title: Constraint Induced Movement Techniques To Facilitate Upper Extremity Use in Stroke Patients.

abstract: A new therapeutic approach to the rehabilitation of movement after stroke, termed constraint-induced (CI) movement therapy, has been derived from basic research with monkeys given somatosensory deafferentation. CI movement therapy consists of a family of therapies; their common element is that they induce stroke patients to greatly increase the use of an affected upper extremity for many hours a day over a period of 10 to 14 consecutive days. The signature intervention involves motor restriction of the contralateral upper extremity in a sling and training of the affected arm. The therapies result in large changes in amount of use of the affected arm in the activities of daily living outside of the clinic that have persisted for the 2 years measured to date. Patients who will benefit from Cl therapy can be identified before the beginning of treatment.


title: Interpersonal attraction and personality: What is attractive--self similarity, ideal similarity, complementarity or attachment security?

abstract: Little is known about whether personality characteristics influence initial attraction. Because adult attachment differences influence a broad range of relationship processes, the authors examined their role in 3 experimental attraction studies. The authors tested four major attraction hypotheses-self similarity, ideal-self similarity, complementarity, and attachment security-and examined both actual and perceptual factors. Replicated analyses across samples, designs, and manipulations showed that actual security and self similarity predicted attraction. With regard to perceptual factors, ideal similarity, self similarity, and security all were significant predictors. Whereas perceptual ideal and self similarity had incremental predictive power, perceptual security's effects were subsumed by perceptual ideal similarity. Perceptual self similarity fully mediated actual attachment similarity effects, whereas ideal similarity was only a partial mediator.


title: Occult Fifth Nerve Dysfunction In Multiple Sclerosis

abstract: Somatosensory evoked potentials (SEPs) were recorded following trigeminal nerve stimulation in 25 normal subjects. Mucosal stimulation of the lip resulted in a reduced stimulus artefact. The three initial peaks, N13, P19, and N30, measured respectively 12.8 +/- 0.9 S-3, 19.3 +/- 1.4 S-3, and 28.6 +/- 1.7 S-3. Blink reflex studies were also performed in most of these subjects. In 41.4% of 29 patients with established or suspected multiple sclerosis, the trigeminal SEP was abnormal. Additional use of the blink reflex raised the overall incidence of trigeminal nerve dysfunction to 51.7%. None of the patients had clinical evidence of fifth nerve involvement either historically or on examination. Four of seven patients with progressive spinal MS and two patients whose only deficit was that of optic neuritis, had abnormal trigeminal SEPs. It is concluded that occult involvement of the pontine fifth nerve structures occurs frequently in MS despite the rarity of corresponding clinical findings. The trigeminal SEP is a useful additional neurophysiological method.


title: Recommendation system for big data applications based on set similarity of user preferences

abstract: Recommender system techniques are software techniques to provide users with tips on the object they need to devour or the item they want to apply. The conventional approach is to consider this as a decision problem and to solve it using rule based techniques, or cluster analysis. But recommendation systems are mainly employed in applications such as online market, which works with big data. Since, performing data mining on big data is a tedious task due to its distributed nature and enormity, instead of data mining, another method known as set-similarity join can be utilized. This paper proposes a solution for item recommendation for big data applications. The proposed work presents customized and personalized item recommendations and prescribes the most suitable items to the users successfully. In particular, key terms are used to indicate users preferences, and a user-based collaborative filtering algorithm is embraced to create suitable suggestions. Proposed work is designed to work with Hadoop, a broadly chosen distributed computing platform using the MapReduce framework


title: A New Controller for Bidirectional Wireless Power Transfer Systems

abstract: Wireless power transfer (WPT) is a promising technology for supplying power to various applications with no physical contacts. Recently, bidirectional WPT (BD-WPT) systems are gaining popularity for grid-to-vehicle and vehicle-to-grid applications which essentially require power transfer in both directions. However, BD-WPT systems are complex in nature, and require sophisticated control strategies to provide synchronization as well as to regulate the power flow between two sides. This paper proposes a new controller that uses measured active power (P) and reactive power (Q) at the resonant network of BD-WPT systems to regulate the power flow in both directions while providing synchronization between two sides without a dedicated communication interface for controlling power transfer. The controller, located on the pickup side, is applicable to BD-WPT systems with either single or multiple loads and ensures that the volt-ampere rating of the pickup converter is at lowest for battery charging, by keeping the reactive power at the resonant tank intake by the load side minimum. Experimental results obtained from a 1-kW prototype system show good agreement with simulated results, validating that the proposed controller can be used to regulate the power flow in BD-WPT systems.


title: A Dual-polarized Planar Antenna Using Four Folded Dipoles and Its Array for Base Stations

abstract: A ±45° dual-polarized base station antenna with a planar structure is proposed for 2G/3G bands. The +45° antenna and the −45° antenna share the same radiator, which is composed of four folded dipoles and four coplanar striplines. The coplanar striplines work as $\lambda $ /4 impedance transformers. Two Y-shaped feeding lines is designed to obtain a good impedance matching and a planar reflector is employed to achieve a stable radiation pattern. The antenna element has a wide impedance bandwidth of 27.8% with SWR 30 dB), a stable radiation pattern with 3-dB beamwidth 65° ± 5° in the horizontal plane, a high XPD and a high front-to-back ratio were achieved.


PWM and PFM Operation of DC / DC Converters for Portable Applications


title: Visual task solution strategies in tree diagrams

abstract: We investigate visual task solution strategies when exploring traditional, orthogonal, and radial node-link tree layouts, four orientations of the non-radial layouts, as well as varying difficulty of the task. The strategies are identified by examining eye movement data recorded in a controlled user study previously conducted by Burch et al. For detailed analysis of the spatio-temporal structures and patterns in the eye tracking data, we employ visual analytics techniques adopted from related methodology for geographic movement data by Andrienko et al. In this way, we complement the statistical analysis of task completion times and error rates reported by Burch et al. with spatio-temporal strategies that explain the variation in completion times. We identify differences between task solution strategies dependent on layout type, orientation, and task difficulty. Furthermore, we examine differences between groups of participants split according to completion time. Our analysis identifies that for all layouts it took nearly the same time to find the task solution node, but in the radial layout the solution was not confirmed directly. Instead, a more frequent cross-checking occurs afterwards, which is the main reason for the impaired performance of radial layouts.


title: Detection of sleep apnea using sub-frame based temporal variation of energy in beta band in EEG

abstract: Sleep apnea is a sleep disorder that affects one's breathing during sleep. A large number of people all over the world are suffering from this disease. Electroencephalogram (EEG) provides electrical activity of the brain signal that enables physicians to diagnose and monitor sleep apnea events. In this paper, an efficient scheme for classifying apnea and non-apnea events of an apnea patient is proposed based on temporal variation of Beta band energy in a frame of EEG data. Unlike conventional approaches, instead of extracting features from the whole frame at a time, a given test frame of EEG signal is divided into overlapping sub-frames and spectral characteristics are extracted from each pre-processed sub-frame. By investigating the spectro-temporal characteristics of all the traditional frequency bands of EEG signal, it is found that the temporal variation of spectral energy in Beta band plays the dominant role in classifying apnea and non-apnea events. Statistical features are extracted from the temporal pattern of Beta band energy and are used in K nearest neighborhood classifier. Extensive experimentation is carried out on several apnea patients with various apnea indices and a very satisfactory apnea detection performance is achieved in comparison to that obtained by some existing methods.


Places: People, Events, Loci – the Relation of Semantic Frames in the Construction of Place


Proposal Using Python To Teach Object-Oriented Programming in CS 1


title: LABVIEW based expert system for detection of heart abnormalities

abstract: The proposed method deals with the study and analysis of ECG signal and thereafter the detection of heart abnormalities using LabVIEW Biomedical toolkit effectively. In the first phase, ECG signal is acquired which is then followed by filtering the raw ECG signal to remove unwanted noises. The next phase focuses on extracting the features from the acquired signal. Finally, different types of abnormalities particularly Sinus Bradycardia, Sinus Tachycardia, Supraventricular Tachycardia (SVT) Abnormal, Atrial Flutter and 1st degree AV Block are detected on the basis of features obtained. Thus the developed system predicts the heart abnormalities of a person, even before him/her consulting a doctor (preliminary investigation). The technique is user-friendly, low cost and hence anyone skeptic of heart problem can analyze his/her ECG using this efficient method.


title: Efficient Bug Triaging Using Text Mining

abstract: Large open source software projects receive abundant rates of submitted bug reports. Triaging these incoming reports manually is error-prone and time consuming. The goal of bug triaging is to assign potentially experienced developers to new-coming bug reports. To reduce time and cost of bug triaging, we present an automatic approach to predict a developer with relevant experience to solve the new coming report. In this paper, we investigate the use of five term selection methods on the accuracy of bug assignment. In addition, we re-balance the load between developers based on their experience. We conduct experiments on four real datasets. The experimental results show that by selecting a small number of discriminating terms, the F-score can be significantly improved.


