title: Algorithms for simultaneous sparse approximation

abstract: A simultaneous sparse approximation problem requests a good approximation of several input signals at once using different linear combinations of the same elementary signals. At the same time, the problem balances the error in approximation against the total number of elementary signals that participate. These elementary signals typically model coherent structures in the input signals, and they are chosen from a large, linearly dependent collection.The first part of this paper proposes a greedy pursuit algorithm, called simultaneous orthogonal matching pursuit (S-OMP), for simultaneous sparse approximation. Then it presents some numerical experiments that demonstrate how a sparse model for the input signals can be identified more reliably given several input signals. Afterward, the paper proves that the S-OMP algorithm can compute provably good solutions to several simultaneous sparse approximation problems.The second part of the paper develops another algorithmic approach called convex relaxation, and it provides theoretical results on the performance of convex relaxation for simultaneous sparse approximation.


title: Challenges for the Self-Safety in Autonomous Vehicles

abstract: The combination of multiple functions having different and complementary capabilities enables the emergence of Autonomous Vehicles. Their deployment is limited by the level of complexity they represent together with the challenges encountered in real environments with strong safety concerns. Thus a major concern prior to massive deployment is on how to ensure the safety of autonomous vehicles despite likely internal (e.g. malfunctions) and external (e.g., aggressive behaviors) disturbances they might undergo. This paper presents the challenges that undergoes the design and development of autonomous vehicles with respect to their functional architecture and adaptive behaviors from a safety perspective. For the purpose of the rationales, we define needs and requirements that lead to the formulation of an architectural framework. Our approach is based on paradigms and technologies from non-automotive domains to address non-functional system properties like safety, reliability and security. The notion of micro-services is also introduced for the self-safety of autonomous vehicles. These are part of the proposed framework that should facilitate the analysis, design, development and validation for the adequate composition and orchestration of services aimed to warrant the required non-functional properties, such as safety. In the present paper, we introduce the structural and behavioral adaptations of the framework to offer a holistic and scalable vision of the safety over the system.


title: Head pose estimation based on face symmetry analysis

abstract: This paper addresses the problem of head pose estimation in order to infer non-intrusive feedback from users about gaze attention. The proposed approach exploits the bilateral symmetry of the face. Size and orientation of the symmetrical area of the face is used to estimate roll and yaw poses by the mean of decision tree model. The approach does not need the location of interest points on face and presents robustness to partial occlusions. Tests were performed on different datasets (FacePix, CMU PIE, Boston University) and our approach coped with variability in illumination and expressions. Results demonstrate that the changes in the size of the regions that contain a bilateral symmetry provide accurate pose estimation.


title: 24/7 Place Recognition by View Synthesis

abstract: We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings being built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data.


title: Do Barriers to Pediatric Pain Management as Perceived by Nurses Change over Time

abstract: Abstract For decades, nurses (RNs) have identified barriers to providing the optimal pain management that children deserve; yet no studies were found in the literature that assessed these barriers over time or across multiple pediatric hospitals. The purpose of this study was to reassess barriers that pediatric RNs perceive, and how they describe optimal pain management, 3 years after our initial assessment, collect quantitative data regarding barriers identified through comments during our initial assessment, and describe any changes over time. The Modified Barriers to Optimal Pain Management survey was used to measure barriers in both studies. RNs were invited via e-mail to complete an electronic survey. Descriptive and inferential statistics were used to compare results over time. Four hundred forty-two RNs responded, representing a 38% response rate. RNs continue to describe optimal pain management most often in terms of patient comfort and level of functioning. While small changes were seen for several of the barriers, the most significant barriers continued to involve delays in the availability of medications, insufficient physician medication orders, and insufficient orders and time allowed to pre-medicate patients before procedures. To our knowledge, this is the first study to reassess RNs' perceptions of barriers to pediatric pain management over time. While little change was seen in RNs' descriptions of optimal pain management or in RNs' perceptions of barriers, no single item was rated as more than a moderate barrier to pain management. The implications of these findings are discussed in the context of improvement strategies.


title: Design and implementation of a high efficiency AC-AC Buck converter

abstract: This paper presents an AC-AC Buck converter in which a modulation technique was applied in order to reduce losses due to dissipative snubbers. Theoretical analysis and numerical simulation were performed. A 100W prototype was built and tested. Efficiency curves show that snubber losses are minimized with the use of a simple open-loop control technique.


title: Bone development and its relation to fracture repair. The role of mesenchymal osteoblasts and surface osteoblasts

abstract: Bone development occurs by two mechanisms: intramembranous bone formation and endochondral bone formation. Bone tissue forms by eventual differentiation of osteoprogenitor cells into either mesenchymal osteoblasts (MOBL), which synthesize woven bone in random orientation, or surface osteoblasts (SOBL), which synthesize bone on surfaces in a well oriented lamellar array. Bone repair uses the same formation patterns as bone development but the specific mechanism of repair is determined by the biomechanical environment provided. Bone synthesis and maintenance are highly dependent on the blood supply of bone and on cell-cell communication via the lacunar-canalicular system. Recent investigations highlight the molecular cascades leading to cell differentiation, the components of the structural proteins such as the various collagens, and tissue vascularization. The patterning of bone matrix from an initial woven to an eventual lamellar orientation is essential for bone to develop its maximum strength. This review demonstrates the repetitive nature of woven to lamellar bone formation as mediated by MOBLs and SOBLs in both normal vertebrate bones and bone repair. Repair, using endochondral, primary, direct and distraction osteogenesis mechanisms, is reviewed along with the associated molecular, vascular, and biophysical features.


title: Development and test of a model linking safety-specific transformational leadership and occupational safety.

abstract: The authors developed, tested, and replicated a model in which safety-specific transformational leader-ship predicted occupational injuries in 2 separate studies. Data from 174 restaurant workers (Mage 26.75 years, range 15–64) were analyzed using structural equation modeling (LISREL 8; K. G.Jo¨reskog & D. So¨rbom, 1993) and provided strong support for a model whereby safety-specifictransformational leadership predicted occupational injuries through the effects of perceived safetyclimate, safety consciousness, and safety-related events. Study 2 replicated and extended this model withdata from 164 young workers from diverse jobs (M age 19.54 years, range 14–24). Safety-specifictransformational leadership and role overload were related to occupational injuries through the effects ofperceived safety climate, safety consciousness, and safety-related events.


title: Wavelet Convolutional Neural Networks for Texture Classification

abstract: Texture classification is an important and challenging problem in many image processing applications. While convolutional neural networks (CNNs) achieved significant successes for image classification, texture classification remains a difficult problem since textures usually do not contain enough information regarding the shape of object. In image processing, texture classification has been traditionally studied well with spectral analyses which exploit repeated structures in many textures. Since CNNs process images as-is in the spatial domain whereas spectral analyses process images in the frequency domain, these models have different characteristics in terms of performance. We propose a novel CNN architecture, wavelet CNNs, which integrates a spectral analysis into CNNs. Our insight is that the pooling layer and the convolution layer can be viewed as a limited form of a spectral analysis. Based on this insight, we generalize both layers to perform a spectral analysis with wavelet transform. Wavelet CNNs allow us to utilize spectral information which is lost in conventional CNNs but useful in texture classification. The experiments demonstrate that our model achieves better accuracy in texture classification than existing models. We also show that our model has significantly fewer parameters than CNNs, making our model easier to train with less memory.


title: Self-Tuning High-Voltage High-Frequency Switching Power Amplifier for Atmospheric-Based Plasma Sterilization

abstract: In this paper, the implementation of a power supply for the generation of plasma, with the purpose of sterilizing a surface, is presented. A power supply based on a Class-E switching amplifier is proposed. This switch-mode amplifier is capable of producing a high-voltage and high-frequency output for plasma generation. In addition, a feedback mechanism is used to handle load variation. This is achieved through the characterization of the load at high voltages while generating plasma. Portability of the system is the primary goal of this paper. Thus, the implementation of a miniaturized circuit capable of achieving high efficiency and low power consumption at the same time is critical. The design and implementation of the complete battery-powered system, as well as the simulation and measurement results are discussed.


