title: Dear Doctor... Regarding Calcium Channel Blockers

abstract: To the Editor. —In response to the article by Dr Psaty and colleagues, 1 thousands of physicians across the country received a "Dear Doctor" letter from an internationally recognized expert in the treatment of hypertension. The letter's author offered a number of criticisms of the study and suggested that the results do not apply to nifedipine, one of the calcium channel blockers (CCBs) discussed in the study. The letter, which was printed on the author's university stationery and postmarked on the day the article was published, did not acknowledge any drug company sponsorship. In a conversation with the author of the letter on September 11, 1995, he informed one of us (P.L.) that the letter and its distribution had been funded by Bayer Corporation, one of the two US makers of nifedipine. He stated that he had been contacted by a public relations company representing Bayer and had been asked


Interactive Evolution of L-System Grammars for Computer Graphics Modelling


title: Is it an Agent, or just a Program?: A Taxonomy for Autonomous Agents

abstract: The advent of software agents gave rise to much discussion of just what such an agent is, and of how they differ from programs in general. Here we propose a formal definition of an autonomous agent which clearly distinguishes a software agent from just any program. We also offer the beginnings of a natural kinds taxonomy of autonomous agents, and discuss possibilities for further classification. Finally, we discuss subagents and multiagent systems.


title: Collocation method for linear and nonlinear Fredholm and Volterra integral equations

abstract: A collocation procedure is developed for the linear and nonlinear Fredholm and Volterra integral equations, using the globally defined B-spline and auxiliary basis functions. The solution is collocated by cubic B-spline and then the integral equation is approximated by the 5-points Gauss-Turan quadrature formula with respect to the Legendre weight function. Combination of these two approaches is the main idea of this paper to reduce the cost of computation and complexity. The error analysis of proposed numerical method is studied theoretically. Numerical results are given to illustrate the efficiency of the proposed method. The results are compared with the results obtained by other methods to verify that this method is accurate and efficient.


title: Optimal Process Mining for Large and Complex Event Logs

abstract: This paper addresses the problem of process discovery from large and complex event logs. We depart from the existing literature and formulate the problem of optimal process discovery. A formal mathematical programming model is given based on a novel hierarchical structuration of the event logs. Desired properties of event trace score functions are described, and the properties of optimal process models are proved. A combination of Monte Carlo optimization and tabu search is proposed to overcome the complexity related to the huge size of the event logs and the combinatorial solution space. Numerical results show that our approach is suitable for large event logs and that it performs better than the state-of-the-art approaches. We also demonstrate the applicability of our method on a real case study in health care. This paper illustrates the benefits of combining techniques from the operational research and the process mining fields. Note to Practitioners —Though directly applicable to general business process discovery, this paper is motivated by our collaboration with the company HEVA (Lyon, France) and health practitioners on patient care pathway discovery. The French hospitalization database that contains hospitalization history of all patients is used for this purpose and our goal is to determine the most meaningful process model of the patient hospitalization history. The hierarchical event structure of this paper provides a natural way of representing relationship between hospitalization events and is readily obtainable from the classical ICD-10 codes. The formal mathematical model and our optimization algorithm allow the end users to best balance between the faithfulness of the process model and its complexity. A case study of cardiovascular patients is presented to show the capability of the proposed approach to clearly capture the major patient pathways before and after the implementation of defibrillators. The results of this paper are highly valuable for doctors and public health decision makers, as crucial information is provided on patient care pathways for any selected cohort.


title: A multi-mode cavity filter with Jerusalem Cross structure resonator

abstract: In this paper, a novel multi-mode dielectric resonator sandwiched by a Jerusalem Cross metal structure in the middle is proposed. This resonator has one degenerate dual modes and another nearby mode. These three modes are smartly used in a cylindrical cavity to generate two transmission zeros and one transmission pole. Therefore a single-cavity multi-mode bandpass filter is designed. In order to excite the three modes, two ports are placed orthogonally and another two tuning screws are needed to provide similar capacitance effects as the port pins. Simulation results show that the cavity with the resonator successfully becomes a bandpass filter and measurement results also prove this concept.


title: The Google Scholar Experiment: how to index false papers and manipulate bibliometric indicators

abstract: Paper accepted for publication at the Journal of the American Society for Information Science and Technology. http://www.asis.org/jasist.html


of lexical appRoach and machine leaRning methods foR sentiment analysis


title: Comparison of municipal solid waste management systems in Canada and Ghana: A case study of the cities of London, Ontario, and Kumasi, Ghana

abstract: Integrated waste management has been accepted as a sustainable approach to solid waste management in any region. It can be applied in both developed and developing countries. The difference is the approach taken to develop the integrated waste management system. This review looks at the integrated waste management system operating in the city of London, Ontario-Canada and how lessons can be drawn from the system's development and operation that will help implement a sustainable waste management system in the city of Kumasi, Ghana. The waste management system in London is designed such that all waste generated in the city is handled and disposed of appropriately. The responsibility of each sector handling waste is clearly defined and monitored. All major services are provided and delivered by a combination of public and private sector forces. The sustainability of the waste management in the city of London is attributed to the continuous improvement strategy framework adopted by the city based on the principles of integrated waste management. It is perceived that adopting a strategic framework based on the principles of integrated waste management with a strong political and social will, can transform the current waste management in Kumasi and other cities in developing countries in the bid for finding lasting solutions to the problems that have plagued the waste management system in these cities.


title: A waveguide slot array antenna with integrated T-shaped filters in the corporate-feed circuit

abstract: In this paper, we demonstrate a waveguide slot array antenna by integration of T-shaped filters using the diffusion bonding of laminated thin metal plates. The excellent performance of the T-shaped filter has been experimentally verified by the in-band transmission and the out-of-band rejection of the fabricated antenna.


title: Output ripple reduction of an automotive multi-phase bi-directional dc-dc converter

abstract: Bi-directional dc-dc converters for automotive applications typically are limited to generate only a small voltage ripple, especially when a ultra-capacitor with a limited ripple current capability is interfaced by the converter. Thus, to minimize the ripple quantities and the converter volume at the same time, interleaved multi-phase dc-dc converters are utilized. However, tolerances of the buck+boost inductors of the interleaved phases generate sub-harmonics in the ripple spectrum that are counterproductive to the advantage of ripple reduction. A method to cancel the fundamental frequency of the voltage ripple is proposed that is applicable to a converter consisting of three or more phases. It is shown that even for a low phase count the overall ripple amplitude is greatly reduced, only by adjustment of the phase shift angles. Experimental results carried out with a three-phase interleaved bi-directional dc-dc converter proof the concept functionality.


title: Exploring the therapeutic potential of Ayahuasca: acute intake increases mindfulness-related capacities

abstract: Background ::: Ayahuasca is a psychotropic plant tea used for ritual purposes by the indigenous populations of the Amazon. In the last two decades, its use has expanded worldwide. The tea contains the psychedelic 5-HT2A receptor agonist N,N-dimethyltryptamine (DMT), plus β-carboline alkaloids with monoamine-oxidase-inhibiting properties. Acute administration induces an introspective dream-like experience characterized by visions and autobiographic and emotional memories. Studies of long-term users have suggested its therapeutic potential, reporting that its use has helped individuals abandon the consumption of addictive drugs. Furthermore, recent open-label studies in patients with treatment-resistant depression found that a single ayahuasca dose induced a rapid antidepressant effect that was maintained weeks after administration. Here, we conducted an exploratory study of the psychological mechanisms that could underlie the beneficial effects of ayahuasca.


title: DAC-MACS: Effective Data Access Control for Multiauthority Cloud Storage Systems

abstract: Data access control is an effective way to ensure data security in the cloud. However, due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Existing access control schemes are no longer applicable to cloud storage systems, because they either produce multiple encrypted copies of the same data or require a fully trusted cloud server. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising technique for access control of encrypted data. However, due to the inefficiency of decryption and revocation, existing CP-ABE schemes cannot be directly applied to construct a data access control scheme for multiauthority cloud storage systems, where users may hold attributes from multiple authorities. In this paper, we propose data access control for multiauthority cloud storage (DAC-MACS), an effective and secure data access control scheme with efficient decryption and revocation. Specifically, we construct a new multiauthority CP-ABE scheme with efficient decryption, and also design an efficient attribute revocation method that can achieve both forward security and backward security. We further propose an extensive data access control scheme (EDAC-MACS), which is secure under weaker security assumptions.