title: Can One TV Show Make a Difference? Will & Grace and the Parasocial Contact Hypothesis

abstract: Television has an opportunity to influence beliefs about groups with which individuals typically may have little direct social contact. This study describes a synthesis of the Contact Hypothesis and the concept of Parasocial Interaction to pose what we call the Parasocial Contact Hypothesis to test whether exposure to gay men on Will & Grace can influence attitudes toward gay men in general. Based on a study of 245 university students, this study examines the relationships among number and intimacy of gay social contacts, parasocial interaction, viewing frequency of Will & Grace, and scores on Herek's Attitudes Toward Gay Men and Lesbians scale. Increased viewing frequency and parasocial interaction were found to correlate with lower levels of sexual prejudice-a relationship that was most pronounced for those with the least amount of social contact with lesbians and gay men.


title: Pedestrian-Aware Engine Management Strategies for Plug-In Hybrid Electric Vehicles

abstract: Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) are increasingly being seen as a means of mitigating the pressing concerns of traffic-related pollution. While hybrid vehicles are usually designed with the objective of minimising fuel consumption, in this paper we propose engine management strategies that also take into account environmental effects of the vehicles to pedestrians outside of the vehicles. Specifically, we present optimisation based engine energy management strategies for PHEVs, that attempt to minimise the environmental impact of pedestrians along the route of the vehicle, while taking account of route dependent uncertainties. We implement the proposed approach in a real PHEV, and evaluate the performance in a hardware-in-the-loop platform. A variety of simulation results are given to illustrate the efficacy of our proposed approach.


title: Reptile: a Scalable Metalearning Algorithm

abstract: This paper considers metalearning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We present a remarkably simple metalearning algorithm called Reptile, which learns a parameter initialization that can be fine-tuned quickly on a new task. Reptile works by repeatedly sampling a task, training on it, and moving the initialization towards the trained weights on that task. Unlike MAML, which also learns an initialization, Reptile doesn't require differentiating through the optimization process, making it more suitable for optimization problems where many update steps are required. We show that Reptile performs well on some well-established benchmarks for few-shot classification. We provide some theoretical analysis aimed at understanding why Reptile works.


title: A Combined Batteryless Radio and WiFi Indoor Positioning for Hospital Nursing

abstract: This paper proposes a design of an efficient hospital nurse calling system which combines two types of indoor localization systems. The purpose of the first system is to locate patients while the second is to locate nurses equipped with their smart phones. The main goal of developing such system is to decrease the time taking for nurses to provide healthcare for patients. Patients' positioning system is RF based. Indeed, each patient is equipped with a wireless and battery-free call button. When the switch is pressed, a wireless telegram is sent to reference nodes that act like Wireless Sensor Networks (WSN). The positioning of patient is performed using trilateration method with the help of Received Signal Strength Indicator (RSSI) values. Hence, beacons will forward the received signal from patient’s call button to a central receiver module connected to a computer. A dedicated program has been developed to calculate the position of the call button and post it on an online database. On the other hand, the nurses’ localization system is WiFi-based. Nurses' positioning is done by determining the Time of Arrival (ToA) and the Angle of Arrival (AoA) between the mobile phone and the WiFi router. The mobile phone locations are posted to the online database as well. Our program performs a comparison between the nurses' and the patient's coordinates. The nearest nurse gets an alarm. As consequence, a patient gets care from the nearest available nurse in an efficient way and with less time. The proposed system is user-friendly and Internet of Things (IoT) based architecture integrating two heterogeneous localization systems seamlessly.


title: Analysis of Draw Bead Geometry on Wall Thinning and Concave/Convex Feature in Rectangular Deep Drawn Parts

abstract: The application of the draw bead could reduce the concave/convex wall features. However, it also affected the wall thinning. Therefore, it is difficult to determine the suitable draw bead geometry to obtain a straight wall without the wall thinning. In this study, the effects of draw bead geometry of height and width on concave/convex wall feature and wall thinning were investigated by using the finite element method (FEM) and experiments. Based on the stress distribution analysis, the increasing in draw bead width and the decreasing in draw bead height lead to the concave wall feature increased; however, the application of the too small draw bead width and the too large draw bead height generated the convex wall feature. The wall thinning also decreased as the draw bead width increased as well as the draw bead height decreased. Therefore, the application of suitable draw bead height and width significantly suppressed the concave/convex wall feature and wall thinning, which resulted in the straight wall with the smallest wall thinning could be achieved.


title: Ready for What? An Exploration of the Meaning of New Graduate Nurses' Readiness for Practice

abstract: Dialogue continues on the “readiness” of new graduates for practice despite significant advancements in the foundational educational preparation for nurses. In this paper, the findings from an exploratory study about the meaning of new graduate ”readiness” for practice are reported. Data was collected during focus group interviews with one-hundred and fifty nurses and new graduates. Themes were generated using content analysis. Our findings point to agreement about the meaning of new graduate nurses’ readiness for practice as having a generalist foundation and some job specific capabilities, providing safe client care, keeping up with the current realities of nursing practice, being well equipped with the tools needed to adapt to the future needs of clients, and possessing a balance of doing, knowing, and thinking. The findings from this exploratory study have implications for policies and programs targeted towards new graduate nurses entering


title: Uniform Solution Sampling Using a Constraint Solver As an Oracle

abstract: We consider the problem of sampling from solutions defined by a set of hard constraints on a combinatorial space. We propose a new sampling technique that, while enforcing a uniform exploration of the search space, leverages the reasoning power of a systematic constraint solver in a black-box scheme. We present a series of challenging domains, such as energy barriers and highly asymmetric spaces, that reveal the difficulties introduced by hard constraints. We demonstrate that standard approaches such as Simulated Annealing and Gibbs Sampling are greatly affected, while our new technique can overcome many of these difficulties. Finally, we show that our sampling scheme naturally defines a new approximate model counting technique, which we empirically show to be very accurate on a range of benchmark problems.


Real-time Multi-stereo Depth Estimation on GPU with Approximative Discontinuity Handling


title: The vector linear program solver Bensolve -- notes on theoretical background

abstract: Bensolve is an open source implementation of Benson’s algorithm and its dual variant. Both algorithms compute primal and dual solutions of vector linear programs (VLP), which include the subclass of multiple objective linear programs (MOLP). The recent version of Bensolve can treat arbitrary vector linear programs whose upper image does not contain lines. This article surveys the theoretical background of the implementation. In particular, the role of VLP duality for the implementation is pointed out. Some numerical examples are provided. In contrast to the existing literature we consider a less restrictive class of vector linear programs.


title: Action recognition using global spatio-temporal features derived from sparse representations

abstract: Abstract Recognizing actions is one of the important challenges in computer vision with respect to video data, with applications to surveillance, diagnostics of mental disorders, and video retrieval. Compared to other data modalities such as documents and images, processing video data demands orders of magnitude higher computational and storage resources. One way to alleviate this difficulty is to focus the computations to informative (salient) regions of the video. In this paper, we propose a novel global spatio-temporal self-similarity measure to score saliency using the ideas of dictionary learning and sparse coding. In contrast to existing methods that use local spatio-temporal feature detectors along with descriptors (such as HOG, HOG3D, and HOF), dictionary learning helps consider the saliency in a global setting (on the entire video) in a computationally efficient way. We consider only a small percentage of the most salient (least self-similar) regions found using our algorithm, over which spatio-temporal descriptors such as HOG and region covariance descriptors are computed. The ensemble of such block descriptors in a bag-of-features framework provides a holistic description of the motion sequence which can be used in a classification setting. Experiments on several benchmark datasets in video based action classification demonstrate that our approach performs competitively to the state of the art.


title: Computing on the edge of chaos: Structure and randomness in encrypted computation

abstract: This survey, aimed mainly at mathematicians rather than practitioners, covers recent devel- opments in homomorphic encryption (computing on encrypted data) and program obfuscation (gen- erating encrypted but functional programs). Current schemes for encrypted computation all use es- sentially the same "noisy" approach: they encrypt via a noisy encoding of the message, they decrypt using an "approximate" ring homomorphism, and in between they employ techniques to carefully con- trol the noise as computations are performed. This noisy approach uses a delicate balance between structure and randomness: structure that allows correct computation despite the randomness of the en- cryption, and randomness that maintains privacy against the adversary despite the structure. While the noisy approach "works", we need new techniques and insights, both to improve e ciency and to better understand encrypted computation conceptually. Mathematics Subject Classification (2010). Primary 68Qxx; Secondary 68P25.


title: TopicPanorama: A Full Picture of Relevant Topics

abstract: This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as well as distinctive topics from each source. Our approach models each textual corpus as a topic graph. These graphs are then matched using a consistent graph matching method. Next, we develop a level-of-detail (LOD) visualization that balances both readability and stability. Accordingly, the resulting visualization enhances the ability of users to understand and analyze the matched graph from multiple perspectives. By incorporating metric learning and feature selection into the graph matching algorithm, we allow users to interactively modify the graph matching result based on their information needs. We have applied our approach to various types of data, including news articles, tweets, and blog data. Quantitative evaluation and real-world case studies demonstrate the promise of our approach, especially in support of examining a topic-graph-based full picture at different levels of detail.