title: Detecting the historical roots of research fields by reference publication year spectroscopy (RPYS)

abstract: We introduce the quantitative method named "Reference Publication Year Spectroscopy" RPYS. With this method one can determine the historical roots of research fields and quantify their impact on current research. RPYS is based on the analysis of the frequency with which references are cited in the publications of a specific research field in terms of the publication years of these cited references. The origins show up in the form of more or less pronounced peaks mostly caused by individual publications that are cited particularly frequently. In this study, we use research on graphene and on solar cells to illustrate how RPYS functions, and what results it can deliver.


title: SoftGUESS: Visualization and Exploration of Code Clones in Context

abstract: We introduce SoftGUESS, a code clone exploration system. SoftGUESS is built on the more general GUESS system which provides users with a mechanism to interactively explore graph structures both through direct manipulation as well as a domain-specific language. We demonstrate SoftGUESS through a number of mini-applications to analyze evolutionary code-clone behavior in software systems. The mini-applications of SoftGUESS represent a novel way of looking at code-clones in the context of many system features. It is our hope that SoftGUESS will form the basis for other analysis tools in the software- engineering domain.


title: Perceived Privacy and Perceived Security and Their Effects on Trust, Risk, and User Intentions

abstract: This article seeks to review and consolidate existing research modeling the relationships between perceived privacy, security, and consumer intention to use Web-based services such as on-line shopping. This is a crucial area to understand since many consumers are still reluctant to use on-line services due to perceived risk and trust issues. Recent headlines highlighting major privacy and security breaches by firms such as Winners have further inflamed concerns. Past research, however, has focused only on narrowly defined components of the overall picture, such as the link between security and trust. This paper therefore seeks to consolidate their models, creating a single overarching model, which is proposed as a starting point for future research.


title: Automatic Capacity Tuning of Very Large VC-dimension Classifiers

abstract: Large VC-dimension classifiers can learn difficult tasks, but are usually impractical because they generalize well only if they are trained with huge quantities of data. In this paper we show that even high-order polynomial classifiers in high dimensional spaces can be trained with a small amount of training data and yet generalize better than classifiers with a smaller VC-dimension. This is achieved with a maximum margin algorithm (the Generalized Portrait). The technique is applicable to a wide variety of classifiers, including Perceptrons, polynomial classifiers (sigma-pi unit networks) and Radial Basis Functions. The effective number of parameters is adjusted automatically by the training algorithm to match the complexity of the problem. It is shown to equal the number of those training patterns which are closest patterns to the decision boundary (supporting patterns). Bounds on the generalization error and the speed of convergence of the algorithm are given. Experimental results on handwritten digit recognition demonstrate good generalization compared to other algorithms.


title: A 15mW 3.6GS/s CT-ΔΣ ADC with 36MHz bandwidth and 83dB DR in 90nm CMOS

abstract: We propose design techniques that enable the realization of power-efficient single-bit CT-ΔΣ ADCs at multi-Gb/s speeds. An FIR DAC [1] is used to reduce sensitivity to clock jitter and relax loop filter linearity. A mostly analog path compensates the modulator for the delay introduced by the FIR DAC. The CTDSM samples at 3.6GS/s, has 83dB DR in 36MHz BW, and occupies 0.12mm2 in 90nm CMOS. Dissipating 15mW from a 1.2V supply, it thereby achieves an FoM SNDR of 72.8fJ/level, which is an improvement over the state of the art for converters with bandwidths greater than 20MHz.


title: Detecting privacy violations in database publishing using disjoint queries

abstract: We present a new method of detecting privacy violations in the context of database publishing. Our method defines a published view V to preserve the privacy of a secret query Q if V and Q return no tuples in common, over all possible database instances. We then establish necessary and sufficient conditions that characterize when V preserves the privacy of Q in terms of the projected inequalities in the queries, both for conjunctive queries and queries with negation. We also show that integrity constraints have an effect on privacy, and derive a test for ensuring privacy preservation in the presence of FD constraints. The issue of privacy preservation in the presence of multiple views is investigated, and we show that it can reduced to the single view case for a suitably chosen view.


title: Interactive Grounded Language Acquisition and Generalization in a 2D World

abstract: We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher’s language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control. By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction. Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences. In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix.


title: Piccolo: Building Fast, Distributed Programs with Partitioned Tables

abstract: Piccolo is a new data-centric programming model for writing parallel in-memory applications in data centers. Unlike existing data-flow models, Piccolo allows computation running on different machines to share distributed, mutable state via a key-value table interface. Piccolo enables efficient application implementations. In particular, applications can specify locality policies to exploit the locality of shared state access and Piccolo's run-time automatically resolves write-write conflicts using user-defined accumulation functions. ::: ::: Using Piccolo, we have implemented applications for several problem domains, including the PageRank algorithm, k-means clustering and a distributed crawler. Experiments using 100 Amazon EC2 instances and a 12 machine cluster show Piccolo to be faster than existing data flow models for many problems, while providing similar fault-tolerance guarantees and a convenient programming interface.


title: 3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder

abstract: We present an algorithm for registration between a large-scale point cloud and a close-proximity scanned point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points–local subsets of points–and describes the geometric structure of each with a low-dimensional descriptor. These descriptors are then used to infer potential matching regions for an efficient coarse registration process, followed by a fine-tuning stage. The set of super-points is selected by covering the point clouds with overlapping spheres, and then filtering out those of low-quality or nonsalient regions. The descriptors are computed using state-of-the-art unsupervised machine learning, utilizing the technology of deep neural network based auto-encoders. Abstract This novel framework provides a strong alternative to the common practice of using manually designed key-point descriptors for coarse point cloud registration. Utilizing super-points instead of key-points allows the available geometrical data to be better exploited to find the correct transformation. Encoding local 3D geometric structures using a deep neural network auto-encoder instead of traditional descriptors continues the trend seen in other computer vision applications and indeed leads to superior results. The algorithm is tested on challenging point cloud registration datasets, and its advantages over previous approaches as well as its robustness to density changes, noise, and missing data are shown.


title: Etiquette online: from nice to necessary

abstract: In the early days of the Internet, an occasional sarcastic or confrontational remark was considered part of its "charm." Times have changed.


title: Phylogenetic placement of the sugarcane orange rust pathogen Puccinia kuehnii in a historical and regional context

abstract: Sugarcane orange rust, caused by Puccinia kuehnii, was once considered a minor disease in the Australian sugar industry. However, in 2000 a new race of the pathogen devastated the high-performing sugarcane cultivar Q124, and caused the industry Aus$150–210 million in yield losses. At the time of the epidemic, very little was known about the genetic and pathogenic diversity of the fungus in Australia and neighbouring sugar industries. DNA sequence data from three rDNA regions were used to determine the genetic relationships between isolates within two P. kuehnii collections. The first collection comprised only recent Australian field isolates and limited sequence variation was detected within this population. In the second study, Australian isolates were compared with isolates from Papua New Guinea, Indonesia, China and historical herbarium collections. Greater sequence variation was detected in this collection and phylogenetic analyses grouped the isolates into three clades. All isolates from commercial cane fields clustered together including the recent Australianfield isolates and the Australian historical isolate from 1898.The other two clades included rust isolates from wild and garden canes in Indonesia and PNG. These rusts appeared morphologically similar to P. kuehnii and could potentially pose a quarantine threat to the Australian sugar industry. The results have revealed greater diversity in sugarcane rusts than previously thought.


title: Performance Analysis of Private Blockchain Platforms in Varying Workloads

abstract: This paper conducts a performance analysis of two popular private blockchain platforms, Hyperledger Fabric and Ethereum (private deployment), to assess the performance and limitations of these state-of-the-art platforms. Blockchain, a decentralized transaction and data management technology, is said to be the technology that will have similar impacts as the Internet had on people's lives. Many industries have become interested in adopting blockchain in their IT systems, but scalability is an often- cited concern of current blockchain technology. Therefore, the goals of this preliminary performance analysis are twofold. First, a methodology for evaluating a blockchain platform is developed. Second, the analysis results are presented to inform practitioners in making decisions regarding adoption of blockchain technology in their IT systems. The experimental results, based on varying number of transactions, show that Hyperledger Fabric consistently outperforms Ethereum across all evaluation metrics which are execution time, latency and throughput. Additionally, both platforms are still not competitive with current database systems in term of performances in high workload scenarios.