title: Modularization of Digital Services for Urban Transportation

abstract: The impact of digital mobility services on individual traffic behavior within cities has increased significantly over the last years. Therefore, the aim of this paper is to provide an overview of existing digital services for urban transportation. Towards this end, we analyze 59 digital mobility services available as smartphone applications or web services. Building on a framework for service system modularization, we identified the services’ modules and data sources. While some service modules and data sources are integrated in various mobility services, others are only used in specific services, even though they would generate value in other services as well. This overview provides the basis for future design science research in the area of digital service systems for sustainable transportation. Based on the overview, practitioners from industry and public administration can identify potential for innovative service and foster co-creation and innovation within existing service systems.


title: A miniature DC-70 GHz SP4T switch in 0.13-µm CMOS

abstract: This paper presents a miniature DC-70 GHz single-pole four-throw (SP4T) built in a low-cost 0.13-µm CMOS process. The switch is based on a series-shunt design with input and output matching circuits. Deep n-well (also called triple-well) CMOS transistors are used to minimize the substrate coupling. Also, deep trench isolation is used between the different ports to minimize the port-to-port coupling. The SP4T results in a measured insertion loss of less than 3.5 dB up to 67 GHz with an isolation of greater than 25 dB. The measured port-to-port coupling is less than 28 dB up to 67 GHz. The measured P1dB and IIP3 are independent of frequency and are 9–10 dBm and 20–21 dBm, respectively. The active chip area is 0.24×0.23 mm2. To our knowledge, this work represents the widest bandwidth SP4T switch in any CMOS technology to-date.


title: An Optimal Maximum Power Point Tracking Algorithm for PV Systems With Climatic Parameters Estimation

abstract: This paper presents a maximum power point tracking (MPPT) method for photovoltaic (PV) systems with reduced hardware setup. It is realized by calculating the instantaneous conductance and the junction conductance of the array. The first one is done using the array voltage and current, whereas the second one, which is a function of the array junction current, is estimated using an adaptive neuro-fuzzy (ANFIS) solar cell model. Knowing the difficulties of measuring solar radiation and cell temperature, since those require two extra sensors that will increase the hardware circuitry and measurement noise, an analytical model is proposed to estimate them with a denoising-based wavelet algorithm. The proposed MPPT technique helps to reduce the hardware setup using only one voltage sensor, while increases the array power efficiency and MPPT response time. The simulation and experimental results are provided to validate the MPPT algorithm operation as well as the climatic parameters estimation capabilities.


title: WIMAX Implementation of Smart Grid Wide Area Power System Load Protection Model in MATLAB/SIMULINK

abstract: As the revolutionary change in electric power industry begins with the latest communication infrastructure, it is on the verge of a revolutionary transformation to develop a smart grid to meet the requirements of our digital society. Wide Area Power System is made up of plentiful automated transmission and distribution systems with strong communication infrastructure, all operating in a coordinated, proficient and reliable mode. This paper is fretful with the wide area power system load protection scheme and ensuing design requirement that enhances stability as well as control. It discusses the architecture that upgrades the existing scheme by controlling all the control signals traffic between generating units, server, connected loads, and protection devices using WIMAX. The main theme of the paper is on the use of information technology to obtain more flexibility and smartness in the Wide Area Power System Load Protection by designing the Communication channel using WIMAX. Faults detected in Local area networks and Information regarding the faults of Local Areas is communicated to Load Area Manager (LAM) which takes required control action to handle it. Finally the paper shows islanding operation through WAM for the areas that becomes intensive faulty. Results have been verified in MATLAB/ SIMULIMK.


Multiagent Reinforcement Learning in Stochastic Games


title: What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields

abstract: We use Latent-Dynamic Conditional Random Fields to perform skeleton-based pointing gesture classification at each time instance of a video sequence, where we achieve a frame-wise pointing accuracy of roughly 83%. Subsequently, we determine continuous time sequences of arbitrary length that form individual pointing gestures and this way reliably detect pointing gestures at a false positive detection rate of 0.63%.


title: Case study of a computer based examination system

abstract: Electronic supported assessment or e-Assessment is a field of growing importance, but it has yet to make a significant impact in the Australian higher education sector (Byrnes & Ellis, 2006). Current computer based assessment models focus on the assessment of knowledge rather than deeper understandings, using multiple choice type questions, and blocking access to more sophisticated software tools. This study explored a new system based on a customised version of an open source live CD, based on Ubuntu which was used with three groups of pre-service teachers (N=270). Students had divided opinions about using computers or paper for their examinations, but prior exposure to computer based assessment was a highly significant factor for preferring the computer medium. Reflecting upon their experience, students found the noise of computer keyboards a distraction during the eExamination and preferred fewer on-screen windows. The new system allowed eExaminations to be taken securely on student owned laptop computers and was supervised by invigilators without specialist information technology skills. The system has been made available for other researchers to use at http://www.eExaminations.org/


title: Design and control of an 8-slot radial flux magnetic bearing

abstract: This work presents the design and control of an 8-slot radial flux magnetic bearing. Design of magnetic bearing is carried out with coupled magnetic equivalent circuit model and finite element analysis (FEA). The magnetic design is verified with 2D FEA and good agreement between the two is obtained. Designed magnetic bearing is assembled into a system and fuzzy logic control is applied to the magnetic bearings to adjust the currents in order to balance the system shaft. The control is performed at the equilibrium position under different disturbances and detailed information about the working space of the designed magnetic bearings is provided.


title: Power Saving Strategies in Green Cloud Computing Systems

abstract: The most challenging field of concern these days is energy conservation in various applications. Energy crisis led a way to green computing and green computing needs an efficient redesign of algorithms and mechanisms to meet the desired energy efficiency. Green IT is a study and practice which can reduce energy consumption significantly. In this paper various efficient energy saving Green IT methods are discussed. As cloud computing has become a fast emerging technology this days, some energy saving strategy for cloud computing has also been discussed in the paper. Most of the IT sector energy consumption happens in data centers. Virtual Machine migration an emerging energy aware technology used in data centers is also discussed in the paper. Finally some power conserving matrix parameters are given importance.


title: Design of a High-Efficiency 2.45-GHz Rectenna for Low-Input-Power Energy Harvesting

abstract: This letter presents a high-efficiency 2.45-GHz rectenna that can harvest low input RF power effectively. A new antenna with a simple structure and high gain of 8.6 dBi is proposed for the rectenna. The antenna is designed to directly match the rectifying circuit at 2.45 GHz and mismatch it at the second and third harmonics so that the use of bandpass filter between the antenna and rectifying circuit can be eliminated. The rectenna shows a maximum conversion efficiency of 83% with a load resistance of 1400 Ω. Furthermore, the overall conversion efficiency can remain 50% for the low, -17.2 dBm (corresponding power density 0.22 μW/cm2 ) input power level.


title: Fisheye Lens Distortion Correction Based on an Ellipsoidal Function Model

abstract: With the purpose of correcting the severe distortion in fisheye images captured by fisheye lens, an ellipsoid function model for fisheye lens correction is proposed. Firstly, an ellipsoidal function model need to be built. Secondly, based on this model, make preliminary correction to the fisheye images. Finally, adjust the parameters in the ellipsoidal function model to apply amendment to the images for better corrected results. Experiments with real circular fisheye images and elliptic fisheye images show that the correction model is feasible and effective.


title: Future Cognitive Ability: US IQ Prediction until 2060 Based on NAEP

abstract: The US National Assessment of Educational Progress (NAEP) measures cognitive competences in reading and mathematics of US students (last 2012 survey N = 50,000). The long-term development based on results from 1971 to 2012 allows a prediction of future cognitive trends. For predicting US averages also demographic trends have to be considered. The largest groups' (White) average of 1978/80 was set at M = 100 and SD = 15 and was used as a benchmark. Based on two past NAEP development periods for 17-year-old students, 1978/80 to 2012 (more optimistic) and 1992 to 2012 (more pessimistic), and demographic projections from the US Census Bureau, cognitive trends until 2060 for the entire age cohort and ethnic groups were estimated. Estimated population averages for 2060 are 103 (optimistic) or 102 (pessimistic). The average rise per decade is dec = 0.76 or 0.45 IQ points. White-Black and White-Hispanic gaps are declining by half, Asian-White gaps treble. The catch-up of minorities (their faster ability growth) contributes around 2 IQ to the general rise of 3 IQ; however, their larger demographic increase reduces the general rise at about the similar amount (-1.4 IQ). Because minorities with faster ability growth also rise in their population proportion the interactive term is positive (around 1 IQ). Consequences for economic and societal development are discussed.