title: Lean/Agile Software Development Methodologies in Regulated Environments – State of the Art

abstract: Choosing the appropriate software development methodology is something which continues to occupy the minds of many IT professionals. The introduction of “Agile” development methodologies such as XP and SCRUM held the promise of improved software quality and reduced delivery times. Combined with a Lean philosophy, there would seem to be potential for much benefit. While evidence does exist to support many of the Lean/Agile claims, we look here at how such methodologies are being adopted in the rigorous environment of safety-critical embedded software development due to its high regulation. Drawing on the results of a systematic literature review we find that evidence is sparse for Lean/Agile adoption in these domains. However, where it has been trialled, “out-of-the-box” Agile practices do not seem to fully suit these environments but rather tailored Agile versions combined with more plan-based practices seem to be making inroads.


title: Analysis, Modeling, and Implementation of a Multidevice Interleaved DC/DC Converter for Fuel Cell Hybrid Electric Vehicles

abstract: Multiphase converter topologies for use in high-performance applications have received increasing interest in recent years. This paper proposes a novel multidevice interleaved boost converter (MDIBC) that interfaces the fuel cell with the powertrain of hybrid electric vehicles. In this research, a multidevice structure with interleaved control is proposed to reduce the input current ripples, the output voltage ripples, and the size of passive components with high efficiency compared with the other topologies. In addition, low EMI and low stress in the switches are expected. The proposed dc/dc converter is compared to other converter topologies such as conventional boost converter (BC), multidevice boost converter (MDBC), and two-phase interleaved boost converter (IBC) to verify its dynamic performance. Furthermore, a generalized small-signal model is derived for these dc/dc converters, which has not been previously discussed. A digital dual-loop control is designed to achieve the proper regulator for the converters with fast transient response. The dc/dc converter topologies and their controller are designed and investigated by using MATLAB/Simulink. Finally, the proposed converter (MDIBC) is experimentally validated with results obtained from a 30-kW prototype that has been built and tested in our laboratory based on TMS320F2808 DSP. The simulation and experimental results have demonstrated that the proposed converter is more efficient than other dc/dc converter topologies in achieving high performance and reliability for high-power dc/dc converters.


title: Association of depression with pre-diabetes, undiagnosed diabetes, and previously diagnosed diabetes: a meta-analysis

abstract: We performed a meta-analysis to analyze the associations of depression with pre-diabetes (PreDM), undiagnosed diabetes (UDM), and previously diagnosed diabetes (PDM), and whether the association was affected by important study characteristics. We searched relevant articles published in PubMed and EMBASE up to August, 2015. Studies reporting cross-sectional associations of depression with PreDM, UDM, or PDM compared with normal glucose metabolism (NGM) were included. Odds ratios (ORs) were pooled with random-effect and fixed-effect models. Subgroup analyses by sex, study mean age, different degrees of adjustment, publication year, quality score, and depression assessment scales were also performed. Twenty studies were eligible and included in current analysis. Summary estimates showed that compared with NGM individuals, prevalence of depression was moderately increased in PreDM (random-effect odds ratio (OR) 1.11, 95 % confidence interval (CI) 1.03–1.19) and UDM (OR 1.27, 95 % CI 1.02–1.59), and markedly increased in PDM (OR 1.80, 95 % CI 1.40–2.31). Subgroup analyses showed that the positive association remained only among studies with mean age <60 years old but not among those with mean age ≥60 years old. Summary estimates of ORs with cardiovascular disease adjustment substantially attenuated the association. Our findings suggested that risk of prevalent depression was gradually increased with the deterioration of glucose metabolism among younger age groups but not among older age groups. Comorbid cardiovascular diseases might be an important intermediate factor underlying the association between depression and diabetes.


title: Learning to Program: Gender Differences and Interactive Effects of Students' Motivation, Goals, and Self-Efficacy on Performance

abstract: Previous research in computer science education has demonstrated the importance of motivation for success in introductory programming. Theoretical constructs from self-regulated learning theory (SRL), which integrates several different types of metacognitive processes, as well as motivational constructs, have proved to be important predictors of success in most academic disciplines. These individual components of self-regulated learning (e.g., self-efficacy, metacognitive strategies) interact in complex ways to influence students' affective states and behaviors, which in turn influence learning outcomes. These elements have been previously examined individually in novice programmers, but we do not have a comprehensive understanding of how SRL constructs interact to influence learning to program. This paper reports on a study that examined the interaction of self-efficacy, intrinsic and extrinsic goal orientations, and metacognitive strategies and their impact on student performance in a CS1 course. We also report on significant gender differences in the relationships between SRL constructs and learning outcomes. We found that student performance had the expected motivational and SRL precursors, but the interactions between these constructs revealed some unexpected relationships. Furthermore, we found that females' self-efficacy had a different connection to programming performance than that of their male peers. Further research on success in introductory programming should take account of the unique and complex relationship between SRL and student success, as well as gender differences in these relationships that are specific to CS.


title: Analysis of ISP caching in information-centric networks

abstract: Information-centric networking (ICN) models have recently emerged as novel network architectures for dealing with current Internet usage, where most users are interested in retrieving content without regard to its physical location. In this paper, we consider a noncooperative game between an Internet Service Provider (ISP) and content provider (CP) in an ICN model where the ISP is motivated to cache content and the polarity of the side-payment (from the ISP to the CP) in an ICN is basically different from that in the current Internet model (i.e. host-centric communication model). We analyze the existence of equilibrium in the game where in-network caching and pricing are taken into account. We show a condition with respect to the demand-response and caching cost functions for caching investment of the ISP profitable. The Nash equilibrium, the impact of caching on the utility, and comparison between the ICN and Internet models are studied numerically.


title: Lateralization in male rats and dopaminergic system: Evidence of right-side population bias

abstract: Abstract Recent studies have reported population right-biased lateralization in rats, although with low percentages (between 54–59%). The present study investigated the spatial preference of rats in an electrified T-maze during successive days and the influence of ascending dopaminergic systems using apomorphine, a dopamine agonist, as weel as ipsilateral and contralateral lesions on the side preference. Results showed a marked right-biased lateralization at both individual and population levels (85.71% of the rats presented a behavioral asymmetry, being 80% right-biased and 20% left-biased within the lateralized group). Also, the results suggest that there is a presynaptic dominance in the contralateral dopaminergic system to the spatial preference in the T-maze and postsynaptic dominance in the ipsilateral side.


title: Work climate and the mediating role of workplace bullying related to job performance, job satisfaction, and work ability: A study among hospital nurses

abstract: Aim ::: To increase understanding of workplace bullying and its relation to work climate and different outcomes among nurses. Examine a proposed bullying model including both job resource and job demands, as well as nurse outcomes reflected in job performance, job satisfaction, and work ability. ::: ::: Background ::: Workplace bullying has been identified as some of the most damaging mechanisms in workplace settings. It is important to increase understanding of workplace bullying in relation to work climate and different outcomes among nurses. ::: ::: Design ::: This study adopted a cross-sectional web based survey design. ::: ::: Method ::: A sample of 2946 registered nurses from four public Norwegian hospitals were collected during October 2014. We analysed data using descriptive statistics, correlations Cronbach's alpa, confirmatory factor analyses and structural equation modelling. ::: ::: Results ::: The majority of work climate characteristics confirmed to influence workplace bullying, and additionally had direct influence on nurse outcomes; job performance, job satisfaction, and work ability. Bullying had a mediational role between most of the work climate dimensions and nurse outcomes. ::: ::: Conclusion ::: This study increases our understanding of organisational antecedent of bullying among nurses. Workplace bullying among nurses functions as a mediator between the majority of work climate dimensions and outcomes related to job satisfaction and work ability. Strategies to reduce bullying should look at the study finding and specifically job resources and job demands that influence bullying and nurse outcomes. ::: ::: This article is protected by copyright. All rights reserved.


title: Osteoblast: osteoclast co-cultures on silk fibroin, chitosan and PLLA films.

abstract: This study investigates the growth of a co-culture of osteoblasts and osteoclasts on four different types of degradable biomaterials with bone tissue engineering potential. Single or co-cultures of osteoblasts and osteoclasts (used at a ratio of 1:100 osteoblast:osteoclasts) were cultured on vapour stabilised silk fibroin, methanol stabilised silk fibroin, chitosan and poly (l lactic acid) (PLLA) films for 10 days. Osteoclast differentiation was determined by tartrate resistant acid phosphatase (TRAP) staining, total cell number by a picogreen DNA assay, cell morphology by scanning electron microscopy (SEM) and the material topography by atomic force microscopy (AFM). Samples were also monitored for degradation by differential scanning calorimetry (DSC) and fourier transform infrared (FTIR). Results demonstrated that vapour stabilised silk fibroin, methanol stabilised silk fibroin and chitosan all support the growth of osteoblasts and osteoclasts in both single and co-cultures. PLLA showed poor osteoclast differentiation in both single and co-cultures but supported osteoblast attachment and proliferation. Both silk fibroin materials showed sign of early degradation in the ten-day period, but very little change was seen in chitosan and PLLA samples. This study indicates that this novel co-culture approach for bone tissue engineering may be possible if scaffolds are created from silk fibroin or chitosan.


title: Risk-Sensitive Control of Discrete-Time Markov Processes with Infinite Horizon

abstract: In this paper we study existence of solutions to the Bellman equation corresponding to risk-sensitive ergodic control of discrete-time Markov processes using three different approaches. Also, for particular classes of systems, asymptotics for vanishing risk factor is investigated, showing that in the limit the optimal value for an average cost per unit time is obtained.


