title: Senior Citizens' Adoption of E-Government: In Quest of the Antecedents of Perceived Usefulness

abstract: E-Government, as a vehicle to deliver enhanced services to citizens, is now extending its reach to the elderly population through provision of targeted services. In doing so, the ideals of ubiquitous e-Government may be better achieved. However, there is a lack of studies on e-Government adoption among senior citizens, especially considering that this age group is growing in size and may be averse to new IT applications. This study aims to address this gap by investigating an innovative e- Government service specifically tailored for senior citizens, called CPF e-Withdrawal. Technology adoption model (TAM) is employed as the theoretical foundation, in which perceived usefulness is recognized as the most significant predictor of adoption intention. This study attempts to identify the antecedents of perceived usefulness by drawing from the innovation diffusion literature as well as age-related studies. Our findings agree with TAM and indicate that internet safety perception and perceived ease of use are significant predictors of perceived usefulness.


title: Hyaluronic acid injections for knee osteoarthritis. Systematic review of the literature.

abstract: OBJECTIVE: To determine whether viscosupplementation with intra-articular hyaluronic acid (HA) injections improves pain and function in patients with osteoarthritis (OA) in their knees. DATA SOURCES: We searched MEDLINE, Pre-MEDLINE, and Cochrane databases using the MeSH headings and key words osteoarthritis (knee) and hyaluronic acid. STUDY SELECTION: English-language case series and randomized controlled trials (RCTs) were selected. Studies with biologic, histologic, or arthroscopic outcomes were excluded. SYNTHESIS: Five case series and 13 RCTs were critically appraised. Data from three case series and three RCTs using injections of high-molecular-weight HA (Synvisc) demonstrated significant improvement in pain, activity levels, and function. The beneficial effect started as early as 12 weeks. Studies using low-molecular-weight HA had conflicting results. CONCLUSION: Viscosupplementation with high-molecular-weight HA is an effective treatment for patients with knee OA who have ongoing pain or are unable to tolerate conservative treatment or joint replacement. Viscosupplementation appears to have a slower onset of action than intra-articular steroids, but the effect seems to last longer.


title: Recognition of emotional speech with convolutional neural networks by means of spectral estimates

abstract: Current developments in deep neural architectures achieved remarkable results in the classification of emotions from speech. Recently, also cross-modal approaches gained attention in the community. Such a classification method is the Convolutional Neural Network (CNN). Mainly developed for analyses of images it can be used also in speech processing. In this paper, we present a CNN-based classification architecture adapting spectrograms as representations of emotion-afflicted speech input. Given this approach, we applied our network architecture to three benchmark corpora, namely EmoDB, eNTERFACE, and SUSAS, and investigated the classification ability in a Leave-One-Speaker-Out setting. Especially, for SUSAS, a close-to-real-life corpus, remarkable results were obtained. In addition, we investigated the option of analysing CNN's internal representations of the given input using Deep Dreaming. For this, we were able to identify spectral parts which contribute most to the classification process.


title: Torque Ripple Reduction in Five-Phase IPM Motors by Lowering Interactional MMF

abstract: This paper proposes a torque ripple reduction method for a five-phase interior permanent magnet (PM) motor by shifting asymmetrical rotor poles. The key of this method is to consider the PM torque ripple, reluctance torque ripple, and cogging torque simultaneously from two aspects. First, the main interactional harmonic, contributing to PM torque ripple, between stator magnetic motive force (MMF) and rotor MMF is developed. Then, the appropriate combination of unequal pole arcs is adopted to eliminate the main interactional harmonic. Second, the asymmetrical rotor pole-pair is selected as a repeating unit which produces cogging torque and reluctance torque with consistency in waveforms and phases. Then, three methods for shifting asymmetrical rotor pole-pair are proposed showing that the torque ripple can be reduced greatly and the lower-order harmonics of radial force is acceptable when the shifting angle of the repeating unit is 4.5 mechanical degrees. Through theoretical analysis and experimental test, it is verified that the proposed motor can offer lower torque ripple even under deep flux-weakening region.


APPLICATION OF POINT CLOUDS FROM TERRESTRIAL 3 D LASER SCANNER FOR DEFORMATION MEASUREMENTS


Robust Steganography based on Matching Pixel Locations


title: A static power model for architects

abstract: Static power dissipation due to transistor leakage constitutes an increasing fraction of the total power in modern semiconductor technologies. Current technology trends indicate that the contribution will increase rapidly, reaching one half of total power dissipation within three process generations. Developing power efficient products will require consideration of static power in the earliest phases of design, including architecture and microarchitecture definition. We propose a simple equation for estimating static power consumption at the architectural level: P/sub static/=V/sub CC//spl middot/N/spl middot/k/sub design//spl middot/I/spl circ//sub leak/, where V/sub CC/ is the supply voltage, N is the number of transistors, k/sub design/ is a design dependent parameter, and I/spl circ//sub leak/ is a technology dependent parameter. This model enables high-level reasoning about the likely static power demands of alternative microarchitectures. Reasonably accurate values for the factors within the equation may be obtained directly from the high-level designs or by straightforward scaling arguments. The factors within the equation also suggest opportunities for static power optimization, including reducing the total number of devices, partitioning the design to allow for lower supply voltages or slower, less leaky transistors, turning off unused devices, favoring certain design styles, and favoring high bandwidth over low latency. Speculation is also examined as a means to employ slower transistors without a significant performance penalty.


title: Community Structure in Graphs

abstract: Graph vertices are often organized into groups that seem to live fairly independently of the rest of the graph, with which they share but a few edges, whereas the relationships between group members are stronger, as shown by the large number of mutual connections. Such groups of vertices, or communities, can be considered as independent compartments of a graph. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. The task is very hard, though, both conceptually, due to the ambiguity in the definition of community and in the discrimination of different partitions and practically, because algorithms must find ``good'' partitions among an exponentially large number of them. Other complications are represented by the possible occurrence of hierarchies, i.e. communities which are nested inside larger communities, and by the existence of overlaps between communities, due to the presence of nodes belonging to more groups. All these aspects are dealt with in some detail and many methods are described, from traditional approaches used in computer science and sociology to recent techniques developed mostly within statistical physics.


title: Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification

abstract: Abstract Given a pedestrian image as a query, the purpose of person re-identification is to identify the correct match from a large collection of gallery images depicting the same person captured by disjoint camera views. The critical challenge is how to construct a robust yet discriminative feature representation to capture the compounded variations in pedestrian appearance. To this end, deep learning methods have been proposed to extract hierarchical features against extreme variability of appearance. However, existing methods in this category generally neglect the efficiency in the matching stage whereas the searching speed of a re-identification system is crucial in real-world applications. In this paper, we present a novel deep hashing framework with Convolutional Neural Networks (CNNs) for fast person re-identification. Technically, we simultaneously learn both CNN features and hash functions to get robust yet discriminative features and similarity-preserving hash codes. Thereby, person re-identification can be resolved by efficiently computing and ranking the Hamming distances between images. A structured loss function defined over positive pairs and hard negatives is proposed to formulate a novel optimization problem so that fast convergence and more stable optimized solution can be attained. Extensive experiments on two benchmarks CUHK03 (Li et al., 2014) and Market-1501 (Zheng et al., 2015) show that the proposed deep architecture is efficacy over state-of-the-arts.


title: Hierarchical Clustering of a Mixture Model

abstract: In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model; this is achieved by clustering (grouping) the components. The method minimizes a new, easily computed distance measure between two Gaussian mixtures that can be motivated from a suitable stochastic model and the iterations of the algorithm use only the model parameters, avoiding the need for explicit resampling of datapoints. We demonstrate the method by performing hierarchical clustering of scenery images and handwritten digits.


title: Contextual Text Understanding in Distributional Semantic Space

abstract: Representing discrete words in a continuous vector space turns out to be useful for natural language applications related to text understanding. Meanwhile, it poses extensive challenges, one of which is due to the polysemous nature of human language. A common solution (a.k.a word sense induction) is to separate each word into multiple senses and create a representation for each sense respectively. However, this approach is usually computationally expensive and prone to data sparsity, since each sense needs to be managed discriminatively. In this work, we propose a new framework for generating context-aware text representations without diving into the sense space. We model the concept space shared among senses, resulting in a framework that is efficient in both computation and storage. Specifically, the framework we propose is one that: i) projects both words and concepts into the same vector space; ii) obtains unambiguous word representations that not only preserve the uniqueness among words, but also reflect their context-appropriate meanings. We demonstrate the effectiveness of the framework in a number of tasks on text understanding, including word/phrase similarity measurements, paraphrase identification and question-answer relatedness classification.


