addition, the proposed method operates four orders of magnitude faster than the state of the art. Qualitative and quantitative visual and statistical evidence support the improvement in the depth obtained by the suggested method.


title: Automatic coronary calcium scoring using noncontrast and contrast CT images

abstract: Purpose: Calcium scoring is widely used to assess the risk of coronary heart disease (CHD). Accurate coronary artery calcification detection in noncontrast CTimage is a prerequisite step for coronary calcium scoring. Currently, calcified lesions in the coronary arteries are manually identified by radiologists in clinical practice. Thus, in this paper, a fully automatic calcium scoring method was developed to alleviate the work load of the radiologists or cardiologists. Methods: The challenge of automatic coronary calcification detection is to discriminate the calcification in the coronary arteries from the calcification in the other tissues. Since the anatomy of coronary arteries is difficult to be observed in the noncontrast CTimages, the contrastCTimage of the same patient is used to extract the regions of the aorta, heart, and coronary arteries. Then, a patient-specific region-of-interest (ROI) is generated in the noncontrast CTimage according to the segmentation results in the contrastCTimage. This patient-specific ROI focuses on the regions in the neighborhood of coronary arteries for calcification detection, which can eliminate the calcifications in the surrounding tissues. A support vector machine classifier is applied finally to refine the results by removing possible imagenoise. Furthermore, the calcified lesions in the noncontrast images belonging to the different main coronary arteries are identified automatically using the labeling results of the extracted coronary arteries. Results: Forty datasets from four different CT machine vendors were used to evaluate their algorithm, which were provided by the MICCAI 2014 Coronary Calcium Scoring (orCaScore) Challenge. The sensitivity and positive predictive value for the volume of detected calcifications are 0.989 and 0.948. Only one patient out of 40 patients had been assigned to the wrong risk category defined according to Agatston scores (0, 1–100, 101–300, >300) by comparing with the ground truth. Conclusions: The calcified lesions in the noncontrast CTimages can be detected automatically by using the segmentation results of the aorta, heart, and coronary arteries obtained in the contrastCTimages with a very high accuracy.


title: On reverse-engineering the KUKA Robot Language

abstract: Most commercial manufacturers of industrial robots require their robots to be programmed in a proprietary language tailored to the domain - a typical domain-specific language (DSL). However, these languages oftentimes suffer from shortcomings such as controller-specific design, limited expressiveness and a lack of extensibility. For that reason, we developed the extensible Robotics API for programming industrial robots on top of a general-purpose language. Although being a very flexible approach to programming industrial robots, a fully-fledged language can be too complex for simple tasks. Additionally, legacy support for code written in the original DSL has to be maintained. For these reasons, we present a lightweight implementation of a typical robotic DSL, the KUKA Robot Language (KRL), on top of our Robotics API. This work deals with the challenges in reverse-engineering the language and mapping its specifics to the Robotics API. We introduce two different approaches of interpreting and executing KRL programs: tree-based and bytecode-based interpretation.


On using a SDN-based control plane in 5G mobile networks


Robot Motion Planning on a Chip


title: An efficient and accurate method for evaluating time series similarity

abstract: A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specified e threshold value, such as the LCSS and the EDR techniques, have been shown to be robust in the presence of noise, time shifts, and data scaling. Our work proposes a new algorithm, called the Fast Time Series Evaluation (FTSE) method, which can be used to evaluate such threshold value techniques, including LCSS and EDR. Using FTSE, we show that these techniques can be evaluated faster than using either traditional dynamic programming or even warp-restricting methods such as the Sakoe-Chiba band and the Itakura Parallelogram. We also show that FTSE can be used in a framework that can evaluate a richer range of e threshold-based scoring techniques, of which EDR and LCSS are just two examples. This framework, called Swale, extends the e threshold-based scoring techniques to include arbitrary match rewards and gap penalties. Through extensive empirical evaluation, we show that Swale can obtain greater accuracy than existing methods.