Steganalysis Using Calibrated Side Information Feature


Global Syllable Vectors for Building TTS Front-End with Deep Learning.


title: A wideband circularly polarized antenna for GNSS

abstract: This paper is mainly to explore a wideband circularly polarized antenna, and the frequency bandwidth for the broadband global navigation satellite systems (GNSS) antenna from 1.2GHz to 1.6GHz. The antenna design is about a dual-polarized magneto-electric dipole antenna, and a power divider with phase shifter was mounted under the box-shaped reflector. The antenna structure is excited by two Γ-shaped orthogonal feeds in the middle of the antenna. The 10-dB return loss bandwidth from 1 to 1.7GHz. The gain at 1.2 to 1.6GHz is about 8.1dBi to 9.2 dBi. The 3 dB AR (axial-ratio bandwidth from 1.2 to 1.6GHz for GNSS.


title: A Rectangle Mining Method for Understanding the Semantics of Financial Tables

abstract: Financial statements report crucial information in tables with complex semantic structure, which are desirable, yet challenging, to interpret automatically. For example, in such tables a row of data cells is often explained by the headers of other rows. In a departure from prior art, we propose a rectangle mining framework for understanding complex tables, which considers rectangular regions rather than individual cells or pairs of cells in a table. We instantiate this framework with ReMine, an algorithm for extracting row header semantics of table, and show that it significantly outperforms prior pair-wise classification approaches on two datasets: (i) a set of manually labeled financial tables from multiple companies, and (ii) the ICDAR 2013 Table Competition dataset.


title: Cost-effective instrumentation via NILM to support a residential energy management system

abstract: Effective home energy management requires data on the current power consumption of devices in the home. Individually monitoring every appliance is costly and inconvenient. Non-Intrusive Load Monitoring (NILM) promises to provide individual electrical load information from aggregate power measurements. Application of NILM in residential settings has been constrained by the data provided by utility billing smart meters. Current utility billing smart meters do not deliver data that supports quantifying the harmonic content in the 60 Hz waveforms. Research in NILM has a critical need for a low-cost sensor system to collect energy data with fast sampling and significant precision to demonstrate actual data requirements. Implementation of cost-effective NILM in a residential consumer context requires real-time processing of this data to identify individual loads. This paper describes a system providing a powerful and flexible platform, supporting user configuration of sampling rates and amplitude resolution up to 65 kHz and up to 24 bits respectively. The internal processor is also capable of running NILM algorithms in real time on the sampled measurements. Using this prototype, real time load identification can be provided to the consumer for control, visualization, feedback, and demand response implications.


title: BLDC Motor-Driven Solar PV Array-Fed Water Pumping System Employing Zeta Converter

abstract: This paper proposes a solar photovoltaic (SPV) array fed water pumping system utilizing a zeta converter as an intermediate DC-DC converter in order to extract the maximum available power from the SPV array. Controlling the zeta converter in an intelligent manner through the incremental conductance maximum power point tracking (INC-MPPT) algorithm offers the soft starting of the brushless DC (BLDC) motor employed to drive a centrifugal water pump coupled to its shaft. Soft starting i.e. the reduced current starting inhibits the harmful effect of the high starting current on the windings of the BLDC motor. A fundamental frequency switching of the voltage source inverter (VSI) is accomplished by the electronic commutation of the BLDC motor, thereby avoiding the VSI losses occurred owing to the high frequency switching. A new design approach for the low valued DC link capacitor of VSI is proposed. The proposed water pumping system is designed and modeled such that the performance is not affected even under the dynamic conditions. Suitability of the proposed system under dynamic conditions is demonstrated by the simulation results using MATLAB/Simulink software.


title: CIRCA-GPUs: Increasing Instruction Reuse Through Inexact Computing in GP-GPUs

abstract: The authors introduce a method that exploits fine-grained parallelism and approximate computing in GP-GPU architecture to increase the energy efficiency through spatial and temporal reuse of instructions.


title: A novel EBG structure to improve isolation in MIMO antenna

abstract: A new, and advanced Electromagnetic Band Gap (EBG) structure is reported to reduce the mutual coupling between two tightly spaced rectangular patch antennas. The EBG structure provides more than 20 dB reduction in mutual coupling without degrading far-field patterns, gain, or bandwidth.


title: An Empirical Study on Organizational Culture, Social Capital, Organizational Learning and Enterprise Knowledge Integration Capability

abstract: Knowledge integration is the key for enterprise to capture sustainable competitive advantage. Based on review of preior research and papers on knowledge integration, a model is presented in this paper to explore the relationships among organizational culture, social capital, organizational learning and enterprise knowledge integration capability. Through theory analysis, the research framework was built and structural equation model was constructed. Then an empirical research was carried out based on a sample of 490 Chinese enterprises. Research results proved that organizational culture and organizational learning have positive direct effects on enterprise knowledge integration capability, but social capital does not directly affect enterprise knowledge integration capability. It is through organizational learning that social capital affects enterprise knowledge integration capability indirectly. Meanwhile, organizational culture has a positive effect on social capital, and social capital has a positive significant effect on organizational learning. The research result is of some value for enterprises to improve knowledge integration capability.


title: easIE: Easy-to-Use Information Extraction for Constructing CSR Databases From the Web

abstract: Public awareness of and concerns about companies’ social and environmental impacts have seen a marked increase over recent decades. In parallel, the quantity of relevant information has increased, as states pass laws requiring certain forms of reporting, researchers investigate companies’ performance, and companies themselves seek to gain a competitive advantage by being seen to operate fairly and transparently. However, this information is typically dispersed and non-standardized, making it complicated to collect and analyze. To address this challenge, the WikiRate platform aims to collect this information and store it in a standardized format within a centralized public repository, making it much more amenable to analysis. In the context of WikiRate, this article introduces easIE, an easy-to-use information extraction (IE) framework that leverages general Web IE principles for building datasets with environmental, social, and governance information from the Web. To demonstrate the flexibility and value of easIE, we built a large-scale corporate social responsibility database comprising 654,491 metrics related to 49,009 companies spending less than 16 hours for data engineering, collection, and indexing. Finally, a data collection exercise involving 12 subjects was performed to showcase the ease of use of the developed framework.


title: Assistive mobility devices focusing on Smart Walkers: Classification and review

abstract: In an aging society it is extremely important to develop devices, which can support and aid the elderly in their daily life. This demands means and tools that extend independent living and promote improved health. Thus, the goal of this article is to review the state of the art in the robotic technology for mobility assistive devices for people with mobility disabilities. The important role that robotics can play in mobility assistive devices is presented, as well as the identification and survey of mobility assistive devices subsystems with a particular focus on the walkers technology. The advances in the walkers' field have been enormous and have shown a great potential on helping people with mobility disabilities. Thus it is presented a review of the available literature of walkers and are discussed major advances that have been made and limitations to be overcome.


title: Controlling negative and positive power at the ankle with a soft exosuit

abstract: The soft exosuit is a new approach for applying assistive forces over the wearer's body through load paths configured by the textile architecture. In this paper, we present a body-worn lower-extremity soft exosuit and a new control approach that can independently control the level of assistance that is provided during negative- and positive-power periods at the ankle. The exosuit was designed to create load paths assisting ankle plantarflexion and hip flexion, and the actuation system transmits forces from the motors to the suit via Bowden cables. A load cell and two gyro sensors per leg are used to measure real-time data, and the controller performs position control of the cable on a step-by-step basis with respect to the power delivered to the wearer's ankle by controlling two force parameters, the pretension and the active force. Human subjects testing results demonstrate that the controller is capable of modulating the amount of power delivered to the ankle joint. Also, significant reductions in metabolic rate (11%–15%) were observed, which indicates the potential of the proposed control approach to provide benefit to the wearer during walking.


title: Printed Texture With Triangle Flat Pins for Bandwidth Enhancement of the Ridge Gap Waveguide

abstract: Lately, there has been a growing interest in the ridge gap waveguide (RGW) technology as a guiding structure for high-frequency applications. Low loss and low dispersion are two major advantages of the RGW. On the other hand, the major disadvantage of this technology is the difficulty in fabrication as it requires fabrication with high precision, in particular for high-frequency applications. The operating bandwidth of the RGW is controlled by the stopband of the texture surrounding the ridge. A study to enhance the bandwidth is introduced for possible utilization of the full band achievable by the unit cell in the presence of the ridge. Modifications of the cell filling shape and the ridge structure are carried out to enhance the RGW bandwidth. We have introduced a new RGW based on mixed fabrication technology. The proposed architecture introduces adaptive and straightforward structure with improved bandwidth while keeping the characteristic impedance at the same value. The proposed structure is fabricated and measured. The measured scattering parameters are in excellent agreement with the simulated ones.