title: Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

abstract: Purpose ::: Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images.


title: Wideband Two-Section Impedance Transformer With Flat Real-to-Real Impedance Matching

abstract: This letter presents the design of an impedance transformer with wideband, maximally flat real-to-real impedance matching. The design formulas for two-section quarter-wave transformer are presented and exact solutions for transmission lines' parameters are derived in explicit form for any impedance transformation ratio. The results of this study are useful for a number of practical design problems, especially power amplification circuits. To validate the design formulas, three impedance transformers terminated in a fixed impedance of 50 $\Omega$ and three target impedances of 100, 150, and 200 $\Omega$ are fabricated and measured. Measurements show a good agreement with theory and simulations.


title: On the cognitive informatics foundations of software engineering

abstract: Software is the object of study in software engineering and computer science. Software is an intellectual artifact that provides a solution for a repeatable computer application, which enables existing tasks to be done easier, faster, and smarter, or which provides innovative applications for the industries and daily life. This paper attempts to explore the basic characteristics of software and to search the informatics and cognitive foundations of software in supplement to theories of computer science. In this paper, the nature of software is characterized by its informatics, behavioral, mathematical, and cognitive properties. Then, the cognitive informatics foundations of software engineering are developed on the basis of the informatics laws of software and software engineering psychology.


title: Collaborative matrix factorization with multiple similarities for predicting drug-target interactions

abstract: We address the problem of predicting new drug-target interactions from three inputs: known interactions, similarities over drugs and those over targets. This setting has been considered by many methods, which however have a common problem of allowing to have only one similarity matrix over drugs and that over targets. The key idea of our approach is to use more than one similarity matrices over drugs as well as those over targets, where weights over the multiple similarity matrices are estimated from data to automatically select similarities, which are effective for improving the performance of predicting drug-target interactions. We propose a factor model, named Multiple Similarities Collaborative Matrix Factorization(MSCMF), which projects drugs and targets into a common low-rank feature space, which is further consistent with weighted similarity matrices over drugs and those over targets. These two low-rank matrices and weights over similarity matrices are estimated by an alternating least squares algorithm. Our approach allows to predict drug-target interactions by the two low-rank matrices collaboratively and to detect similarities which are important for predicting drug-target interactions. This approach is general and applicable to any binary relations with similarities over elements, being found in many applications, such as recommender systems. In fact, MSCMF is an extension of weighted low-rank approximation for one-class collaborative filtering. We extensively evaluated the performance of MSCMF by using both synthetic and real datasets. Experimental results showed nice properties of MSCMF on selecting similarities useful in improving the predictive performance and the performance advantage of MSCMF over six state-of-the-art methods for predicting drug-target interactions.


title: Biomarkers for Osteoporosis Management Utility in Diagnosis , Fracture Risk Prediction and Therapy Monitoring

abstract: Osteoporosis is a systemic disease characterized by low bone mass and microarchitectural deterioration of bone tissue, resulting in an increased risk of fracture. While the level of bone mass can be estimated by measuring bone mineral density (BMD) using dual X-ray absorptiometry (DXA), its measurement does not capture all the risk factors for fracture. Quantitative changes in skeletal turnover can be assessed easily and non-invasively by the measurement of serum and urinary biochemical markers; the most sensitive markers include serum osteocalcin, bone specific alkaline phosphatase, the N-terminal propeptide of type I collagen for bone formation, and the crosslinked C- (CTX) and N- (NTX) telopeptides of type I collagen for bone resorption. Advances in our knowledge of bone matrix biochemistry, most notably of post-translational modifications in type I collagen, are likely to lead to the development of new biochemical markers that reflect changes in the material property of bone, an important determinant of bone strength. Among those, the measurement of the urinary ratio of native (α) to isomerized (β) CTX — an index of bone matrix maturation — has been shown to be predictive of fracture risk independently of BMD and bone turnover.In postmenopausal osteoporosis, levels of bone resorption markers above the upper limit of the premenopausal range are associated with an increased risk of hip, vertebral, and nonvertebral fracture, independent of BMD. Therefore, the combined use of BMD measurement and biochemical markers is helpful in risk assessment, especially in those women who are not identified as at risk by BMD measurement alone. Levels of bone markers decrease rapidly with antiresorptive therapies, and the levels reached after 3–6 months of therapy have been shown to be more strongly associated with fracture outcome than changes in BMD. Preliminary studies indicate that monitoring changes of bone formation markers could also be useful to monitor anabolic therapies, including intermittent parathyroid hormone administration and, possibly, to improve adherence to treatment. Thus, repeated measurements of bone markers during therapy may help improve the management of osteoporosis in patients.


title: An Autonomous Aquaponics System Using 6LoWPAN Based WSN

abstract: Aquaponics is a groundbreaking food production technique that combines both traditional methods of aquaculture and hydroponics to grow both fish and crops in a single integrated system. Aquaponics system uses fish wastes to provide essential nutrients to the plants. In return plants serve as a bio-filter for the fish in a symbiotic relationship. The purpose of this paper is to showcase how to build an efficient Internet of Things (IoT) application for aquaponics in order to create an autonomous, self-regulating system with the help of Wireless Sensor Network (WSN). An open standard of WSN called 6LoWPAN is being used in this system which helps us to construct a global infrastructure. The designed aquaponic system is composed of sensor devices that can sense and collect information of the various dimensions of the water quality involved and store it in a cloud database. This means that the human intervention would be considerably less when compared to the traditional methods of aquaponics. In addition to the traditional technique, with the help of Next-Gen Telco technologies, i.e., using their high bandwidth and low latency, infected fishes are detected automatically on time and treat them to ensure the balance in our aquaponics ecosystem. In this paper we propose an end-to-end system to enable "Connected Aquaponics" which includes WSN and Next-Gen Telco to increase the crop yield and provide organic sustainable food to the world community.


title: Design of a Transmission With Gear Trains for Underactuated Mechanisms

abstract: This paper proposes a design method of transmissions for underactuated mechanisms. This transmission consists of active and passive gear trains that align a series of gears over several joints. The transmission has no kinematic singular point that restricts the workspace. Thus, arbitrary joint-connected motions of the underactuated mechanisms can be designed by combining the gear trains. We investigate the kinematics and statics of underactuated mechanisms to find a transmission matrix that realizes the desired connected motions and adaptive grasps. We also develop a method to design gear trains that corresponds to the transmission matrix. We extend this design method with stepped gears and belt–gear mechanisms to increase the design flexibility. The proposed method was experimentally validated using the two- and three-jointed mechanisms.


title: Myiasis associated with penile carcinoma: a new trend in developing countries?

abstract: OBJECTIVES ::: The aim of this study is to report an unusual form of penile cancer presentation associated with myiasis infestation, treatment options and outcomes. ::: ::: ::: MATERIALS AND METHODS ::: We studied 10 patients with suspected malignant neoplasm of the penis associated with genital myiasis infestation. Diagnostic assessment was conducted through clinical history, physical examination, penile biopsy, larvae identification and computerized tomography scan of the chest, abdomen and pelvis. Clinical and pathological staging was done according to 2002 TNM classification system. Radical inguinal lymphadenectomy was conducted according to the primary penile tumor pathology and clinical lymph nodes status. ::: ::: ::: RESULTS ::: Patients age ranged from 41 to 77 years (mean=62.4). All patients presented squamous cell carcinoma of the penis in association with myiasis infestation caused by Psychoda albipennis. Tumor size ranged from 4cm to 12cm (mean=5.3). Circumcision was conducted in 1 (10%) patient, while penile partial penectomy was performed in 5 (50%). Total penectomy was conducted in 2 (20%) patients, while emasculation was the treatment option for 2 (20%). All patients underwent radical inguinal lymphadenectomy. Prophylactic lymphadenectomy was performed on 3 (30%) patients, therapeutic on 5 (50%), and palliative lymphadenectomy on 2 (20%) patients. Time elapsed from primary tumor treatment to radical inguinal lymphadenectomy was 2 to 6 weeks. The mean follow-up was 34.3 months. ::: ::: ::: CONCLUSION ::: The occurrence of myiasis in the genitalia is more common in patients with precarious hygienic practices and low socio-economic level. The treatment option varied according to the primary tumor presentation and clinical lymph node status.