title: Self-Powered Forward Error-Correcting Biosensor Based on Integration of Paper-Based Microfluidics and Self-Assembled Quick Response Codes

abstract: This paper extends our previous work on silver-enhancement based self-assembling structures for designing reliable, self-powered biosensors with forward error correcting (FEC) capability. At the core of the proposed approach is the integration of paper-based microfluidics with quick response (QR) codes that can be optically scanned using a smart-phone. The scanned information is first decoded to obtain the location of a web-server which further processes the self-assembled QR image to determine the concentration of target analytes. The integration substrate for the proposed FEC biosensor is polyethylene and the patterning of the QR code on the substrate has been achieved using a combination of low-cost ink-jet printing and a regular ballpoint dispensing pen. A paper-based microfluidics channel has been integrated underneath the substrate for acquiring, mixing and flowing the sample to areas on the substrate where different parts of the code can self-assemble in presence of immobilized gold nanorods. In this paper we demonstrate the proof-of-concept detection using prototypes of QR encoded FEC biosensors.


title: Important developments for the digital library: Data Ocean and Smart Library

abstract: Since its inception at Zhejiang University in 2005, the International Conference on the Universal Digital Library (ICUDL) has been held around the globe in Alexandria, Egypt, Carnegie Mellon University, USA, and at Allahabad in India. This annual event has been a strong driving force for exchange in digital library technologies, international scientific and cultural cooperation, as well as an influence on the development of the Universal Digital Library. Now the sixth ICUDL is being held in Hangzhou (China) again, which is bound to become a good opportunity for us to summarize the past while casting our eyes into the future. The theme of this year’s conference is ‘Data Ocean and Cloud Computing’. Nowadays, two major changes are taking place in digital library technologies. One is the transition from database into Data Ocean. This not only means a rapid increase in the number of books being digitalized, from 1 million to 5 million or even 10 million books, but also an enrichment in the forms of information, from books, pictures, calligraphy, videos, paintings and photos to a wide variety of relationships between them and their derivatives. The other change is the transition from digital library to Smart Library, where advanced services beyond the means of conventional libraries such as personalized services, hypertext services, computer-aided design (CAD) services, translation services, knowledge mining services, cross-media services are provided. This means that from the Data Ocean, new and various smart cloud services will arise to make the digital library more active, professional and intelligent. It has been nearly ten years since the launching of the China-America Digital Academic Library (CADAL) with the ultimate goal of the digitalization all the library resources of humanity to allow everybody to access the knowledge, anywhere at any time. On completion of Phase I of CADAL, China has scanned over 1 million books in Chinese and English, which were released on the CADAL portal website for users from over 70 countries and regions, which is a great contribution to the conservation and sharing of human intellectual wealth. CADAL has already been marked as a milestone in the history of the Universal Digital Library. It is my great pleasure to inform you that the Chinese Government has decided to invest 150 million Yuan to initiate Phase II of CADAL starting from April, 2010. The project will further increase the coverage and quantity of the Journal of Zhejiang University-SCIENCE C (Computers & Electronics) ISSN 1869-1951 (Print); ISSN 1869-196X (Online) www.zju.edu.cn/jzus; www.springerlink.com E-mail: jzus@zju.edu.cn


title: Perioperative Nursing Leaders Implement Clinical Practice Guidelines Using the Iowa Model of Evidence-Based Practice.

abstract: Many health care organizations, nursing leaders, and individual clinicians are not providing care consistently based on evidence and many are not aware of the evidence that is available. Preventable complications have an adverse effect on hospital reimbursement and the burden is placed on hospital personnel and nursing leaders to use current evidence to improve care and prevent complications, such as surgical site infections. Using AORN resources, leadership involvement and ownership, and implementing a theoretical model will contribute to implementing daily evidence-based practice and help to decrease the chasm between research and practice.


title: Buyer targeting optimization: A unified customer segmentation perspective

abstract: In marketing analytics, customer segmentation (clustering) divides a customer base into groups of similar individuals, while buyer targeting (classification) identifies promising customers. Both customer segmentation and buyer targeting help the business to improve marketing performances by allocating resources to the most profitable customers. Due to the heterogeneity across the customer groups, some studies have been made on combining the tasks of customer segmentation and buyer targeting for tailored marketing strategies. However, these efforts usually combine these two tasks in a simple step-by-step approach. It is still unclear how to implement these two tasks in a more integrated and optimized way, which is the research objective of this paper. Specifically, we formulate customer segmentation and buyer targeting as a unified optimization problem. Then, the customer segments are adaptively realized during the targeting optimization process. In this way, the integrated approach not only improves the buyer targeting performances but also provides a new perspective of segmentation based on the buying decision preferences of the customers. The unified customer segmentation and buyer targeting method not only quantifies the purchase tendency of a specific customer but also characterizes the buying decision behaviors at the segment level. We also develop an efficient K-Classifiers Segmentation algorithm to solve the unified optimization problem. Moreover, we show that the customer segmentation based on the buying decision preferences can also be consistent with the features on customer profiles. Finally, we have performed the extensive experiments on several real-world Business to Business (B2B) marketing data sets. The results show that our approach offers not only more accurate targeting of promising customers but also meaningful customer segmentation solutions with interpretable buying decision preferences for each customer segment.