title: Sentiment analysis of Twitter data within big data distributed environment for stock prediction

abstract: This paper covers design, implementation and evaluation of a system that may be used to predict future stock prices basing on analysis of data from social media services. The authors took advantage of large datasets available from Twitter micro blogging platform and widely available stock market records. Data was collected during three months and processed for further analysis. Machine learning was employed to conduct sentiment classification of data coming from social networks in order to estimate future stock prices. Calculations were performed in distributed environment according to Map Reduce programming model. Evaluation and discussion of results of predictions for different time intervals and input datasets proved efficiency of chosen approach is discussed here.


title: MAST: triage for market-scale mobile malware analysis

abstract: Malware is a pressing concern for mobile application market operators. While current mitigation techniques are keeping pace with the relatively infrequent presence of malicious code, the rapidly increasing rate of application development makes manual and resource-intensive automated analysis costly at market-scale. To address this resource imbalance, we present the Mobile Application Security Triage (MAST) architecture, a tool that helps to direct scarce malware analysis resources towards the applications with the greatest potential to exhibit malicious behavior. MAST analyzes attributes extracted from just the application package using Multiple Correspondence Analysis (MCA), a statistical method that measures the correlation between multiple categorical (i.e., qualitative) data. We train MAST using over 15,000 applications from Google Play and a dataset of 732 known-malicious applications. We then use MAST to perform triage on three third-party markets of different size and malware composition---36,710 applications in total. Our experiments show that MAST is both effective and performant. Using MAST ordered ranking, malware-analysis tools can find 95% of malware at the cost of analyzing 13% of the non-malicious applications on average across multiple markets, and MAST triage processes markets in less than a quarter of the time required to perform signature detection. More importantly, we show that successful triage can dramatically reduce the costs of removing malicious applications from markets.


Automatic Identification of Instrument Classes in Polyphonic and Poly-Instrument Audio


title: The Power of Two Choices in Randomized Load Balancing

abstract: We consider the following natural model: customers arrive as a Poisson stream of rate /spl lambda/n, /spl lambda/<1, at a collection of n servers. Each customer chooses some constant d servers independently and uniformly at random from the n servers and waits for service at the one with the fewest customers. Customers are served according to the first-in first-out (FIFO) protocol and the service time for a customer is exponentially distributed with mean 1. We call this problem the supermarket model. We wish to know how the system behaves and in particular we are interested in the effect that the parameter d has on the expected time a customer spends in the system in equilibrium. Our approach uses a limiting, deterministic model representing the behavior as n/spl rarr//spl infin/ to approximate the behavior of finite systems. The analysis of the deterministic model is interesting in its own right. Along with a theoretical justification of this approach, we provide simulations that demonstrate that the method accurately predicts system behavior, even for relatively small systems. Our analysis provides surprising implications. Having d=2 choices leads to exponential improvements in the expected time a customer spends in the system over d=1, whereas having d=3 choices is only a constant factor better than d=2. We discuss the possible implications for system design.


Reference 3 D Simulated Interactive Drama for Teenagers Coping with a Traumatic Brain Injury in a Parent


title: Classification system for partial edentulism

abstract: The American College of Prosthodontists (ACP) has developed a classification system for partial edentulism based on diagnostic findings. This classification system is similar to the classification system for complete edentulism previously developed by the ACP. These guidelines are intended to help practitioners determine appropriate treatments for their patients. Four categories of partial edentulism are defined, Class I to Class IV, with Class I representing an uncomplicated clinical situation and class IV representing a complex clinical situation. Each class is differentiated by specific diagnostic criteria. This system is designed for use by dental professionals involved in the diagnosis and treatment of partially edentulous patients. Potential benefits of the system include (1) improved intraoperator consistency, (2) improved professional communication, (3) insurance reimbursement commensurate with complexity of care, (4) improved screening tool for dental school admission clinics, (5) standardized criteria for outcomes assessment and research, (6) enhanced diagnostic consistency, and (7) simplified aid in the decision to refer a patient.


title: Unsupervised Learning of Important Objects from First-Person Videos

abstract: A first-person camera, placed at a person's head, captures, which objects are important to the camera wearer. Most prior methods for this task learn to detect such important objects from the manually labeled first-person data in a supervised fashion. However, important objects are strongly related to the camera wearer's internal state such as his intentions and attention, and thus, only the person wearing the camera can provide the importance labels. Such a constraint makes the annotation process costly and limited in scalability. ::: In this work, we show that we can detect important objects in first-person images without the supervision by the camera wearer or even third-person labelers. We formulate an important detection problem as an interplay between the 1) segmentation and 2) recognition agents. The segmentation agent first proposes a possible important object segmentation mask for each image, and then feeds it to the recognition agent, which learns to predict an important object mask using visual semantics and spatial features. ::: We implement such an interplay between both agents via an alternating cross-pathway supervision scheme inside our proposed Visual-Spatial Network (VSN). Our VSN consists of spatial ("where") and visual ("what") pathways, one of which learns common visual semantics while the other focuses on the spatial location cues. Our unsupervised learning is accomplished via a cross-pathway supervision, where one pathway feeds its predictions to a segmentation agent, which proposes a candidate important object segmentation mask that is then used by the other pathway as a supervisory signal. We show our method's success on two different important object datasets, where our method achieves similar or better results as the supervised methods.


title: Chair Massage for Treating Anxiety in Patients Withdrawing from Psychoactive Drugs

abstract: Abstract Context: Therapeutic massage has been proven to be an effective, nonpharmacologic, alternative for managing state and trait anxiety in a variety of clinical situations. However, no controlled study has investigated this effect in an addiction treatment setting. Aim: The aim of this study was to investigate the effectiveness of chair massage for reducing anxiety in persons participating in an inpatient withdrawal management program for psychoactive drugs. Design: The design was a randomized, controlled clinical trial conducted from June 2008 to January 2009. Subjects: Eighty-two (82) adult patients received inpatient treatment for psychoactive drug withdrawal (alcohol, cocaine, and opiates). Setting: This study was conducted at the Withdrawal Management Services at the Capital District Health Authority, Halifax, Nova Scotia. Interventions: Subjects were randomly assigned to receive chair massage (n = 40) or a relaxation control condition (n = 42). Treatments were offered for 3 consecutive days. St...


title: A novel RRT*-based algorithm for motion planning in Dynamic environments

abstract: Sampling-based motion planning has become a powerful framework for solving complex robotic motion-planning tasks. Despite the introduction of a multitude of algorithms, most of these deal with the static case involving non-moving obstacles. In this work, we are extending our memory efficient RRT*FN algorithm to dynamic scenarios. Specifically, we retain the useful parts of the tree (the data structure storing the motion plan information) after a dynamic obstacle invalidates the solution path. We then employ two greedy heuristics to repair the solution instead of running the whole motion planning process from scratch. We call this new algorithm, RRT*FN-Dynamic (RRT*FND). To compare our method to the state-of-the-art motion planners, RRT* and RRT*FN, we conducted an extensive set of benchmark experiments in dynamic environments using two robot models: a non-holonomic mobile robot and an industrial manipulator. The results of these experiments show that RRT*FND finds the solution path in shorter time in most of the cases and verifies the efficacy of it in dynamic settings.


title: Metaperceptions of bias in intimate relationships.

abstract: Meta-awareness of bias in intimate partner judgments was investigated in 3 studies. In Study 1, participants rated fictional partners in happier relationships as more positively biased in their partner perceptions. In Study 2, participants thought their judgments of their own current partners were positively biased and that they were judged by their partners in a positively biased fashion. Using a sample of couples, Study 3 showed that metaperceptions of bias were anchored to actual levels of bias at the individual and relationship levels. In addition, positive bias was accentuated for traits that were more relevant to mate evaluation. These findings (as expected) suggest that positive bias in partner judgments can be a normative and consciously accessible feature of intimate relationships.


title: Studying the impact of social interactions on software quality

abstract: Correcting software defects accounts for a significant amount of resources in a software project. To make best use of testing efforts, researchers have studied statistical models to predict in which parts of a software system future defects are likely to occur. By studying the mathematical relations between predictor variables used in these models, researchers can form an increased understanding of the important connections between development activities and software quality. Predictor variables used in past top-performing models are largely based on source code-oriented metrics, such as lines of code or number of changes. However, source code is the end product of numerous interlaced and collaborative activities carried out by developers. Traces of such activities can be found in the various repositories used to manage development efforts. In this paper, we develop statistical models to study the impact of social interactions in a software project on software quality. These models use predictor variables based on social information mined from the issue tracking and version control repositories of two large open-source software projects. The results of our case studies demonstrate the impact of metrics from four different dimensions of social interaction on post-release defects. Our findings show that statistical models based on social information have a similar degree of explanatory power as traditional models. Furthermore, our results demonstrate that social information does not substitute, but rather augments traditional source code-based metrics used in defect prediction models.


title: On Paraphrase Identification Corpora

abstract: We analyze in this paper a number of data sets proposed over the last decade or so for the task of paraphrase identification. The goal of the analysis is to identify the advantages as well as shortcomings of the previously proposed data sets. Based on the analysis, we then make recommendations about how to improve the process of creating and using such data sets for evaluating in the future approaches to the task of paraphrase identification or the more general task of semantic similarity. The recommendations are meant to improve our understanding of what a paraphrase is, offer a more fair ground for comparing approaches, increase the diversity of actual linguistic phenomena that future data sets will cover, and offer ways to improve our understanding of the contributions of various modules or approaches proposed for solving the task of paraphrase identification or similar tasks. We also developed a data collection tool, called Data Collector, that proactively targets the collection of paraphrase instances covering linguistic phenomena important to paraphrasing.