title: The Isomap algorithm in distance geometry

abstract: The fundamental problem of distance geometry consists in finding a realization of a given weighted graph in a Euclidean space of given dimension, in such a way that vertices are realized as points and edges as straight segments having the same lengths as their given weights. This problem arises in structural proteomics, wireless sensor networks, and clock synchronization protocols to name a few applications. The well-known Isomap method is a dimensionality reduction heuristic which projects finite but high dimensional metric spaces into the "most significant" lower dimensional ones, where significance is measured by the magnitude of the corresponding eigenvalues. We start from a simple observation, namely that Isomap can also be used to provide approximate realizations of weighted graphs very eciently, and then derive and benchmark six new heuristics.


title: Part-Stacked CNN for Fine-Grained Visual Categorization

abstract: In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy. In this paper, we propose a novel Part-Stacked CNN architecture that explicitly explains the finegrained recognition process by modeling subtle differences from object parts. Based on manually-labeled strong part annotations, the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that encodes object-level and part-level cues simultaneously. By adopting a set of sharing strategies between the computation of multiple object parts, the proposed architecture is very efficient running at 20 frames/sec during inference. Experimental results on the CUB-200-2011 dataset reveal the effectiveness of the proposed architecture, from multiple perspectives of classification accuracy, model interpretability, and efficiency. Being able to provide interpretable recognition results in realtime, the proposed method is believed to be effective in practical applications.


title: Surf: Speeded up robust features

abstract: In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. ::: ::: This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.


title: From Agents to Organizations: An Organizational View of Multi-agent Systems

abstract: While multi-agent systems seem to provide a good basis for building complex software systems, this paper points out some of the drawbacks of classical agent centered multi-agent systems. To resolve these difficulties we claim that organization centered multi-agent system, or OCMAS for short, may be used. We propose a set of general principles from which true OCMAS may be designed. One of these principles is not to assume anything about the cognitive capabilities of agents. In order to show how OCMAS models may be designed, we propose a very concise and minimal OCMAS model called AGR, for Agent/Group/Role. We propose a set of notations and a methodological framework to help the designer to build MAS using AGR. We then show that it is possible to design multi-agent systems using only OCMAS models.


title: Omnidirectional planar dipole array antenna for WLAN access point

abstract: In this paper, a low-cost planar dipole array antenna with an omnidirectional radiation characteristic is presented. The proposed omnidirectional planar dipole array antenna comprises two 1/spl times/2 dipole arrays arranged back to back, and can be easily constructed by printing on both sides of a dielectric substrate. By selecting the central ground plane to have a small width (about 6 mm for operating at about 2.4 GHz), the proposed array antenna can have a good omnidirectional radiation pattern in the azimuthal plane (gain variations less than 2 dBi). Prototypes of the proposed antenna for WLAN operation in the 2.4 GHz band (2400-2484 MHz) were constructed and studied.


title: An NFC transceiver with RF-powered RFID transponder mode

abstract: A single chip NFC transceiver supporting not only NFC active and passive mode but also 13.56 MHz RFID reader and tag mode was designed and fabricated. The proposed NFC transceiver can operate as a RFID tag even without external power supply thanks to a dual antenna structure for initiator and target. The area increment due to additional target antenna is negligible because the target antenna is constructed by using a shielding layer of initiator antenna.


title: CONSUMER ATTITUDE TOWARDS PRICING OF LIVE AQUATIC PRODUCTS

abstract: The live aquatic products market can be defined as aquatic products sold live for the food market. This live aquatic market caters to Asian supermarkets and restaurants, and the entire market is being supplied by both wild and farmed aquatic species. This study was designed to gather market information on customer attributes and document how they feel about pricing while purchasing live aquatic products in the Northeastern U.S. The primary objective of this study is to present impact of socio-economic characteristics of aquatic products consumers and their attitude towards importance of price while buying live aquatic products.


title: Language Evolution by Iterated Learning With Bayesian Agents

abstract: Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combining a prior (representing their inductive biases) with the evidence provided by linguistic data. We show that when learners sample languages from this posterior distribution, iterated learning converges to a distribution over languages that is determined entirely by the prior. Under these conditions, iterated learning is a form of Gibbs sampling, a widely-used Markov chain Monte Carlo algorithm. The consequences of iterated learning are more complicated when learners choose the language with maximum posterior probability, being affected by both the prior of the learners and the amount of information transmitted between generations. We show that in this case, iterated learning corresponds to another statistical inference algorithm, a variant of the expectation-maximization (EM) algorithm. These results clarify the role of iterated learning in explanations of linguistic universals and provide a formal connection between constraints on language acquisition and the languages that come to be spoken, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners.


title: Evaluating the relationship between white matter integrity, cognition, and varieties of video game learning

abstract: BACKGROUND ::: Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games. ::: ::: ::: OBJECTIVE ::: To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research. ::: ::: ::: METHODS ::: Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas. ::: ::: ::: RESULTS ::: Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age. ::: ::: ::: CONCLUSION ::: Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related disorders or declines in processing speed, particularly older adults.


title: Attention and Visual Memory in Visualization and Computer Graphics

abstract: A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we "see” details in an image can directly impact a viewer's efficiency and effectiveness. This paper surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.


title: Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research

abstract: Over the last decade, the application of neuroscience to economic research has gained in importance and the number of neuroeconomic studies has grown extensively. The most common method for these investigations is fMRI. However, fMRI has limitations (particularly concerning situational factors) that should be countered with other methods. This review elaborates on the use of functional Near-Infrared Spectroscopy (fNIRS) as a new and promising tool for investigating economic decision making both in field experiments and outside the laboratory. We describe results of studies investigating the reliability of prototype NIRS studies, as well as detailing experiments using conventional and stationary fNIRS devices to analyze this potential. This review article shows that further research using mobile fNIRS for studies on economic decision making outside the laboratory could be a fruitful avenue helping to develop the potential of a new method for field experiments outside the laboratory.


title: Inter-media hashing for large-scale retrieval from heterogeneous data sources

abstract: In this paper, we present a new multimedia retrieval paradigm to innovate large-scale search of heterogenous multimedia data. It is able to return results of different media types from heterogeneous data sources, e.g., using a query image to retrieve relevant text documents or images from different data sources. This utilizes the widely available data from different sources and caters for the current users' demand of receiving a result list simultaneously containing multiple types of data to obtain a comprehensive understanding of the query's results. To enable large-scale inter-media retrieval, we propose a novel inter-media hashing (IMH) model to explore the correlations among multiple media types from different data sources and tackle the scalability issue. To this end, multimedia data from heterogeneous data sources are transformed into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations. Furthermore, we integrate a linear regression model to learn hashing functions so that the hash codes for new data points can be efficiently generated. Experiments conducted on real-world large-scale multimedia datasets demonstrate the superiority of our proposed method compared with state-of-the-art techniques.


title: Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it

abstract: With the rich functionalities and enhanced computing capabilities available on mobile computing devices with touch screens, users not only store sensitive information (such as credit card numbers) but also use privacy sensitive applications (such as online banking) on these devices, which make them hot targets for hackers and thieves. To protect private information, such devices typically lock themselves after a few minutes of inactivity and prompt a password/PIN/pattern screen when reactivated. Passwords/PINs/patterns based schemes are inherently vulnerable to shoulder surfing attacks and smudge attacks. Furthermore, passwords/PINs/patterns are inconvenient for users to enter frequently. In this paper, we propose GEAT, a gesture based user authentication scheme for the secure unlocking of touch screen devices. Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, GEAT authenticates users mainly based on how they input, using distinguishing features such as finger velocity, device acceleration, and stroke time. Even if attackers see what gesture a user performs, they cannot reproduce the behavior of the user doing gestures through shoulder surfing or smudge attacks. We implemented GEAT on Samsung Focus running Windows, collected 15009 gesture samples from 50 volunteers, and conducted real-world experiments to evaluate GEAT's performance. Experimental results show that our scheme achieves an average equal error rate of 0.5% with 3 gestures using only 25 training samples.