title: Five-Level Inverter for Renewable Power Generation System

abstract: In this paper, a five-level inverter is developed and applied for injecting the real power of the renewable power into the grid to reduce the switching power loss, harmonic distortion, and electromagnetic interference caused by the switching operation of power electronic devices. Two dc capacitors, a dual-buck converter, a full-bridge inverter, and a filter configure the five-level inverter. The input of the dual-buck converter is two dc capacitor voltage sources. The dual-buck converter converts two dc capacitor voltage sources to a dc output voltage with three levels and balances these two dc capacitor voltages. The output voltage of the dual-buck converter supplies to the full-bridge inverter. The power electronic switches of the full-bridge inverter are switched in low frequency synchronous with the utility voltage to convert the output voltage of the dual-buck converter to a five-level ac voltage. The output current of the five-level inverter is controlled to generate a sinusoidal current in phase with the utility voltage to inject into the grid. A hardware prototype is developed to verify the performance of the developed renewable power generation system. The experimental results show that the developed renewable power generation system reaches the expected performance.


title: PERFORMANCE COMPARISONS OF FACIAL EXPRESSION RECOGNITION IN JAFFE DATABASE

abstract: Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we investigate various feature representation and expression classification schemes to recognize seven different facial expressions, such as happy, neutral, angry, disgust, sad, fear and surprise, in the JAFFE database. Experimental results show that the method of combining 2D-LDA (Linear Discriminant Analysis) and SVM (Support Vector Machine) outperforms others. The recognition rate of this method is 95.71% by using leave-one-out strategy and 94.13% by using cross-validation strategy. It takes only 0.0357 second to process one image of size 256 × 256.


title: A Discriminatively Learned CNN Embedding for Person Reidentification

abstract: In this article, we revisit two popular convolutional neural networks in person re-identification (re-ID): verification and identification models. The two models have their respective advantages and limitations due to different loss functions. Here, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a Siamese network that simultaneously computes the identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two input images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus taking full usage of the re-ID annotations. Our method can be easily applied on different pretrained networks. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show that our architecture can also be applied to image retrieval. The code is available at https://github.com/layumi/2016_person_re-ID.


Enterprise Information Portals and Enterprise Knowledge Portal


title: A Needle in a Haystack: What Do Twitter Users Say about Software?

abstract: Users of the Twitter microblogging platform share a vast amount of information about various topics through short messages on a daily basis. Some of these so called tweets include information that is relevant for software companies and could, for example, help requirements engineers to identify user needs. Therefore, tweets have the potential to aid in the continuous evolution of software applications. Despite the existence of such relevant tweets, little is known about their number and content. In this paper we report on the results of an exploratory study in which we analyzed the usage characteristics, content and automatic classification potential of tweets about software applications by using descriptive statistics, content analysis and machine learning techniques. Although the manual search of relevant information within the vast stream of tweets can be compared to looking for a needle in a haystack, our analysis shows that tweets provide a valuable input for software companies. Furthermore, our results demonstrate that machine learning techniques have the capacity to identify and harvest relevant information automatically.


title: Time series prediction based on NARX neural networks: An advanced approach

abstract: The NARX network is a dynamical neural architecture commonly used for input-output modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward time delay neural network (TDNN), i.e., without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, it is shown that the original architecture of the NARX network can be easily and efficiently applied to prediction of time series using embedding theory to reconstruct the input of NARX network. We evaluate the proposed approach using a real-world data set, which is the vibration data measured from a Co2 compressor. The results show that the proposed approach consistently outperforms standard neural network based predictors, such as the TDNN architecture.