title: Real-time selective sequencing using nanopore technology

abstract: The Oxford Nanopore Technologies MinION sequencer enables the selection of specific DNA molecules for sequencing by reversing the driving voltage across individual nanopores. To directly select molecules for sequencing, we used dynamic time warping to match reads to reference sequences. We demonstrate our open-source Read Until software in real-time selective sequencing of regions within small genomes, individual amplicon enrichment and normalization of an amplicon set.


title: MM-wave LTE-A small-cell wireless backhauling based on TH-IR techniques

abstract: The backhauling will be one of the key issues for future wireless networking in the framework of LTE-A (Long Term Evolution-Advanced) standardization. 3GPP (3rd Generation Partnership Project) standardization committee has decided to study different alternative solutions for small cell backhauling, considering also broadband wireless solutions based on MM-wave Point-to-Point (P-t-P) and Point-to-multiPoint (P-t-mP) transmissions. Currently, E-band (81–86 GHz) is preferred for LOS backhauling, while the 28 GHz band has been intensively studied for NLOS (Not Line of Sight) backhauling. In this paper, we aim at proposing feasible and effective solutions for LOS small-cell wireless backhaul based on UWB (Ultra Wide Band) Time-Hopping Impulse Radio (TH-IR) techniques in E-band. The motivation of the choice of TH-IR lies in the robustness of such kind of signals and in ease of generation and detection. The intrinsically low spectral efficiency is compensated by the possibility of spanning the transmitted signal over the entire available bandwidth, keeping the power spectral density as low as required. Typical channel impairments affecting MM-wave transmission (nonlinear distortions, rain fading, oxygen absorption, phase noise, etc.) have been considered in our simulations. Results have shown the capability of TH-IR to reach a net capacity of 3.48 Gb/s at a distance superior to 1 Km in a point-to-multipoint 4-to-1 backhaul configuration.


title: Multi-candidate reduction: Sentence compression as a tool for document summarization tasks

abstract: This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization-a ''parse-and-trim'' approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations demonstrate that sentence compression is a valuable component of a larger multi-document summarization framework.


title: Privacy preserving EHR system using attribute-based infrastructure

abstract: Secure management of Electronic Health Records (EHR) in a distributed computing environment such as cloud computing where computing resources including storage is provided by a third party service provider is a challenging task. In this paper, we explore techniques which guarantees security and privacy of medical data stored in the cloud. We show how new primitives in attribute-based cryptography can be used to construct a secure and privacy-preserving EHR system that enables patients to share their data among healthcare providers in a flexible, dynamic and scalable manner.


title: Assessing Emotions by Cursor Motions: An Affective Computing Approach.

abstract: Choice reaching, e.g., reaching a targeted object by hand, involves a dynamic online integration of perception, action and cognition, where neural activities of prefrontal cortical regions are concurrently coordinated with sensori-motor subsystems. On the basis of this theoretical development, the authors investigate the extent to which cursor movements in a simple choice-reaching task reveal people’s emotions, such as anxiety. The results show that there is a strong correlation between cursor trajectory patterns and self-reported anxiety in male participants. Because computer cursors are ubiquitous, our trajectory analysis can be augmented to existing affective computing technologies.


title: Effect of fenfluramine-induced increases in serotonin release on [18F]MPPF binding: A continuous infusion PET study in conscious monkeys

abstract: [F-18]MPPF is a selective and reversible antagonist to the serotonin-1A (5-HT1A) receptor. The aim of the present study was to investigate whether the binding of [18F]MPPF is sensitive to increases in 5-HT levels. We used the 5-HT releasing agent and reuptake inhibitor fenfluramine (FEN) to increase the concentration of 5-HT. [F-18]MPPF binding was assessed using positron emission tomography (PET) in conscious monkeys. Possible effects of blood flow on ligand binding were excluded by using a bolus-infusion paradigm. Control scans were obtained to assess the state of ligand equilibrium. FEN (5 or 10 mg/kg, i.v.) was administered between 90 and 130 min after the start of the [F-18]MPPF infusion. The binding potential (BP) was calculated for an early interval (30 min preceding FEN administration) and late interval (20-50 min after administration of FEN). Microdialyses results showed a 20- and 35-fold increase in extracellular 5-HT levels in the prefrontal cortex after injection of FEN at a dose of 5 mg/kg and 10 mg/kg respectively. However, despite these large increases in 5-HT levels, no differences in BP were found between the control and FEN scans. These results may imply that the majority of 5-HT1A receptors is in the low affinity state in the living brain.


title: Support for family carers of children and young people with developmental disabilities and challenging behaviour

abstract: Background This study gathered information about perceptions of family carers of children and young people with developmental disabilities and challenging behaviour of the help, support and treatment received from services and professionals. ::: ::: Methods A total of 66 family carers completed postal questionnaires on the nature of, and their satisfaction with, services, professional help and advice received in respect of their family member's challenging behaviour. ::: ::: Results Most carers were dissatisfied with support and services received. Almost half reported receiving no professional input or none that was helpful. Over two-thirds reported receiving respite care but, in a third of these, the child had been excluded because of challenging behaviour. ::: ::: Conclusions Families of children with challenging behaviour often do not receive services and supports that they find helpful. Treatments provided are not always evidence based. The ‘rationing’ of services creates a danger of inequality of access. We need a more proactive approach to identifying and meeting the need for family support.


title: A Machine Learning Approach to Horizon Line Detection Using Local Features

abstract: Planetary rover localization is a challenging problem since no conventional methods such as GPS, structural landmarks etc. are available. Horizon line is a promising visual cue which can be exploited for estimating the rover's position and orientation during planetary missions. By matching the horizon line detected in 2D images captured by the rover with virtually generated horizon lines from 3D terrain models e.g., Digital Elevation MapsDEMs, the localization problem can be solved in principle. In this paper, we propose a machine learning approach for horizon line detection using edge images and local features i.e., SIFT. Given an image, first we apply Canny edge detection using various parameters and keep only those edges which survive over a wide range of thresholds. We refer to these edges as Maximally Stable Extremal Edges MSEEs. Using ground truth information, we then train an Support Vector Machine SVM classifier to classify MSEE pixels into two categories: horizon and non-horizon. Each MSSE pixel is described using SIFT features which becomes input to the SVM classifier. Given a novel image, we use the same procedure to extract MSSEs; then, we classify each MSEE pixel as horizon or non-horizon using the SVM classifier. MSEE pixels classified as horizon are then provided to a Dynamic Programming shortest path finding algorithm which returns a consistent horizon line. In general, Dynamic Programming returns different solutions i.e., due to gaps when searching for the optimum horizon line in a left-to-right or right-to-left fashion. We use the actual output of the SVM classifier to resolve ambiguities in places where the left-to-right and right-to-left solutions are different. The final solution, is typically a combination of edge segments from the left-to-right or right-to-left solutions. Moreover, we use the SVM classifier to fill in small gaps in the horizon line; this is in contrast to the traditional dynamic programming approach which relies on mere interpolation. We report promising experimental results using a set of real images.


title: Design and control of omni-directional mobile robot for Mobile Haptic Interface

abstract: A mobile haptic interface (MHI) refers to a system where a grounded force-feedback haptic interface is mounted onto a mobile robot to provide the user with unlimited workspace, especially for large virtual environments. In MHI, the mobile base needs to quickly change the movement direction, thus a omni-directional robot is preferred. In this paper, we present a novel omni-directional mobile robot designed for a mobile haptic interface (MHI), which uses four custom-made Mecanum wheels to provide higher operation stability. We also implemented two PI control methods (the conventional independent motor control and one combined with the Cartesian velocity control) and empirically evaluated their performance through an experiment. The experimental results indicated that the developed holonomic mobile robot allows accurate velocity control required for the MHI.


title: An index to quantify an individual's scientific research output that takes into account the effect of multiple coauthorship

abstract: I propose the index $$\hbar$$ ("hbar"), defined as the number of papers of an individual that have citation count larger than or equal to the $$\hbar$$ of all coauthors of each paper, as a useful index to characterize the scientific output of a researcher that takes into account the effect of multiple authorship. The bar is higher for $$\hbar.$$


title: Toward Covert Iris Biometric Recognition: Experimental Results From the NICE Contests

abstract: This paper announces and discusses the experimental results from the Noisy Iris Challenge Evaluation (NICE), an iris biometric evaluation initiative that received worldwide participation and whose main innovation is the use of heavily degraded data acquired in the visible wavelength and uncontrolled setups, with subjects moving and at widely varying distances. The NICE contest included two separate phases: 1) the NICE.I evaluated iris segmentation and noise detection techniques and 2) the NICE:II evaluated encoding and matching strategies for biometric signatures. Further, we give the performance values observed when fusing recognition methods at the score level, which was observed to outperform any isolated recognition strategy. These results provide an objective estimate of the potential of such recognition systems and should be regarded as reference values for further improvements of this technology, which-if successful-may significantly broaden the applicability of iris biometric systems to domains where the subjects cannot be expected to cooperate.