title: An efficient ad recommendation system for TV programs

abstract: With broadcast Television (TV) going digital, the number of channels and the programs aired have increased tremendously. Millions of audiences of various categories such as adults, children, youth and families watch these programs. Advertisements (ads) aired during these programs are targeted to reach these varied audiences and are the main revenue earners for TV broadcasters. While TV broadcasters have the task of scheduling hundreds of ads during the various ad breaks of programs, it is important that the ads shown during any ad break have a good impact on the viewers. An intelligent ad recommendation system that takes into account various factors such as ad/program content, viewers' interests, sponsors' preferences, program timing, program popularity and the available ad slot that help increasing the ad revenue would be useful for sponsors and broadcasters. We present in this paper a single end-to-end ad recommender system that considers all of these factors and recommends a set of well scheduled and sequenced ads that are the best suited for a given TV ad break. The proposed recommendation system captures the features of the ad video in terms of annotations derived from MPEG-7 descriptions and these annotation keywords are systematically grouped into a number of pre-defined semantic categories by using a categorization technique. A fuzzy categorical data clustering technique is then applied on the categorized data for grouping the best suited ads for a set of pre-defined program classes such as News, Sports, Cartoons etc. The program classes considered are selected to match with the TV program genres proposed in the TV-anytime standard. Since the same ad can be recommended to more than one program depending upon multiple parameters, fuzzy clustering acts as a well suited (and perhaps also the best suited) technique for ad recommendation. The relative fuzzy score called "degree of membership" calculated for each ad is an indicator of the number of program clusters to which the given ad belongs to. The clustered ads are then scheduled using an algorithm that takes into consideration parameters such as program popularity, program timing and available ad slots, to provide the best possible package for sponsors to show their ads. The scheduled set of ads if played randomly during an ad break might make viewers (sponsors) unhappy, for instance, when similar (competing) product ads get played consecutively. Hence, the system employs sequencing algorithm that takes into account the pre- and post-ad sequences in order to better order the scheduled set of ads in any ad break. We show that our proposed recommendation system provides an effective way of recommending the right ads for broadcast TV programs. We also demonstrate that this strategy does indeed help sponsors to attract viewers' attention while playing their ads during ad breaks of TV programs. The proposed ad recommendation system is compared and evaluated subjectively with the current ad display system, by ten different people, and is rated with a high success score.


title: An empirical model to predict security vulnerabilities using code complexity metrics

abstract: Complexity is often hypothesized to be the enemy of software security. If this hypothesis is true, complexity metrics may be used to predict the locale of security problems and can be used to prioritize inspection and testing efforts. We performed statistical analysis on nine complexity metrics from the JavaScript Engine in the Mozilla application framework to find differences in code metrics between vulnerable and nonvulnerable code and to predict vulnerabilities. Our initial results show that complexity metrics can predict vulnerabilities at a low false positive rate, but at a high false negative rate.


title: Irradiance-Adaptive PV Module Integrated Converter for High Efficiency and Power Quality in Standalone and DC Microgrid Applications

abstract: The strive for efficient and cost-effective photovoltaic (PV) systems motivated the power electronic design developed here. The work resulted in a dc–dc converter for module integration and distributed maximum power point tracking (MPPT) with a novel adaptive control scheme. The latter is essential for the combined features of high energy efficiency and high power quality over a wide range of operating conditions. The switching frequency is optimally modulated as a function of solar irradiance for power conversion efficiency maximization. With the rise of irradiance, the frequency is reduced to reach the conversion efficiency target. A search algorithm is developed to determine the optimal switching frequency step. Reducing the switching frequency may, however, compromise MPPT efficiency. Furthermore, it leads to increased ripple content. Therefore, to achieve a uniform high power quality under all conditions, interleaved converter cells are adaptively activated. The overall cost is kept low by selecting components that allow for implementing the functions at low cost. Simulation results show the high value of the module integrated converter for dc standalone and microgrid applications. A 400-W prototype was implemented at 0.14 Euro/W. Testing showed efficiencies above 95 %, taking into account all losses from power conversion, MPPT, and measurement and control circuitry.


title: Face shape and behavior: Implications of similarities in infants and adults

abstract: We investigated conceptual overlap between literature demonstrating links between adult facial width-to-height ratio (FWHR) and behavior and that demonstrating links between infant FWHR and temperament by investigating whether babyfaceness is associated with FWHR and behavior at both ages. Babyfaceness was positively correlated with FWHR in both infants and adults. Babyfaceness also was correlated with an infant temperament that is a precursor of bolder behavior in childhood and adulthood, just as a broader infant FWHR was previously shown to be. These results call into question existing explanations for relationships between facial appearance and adult assertive or aggressive behavior. Previously, behavioral correlates of adult FWHR have been attributed to influences of pubertal testosterone, and correlates of adult babyfaceness have been attributed to compensation for undesirable stereotypes. Our findings indicate that the pre-natal developmental influences required to explain appearance-temperament relationships in infancy also should be considered as explanations for appearance-behavior relationships in adulthood.


title: Security through amnesia: a software-based solution to the cold boot attack on disk encryption

abstract: Disk encryption has become an important security measure for a multitude of clients, including governments, corporations, activists, security-conscious professionals, and privacy-conscious individuals. Unfortunately, recent research has discovered an effective side channel attack against any disk mounted by a running machine [23]. This attack, known as the cold boot attack, is effective against any mounted volume using state-of-the-art disk encryption, is relatively simple to perform for an attacker with even rudimentary technical knowledge and training, and is applicable to exactly the scenario against which disk encryption is primarily supposed to defend: an adversary with physical access. While there has been some previous work in defending against this attack [27], the only currently available solution suffers from the twin problems of disabling access to the SSE registers and supporting only a single encrypted volume, hindering its usefulness for such common encryption scenarios as data and swap partitions encrypted with different keys (the swap key being a randomly generated throw-away key). We present Loop-Amnesia, a kernel-based disk encryption mechanism implementing a novel technique to eliminate vulnerability to the cold boot attack. We contribute a novel technique for shielding multiple encryption keys from RAM and a mechanism for storing encryption keys inside the CPU that does not interfere with the use of SSE. We offer theoretical justification of Loop-Amnesia's invulnerability to the attack, verify that our implementation is not vulnerable in practice, and present measurements showing our impact on I/O accesses to the encrypted disk is limited to a slowdown of approximately 2x. Loop-Amnesia is written for x86-64, but our technique is applicable to other register-based architectures. We base our work on loop-AES, a state-of-the-art open source disk encryption package for Linux.


title: Predicting future personal life events on twitter via recurrent neural networks

abstract: Social network users publicly share a wide variety of information with their followers and the general public ranging from their opinions, sentiments and personal life activities. There has already been significant advance in analyzing the shared information from both micro (individual user) and macro (community level) perspectives, giving access to actionable insight about user and community behaviors. The identification of personal life events from user’s profiles is a challenging yet important task, which if done appropriately, would facilitate more accurate identification of users’ preferences, interests and attitudes. For instance, a user who has just broken his phone, is likely to be upset and also be looking to purchase a new phone. While there is work that identifies tweets that include mentions of personal life events, our work in this paper goes beyond the state of the art by predicting a future personal life event that a user will be posting about on Twitter solely based on the past tweets. We propose two architectures based on recurrent neural networks, namely the classification and generation architectures, that determine the future personal life event of a user. We evaluate our work based on a gold standard Twitter life event dataset and compare our work with the state of the art baseline technique for life event detection. While presenting performance measures, we also discuss the limitations of our work in this paper.


title: ROWLBAC: representing role based access control in OWL

abstract: There have been two parallel themes in access control research in recent years. On the one hand there are efforts to develop new access control models to meet the policy needs of real world application domains. In parallel, and almost separately, researchers have developed policy languages for access control. This paper is motivated by the consideration that these two parallel efforts need to develop synergy. A policy language in the abstract without ties to a model gives the designer little guidance. Conversely a model may not have the machinery to express all the policy details of a given system or may deliberately leave important aspects unspecified. Our vision for the future is a world where advanced access control concepts are embodied in models that are supported by policy languages in a natural intuitive manner, while allowing for details beyond the models to be further specified in the policy language. This paper studies the relationship between the Web Ontology Language (OWL) and the Role Based Access Control (RBAC) model. Although OWL is a web ontology language and not specifically designed for expressing authorization policies, it has been used successfully for this purpose in previous work. OWL is a leading specification language for the Semantic Web, making it a natural vehicle for providing access control in that context. In this paper we show two different ways to support the NIST Standard RBAC model in OWL and then discuss how the OWL constructions can be extended to model attribute-based RBAC or more generally attribute-based access control. We further examine and assess OWL's suitability for two other access control problems: supporting attribute based access control and performing security analysis in a trust-management framework.