title: Asymmetrical Duty Cycle-Controlled LLC Resonant Converter With Equivalent Switching Frequency Doubler

abstract: In the conventional full-bridge LLC converter, the duty cycle is kept as 0.5 and the phase-shift angle of the half-bridge modules is 0° to be a symmetrical operation, which makes the resonant tank operating frequency only equal to the switching frequency of the power devices. By regulating the duty cycles of the upper and lower switches in each half-bridge module to be 0.75 and 0.25 and the phase-shift angle of the half-bridge modules to be 180°, the asymmetrical duty cycle controlled full-bridge LLC resonant converter is derived. The proposed asymmetrical duty cycle controlled scheme halves the switching frequency of the primary switches. As a result, the driving losses are effectively reduced. Compared with the conventional full-bridge LLC converter, the soft-switching condition of the derived asymmetrical controlled LLC converter becomes easier to reduce the resonant current. Consequently, the conduction losses are decreased and the conversion efficiency is improved. The asymmetrical control scheme can be also extended to the stacked structure for high input voltage applications. Finally, two LLC converter prototypes both with 200-kHz resonant frequency for asymmetrical and symmetrical control schemes are built and compared to validate the effectiveness of the proposed control strategy.


title: What Does This Imply? Examining the Impact of Implicitness on the Perception of Hate Speech

abstract: We analyze whether implicitness affects human perception of hate speech. To do so, we use Tweets from an existing hate speech corpus and paraphrase them with rules to make the hate speech they contain more explicit. Comparing the judgment on the original and the paraphrased Tweets, our study indicates that implicitness is a factor in human and automatic hate speech detection. Hence, our study suggests that current automatic hate speech detection needs features that are more sensitive to implicitness.


title: Flat-tree: A Convertible Data Center Network Architecture from Clos to Random Graph

abstract: Clos networks are easy to implement, whereas random graphs have good performance. We propose flat-tree, a convertible data center network architecture, to combine the best of both worlds. Flat-tree can change the network topology dynamically, so the data center can be implemented as a Clos network and be converted to approximate random graphs of different sizes. To serve the heterogeneous workloads in data centers, flat-tree can organize the network as functionally separate zones each having a different topology. Workloads are placed into suitable zones that best optimize the performance. Simulation results demonstrate that flat-tree has similar performance to random graphs.


title: Classroom Emotional Climate, Student Engagement, and Academic Achievement

abstract: The emotional connections students foster in their classrooms are likely to impact their success in school. Using a multimethod, multilevel approach, this study examined the link between classroom emotional climate and academic achievement, including the role of student engagement as a mediator. Data were collected from 63 fifth- and sixth-grade classrooms (N 1,399 students) and included classroom observations, student reports, and report card grades. As predicted, multilevel mediation analyses showed that the positive relationship between classroom emotional climate and grades was mediated by engagement, while controlling for teacher characteristics and observations of both the organizational and instructional climates of the classrooms. Effects were robust across grade level and student gender. The discussion highlights the role of classroom-based, emotion-related interactions to promote academic achievement.


title: A High-Sensitivity and Low-Walk Error LADAR Receiver for Military Application

abstract: An integrated receiver with high sensitivity and low walk error for a military purpose pulsed time-of-flight (TOF) LADAR system is proposed. The proposed receiver adopts a dual-gain capacitive-feedback TIA (C-TIA) instead of widely used resistive-feedback TIA (R-TIA) to increase the sensitivity. In addition, a new walk-error improvement circuit based on a constant-delay detection method is proposed. Implemented in 0.35 μm CMOS technology, the receiver achieves an input-referred noise current of 1.36 pA/√Hz with bandwidth of 140 MHz and minimum detectable signal (MDS) of 10 nW with a 5 ns pulse at SNR=3.3, maximum walk-error of 2.8 ns, and a dynamic range of 1:12,000 over the operating temperature range of -40 °C to +85 °C.


title: ClouNS - a Cloud-Native Application Reference Model for Enterprise Architects

abstract: The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies.


title: Cooperative impedance control for multiple UAVs with a robotic arm

abstract: In this paper, an impedance control scheme for cooperative quadrotors with robotic arms is proposed in order to limit both the contact forces, due to the object/environment interaction, and the internal forces, due to the manipulators/ object interaction. To this aim, two impedance filters are used to determine the reference trajectories for manipulator end effectors: the first is aimed at conferring a compliant behavior at the object level (external impedance), while the second filter, is aimed at avoiding large internal loading of the object (internal impedance). Such trajectories are fed to a motion controller including an inverse kinematics algorithm and a PD controller with gravity compensation. The effectiveness of the proposed approach is then verified in simulation.


title: A single-item inventory model for a nonstationary demand process

abstract: In this paper, we consider an adaptive base-stock policy for a single-item inventory system, where the demand process is nonstationary. In particular, the demand process is an integrated moving average process of order (0, 1, 1), for which an exponential-weighted moving average provides the optimal forecast. For the assumed control policy we characterize the inventory random variable and use this to find the safety stock requirements for the system. From this characterization, we see that the required inventory, both in absolute terms and as it depends on the replenishment lead-time, behaves much differently for this case of nonstationary demand compared with stationary demand. We then show how the single-item model extends to a multi-stage, or supply-chain context; in particular we see that the demand process for the upstream stage is not only nonstationary but also more variable than that for the downstream stage. We also show that for this model there is no value from letting the upstream stages see the exogenous demand. The paper concludes with some observations about the practical implications of this work.


title: Improving Recurrent Neural Networks For Sequence Labelling

abstract: In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more traditional Elman and Jordan RNNs. We compare all models, either traditional or new, on four distinct tasks of sequence labeling: two on Spoken Language Understanding (ATIS and MEDIA); and two of POS tagging for the French Treebank (FTB) and the Penn Treebank (PTB) corpora. The results show that our new variants of RNNs are always more effective than the others.


title: Near-infrared imaging and structured light ranging for automatic catheter insertion

abstract: Vein localization and catheter insertion constitute the first and perhaps most important phase of many medical procedures. Currently, catheterization is performed manually by trained personnel. This process can prove problematic, however, depending upon various physiological factors of the patient. We present in this paper initial work for localizing surface veins via near-infrared (NIR) imaging and structured light ranging. The eventual goal of the system is to serve as the guidance for a fully automatic (i.e., robotic) catheterization device. Our proposed system is based upon near-infrared (NIR) imaging, which has previously been shown effective in enhancing the visibility of surface veins. We locate the vein regions in the 2D NIR images using standard image processing techniques. We employ a NIR line-generating LED module to implement structured light ranging and construct a 3D topographic map of the arm surface. The located veins are mapped to the arm surface to provide a camera-registered representation of the arm and veins. We describe the techniques in detail and provide example imagery and 3D surface renderings.


Molecular Padlock Assay of Crude Plant Leaf Extracts for Detection of Listeria Monocytogenes


Toward real-time indoor semantic segmentation using depth information


title: A portable ARINC 653 standard interface

abstract: The ARINC 653 specification defines the functionality that an operating system (OS) must guarantee to enforce robust spatial and temporal partitioning as well as an avionics application programming interface for the system. The standard application interface - the ARINC 653 application executive (APEX) - is defined as a set of software services a compliant OS must provide to avionics application developers. The ARINC 653 specification defines the interfaces and the behavior of the APEX but leaves implementation details to OS vendors. This paper describes an OS independent design approach of a portable APEX interface. POSIX, as a programming interface available on a wide range of modern OS, will be used to implement the APEX layer. This way the standardization of the APEX is taken a step further: not only the definition of services is standardized but also its interface to the underlying OS. Therefore, the APEX operation does not depend on a particular OS but relies on a well defined set of standardized components.


title: A first look at the usability of bitcoin key management

abstract: Bitcoin users are directly or indirectly forced to deal with public key cryptography, which has a number of security and usability challenges that differ from the password-based authentication underlying most online banking services. Users must ensure that keys are simultaneously accessible, resistant to digital theft and resilient to loss. In this paper, we contribute an evaluation framework for comparing Bitcoin key management approaches, and conduct a broad usability evaluation of six representative Bitcoin clients. We find that Bitcoin shares many of the fundamental challenges of key management known from other domains, but that Bitcoin may present a unique opportunity to rethink key management for end users.


title: Event Nugget Detection with Forward-Backward Recurrent Neural Networks

abstract: Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But such efforts, like tradition methods, have so far only focused on single-token event mentions, whereas in practice events can also be a phrase. We instead use forward-backward recurrent neural networks (FBRNNs) to detect events that can be either words or phrases. To the best our knowledge, this is one of the first efforts to handle multi-word events and also the first attempt to use RNNs for event detection. Experimental results demonstrate that FBRNN is competitive with the state-of-the-art methods on the ACE 2005 and the Rich ERE 2015 event detection tasks.