title: Real-time traffic sign recognition based on a general purpose GPU and deep-learning

abstract: We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).


title: Negotiation-based Human-Robot Collaboration via Augmented Reality

abstract: Effective human-robot collaboration (HRC) requires extensive communication among the human and robot teammates, because their actions can potentially produce conflicts, synergies, or both. In this paper, we develop an augmented reality-driven, negotiation-based (ARN) framework for HRC, where ARN supports planning-phase negotiations within human-robot teams through a novel AR-based interface. We have conducted experiments in an office environment, where multiple mobile robots work on delivery tasks. The robots could not complete the tasks on their own, but sometimes need help from their human teammate, rendering human-robot collaboration necessary. From the experimental results, we observe that ARN significantly reduced the human-robot team's task completion time in comparison to a non-AR baseline.


title: Awesome Typography: Statistics-Based Text Effects Transfer

abstract: In this work, we explore the problem of generating fantastic special-effects for the typography. It is quite challenging due to the model diversities to illustrate varied text effects for different characters. To address this issue, our key idea is to exploit the analytics on the high regularity of the spatial distribution for text effects to guide the synthesis process. Specifically, we characterize the stylized patches by their normalized positions and the optimal scales to depict their style elements. Our method first estimates these two features and derives their correlation statistically. They are then converted into soft constraints for texture transfer to accomplish adaptive multi-scale texture synthesis and to make style element distribution uniform. It allows our algorithm to produce artistic typography that fits for both local texture patterns and the global spatial distribution in the example. Experimental results demonstrate the superiority of our method for various text effects over conventional style transfer methods. In addition, we validate the effectiveness of our algorithm with extensive artistic typography library generation.


title: A critical look at technological innovation typology and innovativeness terminology: a literature review

abstract: Abstract A plethora of definitions for innovation types has resulted in an ambiguity in the way the terms ‘innovation’ and ‘innovativeness’ are operationalized and utilized in the new product development literature. The terms radical, really-new, incremental and discontinuous are used ubiquitously to identify innovations. One must question, what is the difference between these different classifications? To date consistent definitions for these innovation types have not emerged from the new product research community. A review of the literature from the marketing, engineering, and new product development disciplines attempts to put some clarity and continuity to the use of these terms. This review shows that it is important to consider both a marketing and technological perspective as well as a macrolevel and microlevel perspective when identifying innovations. Additionally, it is shown when strict classifications from the extant literature are applied, a significant shortfall appears in empirical work directed toward radical and really new innovations. A method for classifying innovations is suggested so that practitioners and academics can talk with a common understanding of how a specific innovation type is identified and how the innovation process may be unique for that particular innovation type. A recommended list of measures based on extant literature is provided for future empirical research concerning technological innovations and innovativeness.


title: Attribute weighted Naive Bayes classifier using a local optimization

abstract: The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.


title: A Simulated Annealing Global Maximum Power Point Tracking Approach for PV Modules Under Partial Shading Conditions

abstract: This paper proposes a simulated annealing (SA)-based global maximum power point tracking (GMPPT) technique designed for photovoltaic (PV) systems which experience partial shading conditions (PSC). The proposed technique is compared with the common perturb and observe MPPT technique and the particle swarm optimization method for GMPPT. The performance is assessed by considering the time taken to converge and the number of sample cases where the technique converges to the GMPP. Simulation results indicate the improved performance of the SA-based GMPPT algorithm, with arbitrarily selected parameters, in tracking to the global maxima in a multiple module PV system which experiences PSC. Experimental validation of the technique is presented based on PV modules that experience nonuniform environmental conditions. Additionally, studies regarding the influence of the key parameters of the SA-based algorithm are described. Simulation and experimental results verify the effectiveness of the proposed GMPPT method.


title: Reduced Overhead Training for Multi Reconfigurable Antennas with Beam-Tilting Capability

abstract: This paper proposes low overhead training techniques for a wireless communication system equipped with a Multifunctional Reconfigurable Antenna (MRA) capable of dynamically changing beamwidth and beam directions. A novel microelectromechanical system (MEMS) MRA antenna is presented with radiation patterns (generated using complete electromagnetic full-wave analysis) which are used to quantify the communication link performance gains. In particular, it is shown that using the proposed Exhaustive Training at Reduced Frequency (ETRF) consistently results in a reduction in training overhead. It is also demonstrated that further reduction in training overhead is possible using statistical or MUSIC-based training schemes. Bit Error Rate (BER) and capacity simulations are carried out using an MRA, which can tilt its radiation beam into one of Ndir = 4 or 8 directions with variable beamwidth (≈2π/Ndir). The performance of each training scheme is quantified for OFDM systems operating in frequency selective channels with and without Line of Sight (LoS). We observe 6 dB of gain at BER = 10-4 and 6 dB improvement in capacity (at capacity = 6 bits/sec/subcarrier) are achievable for an MRA with Ndir= 8 as compared to omni directional antennas using ETRF scheme in a LoS environment.


title: STANFORD UNIVERSITYVenture-Capital Financing and the Growth of Startup Firms

abstract: Abstract This study examines the association between the presence of venture capital (VC) and the employee growth of startups. Grounded in signaling theory, it investigates the impact, if any, of VC financing events upon the growth of these companies and whether the amount of funding affects the intensity of the signal. It further explores whether VC leads to growth or, alternatively, whether growth signals the need for VC. Finally, it documents the relationship between growth in startup financial valuation and changes in the number of employees over successive rounds of financing.


title: Not All Contexts Are Created Equal: Better Word Representations with Variable Attention

abstract: We introduce an extension to the bag-ofwords model for learning words representations that take into account both syntactic and semantic properties within language. This is done by employing an attention model that finds within the contextual words, the words that are relevant for each prediction. The general intuition of our model is that some words are only relevant for predicting local context (e.g. function words), while other words are more suited for determining global context, such as the topic of the document. Experiments performed on both semantically and syntactically oriented tasks show gains using our model over the existing bag of words model. Furthermore, compared to other more sophisticated models, our model scales better as we increase the size of the context of the model.


title: Online Semi-Supervised Discriminative Dictionary Learning for Sparse Representation

abstract: We present an online semi-supervised dictionary learning algorithm for classification tasks. Specifically, we integrate the reconstruction error of labeled and unlabeled data, the discriminative sparse-code error, and the classification error into an objective function for online dictionary learning, which enhances the dictionary's representative and discriminative power. In addition, we propose a probabilistic model over the sparse codes of input signals, which allows us to expand the labeled set. As a consequence, the dictionary and the classifier learned from the enlarged labeled set yield lower generalization error on unseen data. Our approach learns a single dictionary and a predictive linear classifier jointly. Experimental results demonstrate the effectiveness of our approach in face and object category recognition applications.


title: Boltzmann Machines and Denoising Autoencoders for Image Denoising

abstract: Image denoising based on a probabilistic model of local image patches has been employed by various researchers, and recently a deep (denoising) autoencoder has been proposed by Burger et al. [2012] and Xie et al. [2012] as a good model for this. In this paper, we propose that another popular family of models in the field of deep learning, called Boltzmann machines, can perform image denoising as well as, or in certain cases of high level of noise, better than denoising autoencoders. We empirically evaluate the two models on three different sets of images with different types and levels of noise. Throughout the experiments we also examine the effect of the depth of the models. The experiments confirmed our claim and revealed that the performance can be improved by adding more hidden layers, especially when the level of noise is high.


title: Six-Phase Voltage Source Inverter Driven Induction Motor

abstract: A six-phase six-step voltage-fed induction motor is presented. The inverter is a transistorized six-step voltage source inverter, while the motor is a modified standard three-phase squirrel-cage motor. The stator is rewound with two three-phase winding sets displaced from each other by 30 electrical degrees. A model for the system is developed to simulate the drive and predict its performance. The simulation results for steady-state conditions and experimental measurements show very good correlation. It is shown that this winding configuration results in the elimination of all air-gap flux time harmonics of the order (6v ±1, v = 1,3,5,...). Consequently, all rotor copper losses produced by these harmonics as well as all torque harmonics of the order (6v, v = 1,3,5,...) are eliminated. A comparison between-the measured instantaneous torque of both three-phase and six-phase six-step voltage-fed induction machines shows the advantage of the six-phase system over the three-phase system in eliminating the sixth harmonic dominant torque ripple.


title: Flea-Inspired Catapult Mechanism for Miniature Jumping Robots

abstract: Fleas can jump more than 200 times their body length. They do so by employing a unique catapult mechanism: storing a large amount of elastic energy and releasing it quickly by torque reversal triggering. This paper presents a flea-inspired catapult mechanism for miniature jumping robots. A robotic design was created to realize the mechanism for the biological catapult with shape memory alloy (SMA) spring actuators and a smart composite microstructure. SMA spring actuators replace conventional actuators, transmissions, and the elastic element to reduce the size. The body uses a four-bar mechanism that simulates a flea's leg kinematics with reduced degrees of freedom. Dynamic modeling was derived, and theoretical jumping was simulated to optimize the leg design for increased takeoff speed. A robotic prototype was fabricated with 1.1-g weight and 2-cm body size that can jump a distance of up to 30 times its body size.