title: Weighted Bilinear Coding over Salient Body Parts for Person Re-identification

abstract: Deep convolutional neural networks (CNNs) have demonstrated dominant performance in person re-identification (Re-ID). Existing CNN based methods utilize global average pooling (GAP) to aggregate intermediate convolutional features for Re-ID. However, this strategy only considers the first-order statistics of local features and treats local features at different locations equally important, leading to sub-optimal feature representation. To deal with these issues, we propose a novel weighted bilinear coding (WBC) framework for local feature aggregation in CNN networks to pursue more representative and discriminative feature representations, which can adapt to other state-of-the-art methods and improve their performance. In specific, bilinear coding is used to encode the channel-wise feature correlations to capture richer feature interactions. Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation. To handle the spatial misalignment issue, we use a salient part net (spatial attention module) to derive salient body parts, and apply the WBC model on each part. The final representation, formed by concatenating the WBC encoded features of each part, is both discriminative and resistant to spatial misalignment. Experiments on three benchmarks including Market-1501, DukeMTMC-reID and CUHK03 evidence the favorable performance of our method against other outstanding methods.


title: Investigating the Performance of Pull-Based Dynamic Adaptive Streaming in NDN

abstract: Adaptive content delivery is the state of the art in real-time multimedia streaming. Leading streaming approaches, e.g., MPEG-DASH and Apple HTTP Live Streaming (HLS), have been developed for classical IP-based networks, providing effective streaming by means of pure client-based control and adaptation. However, the research activities of the Future Internet community adopt a new course that is different from today’s host-based communication model. So-called information-centric networks are of considerable interest and are advertised as enablers for intelligent networks, where effective content delivery is to be provided as an inherent network feature. This paper investigates the performance gap between pure client-driven adaptation and the theoretical optimum in the promising Future Internet architecture named data networking (NDN). The theoretical optimum is derived by modeling multimedia streaming in NDN as a fractional multi-commodity flow problem and by extending it taking caching into account. We investigate the multimedia streaming performance under different forwarding strategies, exposing the interplay of forwarding strategies and adaptation mechanisms. Furthermore, we examine the influence of network inherent caching on the streaming performance by varying the caching polices and the cache sizes.


title: The Kinetics of the Precipitation of Co from Supersaturated Cu-Co Alloy

abstract: The kinetics of the precipitation of Co from a supersaturated solid solution of Cu-0.95 at. pct Co was investigated by isochronal annealing applying differential scanning calorimetry (DSC) with heating rates in the range 5 to 20 K min–1. The corresponding microstructural evolution was investigated by high-resolution transmission electron microscopy (HRTEM) in combination with electron energy loss spectroscopy (EELS). Upon isochronal annealing, spherical Co precipitates of fcc crystal structure form. Kinetic analysis by fitting of a modular phase transformation model to, simultaneously, all DSC curves of variable heating rate measured for Cu-0.95 at. pct Co showed that the precipitation-process mechanism can be described within the framework of this general phase transformation model by continuous nucleation and diffusion-controlled growth. By introducing additional microstructural information (here, the precipitate-particle density), for the first time, values for the separate activation energies of nucleation and growth could be deduced from the transformation kinetics.


title: Examining the relationship to gender and personality on the purpose of Facebook usage of Turkish university students

abstract: Abstract This study examined whether the purposes of Facebook usage is associated with gender and big five personalities in a sample of Turkish university students. Seven hundred and fifty one students filled in a set of questionnaires. While males use Facebook to express themselves, meet new people, and store and organize things more, females use Facebook for educational purposes. Agreeable and conscientious people use Facebook to maintain existing relationship more. It was concluded that gender and agreeableness, which is one of the personality types, are significant variables on the purposes of Facebook usage.


title: Contrastive Summarization: An Experiment with Consumer Reviews

abstract: Contrastive summarization is the problem of jointly generating summaries for two entities in order to highlight their differences. In this paper we present an investigation into contrastive summarization through an implementation and evaluation of a contrastive opinion summarizer in the consumer reviews domain.


title: Comparative Evaluation of Anomaly Detection Techniques for Sequence Data

abstract: We present a comparative evaluation of a large number of anomaly detection techniques on a variety of publicly available as well as artificially generated data sets. Many of these are existing techniques while some are slight variants and/or adaptations of traditional anomaly detection techniques to sequence data.


title: A Case Study on Legal Case Annotation.

abstract: The paper reports the outcomes of a study with law school students to annotate a corpus of legal cases for a variety of annotation types, e.g. citation indices, legal facts, rationale, judgement, cause of action, and others. An online tool is used by a group of annotators that results in an annotated corpus. Differences amongst the annotations are curated, producing a gold standard corpus of annotated texts. The annotations can be extracted with semantic searches of complex queries. There would be many such uses for the development and analysis of such a corpus for both legal education and legal research.


Measuring Parsing Difficulty Across Treebanks


title: Regularization of Deep Neural Networks with Spectral Dropout

abstract: The big breakthrough on the ImageNet challenge in 2012 was partially due to the `dropout' technique used to avoid overfitting. Here, we introduce a new approach called `Spectral Dropout' to improve the generalization ability of deep neural networks. We cast the proposed approach in the form of regular Convolutional Neural Network (CNN) weight layers using a decorrelation transform with fixed basis functions. Our spectral dropout method prevents overfitting by eliminating weak and `noisy' Fourier domain coefficients of the neural network activations, leading to remarkably better results than the current regularization methods. Furthermore, the proposed is very efficient due to the fixed basis functions used for spectral transformation. In particular, compared to Dropout and Drop-Connect, our method significantly speeds up the network convergence rate during the training process (roughly x2), with considerably higher neuron pruning rates (an increase of ~ 30%). We demonstrate that the spectral dropout can also be used in conjunction with other regularization approaches resulting in additional performance gains.


title: A Time-Driven Scheduling Model for Real-Time Operating Systems

abstract: Process scheduling in real-time systems has almost invariably used one or more of three algorithms: fixed priority, FIFO, or round robin. The reasons for these choices are simplicity and speed in the operating system, but the cost to the system in terms of reliability and maintainability have not generally been assessed. This paper originates from the notion that the primary distinguishing characteristic of a real-time system is the concept that completion of a process or a set of processes has a value to the system which can be expressed as a function of time. This notion is described in terms of a time-driven scheduling model for real-time operating systems and provides a tool for measuring the effectiveness of most of the currently used process schedulers in real-time systems. Applying this model, we have constructed a multiprocessor real-time system simulator with which we measure a number of well-known scheduling algorithms such as Shortest Process Time (SPT), Deadline, Shortest Slack Time, FIFO, and a fixed priority scheduler, with respect to the resulting total system values. This approach to measuring the process scheduling effectiveness is a first step in our longer term effort to produce a scheduler which will explicitly schedule real-time processes in such a way that their execution times maximize their collective value to the system, either in a shared memory multiprocessing environment or in multiple nodes of a distributed processing environment.