title: A functional approach to automatic melody harmonisation

abstract: Melody harmonisation is a centuries-old problem of long tradition, and a core aspect of composition in Western tonal music. In this work we describe FHarm, an automated system for melody harmonisation based on a functional model of harmony. Our system first generates multiple harmonically well-formed chord sequences for a given melody. From the generated sequences, the best one is chosen, by picking the one with the smallest deviation from the harmony model. Unlike all existing systems, FHarm guarantees that the generated chord sequences follow the basic rules of tonal harmony. We carry out two experiments to evaluate the quality of our harmonisations. In one experiment, a panel of harmony experts is asked to give its professional opinion and rate the generated chord sequences for selected melodies. In another experiment, we generate a chord sequence for a selected melody, and compare the result to the original harmonisation given by a harmony scholar. Our experiments confirm that FHarm generates realistic chords for each melody note. However, we also conclude that harmonising a melody with individually well-formed chord sequences from a harmony model does not guarantee a well-sounding coherence between the chords and the melody. We reflect on the experience gained with our experiment, and propose future improvements to refine the quality of the harmonisation.


title: An Adaptive Method for Cross-Recording Speaker Diarization

abstract: Nowadays, state-of-the-art speaker diarization systems heavily rely on between-recording variability compensation methods to accurately process large collections of recordings. Variability estimation is performed on consequent training datasets, which must be labeled by speaker. One major problem of such systems is the acoustic mismatch between training and target data that degrades performances. Most of the collections contain lots of speakers speaking in various acoustic conditions. In this paper, we investigate how unlabeled speakers can help improve between-recording variability estimation, to overcome the mismatch issue. We propose a scalable unsupervised adaptation framework for two types of variability compensation. The proposed framework consists in adapting a state-of-the-art diarization and linking system, trained on out-of-domain data, using the data of the collection itself. Experiments in mismatch condition are run on two French Television shows, while the initial training dataset is composed of Radio recordings. Results indicate that the proposed adaptation framework reduces the cross-recording DER of 13% in average for variable collection sizes.


title: Is Danmaku an Effective Way for Promoting Event based Social Network?

abstract: Recently, a new type of social network called event-based social network(EBSN) is emerging to promote real-time online to offline social interactions. On the other hand, Danmaku (Danmu) is becoming increasingly popular on video sharing sites in Asia. Danmu creates a one to many social experiences suitable for EBSNs. It enables real-time commentary and serves as a valuable source for further analyzing social networks. In this poster, we use Danmu to create real-time online to offline event based social network. A method for analyzing user's activity via Danmu is proposed. Finally, we conclude that Danmu is effective in promoting EBSN by presenting the results of analyzing Danmu contents gathered by a social network App called "USee" on three social events.


title: DeepCoder: Learning to Write Programs

abstract: We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network's predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver. Empirically, we show that our approach leads to an order of magnitude speedup over the strong non-augmented baselines and a Recurrent Neural Network approach, and that we are able to solve problems of difficulty comparable to the simplest problems on programming competition websites.


title: Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection

abstract: In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.


title: Empathy Training for Resident Physicians: A Randomized Controlled Trial of a Neuroscience-Informed Curriculum

abstract: BACKGROUND ::: Physician empathy is an essential attribute of the patient-physician relationship and is associated with better outcomes, greater patient safety and fewer malpractice claims. ::: ::: ::: OBJECTIVE ::: We tested whether an innovative empathy training protocol grounded in neuroscience could improve physician empathy as rated by patients. ::: ::: ::: DESIGN ::: Randomized controlled trial. ::: ::: ::: INTERVENTION ::: We randomly assigned residents and fellows from surgery, medicine, anesthesiology, psychiatry, ophthalmology, and orthopedics (N=99, 52% female, mean age 30.6 ± 3.6) to receive standard post-graduate medical education or education augmented with three 60-minute empathy training modules. ::: ::: ::: MAIN MEASURE ::: Patient ratings of physician empathy were assessed within one-month pre-training and between 1-2 months post-training with the use of the Consultation and Relational Empathy (CARE) measure. Each physician was rated by multiple patients (pre-mean=4.6 ± 3.1; post-mean 4.9 ± 2.5), who were blinded to physician randomization. The primary outcome was change score on the patient-rated CARE. ::: ::: ::: KEY RESULTS ::: The empathy training group showed greater changes in patient-rated CARE scores than the control (difference 2.2; P=0.04). Trained physicians also showed greater changes in knowledge of the neurobiology of empathy (difference 1.8; P<0.001) and in ability to decode facial expressions of emotion (difference 1.9; P<0.001). ::: ::: ::: CONCLUSIONS ::: A brief intervention grounded in the neurobiology of empathy significantly improved physician empathy as rated by patients, suggesting that the quality of care in medicine could be improved by integrating the neuroscience of empathy into medical education.


title: Nonnegative Tucker decomposition with alpha-divergence

abstract: Nonnegative tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnega- tivity constraints are incorporated into Tucker model. In this paper we consider alpha-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD. The proposed multiplicative algorithm includes some existing NMF and NTD algorithms as its special cases, since alpha-divergence is a one-parameter family of divergences which accommodates KL-divergence, Hellinger divergence, X2 divergence, and so on. Numerical experiments on face images show how different values of alpha affect the factorization results under different types of noise.


title: Early-Stage Design for Electric Ship

abstract: One of the truisms of ship design is that the decisions of greatest impact are made in the early stages of design when the least information and the greatest uncertainty are present. In response to this, ongoing efforts in developing ship design tools are directed toward making more information available sooner and pushing decision points later. The integrated nature of electric ship in which performance of support systems directly affects performance of primary mission systems compounds this concern; design and simulation of support systems are needed earlier, and collaborative design among multiple engineering disciplines is required in the process. This paper reviews the Navy ship design process and recent developments in ship design tools to illuminate the areas in which further development is necessary.


title: Accurate Eye Centre Localisation by Means of Gradients.

abstract: The estimation of the eye centres is used in several computer vision applications such as face recognition or eye tracking. Especially for the latter, systems that are remote and rely on available light have become very popular and several methods for accurate eye centre localisation have been proposed. Nevertheless, these methods often fail to accurately estimate the eye centres in difficult scenarios, e.g. low resolution, low contrast, or occlusions. We therefore propose an approach for accurate and robust eye centre localisation by using image gradients. We derive a simple objective function, which only consists of dot products. The maximum of this function corresponds to the location where most gradient vectors intersect and thus to the eye’s centre. Although simple, our method is invariant to changes in scale, pose, contrast and variations in illumination. We extensively evaluate our method on the very challenging BioID database for eye centre and iris localisation. Moreover, we compare our method with a wide range of state of the art methods and demonstrate that our method yields a significant improvement regarding both accuracy and robustness.


title: Epidemiology of Internet Behaviors and Addiction Among Adolescents in Six Asian Countries

abstract: Abstract Internet addiction has become a serious behavioral health problem in Asia. However, there are no up-to-date country comparisons. The Asian Adolescent Risk Behavior Survey (AARBS) screens and compares the prevalence of Internet behaviors and addiction in adolescents in six Asian countries. A total of 5,366 adolescents aged 12–18 years were recruited from six Asian countries: China, Hong Kong, Japan, South Korea, Malaysia, and the Philippines. Participants completed a structured questionnaire on their Internet use in the 2012–2013 school year. Internet addiction was assessed using the Internet Addiction Test (IAT) and the Revised Chen Internet Addiction Scale (CIAS-R). The variations in Internet behaviors and addiction across countries were examined. The overall prevalence of smartphone ownership is 62%, ranging from 41% in China to 84% in South Korea. Moreover, participation in online gaming ranges from 11% in China to 39% in Japan. Hong Kong has the highest number of adolescents reporting daily o...