title: SDM for Power-Efficient Undersea Transmission

abstract: We discuss space division multiplexing as a means of increasing link capacity for power limited transmission in undersea communication. Theoretical study of erbium-doped fiber amplifiers-based space division multiplexing (SDM) transmission shows the existence of optimal values of spectral efficiency, signal-to-noise ratio, and span length that maximize power efficiency. We experimentally confirm the existence of an optimal spectral efficiency and demonstrate power efficient SDM transmission using 12-core multicore fiber. Modulation format, amplifier, and link design are discussed in connection to SDM power efficient transmission.


title: A high gain patch antenna using negative permeability metamaterial structures

abstract: In this paper, a high gain patch antenna realized by using negative permeability electromagnetic metamaterials is presented to operate at 5–5.5 GHz. The proposed metamaterial cell is investigated and analyzed by numerical methods. We found that the proposed metamaterial structure provides a negative permeability from 5 GHz to 5.5 GHz, which overlaps with the operating frequency band of the reference microstrip antenna. By periodically loading a number of the proposed metamaterial cells on the substrate around the patch antenna, the gain of the antenna at operating frequency is enhanced by about 2.5 dBi, while there is no effect on the operating bandwidth of the reference antenna.


title: Objectives and State-of-the-Art of Location-Based Social Network Recommender Systems

abstract: Because of the widespread adoption of GPS-enabled devices, such as smartphones and GPS navigation devices, more and more location information is being collected and available. Compared with traditional ones (e.g., Amazon, Taobao, and Dangdang), recommender systems built on location-based social networks (LBSNs) have received much attention. The former mine users’ preferences through the relationship between users and items, e.g., online commodity, movies and music. The latter add location information as a new dimension to the former, hence resulting in a three-dimensional relationship among users, locations, and activities. In this article, we summarize LBSN recommender systems from the perspective of such a relationship. User, activity, and location are called objects, and recommender objectives are formed and achieved by mining and using such 3D relationships. From the perspective of the 3D relationship among these objects, we summarize the state-of-the-art of LBSN recommender systems to fulfill the related objectives. We finally indicate some future research directions in this area.


title: A Novel Approach to Improving the Efficiency of Storing and Accessing Small Files on Hadoop: A Case Study by PowerPoint Files

abstract: Hadoop distributed file system (HDFS) becomes a representative cloud storage platform, benefiting from its reliable, scalable and low-cost storage capability. HDFS has been utilized in BlueSky, one of the most prevalent e-Learning resource sharing systems in China, to store and share courseware majorly in the form of PowerPoint (PPT) files and video clips. Unfortunately, HDFS does not perform well for massive small files since huge numbers of small files imposed heavy burden on NameNode of HDFS, correlations between files were not considered for data placement, and no prefetching mechanism was provided to improve I/O performance. This paper introduces a novel approach to improve the efficiency of storing and accessing small files on HDFS. Characteristics of file correlations and access locality remaining among small files in the context of courseware are well considered for storing and accessing them. Firstly, all correlated small files of a PPT courseware are merged into a larger file to reduce the metadata burden on NameNode. Secondly, a two-level prefetching mechanism is introduced to improve the efficiency of accessing small files. The experimental results indicate that the proposed approach is able to effectively mitigate the load of NameNode and to improve the efficiency of storing and accessing massive small files on HDFS.


title: Putting Users in Control of their Recommendations

abstract: The essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions affect the resulting recommendations. We hypothesize that any recommender algorithm will better fit some users' expectations than others, leaving opportunities for improvement. To address this challenge, we study a recommender that puts some control in the hands of users. Specifically, we build and evaluate a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like "show more popular items". We find that users who are given these controls evaluate the resulting recommendations much more positively. Further, we find that users diverge in their preferred settings, confirming the importance of giving control to users.


title: Text/Non-text Classification in Online Handwritten Documents with Recurrent Neural Networks

abstract: In this paper, we propose a novel method for text/non-text classification in online handwritten document based on Recurrent Neural Network (RNN) and its improved version, Long Short-Term Memory (LSTM) network. The task of classifying strokes in a digital ink document into two classes (text and non-text) can be seen as a sequence labelling task. The bidirectional architecture is used in these networks to access to the complete global context of the sequence being classified. Moreover, a simple but effective model is adopted for the temporal local context of adjacent strokes. By integrating local context and global context, the classification accuracy is improved. In our experiments on the Japanese ink documents (Kondate database), the proposed method achieves a classification rate of 98.75%, which is significantly higher than the 96.61% in the previous work. Similarly, on the English ink documents (IAMonDo database), it produces a classification rate of 97.68%, which is also higher than other results reported in the literature. KeywordsText/Non-text classification; Text/Non-text separation; ink stroke classification; Recurrent Neural Networks; RNN; Long Short-Term Memory; LSTM