Provably Private Data Anonymization: Or, k-Anonymity Meets Differential Privacy


title: MEGA-CC : computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis

abstract: Summary: There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA’s graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis. ::: ::: Availability: http://www.megasoftware.net/ ::: ::: Contact: [email protected]


title: Evolutionary Algorithm for Extractive Text Summarization

abstract: Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization may compose novel sentences, unseen in the original sources. In our study we focus on sentence based extractive document summarization. The extractive summarization systems are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document. In this paper, we propose unsupervised document summarization method that creates the summary by clustering and extracting sentences from the original document. For this purpose new criterion functions for sentence clustering have been proposed. Similarity measures play an increasingly important role in document clustering. Here we’ve also developed a discrete differential evolution algorithm to optimize the criterion functions. The experimental results show that our suggested approach can improve the performance compared to sate-of-the-art summarization approaches.


title: Representation Learning for Visual-Relational Knowledge Graphs

abstract: A visual-relational knowledge graph (KG) is a KG whose entities are associated with images. We propose representation learning for relation and entity prediction in visual-relational KGs as a novel machine learning problem. We introduce \textsc{ImageGraph}, a KG with 1,330 relation types, 14,870 entities, and 829,931 images. Visual-relational KGs lead to novel probabilistic query types treating images as first-class citizens. We approach the query answering problems by combining ideas from the areas of computer vision and embedding learning for KGs. The resulting ML models can answer queries such as \textit{"How are these two unseen images related to each other?"} We also explore a novel zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. Our experiments show that the proposed deep neural networks are able to answer the visual-relational queries efficiently and accurately.


title: Finding New Tricks For Old Drugs: An Efficient Route For Public-Sector Drug Discovery

abstract: With the annotation of the human genome approaching completion, public-sector researchers - spurred in part by various National Institutes of Health Roadmap Initiatives - have become increasingly engaged in drug discovery and development efforts. Although large and diverse chemical libraries of 'drug-like' compounds can be readily screened to yield chemically novel scaffolds, transforming these 'chemical probes' into drugs is a daunting endeavour. A more efficient approach involves screening libraries of approved and off-patent medications; both phenotypic- and molecular target-based screening of 'old drugs' can readily yield compounds that could be immediately used in clinical trials. Using case studies, we describe how this approach has rapidly identified candidate medications suitable for clinical trials in disorders such as progressive multifocal leukoencephalopathy and amyotrophic lateral sclerosis. This approach has also led to the discovery of the molecular targets responsible for serious drug side effects, thereby allowing efficient 'counter-screening' to avoid these side effects.


title: Energy harvesting devices for high voltage transmission line monitoring

abstract: Sensor nodes with wireless communication capability are essential elements in future intelligent transmission line monitoring system. This paper focuses on designing two types of transmission line mounted energy harvesting devices to realize the self-power of these nodes. First, the well-known electric field based energy harvesting method is further analyzed to identify circuit design guidelines. Then, a feasibility study of a low cost linear generator that utilizes the magnetic field produced by high voltage dc lines is presented. Although the power density of the linear generator is low, a case study shows that it could be utilized to continuously charge a battery for pulsed communications.


title: Recursive Lagrangian Dynamics of Flexible Manipulator Arms via Transformation Matrices

abstract: Abstract The nonlinear equations of motion for flexible manipulator arms consisting of rotary joints connecting two flexible links are developed. Kinematics of both the rotary joint motion and the link deformation are described by 4×4 transformation matrices. The link deflection is assumed small so that the link transformation can be composed of summations of assumed link shapes. The resulting equations are presented as scalar and 4×4 matrix operations ready for programming. The efficiency of this formulation is compared to rigid link cases reported in the literature.


title: Mail Versus Mall: a Strategic Analysis of Competition Between Direct Marketers and Conventional Retailers

abstract: Consumers now purchase several offerings from direct sellers, including catalog and Internet marketers. These direct channels exist in parallel with the conventional retail stores. The availability of multiple channels has significant implications for the performance of consumer markets. The literature in marketing and economics has, however, been dominated by a focus on the conventional retail sector. This paper is an effort toward modeling competition in the multiple-channel environment from a strategic viewpoint. ::: ::: At the outset, a parsimonious model that accommodates the following consumer and market characteristics is introduced. First, the relative attractiveness of retail shopping varies across consumers. Second, the fit with the direct channel varies across product categories. Third, the strength of existing retail presence in local markets moderates competition. Fourth, in contrast with the fixed location of the retail store that anchors its localized market power, the location of the direct marketer is irrelevant to the competitive outcome. ::: ::: The model is first applied in a setting where consumers have complete knowledge of product availability and prices in all channels. In the resulting equilibrium, the direct marketer acts as a competitive wedge between retail stores. The direct presence is so strong that each retailer competes against the remotely located direct marketer, rather than against neighboring retailers. This outcome has implications for the marketing mix of retailers, which has traditionally been tuned to attract consumers choosing between retail stores. In the context of market entry, conditions under which a direct channel can access a local market in retail entry equilibrium are derived. Our analysis suggests that the traditional focus on retail entry equilibria may not yield informative or relevant findings when direct channels are a strong presence. ::: ::: Next, the role of information in multiple-channel markets is modeled. This issue is particularly relevant in the context of direct marketing where the seller can typically control the level of information in the marketplace, sometimes on a customer-by-customer basis e.g., by deciding on the mailing list for a catalog campaign. When a certain fraction of consumers does not receive information from the direct marketer, the retailers compete with each other for that fraction of the market. The retailer's marketing mix has to be tuned, in this case, to jointly address direct and neighboring retail competition. ::: ::: The level of information disseminated by the direct marketer is shown to have strategic implications, and the use of market coverage as a lever to control competition is described. Even with zero information costs, providing information to all consumers may not be optimal under some circumstances. In particular, when the product is not well adapted to the direct channel, the level of market information about the direct option should ideally be lowered. The only way to compete with retailers on a larger scale with a poorly adapted product is by lowering direct prices, which lowers profits. Lowering market information levels and allowing retailers to compete more with each other facilitates a higher equilibrium retail price. In turn, this allows a higher direct price to be charged and improves overall direct profit. On the other hand, when the product is well adapted, increasing direct market presence and engaging in greater competition with the retail sector yields higher returns. ::: ::: The finding that high market coverage may depress profits raises some issues for further exploration. First, implementing the optimal coverage is straightforward when the seller controls the information mechanism, as in the case of catalog marketing. The Internet, in contrast, is an efficient mechanism to transmit information, but does not provide the sellers with such control over the level of market information. A key reason is that the initiative to gather information on the Internet lies largely with consumers. The design and implementation of mechanisms to control aggregate information levels in electronic markets can, therefore, be an important theme for research and managerial interest. Second, direct marketers have traditionally relied on the statistical analysis of customer records to decide on contact policies. The analysis in this paper reveals that these policies can have significant strategic implications as well. Research that integrates the statistical and strategic aspects could make a valuable contribution. ::: ::: The paper concludes with a discussion of issues for future research in multiple-channel markets, including avenues to model competition in settings with multiple direct marketers.


title: Fabrication and Evaluation of Membranes as Specific Electrodes for Calcium ions

abstract: Membranes fabricated from an inert polyvinyl chloride matrix impregnated with tributyl phosphate and with tributyl phosphate plus thenoyl trifluoroacetone were tested as electrodes specific for calcium ions. Both types of membrane exhibited a high specificity for calcium ions in the presence of sodium, magnesium, and barium ions. In the presence of perturbing ions, the usefulness of membranes is limited by both the transport of current and development of potentials by the interfering ion. An experimental method for evaluating these interfering quantities is presented.


title: A Research of Job Recommendation System Based on Collaborative Filtering

abstract: Dealing with the enormous amount of recruiting information on the Internet, a job seeker always spends hours to find useful ones. To reduce this laborious work, we design and implement a recommendation system for online job-hunting. In this paper, we contrast user-based and item-based collaborative filtering algorithm to choose a better performed one. We also take background information including students' resumes and details of recruiting information into consideration, bring weights of co-apply users (the users who had applied the candidate jobs) and weights of student used-liked jobs into the recommendation algorithm. At last, the model we proposed is verified through experiments study which is using actual data. The recommended results can achieve higher score of precision and recall, and they are more relevant with users' preferences before.


title: DomainSieve: a protein domain-based screen that led to the identification of dam-associated genes with potential link to DNA maintenance

abstract: MOTIVATION ::: The Dam methyltransferase (DamMT) activity, broadly distributed in association with restriction endonucleases, as part of the restriction-modification defense systems, has evolved to become intimately associated with essential biological functions in a few organisms. In Escherichia coli, DamMT is involved in multiple aspects of DNA maintenance, replication initiation, daughter chromosome segregation, DNA mismatch repair, gene expression control, etc. The participation of DamMT in such a diverse set of functions required that other genes adapted, or emerged through evolution, in response to the DamMT-induced modification of the genomic environment. One example is SeqA, a protein that senses the methylation status of the origin of replication of the chromosome to control the timing of replication initiation. Interestingly, seqA is only present in a few DamMT-specifying proteobacteria. This observation led us to hypothesize that other genes, specifying related functions, might also be found in these organisms. To test this hypothesis, we implemented a large-scale comparative genomic screen meant to identify genes specifying DNA methylation sensing domains, probably involved in DNA maintenance functions. ::: ::: ::: RESULTS ::: We carried out a phylogenetic analysis of DamMT, identifying two contrasting behaviors of the protein. Based on this phylogeny, we defined precisely a set of genomes, in which the protein activity is likely to be involved in DNA maintenance functions, the 'resident' dam genomes. We defined a second set of genomes, in which DamMT is not resident. We developped a new tool, 'DomainSieve', in order to screen these two sets for protein domains that are strictly associated with 'resident' dam genomes. This approach was rewarding and generated a list of genes, among which some, at least, specify activities with clear linkage to DamMT-dependent DNA methylation and DNA maintenance. ::: ::: ::: AVAILABILITY ::: DomainSieve is implemented as a web resource and is accessible at http://stat.genopole.cnrs.fr/ds/.