title: Pattern-Reconfigurable Planar Circular Ultra-Wideband Monopole Antenna

abstract: A novel pattern-reconfigurable compact planar ultra-wideband monopole antenna is presented. By the incorporation of four p-i-n diode switches and two parasitic elements, the antenna's radiation patterns can be shaped to concentrate energy in specific directions while minimising the gain in other unwanted directions without significantly affecting the impedance bandwidth of the antenna. A fully functional prototype has been developed and tested. The measured results of the return loss, radiation patterns, and realised gain verify the effectiveness of the proposed antenna configuration. The antenna switches its radiation patterns between an omni-directional mode and two directional modes with opposite directions in the operating range from 3 to 6 GHz. The proposed antenna could be a suitable candidate for advanced and smart radio applications such as cognitive radio (CR) as it can enhance the radio front-end flexibility and performance by adding the benefits of pattern diversity, specifically in multipath environments.


title: Self-configuration and self-optimization in LTE-advanced heterogeneous networks

abstract: Self-organizing network, or SON, technology, which is able to minimize human intervention in networking processes, was proposed to reduce the operational costs for service providers in future wireless systems. As a cost-effective means to significantly enhance capacity, heterogeneous deployment has been defined in the 3GPP LTEAdvanced standard, where performance gains can be achieved through increasing node density with low-power nodes, such as pico, femto, and relay nodes. The SON has great potential for application in future LTE-Advanced heterogeneous networks, also called HetNets. In this article, state-of-the-art research on self-configuring and self-optimizing HetNets are surveyed, and their corresponding SON architectures are introduced. In particular, we discuss the issues of automatic physical cell identifier assignment and radio resource configuration in HetNets based on selfconfiguring SONs. As for self-optimizing SONs, we address the issues of optimization strategies and algorithms for mobility management and energy saving in HetNets. At the end of the article, we show a testbed designed for evaluating SON technology, with which the performance gain of SON algorithms is demonstrated.


title: Ten Years of iCTF: The Good, The Bad, and The Ugly.

abstract: Author(s): Vigna, Giovanni; Borgolte, Kevin; Corbetta, Jacopo; Doupe, Adam; Fratantonio, Yanick; Invernizzi, Luca; Kirat, Dhilung; Shoshitaishvili, Yan | Abstract: Security competitions have become a popular way to foster security education by creating a competitive environment in which participants go beyond the effort usually required in traditional security courses. Live security competitions (also called “Capture The Flag,” or CTF competitions) are particularly well-suited to support hands-on experience, as they usually have both an attack and a defense component. Unfortunately, because these competitions put several (possibly many) teams against one another, they are difficult to design, implement, and run. This paper presents a framework that is based on the lessons learned in running, for more than 10 years, the largest educational CTF in the world, called iCTF. The framework’s goal is to provide educational institutions and other organizations with the ability to run customizable CTF competitions. The framework is open and leverages the security community for the creation of a corpus of educational security challenges.


title: Implementation of Bit-Vector Algorithm for Approximate String Matching on Rhodopsin Protein Sequence

abstract: Approximate string matching has been used in many applications such as, text retrieval, spell checker and DNA sequence matching in computational biology. In this paper, we implemented bit-vector algorithm using MATLAB for approximate string matching on Rhodopsin protein sequence of class Aves. Our experiments on real data of Rhodopsin protein sequences demonstrate that the algorithm can work as expected. The experiment results shows that the Rhodopsin protein sequence of the species in same genus is more approximately match each other compared to the species from different genus in the same family, Furthermore, for the species from different genus in the same family, its Rhodopsin protein sequence is more approximately match each other compared to the species from different family in the same order.


title: A perceptual control space for garment simulation

abstract: We present a perceptual control space for simulation of cloth that works with any physical simulator, treating it as a black box. The perceptual control space provides intuitive, art-directable control over the simulation behavior based on a learned mapping from common descriptors for cloth (e.g., flowiness, softness) to the parameters of the simulation. To learn the mapping, we perform a series of perceptual experiments in which the simulation parameters are varied and participants assess the values of the common terms of the cloth on a scale. A multi-dimensional sub-space regression is performed on the results to build a perceptual generative model over the simulator parameters. We evaluate the perceptual control space by demonstrating that the generative model does in fact create simulated clothing that is rated by participants as having the expected properties. We also show that this perceptual control space generalizes to garments and motions not in the original experiments.


title: Sex Differences in Response to Visual Sexual Stimuli: A Review

abstract: This article reviews what is currently known about how men and women respond to the presentation of visual sexual stimuli. While the assumption that men respond more to visual sexual stimuli is generally empirically supported, previous reports of sex differences are confounded by the variable content of the stimuli presented and measurement techniques. We propose that the cognitive processing stage of responding to sexual stimuli is the first stage in which sex differences occur. The divergence between men and women is proposed to occur at this time, reflected in differences in neural activation, and contribute to previously reported sex differences in downstream peripheral physiological responses and subjective reports of sexual arousal. Additionally, this review discusses factors that may contribute to the variability in sex differences observed in response to visual sexual stimuli. Factors include participant variables, such as hormonal state and socialized sexual attitudes, as well as variables specific to the content presented in the stimuli. Based on the literature reviewed, we conclude that content characteristics may differentially produce higher levels of sexual arousal in men and women. Specifically, men appear more influenced by the sex of the actors depicted in the stimuli while women’s response may differ with the context presented. Sexual motivation, perceived gender role expectations, and sexual attitudes are possible influences. These differences are of practical importance to future research on sexual arousal that aims to use experimental stimuli comparably appealing to men and women and also for general understanding of cognitive sex differences.


title: Practical and secure solutions for integer comparison

abstract: Yao's classical millionaires' problem is about securely determining whether x > y, given two input values x, y, which are held as private inputs by two parties, respectively. The output x > y becomes known to both parties. ::: ::: In this paper, we consider a variant of Yao's problem in which the inputs x, y as well as the output bit x > y are encrypted. Referring to the framework of secure n-party computation based on threshold homomorphic cryptosystems as put forth by Cramer, Damgard, and Nielsen at Eurocrypt 2001, we develop solutions for integer comparison, which take as input two lists of encrypted bits representing x and y, respectively, and produce an encrypted bit indicating whether x > y as output. Secure integer comparison is an important building block for applications such as secure auctions. ::: ::: In this paper, our focus is on the two-party case, although most of our results extend to the multi-party case. We propose new logarithmicround and constant-round protocols for this setting, which achieve simultaneously very low communication and computational complexities. We analyze the protocols in detail and show that our solutions compare favorably to other known solutions.


title: A coinductive monad for prop-bounded recursion

abstract: This paper develops machinery necessary to mechanically import arbitrary functional programs into Coq's type theory, manually strengthen their specifications with additional proofs, and then mechanicaly re-extracting the newly-certified program in a form which is as efficient as the original program. In order to facilitate this goal, the coinductive technique of[Capretta2005] is modified to form a monad whose operators are the constructors of a coinductive type rather than functions defined over the type. The inductive invariant technique of [Krstic2003] is extended to allow optional "after the fact" termination proofs. These proofs inhabit members of Prop, and therefore do not affect extracted code. Compared to [Capretta2005], the new monad makes it possible to directly represent unrestricted recursion without violating productivity requirements [Gimenez1995], and it produces efficient code via Coq's extraction mechanism. The disadvantages of this technique include reliance on the JMeq axiom [McBride2000] and a significantly more complex notion of equality. The resulting technique is packaged as a Coq library, and is suitable for formalizing programs written in any side-effect-free functional language with call-by-value semantics. It can be downloaded from: http://www.cs.berkeley.edu/~megacz/computation.


title: Entity resolution in the web of data

abstract: This tutorial provides an overview of the key research results in the area of entity resolution that are relevant to addressing the new challenges in entity resolution posed by the Web of data, in which real world entities are described by interlinked data rather than documents. Since such descriptions are usually partial, overlapping and sometimes evolving, entity resolution emerges as a central problem both to increase dataset linking but also to search the Web of data for entities and their relations.