title: Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry

abstract: Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis with applications such as: image rigid registration, anatomical standard plane detection, tracking and device/camera pose estimation. Deep learning methods often parameterise a pose with a representation that separates rotation and translation. As commonly available frameworks do not provide means to calculate loss on a manifold, regression is usually performed using the L2-norm independently on the rotation's and the translation's parameterisations, which is a metric for linear spaces that does not take into account the Lie group structure of SE(3). In this paper, we propose a general Riemannian formulation of the pose estimation problem. We propose to train the CNN directly on SE(3) equipped with a left-invariant Riemannian metric, coupling the prediction of the translation and rotation defining the pose. At each training step, the ground truth and predicted pose are elements of the manifold, where the loss is calculated as the Riemannian geodesic distance. We then compute the optimisation direction by back-propagating the gradient with respect to the predicted pose on the tangent space of the manifold SE(3) and update the network weights. We thoroughly evaluate the effectiveness of our loss function by comparing its performance with popular and most commonly used existing methods, on tasks such as image-based localisation and intensity-based 2D/3D registration. We also show that hyper-parameters, used in our loss function to weight the contribution between rotations and translations, can be intrinsically calculated from the dataset to achieve greater performance margins.


title: A robust adaptive mixing control for improved forward flight of a tilt-rotor UAV

abstract: This work presents the modeling and control of a tilt-rotor UAV, with tail controlled surfaces, for path tracking with improved forward flight. The dynamic model is obtained using the Euler-Lagrange formulation considering the aerodynamic forces and torques exerted on the horizontal and vertical stabilizers, and fuselage. For control design purposes, the equations of motion are linearized around different operation points to cover a large range of forward velocity. Based on these linearized dynamic models, a mixed H 2 /H ∞ robust controller is designed for each operation point. Therefore, an adaptive mixing scheme is used to perform an on-line smooth gain-scheduling between them. Simulation results show the control strategy efficiency when the UAV is designated to have a forward acceleration and perform a circular trajectory subject to a wind disturbance.


title: RFID and GPS integrated navigation system for the visually impaired

abstract: This paper describes an RFID and GPS integrated navigation system, Smart-Robot (SR) for the visually impaired. The SR uses RFID and GPS based localization while operating indoor and outdoor respectively. The portable terminal unit is an embedded system equipped with an RFID reader, GPS, and analog compass as input devices to obtain location and orientation. The SR can guide the user to a predefined destination, or create a new route on-the-fly for later use. While in navigation mode, the SR reaches the destination by avoiding obstacles using ultrasonic and infrared sensor inputs. The SR also provides user feedback through a speaker, and vibrating motors on the glove. The SR prototype has been successfully implemented and is operational.


title: High-Performance Planar Isolated Current Sensor for Power Electronics Applications

abstract: A planar current sensor, comprised of a magnetic current transformer and a hall-effect element, is presented. The sensor has a broad frequency bandwidth from dc up to 30 MHz, a high current rating (40-A dc), superior linearity, high EMI immunity, small size, robustness, and low realization cost. The main design formulations are given analytically; simulations and finite-element results are presented for verification. Experimental results of current step response and dv/dt immunity are included.


title: Precocious puberty and statural growth

abstract: Precocious puberty results mostly from the precocious activation of the gonadotropic axis. Although the age limits have recently been discussed, most physicians consider that onset of pubertal development before the age of 8 years in a girl or 9 years in a boy warrants at least a clinical and bone age evaluation by a paediatric endocrinologist. The major concern in precocious puberty is the underlying condition, and central nervous system or gonadal neoplasm have to be formally excluded as a first step in the diagnosis. A secondary concern is height, since precocious puberty leads to accelerated growth, accelerated bone maturation and ultimately reduced stature. Precocious puberty is heterogeneous and strict criteria should be used to define it, both in terms of age and in terms of potential for progression. Depot forms of GnRH agonists are now the standard treatment for progressive central precocious puberty and aim at alleviating the clinical symptoms of early pubertal development, their psychological consequences and the effects on growth. Here, we review the consequences of both central and gonadotropin-independent precocious puberty on adult stature and the information available on outcomes using the therapeutic regimens currently available. In girls with progressive precocious puberty, all published evidence indicates a gain of adult height over height predicted before treatment or over untreated historical controls. However, the apparent height gain (derived from the comparison of predicted and actual heights) is very variable, in large part due to the inaccuracy of height prediction methods. In girls with onset of puberty at the lower half of the normal age (8‐10 years) distribution, trials using GnRH agonists have given negative results (no benefit of treatment). In boys, precocious puberty is rare and fewer results are available but point in the same direction. The most appropriate time for interrupting the treatment is still controversial. In conclusion, GnRH agonists restore adult height in children when it is compromised by precocious puberty.


title: Large-scale image analysis using docker sandboxing

abstract: With the advent of specialized hardware such as Graphics Processing Units (GPUs), large scale image localization, classification and retrieval have seen increased prevalence. Designing scalable software architecture that co-evolves with such specialized hardware is a challenge in the commercial setting. In this paper, we describe one such architecture (\textit{Cortexica}) that leverages scalability of GPUs and sandboxing offered by docker containers. This allows for the flexibility of mixing different computer architectures as well as computational algorithms with the security of a trusted environment. We illustrate the utility of this framework in a commercial setting i.e., searching for multiple products in an image by combining image localisation and retrieval.


Penile ulceration: a rare manifestation of cutaneous crohn's successfully treated with subcutaneous methotrexate


title: Brain function in coma, vegetative state, and related disorders

abstract: Summary We review the nosological criteria and functional neuroanatomical basis for brain death, coma, vegetative state, minimally conscious state, and the locked-in state. Functional neuroimaging is providing new insights into cerebral activity in patients with severe brain damage. Measurements of cerebral metabolism and brain activations in response to sensory stimuli with PET, fMRI, and electrophysiological methods can provide information on the presence, degree, and location of any residual brain function. However, use of these techniques in people with severe brain damage is methodologically complex and needs careful quantitative analysis and interpretation. In addition, ethical frameworks to guide research in these patients must be further developed. At present, clinical examinations identify nosological distinctions needed for accurate diagnosis and prognosis. Neuroimaging techniques remain important tools for clinical research that will extend our understanding of the underlying mechanisms of these disorders.


title: Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic

abstract: The Early Detection, Alert and Response (eDare) system is aimed at purifying Web traffic propagating via the premises of Network Service Providers (NSP) from malicious code. To achieve this goal, the system employs powerful network traffic scanners capable of cleaning traffic from known malicious code. The remaining traffic is monitored and Machine Learning (ML) algorithms are invoked in an attempt to pinpoint unknown malicious code exhibiting suspicious morphological patterns. Decision trees, Neural Networks and Bayesian Networks are used for static code analysis in order to determine whether a suspicious executable file actually inhabits malicious code. These algorithms are being evaluated and preliminary results are encouraging.


title: FACE RECOGNITION AS A BIOMETRICS APPROACH USING THERMAL IMAGES

abstract: Biometrics is a technology used for measuring and analyzing a person's unique characteristics. Making a secure biometric system is not as easy as it might appear. The word biometrics is very often used as a synonym for the perfect security. Human Biometrics is now moving towards thermal imaging. Thermal imaging is simply the technique of using the heat given off by an object to produce an image of it. A thermal image provides more accurate information than the visual image. Hence a novel approach to face recognition based on thermal imaging is implemented here. The thermogram of the human face was captured from an infrared camera. In this work, an effective method to extract the thermal pattern of the human face using thermal images is presented. Thermal pattern of the human face is obtained by implementing the image processing techniques. Segmentation of human face from the rest of the image is the fundamental step in this process. After the face segmentation, the noise in the thermal image is removed using an appropriate standard filter. Then the morphological operations such as opening operation and top-hat method are performed on the image. From the result of the top-hat segmentation method, the features such as three quantitative distance measures are selected and compared with different subjects. Thirty volunteers were involved in this experimental procedure.


title: Executive function in preschoolers: A review using an integrative framework.

abstract: During the last 2 decades, major advances have been made in understanding the development of executive functions (EFs) in early childhood. This article reviews the EF literature during the preschool period using an integrative framework. The framework adopted considers EF to be a unitary construct with partially dissociable components (A. Miyake et al., 2000). The authors focus on 3 EF components: working memory, response inhibition, and shifting. For the present purposes, the central executive is conceived of as a central attention system that is involved in all EF component operations. Research to date suggests that elementary forms of the core EF components are present early during the preschool period. Changes in EF during the latter half of the preschool period appear to be due to the development of attention and integration of component EFs. Finally, the review outlines a number of areas that warrant further investigation if researchers are to move forward in understanding early EF development.