title: A joint solution for the hidden and exposed terminal problems in CSMA/CA wireless networks

abstract: It is well-known that the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)-based wireless networks suffer seriously from the hidden terminal problem and the exposed terminal problem. So far, no satisfactory solutions that can resolve both problems simultaneously have been found. In this paper, we present a joint solution to the two problems. Our approach avoids the drawback of lessening one problem but aggregating the other. It is compatible with the IEEE 802.11 MAC and requires no protocol change. Analysis and simulations show that the proposed scheme can significantly reduce the hidden and exposed terminal problems. Not only it can significantly improve the throughput of the network and the fairness among different flows, it can also provide a much more stable link layer. In simulated scenarios under heavy traffic conditions, compared to the conventional IEEE 802.11 MAC, the new method can achieve up to 1.8 times gain in network throughput for single-hop flows and up to 2.6 times gain for multihop flows.


title: Maximin affinity learning of image segmentation

abstract: Images can be segmented by first using a classifier to predict an affinity graph that reflects the degree to which image pixels must be grouped together and then partitioning the graph to yield a segmentation. Machine learning has been applied to the affinity classifier to produce affinity graphs that are good in the sense of minimizing edge misclassification rates. However, this error measure is only indirectly related to the quality of segmentations produced by ultimately partitioning the affinity graph. We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure. The Rand index measures segmentation performance by quantifying the classification of the connectivity of image pixel pairs after segmentation. By using the simple graph partitioning algorithm of finding the connected components of the thresholded affinity graph, we are able to train an affinity classifier to directly minimize the Rand index of segmentations resulting from the graph partitioning. Our learning algorithm corresponds to the learning of maximin affinities between image pixel pairs, which are predictive of the pixel-pair connectivity.


Combining Fluorescent Probes and Biofunctional Magnetic Nanoparticles for Rapid Detection of Bacteria in Human Blood


title: PRIVAaaS: Privacy Approach for a Distributed Cloud-Based Data Analytics Platforms

abstract: Data privacy is a key challenge that is exacerbated by Big Data storage and analytics processing requirements. Big Data and Cloud Computing are related and allow the users to access data from any device, making data privacy essential as the data sets are exposed through the web. Organizations care about data privacy as it directly affects the confidence that clients have that their personal data are safe. This paper presents a data privacy approach – PRIVAaaS – and its inte-gration to the LEMONADE Web-based platform, developed to compose ETL (Extract, Transform, Load) process and Machine Learning workflows. The 3-level approach of PRIVAaaS, based on data anonymization policies, is implemented in a software toolkit that provides a set of libraries and tools which allows controlling and reducing data leakage in the context of Big Data processing.


title: Differential effects of attentional focus strategies during long-term resistance training

abstract: AbstractThe purpose of this study was to investigate the effects of using an internal versus external focus of attention during resistance training on muscular adaptations. Thirty untrained college-aged men were randomly assigned to an internal focus group (INTERNAL) that focused on contracting the target muscle during training (n = 15) or an external focus group (EXTERNAL) that focused on the outcome of the lift (n = 15). Training for both routines consisted of 3 weekly sessions performed on non-consecutive days for 8 weeks. Subjects performed 4 sets of 8–12 repetitions per exercise. Changes in strength were assessed by six repetition maximum in the biceps curl and isometric maximal voluntary contraction in knee extension and elbow flexion. Changes in muscle thickness for the elbow flexors and quadriceps were assessed by ultrasound. Results show significantly greater increases in elbow flexor thickness in INTERNAL versus EXTERNAL (12.4% vs. 6.9%, respectively); similar changes were noted in quadriceps th...


title: Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

abstract: A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.


title: Grid interactive bidirectional solar PV array fed water pumping system

abstract: This paper proposes a grid interactive bidirectional solar water pumping system using a three phase induction motor drive (IMD). A single phase voltage source converter (VSC) is used to direct the flow of power from grid supply to the pump and back to the grid from SPV array. A boost converter is used for the maximum power point tracking (MPPT) of the SPV array. A smart power sharing control is proposed, with preference given to the power from SPV array over the grid power. Moreover, the grid input power quality is also improved. Various modes of operation of the pump are elaborated and the performance of the system at starting, in steady state and dynamic conditions are simulated. The simulated results show the novelty and the satisfactory performance of the system.


title: Pathophysiology, Diagnosis, and Treatment of Orthostatic Hypotension and Vasovagal Syncope

abstract: Orthostatic hypotension (OH) occurs in 0.5% of individuals and as many as 7-17% of patients in acute care settings. Moreover, OH may be more prevalent in the elderly due to the increased use of vasoactive medications and the concomitant decrease in physiologic function, such as baroreceptor sensitivity. OH may result in the genesis of a presyncopal state or result in syncope. OH is defined as a reduction of systolic blood pressure (SBP) of at least 20 mm Hg or diastolic blood pressure (DBP) of at least 10 mm Hg within 3 minutes of standing. A review of symptoms, and measurement of supine and standing BP with appropriate clinical tests should narrow the differential diagnosis and the cause of OH. The fall in BP seen in OH results from the inability of the autonomic nervous system (ANS) to achieve adequate venous return and appropriate vasoconstriction sufficient to maintain BP. An evaluation of patients with OH should consider hypovolemia, removal of offending medications, primary autonomic disorders, secondary autonomic disorders, and vasovagal syncope, the most common cause of syncope. Although further research is necessary to rectify the disease process responsible for OH, patients suffering from this disorder can effectively be treated with a combination of nonpharmacologic treatment, pharmacologic treatment, and patient education. Agents such as fludrocortisone, midodrine, and selective serotonin reuptake inhibitors have shown promising results. Treatment for recurrent vasovagal syncope includes increased salt and water intake and various drug treatments, most of which are still under investigation.


title: A Study on Human Activity Recognition Using Accelerometer Data from Smartphones

abstract: Abstract This paper describes how to recognize certain types of human physical activities using acceleration data generated by a user's cell phone. We propose a recognition system in which a new digital low-pass filter is designed in order to isolate the component of gravity acceleration from that of body acceleration in the raw data. The system was trained and tested in an experiment with multiple human subjects in real-world conditions. Several classifiers were tested using various statistical features. High-frequency and low-frequency components of the data were taken into account. We selected five classifiers each offering good performance for recognizing our set of activities and investigated how to combine them into an optimal set of classifiers. We found that using the average of probabilities as the fusion method could reach an overall accuracy rate of 91.15%.


Bioprospecting potential of halogenases from Arctic marine actinomycetes


Soot - a Java Bytecode Optimization Framework


title: Graph-based Method for Summarized Storyline Generation in Twitter

abstract: Twitter has become a leading source of real-time world-wide information and a great medium for exploring emerging events, breaking news and general topics which most matter to a broad audience. On the other hand, the explosive rate of incoming information in Twitter leads users to experience information overload. Whereas, a significant fraction of tweets are about news events, summarizing the storyline of events can be helpful for users to easily access to the relevant and key information hidden among tweets and thereby draw high level conclusions. Storytelling is the task of providing chronological summaries of significant sub-events development and sketching the relationship between sub-events. In this paper, we propose a novel framework to generate a summarized storyline of news events from social point of view. Utilizing the concepts in graph-theory, we identify sub-events, summarize the evolution of sub-events and generate a coherent storyline of them. Our approach models a storyline as a directed tree of social salient sub-events evolving over time. To overcome the enormous number of redundant tweets, we keep distilled information in super-tweets. Experiments performed on a large scale data set from tweets sent during the Iranian Presidential Election (#IranElection) and the results demonstrate the efficiency and effectiveness of our framework.


title: Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding

abstract: We present a novel vision-based grasp point detection algorithm that can reliably detect the corners of a piece of cloth, using only geometric cues that are robust to variation in texture. Furthermore, we demonstrate the effectiveness of our algorithm in the context of folding a towel using a general-purpose two-armed mobile robotic platform without the use of specialized end-effectors or tools. The robot begins by picking up a randomly dropped towel from a table, goes through a sequence of vision-based re-grasps and manipulations—partially in the air, partially on the table—and finally stacks the folded towel in a target location. The reliability and robustness of our algorithm enables for the first time a robot with general purpose manipulators to reliably and fully-autonomously fold previously unseen towels, demonstrating success on all 50 out of 50 single-towel trials as well as on a pile of 5 towels.