title: False Data Attacks Against AC State Estimation With Incomplete Network Information

abstract: Power systems are being exposed to cyber-attacks due to the high integration of information technology and the vulnerability of communication networks. Existing false data attacks research focus on dc state estimation. In this paper, we show that an attacker can construct an undetectable attack vector against ac state estimation based on a few measurements in the attacking region associated with boundary buses without knowing the full topology and parameter information of the entire power network. An iteration approach is adopted to obtain the attack vector. The simulations on the IEEE 14-bus and 118-bus systems are used to demonstrate the correctness and effectiveness of the proposed attack scheme. This paper provides a basis to study the attack behaviors under the ac case, and a theoretical guide to develop protection strategies and detection methods.


title: Colorization by patch-based local low-rank matrix completion

abstract: Colorization aims at recovering the original color of a monochrome image from only a few color pixels. A state-of-the-art approach is based on matrix completion, which assumes that the target color image is low-rank. However, this low-rank assumption is often invalid on natural images. In this paper, we propose a patch-based approach that divides the image into patches and then imposes a low-rank structure only on groups of similar patches. Each local matrix completion problem is solved by an accelerated version of alternating direction method of multipliers (ADMM), and each ADMM subproblem is solved efficiently by divide-and-conquer. Experiments on a number of benchmark images demonstrate that the proposed method outperforms existing approaches.


title: Speciation of Chromium in Soil and Sludge in the Surrounding Tannery Region, Ranipet, Tamil Nadu

abstract: The distribution and mobility of chromium in the soils and sludge surrounding a tannery waste dumping area was investigated to evaluate its vertical and lateral movement of operational speciation which was determined in six steps to fractionate the material in the soil and sludge into (i) water soluble, (ii) exchangeable, (iii) carbonate bound, (iv) reducible, (v) oxidizable, and (vi) residual phases. The present study shows that about 63.7% of total chromium is mobilisable, and 36.3% of total chromium is nonbioavailable in soil, whereas about 30.2% of total chromium is mobilisable, and 69.8% of total chromium is non-bioavailable in sludge. In contaminated sites the concentration of chromium was found to be higher in the reducible phase in soils (31.3%) and oxidisable phases in sludge (56.3%) which act as the scavenger of chromium in polluted soils. These results also indicate that iron and manganese rich soil can hold chromium that will be bioavailable to plants and biota. Thus, results of this study can indicate the status of bioavailable of chromium in this area, using sequential extraction technique. So a suitable and proper management of handling tannery sludge in the said area will be urgently needed to the surrounding environment as well as ecosystems.


title: A new method of load-shedding control on centrifugal water chiller sequencing

abstract: This paper presents a simplified method of accomplishing the direct load control (DLC) program from Taiwan Power Company (TPC) with the building HVAC system efficiency maximum. Instead of the coefficient of performance (COP) maximum for multiple centrifugal water chillers system, a program with cooling load maximum is approved to be more effective for the electricity load-shedding customer. Based on the hardware resource on I-7188XA with 512k SRAM, we employed an algorithm with Penalty Function Method (PFM) and Simplex Method (SM) compiled by C++. Comparing with the Lingo simulation, this result was close to the global optimal result. Because of this algorithm leads to the rapid computation speed and minimal ROM requirements, it is applicable to the actual control hardware condition.


title: Automatic de-identification of textual documents in the electronic health record: a review of recent research

abstract: BackgroundIn the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here.MethodsThis review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers.ResultsThe literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries.ConclusionsIn general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.


title: Game Data Mining

abstract: During the years of the Information Age, technological advances in the computers, satellites, data transfer, optics, and digital storage has led to the collection of an immense mass of data on everything from business to astronomy, counting on the power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century, data were stored on disparate structures and very rapidly became overwhelming. The initial chaos led to the creation of structured databases and database management systems to assist with the management of large corpuses of data, and notably, the effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information gathering.


Geometric Programming for Aircraft Design Optimization


title: Effects of hormonal priming on seed germination of pigeon pea under cadmium stress

abstract: In this work we investigated whether priming with auxin, cytokinin, gibberellin, abscisic acid and ethylene, alters the physiological responses of seeds of pigeon pea germinated under water and cadmium stress. Seeds treated with water or non-treated seeds were used as control. Although compared to non-treated seeds we found that the hormone treatments improve the germination of pigeon pea under cadmium stress, however, these treatments did not differ from water. However, we also observed a trend of tolerance to the effects of cadmium in the presence of ethylene, suggesting that the use of this hormone may be an efficient method to overcome seed germination under metal stress.


title: The Feasibility of Using an Automated Net Asset Value Validation Tool in an International Investment Bank

abstract: Fund administration is a relatively new service that some banks and back office offer Investment Company’s. This service was regarded as “boutique” in some countries as it was not a necessity hence not enforced by law to have independent calculation and verification of a fund price. However, this sector of business was and has been a major factor in the economic boom for many countries worldwide. In general most companies have many human resources tagged to this service. This is mainly due to the high volume of manual work that needs to be carried out to validate a Net Asset Value. If the Net Asset Value is calculated incorrectly and hence not validated correctly then there is huge repercussions for the company that calculated the Net Asset Value (monetary, reputation, losing a client). With the turn in the current climate the operational requirements that was once affordable has snowballed out of control, this is why invest company’s are finding ways to reduce costs and hence use less labour intensive methods or relocate these specific jobs to lower cost countries such as Eastern Europe and India. However, this is not without its own set of problems, some being that most companies and in our case, the company always employs a distributed service requirement. Within the scope of a collaboration project which focuses on a Net Asset Value automated validation solution to replace a labour intensive manual approach. In this paper, we research the feasibility of using such a tool in a funds business of an international investment bank where parts of this process are based in Asia and Europe. Our approach is based on surveying people that are currently working in the Net Asset Value validation process, and in turn analyse the results attained. Throughout this process, we must not only focus on the efficient method of applying a Net Asset Value validation automated solution but we must also provide an overview of the important factors in building a solutions to be used in a fund administration environment.


title: An agent-based collaborative information processing system for mixed reality applications — Part B: Agent-based collaborative information processing and coordination

abstract: This paper mainly presents the system architecture of an agent-based collaborative information processing in mixed reality applications. We model system elements as agents with different processes, data, and applications. Various types of agents and mixed-agent groups are discussed, and the combination of agents as agent-groups are designed to perform the tasks including sensing, user-identification, feature learning and target of interest differentiation, 3D modeling, mixed reality fusion, and interaction. We design an agent-based collaboration strategy with concurrent operation coordination using a serial logic scheduling strategy. The integrity of the system and the correctness of scheduling is guaranteed by using lock-based coordination with timeouts strategy.


title: Low latency sound source separation using convolutional recurrent neural networks

abstract: Deep neural networks (DNN) have been successfully employed for the problem of monaural sound source separation achieving state-of-the-art results. In this paper, we propose using convolutional recurrent neural network (CRNN) architecture for tackling this problem. We focus on a scenario where low algorithmic delay (< 10 ms) is paramount, and relatively little training data is available. We show that the proposed architecture can achieve slightly better performance as compared to feedforward DNNs and long short-term memory (LSTM) networks. In addition to reporting separation performance metrics (i.e., source to distortion ratios), we also report extended short term objective intelligibility (ESTOI) scores which better predict intelligibility performance in presence of non-stationary interferers.


title: Comprehending Pronouns: A Role for Word-Specific Gender Stereotype Information

abstract: The research investigated how word-specific gender stereotype information can be used during language comprehension. In a rating study, the gender stereotypes for 405 noun and noun compounds were assessed. From these norms, 32 words strongly stereotyped to refer mostly to males and 32 words strongly stereotyped to refer mostly to females were selected and used in a reading experiment. Comprehenders read pairs of sentences. The subject of the first sentence was a gender stereotyped word (e.g., executive or secretary). The subject of the second sentence was either the pronoun he or she. Reading time was significantly longer when gender of the pronoun mismatched the gender stereotype of the antecedent than when the genders of pronoun and antecedent matched. This gender mismatch effect occurred on the two regions immediately following the region containing the pronoun. The results indicated that word-specific gender stereotypes can influence coreference resolution of pronouns.


title: A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices

abstract: The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.