title: Kinematic seismic response and bending of free-head piles in layered soil

abstract: The paper studies the kinematic response of free-head piles. Such pile deformation has triggered structural damage in many strong earthquakes. In this Paper dimensionless parametric graphs for pile bending moments are presented which pertain to characteristic two-layer soil profiles. The results are derived by using an existing rigorous dynamic finite-element code, and by implementing a realistic beam-on-dynamic-Winkler-foundation formulation specifically developed for the kinematic response of piles in layered soil. The Winkler model is shown to reproduce quantitatively even detailed trends observed in the finite-element results; a simple analytical expression is thereby developed for estimating the Winkler stiffness in terms of the local soil Young's modulus and key dimensionless pile/ soil parameters. The study concludes that even relatively flexible piles may not exactly experience the wavy and abruptly changing ground deformation of the free field. The critical region of pile distress due to such kin...


title: Hand rehabilitation based on augmented reality

abstract: This paper presents an augmented reality based system for hand rehabilitation. In this system, digital data gloves are used to detect the movements of the patients' hands and collect the physical information from the patients. Using augmented reality technology, a highly controllable environment with tasks of different levels of difficulty is provided to the patients for them to perform the rehabilitation exercises gradually. The targets of the exercises can be adjusted dynamically with respect to the physical conditions and progress of the patients. Multimodal feedbacks are provided to facilitate and encourage the patients during the rehabilitation sessions.


title: Machine learning for attack vector identification in malicious source code

abstract: As computers and information technologies become ubiquitous throughout society, the security of our networks and information technologies is a growing concern. As a result, many researchers have become interested in the security domain. Among them, there is growing interest in observing hacker communities for early detection of developing security threats and trends. Research in this area has often reported hackers openly sharing cybercriminal assets and knowledge with one another. In particular, the sharing of raw malware source code files has been documented in past work. Unfortunately, malware code documentation appears often times to be missing, incomplete, or written in a language foreign to researchers. Thus, analysis of such source files embedded within hacker communities has been limited. Here we utilize a subset of popular machine learning methodologies for the automated analysis of malware source code files. Specifically, we explore genetic algorithms to resolve questions related to feature selection within the context of malware analysis. Next, we utilize two common classification algorithms to test selected features for identification of malware attack vectors. Results suggest promising direction in utilizing such techniques to help with the automated analysis of malware source code.


title: Supervised Kernel Descriptors for Visual Recognition

abstract: In visual recognition tasks, the design of low level image feature representation is fundamental. The advent of local patch features from pixel attributes such as SIFT and LBP, has precipitated dramatic progresses. Recently, a kernel view of these features, called kernel descriptors (KDES), generalizes the feature design in an unsupervised fashion and yields impressive results. In this paper, we present a supervised framework to embed the image level label information into the design of patch level kernel descriptors, which we call supervised kernel descriptors (SKDES). Specifically, we adopt the broadly applied bag-of-words (BOW) image classification pipeline and a large margin criterion to learn the low-level patch representation, which makes the patch features much more compact and achieve better discriminative ability than KDES. With this method, we achieve competitive results over several public datasets comparing with state-of-the-art methods.


title: Single-Master Dual-Slave Surgical Robot With Automated Relay of Suture Needle

abstract: Automating ubiquitous surgical subtasks such as suturing makes surgery more efficient, e.g., reduction of surgeon fatigue and/or surgery time. However, fully autonomous control in a surgical robot is still difficult due to individual differences in layout and deformation of human organs. In this paper, we propose a system consisting of a single master and dual slave, in which the operator commands one of the slaves manually through a master and the other slave autonomously moves. The operator inserts the needle to organ manually using one of the slaves (Slave A1) with a master. The other slave (Slave A2) automatically grasps the tip of the needle and pulls it out from the organ, and hands the needle back to Slave A1 automatically. Finally, Slave A1 regrasps the needle and the suturing is performed by repeating this procedure. The estimated external force is used to recognize the needle insertion by Slave A1, which is manually controlled with the master. This recognition of external force is used as a trigger to start the autonomous approach of Slave A2 to grasp the needle. We conducted single throwing and reinsertion in the experiment and compared the completion time using the proposed system and conventional bimanual teleoperation. The completion time was decreased by an average of 20% in total.


title: Flipped Voltage Follower Based Low Dropout (LDO) Voltage Regulators: A Tutorial Overview

abstract: This tutorial introduces flipped voltage follower (FVF) based low dropout voltage regulators (LDOs) and their recent advances in the literature. Conventional off-chip output capacitor LDOs deploy an off-chip output capacitor in the range of hundreds of nanofarads to tens of microfarads with an inherent equivalent series resistance (ESR) in order to attain stability and low overshoot and undershoot during load transients. Because of the large value of the output capacitor, the bandwidth of the LDO is often limited and the transient response is slow. Advancements in System-on-Chips (SoCs) and bill of materials cost often restrict designers from using off-chip components or too many inputoutput (IO) pins. Thus low value integrated on-chip capacitor LDOs are gaining importance. One of the major requirements of on-chip capacitor LDOs is to achieve high bandwidth. An FVF is an analog voltage buffer circuit with fast local feedback. It is capable of sourcing high currents, which makes it ideal candidate for LDOs. Design details such as pole-zero equations, stability, output resistance and transient response of basic and advanced FVF LDOs are discussed in this tutorial. The designs are analyzed with mathematical analysis and simulation results.


title: Exploiting Three-Dimensional Gaze Tracking for Action Recognition During Bimanual Manipulation to Enhance Human–Robot Collaboration

abstract: Human-robot collaboration could be advanced by facilitating the intuitive, gaze-based control of robots, and enabling robots to recognize human actions, infer human intent, and plan actions that support human goals. Traditionally, gaze tracking approaches to action recognition have relied upon computer vision-based analyses of 2D egocentric camera videos. The objective of this study was to identify useful features that can be extracted from 3D gaze behavior and used as inputs to machine learning algorithms for human action recognition. We investigated human gaze behavior and gaze-object interactions in 3D during the performance of a bimanual, instrumental activity of daily living: the preparation of a powdered drink. A marker-based motion capture system and binocular eye tracker were used to reconstruct 3D gaze vectors and their intersection with 3D point clouds of objects being manipulated. Statistical analyses of gaze fixation duration and saccade size suggested that some actions (pouring, stirring) may require more visual attention than other actions (reach, pick up, set down, move). Three-dimensional gaze saliency maps, generated with high spatial resolution for six subtasks, appeared to encode action-relevant information. The "gaze object sequence" was used to capture information about the identity of objects in concert with the temporal sequence in which the objects were visually regarded. Dynamic time warping barycentric averaging was used to create a population-based set of characteristic gaze object sequences that accounted for intra- and inter-subject variability. The gaze object sequence was used to demonstrate the feasibility of a simple action recognition algorithm that utilized a dynamic time warping Euclidean distance metric. Averaged over the six subtasks, the action recognition algorithm yielded an accuracy of 96.4%, precision of 89.5%, and recall of 89.2%. This level of performance suggests that the gaze object sequence is a promising feature for action recognition whose impact could be enhanced through the use of sophisticated machine learning classifiers and algorithmic improvements for real-time implementation. Robots capable of robust, real-time recognition of human actions during manipulation tasks could be used to improve quality of life in the home as well as quality of work in industrial environments.


title: Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs

abstract: RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. More specifically, we propose two different frameworks to build the semantic query graph, one is relation (edge)-first and the other one is node-first. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.