title: Evolving Concepts of Pathogenesis in Atopic Dermatitis and Other Eczemas

abstract: The eczemas represent a common and diverse group of inflammatory skin diseases whose definitions and pathogenic mechanisms have often been confused and controversial. Since the millennium, fresh approaches are providing better insight. Research has focused much more upon the epidermis and the very relevant signaling pathways that contribute to spongiosis, proliferation, generation of proinflammatory factors, and differentiation to form an effective stratum corneum barrier. A major step in understanding has come from the solidly confirmed association between filaggrin null mutations of ichthyosis vulgaris and atopic dermatitis. Similar associations relating to protease and lipid defects have highlighted the role of barrier disruption that allows greater access of environmental toxins, microbes, and allergens. Animal models are beginning to predict mechanisms in which such direct perturbation of keratinocytes may initiate inflammation and condition immune responses in irritant contact dermatitis and atopic dermatitis. These conceptual shifts are nurturing more balanced approaches to understanding eczema and hold the hope for better prevention efforts and more specific molecular targeting for therapy.


title: An efficient protocol for oblivious DFA evaluation and applications. IACR Cryptology ePrint Archive

abstract: In this paper, we design an efficient protocol for oblivious DFA evaluation between an input holder (client) and a DFA holder (server). The protocol runs in a single round, and only requires a small amount of computation by each party. The most efficient version of our protocol only requires O(k) asymmetric operations by either party, where k is the security parameter. Moreover, the client's total computation is only linear in his own input and independent of the size of the DFA. We prove the protocol fully-secure against a malicious client and private against a malicious server, using the standard simulation-based security definitions for secure two-party computation. ::: ::: We show how to transform our construction in order to solve multiple variants of the secure pattern matching problem without any computational overhead. The more challenging variant is when parties want to compute the number of occurrences of a pattern in a text (but nothing else). We observe that, for this variant, we need a protocol for counting the number of accepting states visited during the evaluation of a DFA on an input. We then introduce a novel modification to our original protocol in order to solve the counting variant, without any loss in efficiency or security. ::: ::: Finally, we fully implement our protocol and run a series of experiments on a client/server network environment. Our experimental results demonstrate the efficiency of our proposed protocol and, confirm the particularly low computation overhead of the client.


title: An Approach to Data Privacy in Smart Home using Blockchain Technology

abstract: Nowadays, numerous applications of smart home systems provide recommendations for users, including reducing their energy consumption, warnings of defective devices, selecting reliable devices and software, diagnoses, etc [1]. The internet connected, dynamic and heterogeneous nature of the smart home environment creates new security, authentication, and privacy challenges [2]. To solve those challenges, an approach to data privacy in smart home using blockchain technology, which is called smart home based the IoT-Blockchain (SHIB), is proposed in this paper. In order to demonstrate the proposed architecture, an experimental scenario using Ganache, Remix, and web3. js is built among the user, service provider, and smart home to evaluate the performance of the smart contract in the SHIB. Based on the experiment results, the SHIB architecture brings the advantages like data privacy, trust access control, and high extension ability. In addition, the comparison between the proposed architecture and existing models in different parameters such as smart contract, the privacy of data, usage of tokens, updating the policies, and misbehavior judging are performed.


title: A way to reverse CAD

abstract: PURPOSE ::: Plant-based nutrition achieved coronary artery disease (CAD) arrest and reversal in a small study. However, there was skepticism that this approach could succeed in a larger group of patients. The purpose of our follow-up study was to define the degree of adherence and outcomes of 198 consecutive patient volunteers who received counseling to convert from a usual diet to plant-based nutrition. ::: ::: ::: METHODS ::: We followed 198 consecutive patients counseled in plant-based nutrition. These patients with established cardiovascular disease (CVD) were interested in transitioning to plant-based nutrition as an adjunct to usual cardiovascular care. We considered participants adherent if they eliminated dairy, fish, and meat, and added oil. ::: ::: ::: RESULTS ::: Of the 198 patients with CVD, 177 (89%) were adherent. Major cardiac events judged to be recurrent disease totaled one stroke in the adherent cardiovascular participants—a recurrent event rate of .6%, significantly less than reported by other studies of plant-based nutrition therapy. Thirteen of 21 (62%) nonadherent participants experienced adverse events. ::: ::: ::: CONCLUSION ::: Most of the volunteer patients with CVD responded to intensive counseling, and those who sustained plant-based nutrition for a mean of 3.7 years experienced a low rate of subsequent cardiac events. This dietary approach to treatment deserves a wider test to see if adherence can be sustained in broader populations. Plant-based nutrition has the potential for a large effect on the CVD epidemic.


title: Modulation strategy to operate the dual active bridge DC-DC converter under soft switching in the whole operating range

abstract: A new modulation strategy that allows operating the dual active bridge (DAB) dc-dc converter under soft switching in the whole operating range is proposed. This strategy is ruled by imposing a certain modulation index in one of the two bridges and a phase shift between the transformer primary and secondary voltages. Moreover, the proposed algorithm reduces the reactive power and thus reducing the converter conduction losses. An experimental prototype was implemented and some experimental results are presented to validate the theoretical analysis. The experimental results reveal that the overall efficiency of the DAB topology can be improved up to 20% by implementing the proposed modulation strategy instead of the conventional one.


title: Chiron: a parallel engine for algebraic scientific workflows

abstract: Large-scale scientific experiments based on computer simulations are typically modeled as scientific workflows, which eases the chaining of different programs. These scientific workflows are defined, executed and monitored by Scientific Workflow Management Systems (SWfMS). As these experiments manage large amounts of data, it becomes critical to execute them in High Performance Computing (HPC) environments, such as clusters, grids and clouds. However, few SWfMS provide parallel support. The ones that do so are usually labor-intensive for workflow developers and have limited primitives to optimize workflow execution. To address these issues, we developed a workflow algebra to specify and enable the optimization of parallel execution of scientific workflows. In this paper, we show how the workflow algebra is efficiently implemented in Chiron, an algebraic based parallel scientific workflow engine. Chiron has a unique native distributed provenance mechanism that enables run-time queries in a relational database. We developed two studies to evaluate the performance of our algebraic approach implemented in Chiron; the first study compares Chiron with different approaches while the second one evaluates the scalability of Chiron. By analyzing the results, we conclude that Chiron is efficient in executing scientific workflows, with the benefits of declarative specification and runtime provenance support.


title: Map-BasedPrecisionVehicleLocalization

abstract: Many urban navigation applications (e.g., autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy. Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings. We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments. Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps. Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap. By reducing the final map to the flat road surface, imprints of other vehicles are removed. The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution. To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map. As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude. Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10centimeter range. Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic. The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads.


title: Morphology Control of TiO2-Based Nanomaterials for Sustainable Energy Applications

abstract: TiO2-based nanomaterials (including layered titanates, H2TinO2n+1, which transform into TiO2 at high temperatures) have attracted much attention because of their fascinating characteristics relating to renewable energy and environmental applications. Recently, one-dimensional (1D) TiO2 nanomaterials, such as nanotube, nanowire, nanorod, nanofiber, have been investigated for electrode materials in dye-sensitized solar cells (DSC) to improve light-to-electricity conversion efficiency. These 1D nanomaterials are also applied for lithium ion or hydrogen storage, photocatalysts, multifunctional filter materials and so on. In this paper, our recent studies on 1D TiO2 nanomaterials, particularly on sustainable energy applications, are briefly overviewed. Some collaborative studies on TiO2 nanomaterials with French groups, i.e., Mines ParisTech (from 2006) and Strasbourg University (from 2008), are introduced.


title: Oral Health and Salivary Profiles of Geriatric Outpatients in Cipto Mangunkusumo General Hosp ital

abstract: Introduction: Population of geriatric patients would increase in line with that of elderly population. Health problems related to this group of people would have impact on general and oral health maintenance aiming for good quality of life. Objectives: This study aimed to determine the profile of oral health, saliva, and oral mucosa on geriatric patients at Geriatric Policlinic in Cipto Mangunkusumo Hospital. Materials and Methods: This research was a descriptive crosssectional study, which data was taken using questionnaire containing basic demographical information and oral clinical examination using standard form used by Faculty of Dentistry Universitas Indonesia. Results: The study included 75 geriatric patients, ranging from 60 to 86 of age. The mean of DMF-T score of 69 geriatric patients was 4.68 ±2.893, OHI-S 2.790 ±1.102, PBI 0.779±0.585. The mean of unstimulated salivary pH score was 6.618 ± 0.54 and stimulated buffer capacity of saliva was 7.46±2.827. Mean of salivary flow rate is 0.24mL/min and 39 people had hiposalivation. 93 normal variations and 117 pathological oral lesion was found. Conclusion: This study showed that poor oral health status and pathological oral lesion found in this study elderly population could be caused from poor salivary flow, pH saliva, and buffer capacity of saliva. Systematic oral examination of the elderly is of considerable importance and ought be carried out regularly by a dentist in collaboration with the physician; making holistic management of the elderly properly performed. DOI: 10.14693/jdi.v17i2.46