title: Mouse genetic background influences whether HrasG12V expression plus Cdkn2a knockdown causes angiosarcoma or undifferentiated pleomorphic sarcoma

abstract: Soft tissue sarcomas are rare mesenchymal tumours accounting for 1% of adult malignancies and are fatal in approximately one third of patients. Two of the most aggressive and lethal forms of soft tissue sarcomas are angiosarcomas and undifferentiated pleomorphic sarcomas (UPS). To examine sarcoma-relevant molecular pathways, we employed a lentiviral gene regulatory system to attempt to generate in vivo models that reflect common molecular alterations of human angiosarcoma and UPS. Mice were intraveneously injected with MuLE lentiviruses expressing combinations of shRNA against Cdkn2a, Trp53, Tsc2 and Pten with or without expression of HrasG12V , PIK3CAH1047R or Myc. The systemic injection of an ecotropic lentivirus expressing oncogenic HrasG12V together with the knockdown of Cdkn2a or Trp53 was sufficient to initiate angiosarcoma and/or UPS development, providing a flexible system to generate autochthonous mouse models of these diseases. Unexpectedly, different mouse strains developed different types of sarcoma in response to identical genetic drivers, implicating genetic background as a contributor to the genesis and spectrum of sarcomas.


title: Sparse-based neural response for image classification

abstract: Image classification is a popular and challenging topic in the computer vision field. On the basis of advances in neuroscience, this paper proposes a sparse-based neural response feature extraction method for image classification. The approach extracts discriminative and invariant representations of images by alternating between non-negative sparse coding and maximum pooling operation with effectiveness. Additionally, effective template selection methods are proposed to further enhance the performance of the algorithm. In comparison with traditional hierarchical methods, our proposed model accounts for the neural processing of visual cortex in human brain, which appears to gain more beneficial discriminative and robust properties for image classification tasks. A variety of benchmarks are used to evaluate the algorithm. The experiment results demonstrate that our proposed algorithm achieves quite excellent or state-of-the-art performance compared with other popular methods.


title: Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis

abstract: The frequently changing user preferences and/or item profiles have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user/item records when splitting the already sparse data across time dimension, previous methods have to restrict the drifting purchasing patterns to pre-assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to alleviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts. In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features/aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analysis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records. Technically, we develop the Fourier-assisted Auto-Regressive Integrated Moving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-aware preference predictions, and we leverage the conditional opportunity models for daily-aware personalized recommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and efficiency of our approach.


title: Capabilities, processes, and performance of knowledge management: A structural approach

abstract: The purpose of this study is to examine structural relationships among the capabilities, processes, and performance of knowledge management, and suggest strategic directions for the successful implementation of knowledge management. To serve this purpose, the authors conducted an extensive survey of 68 knowledge management-adopting Korean firms in diverse industries and collected 215 questionnaires. Analyzing hypothesized structural relationships with the data collected, they found that there exists statistically significant relationships among knowledge management capabilities, processes, and performance. The empirical results of this study also support the wellknown strategic hypothesis of the balanced scorecard (BSC). © 2007 Wiley Periodicals, Inc.


title: A VECTOR SPACE MODEL FOR INFORMATION RETRIEVAL: A MATLAB APPROACH

abstract: By and large, three classic framework models have been used in the process of retrieving information: Boolean, Vector Space and Probabilistic. Boolean model is a light weight model which matches the query with precise semantics. Because of its boolean nature, results may be tides, missing partial matching, while on the contrary, vector space model, considering term-frequency, inverse document frequency measures, achieves utmost relevancy in retrieving documents in information retrieval. This paper implements and discusses the issues of information retrieval system with vector space model using MATLAB on Cranfield data collection of aerodynamics domain.


title: Intelligent User Interfaces for Social Music Discovery and Exploration of Large-scale Music Repositories

abstract: In this position paper, we address the question of how to make music search and discovery more appealing, more exciting, and more joyful. In particular, we argue to research methods that foster serendipitous encounters with music items and to integrate ways for social interaction while exploring music collections and discovering the gems in today's huge catalogs available through online streaming platforms. We identify two major challenges here: the need for (i) highly efficient clustering and information visualization techniques that scale to these music catalogs and (ii) novel user interfaces that explain the clustering of music items and provide means to make the exploration of music a social event.


title: Short Segment Screw Fixation without Fusion for Unstable Thoracolumbar and Lumbar Burst Fracture : A Prospective Study on Selective Consecutive Patients

abstract: Objective ::: The purpose of this prospective study was to evaluate the efficacy and safety of screw fixation without bone fusion for unstable thoracolumbar and lumbar burst fracture.


title: Model-free simultaneous design of multiloop PID controllers for TITO interactive processes with time delays

abstract: This paper presents a model-free simultaneous design method of multiloop PID controllers for two-input-two-output (TITO) interactive processes with time delays. The controller design directly exploits experimental process data without identifying process models. By employing the virtual reference approach, structure of the reference model matrix achievable by the multiloop control scheme is derived and the decentralized multiloop controllers that exactly solve a model-reference control problem are synthesized accordingly. Then, each controller is approximated to PID structure through a frequency-domain approach. The PID controllers design depends on the reference models that are optimally specified by minimizing a model-reference control criterion. Because the controller design is applicable to closed-loop data, it is preferable in industrial applications and the method can be used to improve existing underperforming controllers. Simulation studies show the effectiveness of the proposed design method.


title: Complex signal demodulation and random body movement cancellation techniques for non-contact vital sign detection

abstract: A complex signal demodulation technique is proposed to eliminate the null detection point problem in non-contact vital sign detection. This technique is robust against DC offset in direct conversion system. Based on the complex signal demodulation, a random body movement cancellation technique is developed to cancel out strong noise caused by random body movement in non-contact vital sign monitoring. Multiple transceivers and antennas with polarization and frequency multiplexing are used to detect signals from different body orientations. The noise due to random body movement is cancelled out based on different patterns of the desired and undesired signals. Experiments by highly compact 5–6 GHz portable radar systems have been performed to verify these two techniques.


title: A systematic literature review of empirical evidence on computer games and serious games

abstract: This paper examines the literature on computer games and serious games in regard to the potential positive impacts of gaming on users aged 14 years or above, especially with respect to learning, skill enhancement and engagement. Search terms identified 129 papers reporting empirical evidence about the impacts and outcomes of computer games and serious games with respect to learning and engagement and a multidimensional approach to categorizing games was developed. The findings revealed that playing computer games is linked to a range of perceptual, cognitive, behavioural, affective and motivational impacts and outcomes. The most frequently occurring outcomes and impacts were knowledge acquisition/content understanding and affective and motivational outcomes. The range of indicators and measures used in the included papers are discussed, together with methodological limitations and recommendations for further work in this area.


title: RecipeScape: Mining and Analyzing Diverse Processes in Cooking Recipes

abstract: In culture analytics, it is important to ask fundamental questions that address salient characteristics of collective human behavior. This paper explores how analyzing cooking recipes in aggregate and at scale identifies these characteristics in the cooking culture, and answer fundamental questions like 'what makes a chocolate chip cookie a chocolate chip cookie?'. Aspiring cooks, professional chefs and cooking hobbyists share their recipes online resulting in thousands of different procedural instructions towards a shared goal. However, existing approaches focus merely on analysis at the ingredient level, for example, extracting ingredient information from individual recipes. We introduce RecipeScape, a prototype interface which supports visually querying, browsing and comparing cooking recipes at scale. We also present the underlying computational pipeline of RecipeScape that scrapes recipes online, extracts their ingredient and instruction information, constructs a graphical representation, and computes similarity between pairs of recipes.