title: Wireless big data: transforming heterogeneous networks to smart networks

abstract: In HetNets (Heterogeneous Networks), each network is allocated with fixed spectrum resource and provides service to its assigned users using specific RAT (Radio Access Technology). Due to the high dynamics of load distribution among different networks, simply optimizing the performance of individual network can hardly meet the demands from the dramatically increasing access devices, the consequent upsurge of data traffic, and dynamic user QoE (Quality-of-Experience). The deployment of smart networks, which are supported by SRA (Smart Resource Allocation) among different networks and CUA (Cognitive User Access) among different users, is deemed a promising solution to these challenges. In this paper, we propose a frame-work to transform HetNets to smart networks by leveraging WBD (Wireless Big Data), CR (Cognitive Radio) and NFV (Network Function Virtualization) techniques. CR and NFV support resource slicing in spectrum, physical layers, and network layers, while WBD is used to design intelligent mechanisms for resource mapping and traffic prediction through powerful AI (Artificial Intelligence) methods. We analyze the characteristics of WBD and review possible AI methods to be utilized in smart networks. In particular, the potential of WBD is revealed through high level view on SRA, which intelligently maps radio and network resources to each network for meeting the dynamic traffic demand, as well as CUA, which allows mobile users to access the best available network with manageable cost, yet achieving target QoS (Quality-of-Service) or QoE.


title: Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging

abstract: A magnetic resonance imaging method is presented for quantifying the degree to which water diffusion in biologic tissues is non-Gaussian. Since tissue structure is responsible for the deviation of water diffusion from the Gaussian behavior typically observed in homogeneous solutions, this method provides a specific measure of tissue structure, such as cellular compartments and membranes. The method is an extension of conventional diffusion-weighted imaging that requires the use of somewhat higher b values and a modified image postprocessing procedure. In addition to the diffusion coefficient, the method provides an estimate for the excess kurtosis of the diffusion displacement probability distribution, which is a dimensionless metric of the departure from a Gaussian form. From the study of six healthy adult subjects, the excess diffusional kurtosis is found to be significantly higher in white matter than in gray matter, reflecting the structural differences between these two types of cerebral tissues. Diffusional kurtosis imaging is related to q-space imaging methods, but is less demanding in terms of imaging time, hardware requirements, and postprocessing effort. It may be useful for assessing tissue structure abnormalities associated with a variety of neuropathologies. Magn Reson Med 53:1432–1440, 2005. © 2005 Wiley-Liss, Inc.


title: DSAC — Differentiable RANSAC for Camera Localization

abstract: RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far not been used as part of such deep learning pipelines, because its hypothesis selection procedure is non-differentiable. In this work, we present two different ways to overcome this limitation. The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w.r.t. to all learnable parameters. We call this approach DSAC, the differentiable counterpart of RANSAC. We apply DSAC to the problem of camera localization, where deep learning has so far failed to improve on traditional approaches. We demonstrate that by directly minimizing the expected loss of the output camera poses, robustly estimated by RANSAC, we achieve an increase in accuracy. In the future, any deep learning pipeline can use DSAC as a robust optimization component.


title: Tutorial on Variational Autoencoders

abstract: In just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.


title: Class-Specific Image Deblurring

abstract: In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably. In this paper, we explore the potential of a class-specific image prior for recovering spatial frequencies attenuated by the blurring process. Specifically, we devise a prior based on the class-specific subspace of image intensity responses to band-pass filters. We learn that the aggregation of these subspaces across all frequency bands serves as a good class-specific prior for the restoration of frequencies that cannot be recovered with generic image priors. In an extensive validation, our method, equipped with the above prior, yields greater image quality than many state-of-the-art methods by up to 5 dB in terms of image PSNR, across various image categories including portraits, cars, cats, pedestrians and household objects.


Evaluation of Elastic Lift for Neck Rejuvenation


title: Why Some Schools Have More Underqualified Teachers Than Others

abstract: Unwanted localized image effects in diffusion transfer photographs are minimized by partially removing portions of the two transverse cassette walls which constrain retained film units against lateral movement.


title: CPU Scheduling for Power/Energy Management on Multicore Processors Using Cache Miss and Context Switch Data

abstract: Power and energy have become increasingly important concerns in the design and implementation of today's multicore/manycore chips. In this paper, we present two priority-based CPU scheduling algorithms, Algorithm Cache Miss Priority CPU Scheduler (CM-PCS) and Algorithm Context Switch Priority CPU Scheduler (CS-PCS), which take advantage of often ignored dynamic performance data, in order to reduce power consumption by over 20 percent with a significant increase in performance. Our algorithms utilize Linux cpusets and cores operating at different fixed frequencies. Many other techniques, including dynamic frequency scaling, can lower a core's frequency during the execution of a non-CPU intensive task, thus lowering performance. Our algorithms match processes to cores better suited to execute those processes in an effort to lower the average completion time of all processes in an entire task, thus improving performance. They also consider a process's cache miss/cache reference ratio, number of context switches and CPU migrations, and system load. Finally, our algorithms use dynamic process priorities as scheduling criteria. We have tested our algorithms using a real AMD Opteron 6134 multicore chip and measured results directly using the “KillAWatt” meter, which samples power periodically during execution. Our results show not only a power (energy/execution time) savings of 39 watts (21.43 percent) and 38 watts (20.88 percent), but also a significant improvement in the performance, performance per watt, and execution time · watt (energy) for a task consisting of 24 concurrently executing benchmarks, when compared to the default Linux scheduler and CPU frequency scaling governor.


Crowdsourcing HIV Test Promotion Videos: A Non-Inferiority Randomized Controlled Trial in China **In Press at Clinical Infectious Diseases**


title: Visual Saliency Based on Scale-Space Analysis in the Frequency Domain

abstract: We address the issue of visual saliency from three perspectives. First, we consider saliency detection as a frequency domain analysis problem. Second, we achieve this by employing the concept of nonsaliency. Third, we simultaneously consider the detection of salient regions of different size. The paper proposes a new bottom-up paradigm for detecting visual saliency, characterized by a scale-space analysis of the amplitude spectrum of natural images. We show that the convolution of the image amplitude spectrum with a low-pass Gaussian kernel of an appropriate scale is equivalent to an image saliency detector. The saliency map is obtained by reconstructing the 2D signal using the original phase and the amplitude spectrum, filtered at a scale selected by minimizing saliency map entropy. A Hypercomplex Fourier Transform performs the analysis in the frequency domain. Using available databases, we demonstrate experimentally that the proposed model can predict human fixation data. We also introduce a new image database and use it to show that the saliency detector can highlight both small and large salient regions, as well as inhibit repeated distractors in cluttered images. In addition, we show that it is able to predict salient regions on which people focus their attention.


THE EFFECTS OF MYOFASCIAL RELEASE VS STATIC STRETCHING ON HAMSTRINGS RANGE OF MOTION


title: Prevalence of overweight and obesity in Turkey.

abstract: The objectives of this study were to determine the prevalence of overweight and obesity in Turkey, and to investigate their association with age, gender, and blood pressure. A crosssectional population-based study was performed. A total of 20,119 inhabitants (4975 women and 15,144 men, age > 20 years) from 11 Anatolian cities in four geographic regions were screened for body weight, height, and systolic and diastolic blood pressure between the years 1999 and 2000. The overall prevalence rate of overweight was 25.0% and of obesity was 19.4%. The prevalence of overweight among women was 24.3% and obesity 24.6%; 25.9% of men were overweight, and 14.4% were obese. Mean body mass index (BMI) of the studied population was 27.59 +/- 4.61 kg/m(2). Mean systolic and diastolic blood pressure for women were 131.0 +/- 41.0 and 80.2 +/- 16.3 mm Hg, and for men 135.0 +/- 27.3 and 83.2 +/- 16.0 mm Hg. There was a positive linear correlation between BMI and blood pressure, and between age and blood pressure in men and women. Obesity and overweight are highly prevalant in Turkey, and they constitute independent risk factors for hypertension.


title: Research of security metric architecture for Next Generation Network

abstract: Constructing a security metric architecture in Next Generation Network is a critical part for network security assessment. In this paper, a three-dimensional security metric architecture model is given. In this model, four comprehensive metrics, which are vulnerability, threat, stability and survivability, are first presented. Then based on these four metrics, a hierarchical security metric architecture is devised, and instanced in a prototype system of NGN. Compared with qualitative metrics, the presented metrics are not only accurate, but also measurable and easy to collect based on the quantitative parameters. Since there is limited research result of the security metric architecture for NGN at present, this model can be referenced to a certain extent.