title: Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods

abstract: We introduce a novel framework for the estimation of the posterior distribution of the weights of a neural network, based on a new probabilistic interpretation of adaptive subgradient algorithms such as AdaGrad and Adam. Having a confidence measure of the weights allows several shortcomings of neural networks to be addressed. In particular, the robustness of the network can be improved by performing weight pruning based on signal-to-noise ratios from the weight posterior distribution. Using the MNIST dataset, we demonstrate that the empirical performance of Badam, a particular instance of our framework based on Adam, is competitive in comparison to related Bayesian approaches such as Bayes By Backprop.


title: Measuring IPv6 DNS Reconnaissance Attacks and Preventing Them Using DNS Guard

abstract: Traditional address scanning attacks mainly rely on the naive 'brute forcing' approach, where the entire IPv4 address space is exhaustively searched by enumerating different possibilities. However, such an approach is inefficient for IPv6 due to its vast subnet size (i.e., 2^64). As a result, it is widely assumed that address scanning attacks are less feasible in IPv6 networks. In this paper, we evaluate new IPv6 reconnaissance techniques in real IPv6 networks and expose how to leverage the Domain Name System (DNS) for IPv6 network reconnaissance. We collected IPv6 addresses from 5 regions and 100,000 domains by exploiting DNS reverse zone and DNSSEC records. We propose a DNS Guard (DNSG) to efficiently detect DNS reconnaissance attacks in IPv6 networks. DNSG is a plug and play component that could be added to the existing infrastructure. We implement DNSG using Bro and Suricata. Our results demonstrate that DNSG could effectively block DNS reconnaissance attacks.


title: xStream : Outlier Dete ‘ x ’ ion in Feature-Evolving Data Stream s

abstract: This work addresses the outlier detection problem for feature-evolving streams, which has not been studied before. In this setting both (1) data points may evolve, with feature values changing, as well as (2) feature space may evolve, with newly-emerging features over time. This is notably different from row-streams, where points with fixed features arrive one at a time. We propose a density-based ensemble outlier detector, called xStream, for this more extreme streaming setting which has the following key properties: (1) it is a constant-space and constant-time (per incoming update) algorithm, (2) it measures outlierness at multiple scales or granularities, it can handle (3 i ) high-dimensionality through distance-preserving projections, and (3$ii$) non-stationarity via $O(1)$-time model updates as the stream progresses. In addition, xStream can address the outlier detection problem for the (less general) disk-resident static as well as row-streaming settings. We evaluate xStream rigorously on numerous real-life datasets in all three settings: static, row-stream, and feature-evolving stream. Experiments under static and row-streaming scenarios show that xStream is as competitive as state-of-the-art detectors and particularly effective in high-dimensions with noise. We also demonstrate that our solution is fast and accurate with modest space overhead for evolving streams, on which there exists no competition.


title: Extending a Conventional Chatbot Knowledge Base to External Knowledge Source and Introducing User Based Sessions for Diabetes Education

abstract: Chatbots or conversational agents are computer programs, which interact with users using natural language through artificial intelligence in a way that the user thinks he is having dialogue with a human. One of the main limit of a chatbot technology is associated to the construction of its local knowledge base. A conventional chatbot knowledge base is typically hand constructed, which is a very time-consuming process and may take years to train a chatbot in a particular field of expertise. This work presented in this paper extends the knowledge base of a conventional chatbot beyond its local knowledge base to external knowledge source Wikipedia. This has been achieved by using Media Wiki API to retrieve information from Wikipedia when the chatbot's local knowledge base does not contain the answer to user query. To make the conversation with chatbot more meaningful with regards to the user's previous chat sessions, a user specific session ability has been added to the chatbot architecture. An open source AIML web based chatbot has been modified and programmed for the use in health informatics domain. The chatbot has been named VDMS - Virtual Diabetes Management System. It is intended to be used by the general community and diabetic patients for diabetes education and management.


title: Investor-Imitator: A Framework for Trading Knowledge Extraction

abstract: Stock trading is a popular investment approach in real world. However, since lacking enough domain knowledge and experience, it is very difficult for common investors to analyze thousands of stocks manually. Algorithmic investment provides another rational way to formulate human knowledge as a trading agent. However, it still requires well-built knowledge and experience to design effective trading algorithms in such a volatile market. Fortunately, various kinds of historical trading records are easy to obtain in this big-data era, it is invaluable of us to extract the trading knowledge hidden in the data to help people make better decisions. In this paper, we propose a reinforcement learning driven Investor-Imitator framework to formalize the trading knowledge, by imitating an investor's behavior with a set of logic descriptors. In particular, to instantiate specific logic descriptors, we introduce the Rank-Invest model that can keep the diversity of logic descriptors by learning to optimize different evaluation metrics. In the experiment, we first simulate three types of investors, representing different degrees of information disclosure we may meet in real market. By learning towards these investors, we can tell the inherent trading logic of the target investor with the Investor-Imitator empirically, and the extracted interpretable knowledge can help us better understand and construct trading portfolios. Experimental results in this paper sufficiently demonstrate the designed purpose of Investor-Imitator, it makes the Investor-Imitator an applicable and meaningful intelligent trading framework in financial investment research.


title: Business Unit Strategy, Managerial Characteristics, and Business Unit Effectiveness at Strategy Implementation

abstract: Data from 58 strategic business units (SBUs) reveal that greater marketing/sales experience, greater willingness to take risk, and greater tolerance for ambiguity on the part of the SBU general manager contribute to effectiveness in the case of “build” SBUs but hamper it in the case of “harvest” SBUs.


title: Gather-scatter DRAM: in-DRAM address translation to improve the spatial locality of non-unit strided accesses

abstract: Many data structures (e.g., matrices) are typically accessed with multiple access patterns. Depending on the layout of the data structure in physical address space, some access patterns result in non-unit strides. In existing systems, which are optimized to store and access cache lines, non-unit strided accesses exhibit low spatial locality. Therefore, they incur high latency, and waste memory bandwidth and cache space. We propose the Gather-Scatter DRAM (GS-DRAM) to address this problem. We observe that a commodity DRAM module contains many chips. Each chip stores a part of every cache line mapped to the module. Our idea is to enable the memory controller to access multiple values that belong to a strided pattern from different chips using a single read/write command. To realize this idea, GS-DRAM first maps the data of each cache line to different chips such that multiple values of a strided access pattern are mapped to different chips. Second, instead of sending a separate address to each chip, GS-DRAM maps each strided pattern to a small pattern ID that is communicated to the module. Based on the pattern ID, each chip independently computes the address of the value to be accessed. The cache line returned by the module contains different values of the strided pattern gathered from different chips. We show that this approach enables GS-DRAM to achieve near-ideal memory bandwidth and cache utilization for many common access patterns. We design an end-to-end system to exploit GS-DRAM. Our evaluations show that 1) for in-memory databases, GS-DRAM obtains the best of the row store and the column store layouts, in terms of both performance and energy, and 2) for matrix-matrix multiplication, GS-DRAM seamlessly enables SIMD optimizations and outperforms the best tiled layout. Our framework is general, and can benefit many modern data-intensive applications.


title: Phase retrieval via linear programming: Fundamental limits and algorithmic improvements

abstract: A recently proposed convex formulation of the phase retrieval problem estimates the unknown signal by solving a simple linear program. This new scheme, known as PhaseMax, is computationally efficient compared to standard convex relaxation methods based on lifting techniques. In this paper, we present an exact performance analysis of PhaseMax under Gaussian measurements in the large system limit. In contrast to previously known performance bounds in the literature, our results are asymptotically exact and they also reveal a sharp phase transition phenomenon. Furthermore, the geometrical insights gained from our analysis led us to a novel nonconvex formulation of the phase retrieval problem and an accompanying iterative algorithm based on successive linearization and maximization over a polytope. This new algorithm, which we call PhaseLamp, has provably superior recovery performance over the original PhaseMax method.