title: On being eager and uninhibited: Narcissism and approach–avoidance motivation

abstract: This article demonstrates the validity and utility of conceptualizing narcissistic personality in terms of relative approach–avoidance motivation. Across three studies (N = 1,319), narcissism predicted high approach and low avoidance motivation. That is, narcissists reported being strongly motivated to approach desirable outcomes but only weakly motivated to avoid negative outcomes. Relative approach–avoidance motivation was shown to be useful in terms of explaining behavioral tendencies associated with narcissism (i.e., functional and dysfunctional impulsivity) and distinguishing different “flavors” of narcissism (i.e., overt and covert narcissism). Discussion focuses on how approach–avoidance motivation may be used to explain prior findings in the narcissism literature and generate novel future hypotheses.


title: Typical Dreams of "Being Chased": A Cross-Cultural Comparison Between Tibetan and Han Chinese Dreamers

abstract: The present study is a cross-cultural comparison of typical dreams of “being chased” between Tibetan and Han Chinese dreamers. The research is based on 208 interviews combined with responses to a Dream Questionnaire. In total 569 subjects were involved in the research project: 278 Tibetan and 291 Han undergraduate college students. Results show an overall occurrence of a being-chased dream in 90% of the sample population. Moreover, the results also revealed that when being chased in dreams, there is a significant difference on the chasing agents. In addition, more than 95% of all participants experienced negative feelings while in the dream, especially fearful feelings. Also there were significant differences between Tibetan and Han Chinese on the feelings of anger and other (unspecified feelings). For the first reactions in the dreams, Tibetan and Han Chinese showed a significant difference on running away and seeking help. In addition, even though petrified then waking up is the most common final outcome in both groups, there were significant differences on not being caught and petrified then waking up between the two groups. Tibetan and Han Chinese take significantly different approaches, especially on the way of telling others, chanting and praying, self-interpretation, and ignoring and forgetting. The significance of these differences is analyzed in relation to the living environment


title: Comparisons of randomization and K-degree anonymization schemes for privacy preserving social network publishing

abstract: Many applications of social networks require identity and/or relationship anonymity due to the sensitive, stigmatizing, or confidential nature of user identities and their behaviors. Recent work showed that the simple technique of anonymizing graphs by replacing the identifying information of the nodes with random ids does not guarantee privacy since the identification of the nodes can be seriously jeopardized by applying background based attacks. In this paper, we investigate how well an edge based graph randomization approach can protect node identities and sensitive links. We quantify both identity disclosure and link disclosure when adversaries have one specific type of background knowledge (i.e., knowing the degrees of target individuals). We also conduct empirical comparisons with the recently proposed K-degree anonymization schemes in terms of both utility and risks of privacy disclosures.


title: Media and Children's Aggression, Fear, and Altruism

abstract: Summary Noting that the social and emotional experiences of American children today often heavily involve electronic media, Barbara Wilson takes a close look at how exposure to screen media affects children’s well-being and development. She concludes that media influence on children depends more on the type of content that children find attractive than on the sheer amount of time they spend in front of the screen. Wilson begins by reviewing evidence on the link between media and children’s emotions. She points out that children can learn about the nature and causes of different emotions from watching the emotional experiences of media characters and that they often experience empathy with those characters. Although research on the long-term effects of media exposure on children’s emotional skill development is limited, a good deal of evidence shows that media exposure can contribute to children’s fears and anxieties. Both fictional and news programming can cause lasting emotional upset, though the themes that upset children differ according to a child’s age. Wilson also explores how media exposure affects children’s social development. Strong evidence shows that violent television programming contributes to children’s aggressive behavior. And a growing body of work indicates that playing violent video games can have the same harmful effect. Yet if children spend time with educational programs and situation comedies targeted to youth, media exposure can have more prosocial effects by increasing children’s altruism, cooperation, and even tolerance for others. Wilson also shows that children’s susceptibility to media influence can vary according to their gender, their age, how realistic they perceive the media to be, and how much they identify with characters and people on the screen. She concludes with guidelines to help parents enhance the positive effects of the media while minimizing the risks associated with certain types of content.