title: Horizon mapping: shadows for bump-mapped surfaces

abstract: Bump mapping produces realistic shading by perturbing normal vectors to a surface, but does not show the shadows that the bumps cast on nearby parts of the same surface. In this paper, these shadows are found from precomputed tables of horizon angles, listing, for each position entry, the elevation of the horizon in a sampled collection of directions. These tables are made for bumps on a standard flat surface, and then a transformation is developed so that the same tables can be used for an arbitrary curved parametrized surface patch. This necessitates a new method for scaling the bump size to the patch size. Incremental calculations can be used in a scan line algorithm for polygonal surface approximations. The errors in the bump shadows are discussed, as well as their anti-aliasing. (An earlier version of this article appeared as Max [10].)


title: Blockchain technology in the chemical industry: Machine-to-machine electricity market

abstract: The purpose of this paper is to explore applications of blockchain technology related to the 4th Industrial Revolution (Industry 4.0) and to present an example where blockchain is employed to facilitate machine-to-machine (M2M) interactions and establish a M2M electricity market in the context of the chemical industry. The presented scenario includes two electricity producers and one electricity consumer trading with each other over a blockchain. All participants are supplied with realistic data produced by process flow sheet models. This work contributes a proof-of-concept implementation of the scenario. Additionally, this paper describes and discusses the research and application landscape of blockchain technology in relation to the Industry 4.0. It concludes that this technology has significant under-researched potential to support and enhance the efficiency gains of the revolution and identifies areas for future research.


title: Making Augmented Reality a Reality

abstract: Augmented Reality has attracted interest for its potential as a platform for new compelling usages. This paper provides an overview of technical challenges in imaging and optics encountered in near-eye optical see-through AR display systems.


title: Physiology-Based Face Recognition in the Thermal Infrared Spectrum

abstract: The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as thermal minutia points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area


title: Wideband Inductorless Balun-LNA Employing Feedback for Low-Power Low-Voltage Applications

abstract: A wideband inductorless low-noise-amplifier (LNA) with single-to-differential conversion for multistandard radio applications is proposed. Noise-suppressed current-mirror-based biasing is utilized to ensure stable operation under process, voltage, and temperature variations. The inherent gain of the common-source (CS) stage is re-used to boost the trans-conductance of the common-gate (CG) stage, and hence, a noise- and power-efficient design is achieved without hurting the noise and distortion cancellation properties of the CG-CS-based balun topology. The gain and phase balance is improved by employing an efficient compensation scheme. The prototype was realized in 0.13- μm CMOS, operates from 0.1 to 2 GHz, and dissipates 3 mW from 1.2-V supply while occupying a 0.075-mm2 active area. The balun-LNA, including the output buffer, provides 7.6-dB maximum power gain, 4.15-dB minimum noise figure, better than -10-dB input matching, and 0.5-dBm IIP3 without any on-chip inductor.


Mutual information and minimum mean-square error in multiuser Gaussian channels


Case-based reasoning for improved micromanagement in Real-time


title: PDE-Net: Learning PDEs from Data

abstract: In this paper, we present an initial attempt to learn evolution PDEs from data. Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models. The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses. Comparing with existing approaches, which either assume the form of the nonlinear response is known or fix certain finite difference approximations of differential operators, our approach has the most flexibility by learning both differential operators and the nonlinear responses. A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network. These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory). We also discuss relations of the PDE-Net with some existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network (ResNet). Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.


A Smart Irrigation and Monitoring System


title: Posture and balance control for biped robots based on contact force optimization

abstract: This paper presents a new balancing control approach for regulating the center of mass position and trunk orientation of a bipedal robot in a compliant way. The controller computes a desired wrench (force and torque) required to recover the posture when an unknown external perturbation has changed the posture of the robot. This wrench is later distributed as forces at predefined contact points via a constrained optimization, which aims at achieving the desired wrench while minimizing the Euclidean norm of the contact forces. The formulation of the force distribution as an optimization problem is adopted from the grasping literature and allows to consider restrictions coming from the friction between the contact points and the ground.


title: Quality attributes of Web software applications

abstract: Web applications have very high requirements for numerous quality attributes. This article discusses some of the technological challenges of building today's complex Web software applications, their unique quality requirements, and how to achieve them.


title: Information Content Weighting for Perceptual Image Quality Assessment

abstract: Many state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage structure: local quality/distortion measurement followed by pooling. While significant progress has been made in measuring local image quality/distortion, the pooling stage is often done in ad-hoc ways, lacking theoretical principles and reliable computational models. This paper aims to test the hypothesis that when viewing natural images, the optimal perceptual weights for pooling should be proportional to local information content, which can be estimated in units of bit using advanced statistical models of natural images. Our extensive studies based upon six publicly-available subject-rated image databases concluded with three useful findings. First, information content weighting leads to consistent improvement in the performance of IQA algorithms. Second, surprisingly, with information content weighting, even the widely criticized peak signal-to-noise-ratio can be converted to a competitive perceptual quality measure when compared with state-of-the-art algorithms. Third, the best overall performance is achieved by combining information content weighting with multiscale structural similarity measures.


title: Automatic current-sharing method for multi-phase LLC resonant converter

abstract: An automatic current sharing method is proposed for multi-phase LLC resonant converter for high power application. There are two types, one is common inductor, and the other is common capacitor. Automatic current sharing is achieved by using a common component for all LLC resonant stages. The proposed method can automatically share the load current without any additional circuit and control strategy. The current share performance of the proposed method is analyzed under Fundamental Harmonic Analysis (FHA) assumption. A 600W two-phase LLC converter prototype based on the proposed method is built to verify the feasibility. Excellent current sharing performance has been achieved.


title: Milk Yield During the First Four Months of Lactation and Cow Productivity of Brahman and Tuli Beef Cattle in South-East Botswana

abstract: The climate of Botswana because of its semi-aridity, is mostly suitable for livestock farming, especially beef cattle production under extensive grazing conditions. The major indigenous cattle breed is the Tswana, while the Tuli (TT) and Brahman (BB) are popular and increasing in numbers to the cattle population of 2.5 million. In the present study, the TT (n=15) and BB (n=24) breeds were compared for cow productivity and milk production using the weigh-suckle-weigh technique. The TT cows produced more milk than BB cows (12.4 vs 9.2 kg/d) during the first four months of lactation. However, BB cows produced heavier (P<0.001) calves at weaning than TT cows (164.8 vs 150.4 kg). Similarly, BB cows produced faster (P<0.001) growing calves than TT cows (.69 vs .64 kg/d). Across breeds, calves born earlier in the season had a higher (P<0.01) average daily gain (ADG) than those born later in the season (.69 vs .64 kg/d). It is an advantage to producers to have the majority of calves born early in the calving season (September/October) so that calves are bigger and heavier at weaning than those calves born late in the season (November/December).


title: Structure and function of auditory cortex: music and speech

abstract: Music and speech represent the most cognitivelycomplex uses of sound by the human species. Thesetwo domains share a number of properties, includingthe fact that they take advantage of modulations ofacoustic parameters specifically for information-bearing purposes. Moreover, both music and speechare characterized by their generative nature: that is,complexity is built up by rule-based permutations ofa limited number of discrete elements (phonemes ortones) to yield meaningful structures (words ormelodies), which in turn are subject to furtherhierarchical organization resulting in more complexentities (such as sentences or songs) [1]. Apart fromthese formal considerations, music and speech showother interesting similarities. For example, bothshow specific and relatively fixed developmental timecourses [2], and all known human societies make useof both speech and music, regardless of technologicalsophistication [3].The foregoing ideas do not imply that music andspeech necessarily share either a similar underlyingcognitive or neural representation (for furtherdiscussion see Refs [4,5]), but they do suggest thatboth might derive from certain functional propertiesof our auditory nervous system. Put another way,given that all normal humans seem to be capable ofrelatively sophisticated musical and speechfunctions in the absence of explicit training, then itfollows that these cognitive–behavioral skills arelikely to be related to the functional organization ofthe human auditory nervous system. Just as a bat’sability to use echolocation is related to the uniqueorganization of its auditory system, so the humannervous system may be considered as beingorganized such that it enables people to readilyunderstand speech and music of the culture inwhich they are raised. This article explores whatthis organization might be, with the aim of throwinglight upon the neural mechanisms responsible forthe low-level perceptual input stage that is relevantfor music and speech, and, conversely, takingadvantage of speech and music to understand thefunction of the auditory cortex.