Augmenting a convolutional neural network with local histograms - A case study in crop classification from high-resolution UAV imagery


title: Unified Closed Form Inverse Kinematics for the KUKA youBot

abstract: Mobile manipulators are of high interest to industry because of the increased flexibility and effectiveness they offer. The combination and coordination of the mobility provided by a mobile platform and of the manipulation capabilities provided by a robot arm leads to complex analytical problems for research. These problems can be studied very well on the KUKA youBot, a mobile manipulator designed for education and research applications. Issues still open in research include solving the inverse kinematics problem for the unified kinematics of the mobile manipulator, including handling the kinematic redundancy introduced by the holonomic platform of the KUKA youBot. As the KUKA youBot arm has only 5 degrees of freedom, a unified platform and manipulator system is needed to compensate for the missing degree of freedom. We present the KUKA youBot as an 8 degree of freedom serial kinematic chain, suggest appropriate redundancy parameters, and solve the inverse kinematics for the 8 degrees of freedom. This enables us to perform manipulation tasks more efficiently. We discuss implementation issues, present example applications and some preliminary experimental evaluation along with discussion about redundancies.


title: A Look into 30 Years of Malware Development from a Software Metrics Perspective

abstract: During the last decades, the problem of malicious and unwanted software (malware) has surged in numbers and sophistication. Malware plays a key role in most of today’s cyber attacks and has consolidated as a commodity in the underground economy. In this work, we analyze the evolution of malware since the early 1980s to date from a software engineering perspective. We analyze the source code of 151 malware samples and obtain measures of their size, code quality, and estimates of the development costs (effort, time, and number of people). Our results suggest an exponential increment of nearly one order of magnitude per decade in aspects such as size and estimated effort, with code quality metrics similar to those of regular software. Overall, this supports otherwise confirmed claims about the increasing complexity of malware and its production progressively becoming an industry.


title: Sentiment-aware stock market prediction: A deep learning method

abstract: Stock market prediction has attracted much attention from academia as well as business. However, it is a challenging research topic, in which many advanced computational methods have been proposed, but not yet attained a desirable and reliable performance. This study proposes a new method for stock market prediction, which adopts the Long Short-Term Memory (LSTM) neural network and incorporates investor sentiment and market factors to improve forecasting performance. By extracting investor sentiment from forum posts using Naive Bayes, this paper makes it possible to analyze the irrational component of stock price. Our empirical study on CSI300 index proves that our prediction method provides better prediction performance. It gives a prediction accuracy of 87.86%, outperforming other benchmark models by at least 6%. Furthermore, our empirical study reveals evidence that helps to better understand investor sentiment and stock behaviors. Finally, this work shows the potential of deep learning financial time series in the presence of strong noises.


title: Bullying and cyberbullying: Convergent and divergent predictor variables

abstract: There is certain controversy on whether cyber-bullying is a category of bullying as it appears in a very different scenario away from the schools. The objective of this research has been to know if the variables that predict the involvement of youngsters in traditional bullying are also predictor of the appearance of cyber-bullying. Accordingly, we have looked for the similarities and the differences existing in the involvement on these phenomena. The sample is composed by secondary school pupils (n=893, 45.9% girls; age [email protected]?=13.80, SD=1.47). The results show that there are multiple relations between the predictor variables of school bullying and the specific variables of virtual environments that predict cyber-bullying. It has been obtained a new model that explains both phenomena which could be a strong evidence to base future interventions to prevent and reduce these problems.


Detecting Active Attacks in WiFi Network by Semi-supervised Deep Learning


title: Synchronous Prediction of Arousal and Valence Using LSTM Network for Affective Video Content Analysis

abstract: The affect embedded in video data conveys high-level semantic information about the content and has direct impact on the understanding and perception of reviewers, as well as their emotional responses. Affective Video Content Analysis (AVCA) attempts to generate a direct mapping between video content and the corresponding affective states such as arousal and valence dimensions. Most existing studies establish the mapping for each dimension separately using knowledge-based rules or traditional classifiers such as Support Vector Machine (SVM). The inherent correlations between affective dimensions have largely been unexploited, which are anticipated to include important information for accurate prediction of affective dimensions. To address this issue, this paper presents an approach to predict arousal and valance dimensions synchronously using the Long Short Term Memory (LSTM) network. The approach extracts a set of low-level audio and visual features from video data and projects them synchronously into pairs of arousal and valence values using the LSTM network which automatically incorporates the correlations between arousal and valance dimensions. We evaluate the performance of the proposed approach on a dataset comprising video clips segmented from real-world resources such as film, drama, and news, and demonstrate its superior performance over the traditional SVM based method. The results provide one of the earliest preliminary evidence to the benefit of considering correlations between affective dimensions towards accurate AVCA.


title: Circularly Polarized Implantable Antenna for 915 MHz ISM-Band Far-Field Wireless Power Transmission

abstract: A wireless power link with circular polarization is studied in this letter for far-field wireless power transmission. First, a miniaturized circularly polarized (CP) implantable antenna is designed at 915 ISM band. The proposed antenna features a good miniaturization with the dimensions of 11 × 11 × 1.27 mm3 by employing the stub loading and capacitive coupling among the stubs. It has the smallest size and maintains good radiation performance, compared with previous CP implantable antennas. The simulated impedance bandwidth covers from 889 to 924 MHz for | S 11| less than −10 dB, and the axial-ratio (AR) bandwidth (AR < 3 dB) is from 901 to 912 MHz. The results show that the antenna is suitable for far-field wireless power transmission. The proposed wireless power link achieves higher converted dc power compared to previous similar letter.


title: Management of Network Slicing in 5G Radio Access Networks: Functional Framework and Information Models

abstract: Network slicing is one of the key features for 5G networks to be able to accommodate the anticipated diversity of applications and business actors in a resource-efficient manner. While significant progress has already been achieved at 3GPP specifications level with regard to the system architectural and functional aspects for the realisation of network slicing in 5G networks, management solutions for the exploitation of this feature in the Next Generation Radio Access Network (NG-RAN) are still at a very incipient stage. In this context, this paper presents a framework for the management of network slicing in a NG-RAN infrastructure, identifying the functions, interfaces and information models that are necessary to enable the automation of the RAN slicing provisioning and management processes. Accordingly, a plausible information model intended to describe the manageable characteristics and behaviour of a RAN slice is developed and its applicability discussed in an illustrative neutral host provider scenario.


title: Low-Profile Equiangular Spiral Antenna Backed by an EBG Reflector

abstract: The bi-directional beam from an equiangular spiral antenna (EAS) is changed to a unidirectional beam using an electromagnetic band gap (EBG) reflector. The antenna height, measured from the upper surface of the EBG reflector to the spiral arms, is chosen to be extremely small to realize a low-profile antenna: 0.07 wavelength at the lowest analysis frequency of 3 GHz. The analysis shows that the EAS backed by the EBG reflector does not reproduce the inherent wideband axial ratio characteristic observed when the EAS is isolated in free space. The deterioration in the axial ratio is examined by decomposing the total radiation field into two field components: one component from the equiangular spiral and the other from the EBG reflector. The examination reveals that the amplitudes and phases of these two field components do not satisfy the constructive relationship necessary for circularly polarized radiation. Based on this finding, next, the EBG reflector is modified by gradually removing the patch elements from the center region of the reflector, thereby satisfying the required constructive relationship between the two field components. This equiangular spiral with a modified EBG reflector shows wideband characteristics with respect to the axial ratio, input impedance and gain within the design frequency band (4-9 GHz). Note that, for comparison, the antenna characteristics for an EAS isolated in free space and an EAS backed by a perfect electric conductor are also presented.


title: Secure Dynamic Memory Scheduling Against Timing Channel Attacks

abstract: This paper presents SecMC, a secure memory controller that provides efficient memory scheduling with a strong quantitative security guarantee against timing channel attacks. The first variant, named SecMC-NI, eliminates timing channels while allowing a tight memory schedule by interleaving memory requests that access different banks or ranks. Experimental results show that SecMC-NI significantly (45% on average) improves the performance of the best known scheme that does not rely on restricting memory placements. To further improve the performance, the paper proposes SecMC-Bound, which enables trading-off security for performance with a quantitative information theoretic bound on information leakage. The experimental results show that allowing small information leakage can yield significant performance improvements.


title: A Framework for Systematic Refinement of Trustworthiness Requirements

abstract: The trustworthiness of systems that support complex collaborative business processes is an emergent property. In order to address users’ trust concerns, trustworthiness requirements of software systems must be elicited and satisfied. The aim of this paper is to address the gap that exists between end-users’ trust concerns and the lack of implementation of proper trustworthiness requirements. New technologies like cloud computing bring new capabilities for hosting and offering complex collaborative business operations. However, these advances might bring undesirable side effects, e.g., introducing new vulnerabilities and threats caused by collaboration and data exchange over the Internet. Hence, users become more concerned about trust. Trust is subjective; trustworthiness requirements for addressing trust concerns are difficult to elicit, especially if there are different parties involved in the business process. We propose a user-centered trustworthiness requirement analysis and modeling framework. We integrate the subjective trust concerns into goal models and embed them into business process models as objective trustworthiness requirements. Business process model and notation is extended to enable modeling trustworthiness requirements. This paper focuses on the challenges of elicitation, refinement and modeling trustworthiness requirements. An application example from the healthcare domain is used to demonstrate our approach.


title: Strong Key-Exposure Resilient Auditing for Secure Cloud Storage

abstract: Key exposure is one serious security problem for cloud storage auditing. In order to deal with this problem, cloud storage auditing scheme with key-exposure resilience has been proposed. However, in such a scheme, the malicious cloud might still forge valid authenticators later than the key-exposure time period if it obtains the current secret key of data owner. In this paper, we innovatively propose a paradigm named strong key-exposure resilient auditing for secure cloud storage, in which the security of cloud storage auditing not only earlier than but also later than the key exposure can be preserved. We formalize the definition and the security model of this new kind of cloud storage auditing and design a concrete scheme. In our proposed scheme, the key exposure in one time period doesn’t affect the security of cloud storage auditing in other time periods. The rigorous security proof and the experimental results demonstrate that our proposed scheme achieves desirable security and efficiency.


title: The Medial Axis of a Multi-Layered Environment and Its Application as a Navigation Mesh

abstract: Path planning for walking characters in complicated virtual environments is a fundamental task in simulations and games. A navigation mesh is a data structure that allows efficient path planning. The Explicit Corridor Map (ECM) is a navigation mesh based on the medial axis. It enables path planning for disk-shaped characters of any radius. ::: In this paper, we formally extend the medial axis (and therefore the ECM) to 3D environments in which characters are constrained to walkable surfaces. Typical examples of such environments are multi-storey buildings, train stations, and sports stadiums. We give improved definitions of a walkable environment (WE: a description of walkable surfaces in 3D) and a multi-layered environment (MLE: a subdivision of a WE into connected layers). We define the medial axis of such environments based on projected distances on the ground plane. For an MLE with $n$ boundary vertices and $k$ connections, we show that the medial axis has size $O(n)$, and we present an improved algorithm that constructs the medial axis in $O(n \log n \log k)$ time. ::: The medial axis can be annotated with nearest-obstacle information to obtain the ECM navigation mesh. Our implementations show that the ECM can be computed efficiently for large 2D and multi-layered environments, and that it can be used to compute paths within milliseconds. This enables simulations of large virtual crowds of heterogeneous characters in real-time.


title: A topology-based object representation for clasping, latching and hooking

abstract: We present a loop-based topological object representation for objects with holes. The representation is used to model object parts suitable for grasping, e.g. handles, and it incorporates local volume information about these. Furthermore, we present a grasp synthesis framework that utilizes this representation for synthesizing caging grasps that are robust under measurement noise. The approach is complementary to a local contact-based force-closure analysis as it depends on global topological features of the object. We perform an extensive evaluation with four robotic hands on synthetic data. Additionally, we provide real world experiments using a Kinect sensor on two robotic platforms: a Schunk dexterous hand attached to a Kuka robot arm as well as a Nao humanoid robot. In the case of the Nao platform, we provide initial experiments showing that our approach can be used to plan whole arm hooking as well as caging grasps involving only one hand.


title: Disentangled Variational Auto-Encoder for Semi-supervised Learning

abstract: In this paper, we develop a novel approach for semi-supervised VAE without classifier. Specifically, we propose a new model called SDVAE, which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via equation constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.


title: Routing perturbation for enhanced security in split manufacturing

abstract: Split manufacturing can mitigate security vulnerabilities at untrusted foundries by exposing only partial designs. Even so, attackers can make educated guess according to design conventions and thereby recover entire chip designs. In this work, a routing perturbation-based defense method is proposed such that such attacks become very difficult while wirelength/timing overhead is restricted to be very small. Experimental results on benchmark circuits confirm the effectiveness of the proposed techniques. The new techniques also significantly outperform the latest previous work.


title: Does Bidirectional Traffic Do More Harm Than Good in LoRaWAN Based LPWA Networks?

abstract: The need for low power, long range and low cost connectivity to meet the requirements of IoT applications has led to the emergence of Low Power Wide Area (LPWA) networking technologies. The promise of these technologies to wirelessly connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out `what-if analyses' and pre-deployment studies are needed to understand the implications of choices that are made at design time. While there are several promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts.


title: Stroke Volume Dynamics during Progressive Exercise in Healthy Adolescents

abstract: Understanding cardiac responses to exercise in healthy subjects is important in the evaluation of youth with heart disease. This review article incorporates previously published original research from the authors’ laboratory to examine changes in stroke volume during progressive exercise which are consistent with a model in which circulatory responses are controlled by alterations in the systemic vascular resistance. Stroke volume dynamics and cardiovascular responses to a progressive upright cycle test were examined in three groups of healthy, untrained adolescent subjects. These indicated a) a progressive decease in systemic vascular resistance, b) little change in stroke volume after an initial rise related to orthostatic changes in ventricular refilling, c) evidence of a constant or slightly declining left ventricular end diastolic filling pressure, d) and increases in markers of ventricular contractility. These observations are consistent with peripheral (vascular resistance) rather than central (car...


title: CariGANs: Unpaired Photo-to-Caricature Translation

abstract: Facial caricature is an art form of drawing faces in an exaggerated way to convey humor or sarcasm. In this paper, we propose the first Generative Adversarial Network (GAN) for unpaired photo-to-caricature translation, which we call "CariGANs". It explicitly models geometric exaggeration and appearance stylization using two components: CariGeoGAN, which only models the geometry-to-geometry transformation from face photos to caricatures, and CariStyGAN, which transfers the style appearance from caricatures to face photos without any geometry deformation. In this way, a difficult cross-domain translation problem is decoupled into two easier tasks. The perceptual study shows that caricatures generated by our CariGANs are closer to the hand-drawn ones, and at the same time better persevere the identity, compared to state-of-the-art methods. Moreover, our CariGANs allow users to control the shape exaggeration degree and change the color/texture style by tuning the parameters or giving an example caricature.


title: Robot learning manipulation action plans by Watching unconstrained videos from the world wide web

abstract: In order to advance action generation and creation in robots beyond simple learned schemas we need computational tools that allow us to automatically interpret and represent human actions. This paper presents a system that learns manipulation action plans by processing unconstrained videos from the World Wide Web. Its goal is to robustly generate the sequence of atomic actions of seen longer actions in video in order to acquire knowledge for robots. The lower level of the system consists of two convolutional neural network (CNN) based recognition modules, one for classifying the hand grasp type and the other for object recognition. The higher level is a probabilistic manipulation action grammar based parsing module that aims at generating visual sentences for robot manipulation. Experiments conducted on a publicly available unconstrained video dataset show that the system is able to learn manipulation actions by "watching" unconstrained videos with high accuracy.


title: New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications

abstract: Abstract In this paper, we propose a new method to obtain the Euclidean distance transformation and the Voronoi diagram based on the exact Euclidean metric for an n -dimensional picture. We present four algorithms to perform the transformation which are constructed by the serial composition of n -dimensional filters. When performed by a general purpose computer, they are faster than the method by H. Yamada for a two-dimensional picture. Those algorithms require only one n -dimensional array for storing input/output pictures and a single one-dimensional array for a work area, if an input picture needs not be preserved.


title: E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text

abstract: An end-to-end trainable (fully differentiable) method for multi-language scene text localization and recognition is proposed. The approach is based on a single fully convolutional network (FCN) with shared layers for both tasks.


title: Segmenting MRI brain images using evolutionary computation technique

abstract: Medical image segmentation is a fundamental preprocessing step in most systems that supports diagnosis or planning of surgical operations. The traditional Fuzzy c means clustering algorithm performs well in the absence of noise. Traditional FCM leads to its non robust result mainly due to 1. Not utilizing the spatial information in the image. 2. Use of Euclidean distance. These limitations can be addressed by using robust spatial kernel FCM (RSKFCM). RSKFCM consider the spatial information and uses Gaussian kernel function to calculate the distance between the center and data points. Though RSKFCM gives good result, the main drawback behind this method is the inability of generating global minima for the objective function. To improve the efficiency of RSKFCM method, in this paper we proposed the genetic algorithm based RSKFCM. By using the genetic algorithm, RSKFCM initializes the cluster centers and reaches the global minima of the objective function. Experimentation is carried out on the standard brain image dataset. Experimental result reveals that the proposed genetic algorithm based RSKFCM outperforms other FCM methods with the use of various cluster validity functions.