title: Two's company, three's a crowd: a case study of crowdsourcing software development

abstract: Crowdsourcing is an emerging and promising approach which involves delegating a variety of tasks to an unknown workforce - the crowd. Crowdsourcing has been applied quite successfully in various contexts from basic tasks on Amazon Mechanical Turk to solving complex industry problems, e.g. InnoCentive. Companies are increasingly using crowdsourcing to accomplish specific software development tasks. However, very little research exists on this specific topic. This paper presents an in-depth industry case study of crowdsourcing software development at a multinational corporation. Our case study highlights a number of challenges that arise when crowdsourcing software development. For example, the crowdsourcing development process is essentially a waterfall model and this must eventually be integrated with the agile approach used by the company. Crowdsourcing works better for specific software development tasks that are less complex and stand-alone without interdependencies. The development cost was much greater than originally expected, overhead in terms of company effort to prepare specifications and answer crowdsourcing community queries was much greater, and the time-scale to complete contests, review submissions and resolve quality issues was significant. Finally, quality issues were pushed later in the lifecycle given the lengthy process necessary to identify and resolve quality issues. Given the emphasis in software engineering on identifying bugs as early as possible, this is quite problematic.


title: Reconstructive expert system explanation

abstract: Existing explanation facilities are typically far more appropriate for knowledge engineers engaged in system maintenance than for end-users of the system. This is because the explanation is little more than a trace of the detailed problem-solving steps. An alternative approach recognizes that an effective explanation often needs to substantially reorganize the actual line of reasoning and bring to bear additional information to support the result. Explanation itself becomes a complex problem-solving process that depends not only on the actual line of reasoning, but also on additional knowledge of the domain. This paper presents a new computational model of explanation and argues that it results in significant improvements over traditional approaches. — Authors' Abstract


title: RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information

abstract: Links between issue reports and their corresponding commits in version control systems are often missing. However, these links are important for measuring the quality of various parts of a software system, predicting defects, and many other tasks. A number of existing approaches have been designed to solve this problem by automatically linking bug reports to source code commits via comparison of textual information in commit messages with textual contents in the bug reports. Yet, the effectiveness of these techniques is oftentimes sub optimal when commit messages are empty or only contain minimum information, this particular problem makes the process of recovering trace ability links between commits and bug reports particularly challenging. In this work, we aim at improving the effectiveness of existing bug linking techniques by utilizing rich contextual information. We rely on a recently proposed tool, namely Change Scribe, which generates commit messages containing rich contextual information by using a number of code summarization techniques. Our approach then extracts features from these automatically generated commit messages and bug reports and inputs them into a classification technique that creates a discriminative model used to predict if a link exists between a commit message and a bug report. We compared our approach, coined as RCLinker (Rich Context Linker), to MLink, which is an existing state-of-the-art bug linking approach. Our experiment results on bug reports from 6 software projects show that RCLinker can outperform MLink in terms of F-measure by 138.66%.


title: Optimism and Traumatic Stress: The Importance of Social Support and Coping1

abstract: This study examined the effects of optimism following traumatic stress and pathways through which optimism may act. Rescue and recovery workers at the crash site of US Air Flight 427 (n= 159) were studied 2, 6, 9, and 12 months after the crash to examine optimistic outlook, social support, coping, and stress. As predicted, a more optimistic disposition was associated with less self-reported distress, less use of avoidant and wishful-thinking coping strategies, greater use of problem-focused and seeking-social-support coping, and greater availability of social support. Contrary to expectations, coping did not account for the relationships observed between optimism and stress responding. Social support explained some of the effects of optimism on coping and stress, but these mediational effects varied over time. Findings suggest that optimism affects stress and coping directly and indirectly by affecting how much social support is available.


title: Vulture: a mid-air word-gesture keyboard

abstract: Word-gesture keyboards enable fast text entry by letting users draw the shape of a word on the input surface. Such keyboards have been used extensively for touch devices, but not in mid-air, even though their fluent gestural input seems well suited for this modality. We present Vulture, a word-gesture keyboard for mid-air operation. Vulture adapts touch based word-gesture algorithms to work in mid-air, projects users' movement onto the display, and uses pinch as a word delimiter. A first 10-session study suggests text-entry rates of 20.6 Words Per Minute (WPM) and finds hand-movement speed to be the primary predictor of WPM. A second study shows that with training on a few phrases, participants do 28.1 WPM, 59% of the text-entry rate of direct touch input. Participants' recall of trained gestures in mid-air was low, suggesting that visual feedback is important but also limits performance. Based on data from the studies, we discuss improvements to Vulture and some alternative designs for mid-air text entry.


title: Virtual fitting: real-time garment simulation for online shopping

abstract: Without physically trying on a garment, online clothes shoppers are unable to decide the best size or color, and therefore are more likely to purchase clothes that do not fit. A solution to this online fit problem is virtual fitting. Using 3D modeling to help customers visualize how garments look on them requires garment simulation in real time. This paper proposes an innovative approach that utilizes machine learning to meet this real-time requirement and can be applied to virtual fitting systems.


title: State-of-the-art in string similarity search and join

abstract: String similarity search and its variants are fundamental problems with many applications in areas such as data integration, data quality, computational linguistics, or bioinformatics. A plethora of methods have been developed over the last decades. Obtaining an overview of the state-of-the-art in this field is difficult, as results are published in various domains without much cross-talk, papers use different data sets and often study subtle variations of the core problems, and the sheer number of proposed methods exceeds the capacity of a single research group. In this paper, we report on the results of the probably largest benchmark ever performed in this field. To overcome the resource bottleneck, we organized the benchmark as an international competition, a workshop at EDBT/ICDT 2013. Various teams from different fields and from all over the world developed or tuned programs for two crisply defined problems. All algorithms were evaluated by an external group on two machines. Altogether, we compared 14 different programs on two string matching problems (k-approximate search and k-approximate join) using data sets of increasing sizes and with different characteristics from two different domains. We compare programs primarily by wall clock time, but also provide results on memory usage, indexing time, batch query effects and scalability in terms of CPU cores. Results were averaged over several runs and confirmed on a second, different hardware platform. A particularly interesting observation is that disciplines can and should learn more from each other, with the three best teams rooting in computational linguistics, databases, and bioinformatics, respectively.


title: ArgueApply: A Mobile App for Argumentation

abstract: Formal models developed in the field of argumentation allow for analysing and evaluating problems that have previously been studied by philosophers on an informal level only. Importantly, they also give rise to the development of computational tools for argumentation. In this paper we report on ArgueApply, a mobile app for argumentation that is based on the Grappa framework, an extension of, e.g., abstract argumentation in the sense of Dung. With ArgueApply users can engage in online discussions and evaluate their semantics. Each of the resulting interpretations can be seen as a coherent view on a discussion in which some of the discussions statements are accepted and others rejected. Being a mobile tool, ArgueApply is intended to be more accessible than existing systems for computing argumentation semantics allowing, e.g., for spontaneous analysis of an ongoing discussion or collective preparation for an important debate. While having a practical system for these applications is the final goal of our work, an immediate objective is using the system for exploring which type of Grappa frameworks under which semantics are best suited for such applications.


title: Monkey visual behavior falls into the uncanny valley

abstract: Very realistic human-looking robots or computer avatars tend to elicit negative feelings in human observers. This phenomenon is known as the “uncanny valley” response. It is hypothesized that this uncanny feeling is because the realistic synthetic characters elicit the concept of “human,” but fail to live up to it. That is, this failure generates feelings of unease due to character traits falling outside the expected spectrum of everyday social experience. These unsettling emotions are thought to have an evolutionary origin, but tests of this hypothesis have not been forthcoming. To bridge this gap, we presented monkeys with unrealistic and realistic synthetic monkey faces, as well as real monkey faces, and measured whether they preferred looking at one type versus the others (using looking time as a measure of preference). To our surprise, monkey visual behavior fell into the uncanny valley: They looked longer at real faces and unrealistic synthetic faces than at realistic synthetic faces.


A Quantitative Study of Data in the NLP community


title: Investigating Structural Brain Changes of Dehydration Using Voxel-Based Morphometry

abstract: Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T1-weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of mild cognitive impairment or Alzheime s disease during disease progression. Based on these findings, a potential confound in GM and WM or ventricular volume studies due to the subjects’ hydration state cannot be excluded and should be appropriately addressed in morphometric studies of the brain.


title: A systematic survey on automated concurrency bug detection, exposing, avoidance, and fixing techniques

abstract: Currently, concurrent programs are becoming increasingly widespread to meet the demands of the rapid development of multi-core hardware. However, it could be quite expensive and challenging to guarantee the correctness and efficiency of concurrent programs. In this paper, we provide a systematic review of the existing research on fighting against concurrency bugs, including automated concurrency bug exposing, detection, avoidance, and fixing. These four categories cover the different aspects of concurrency bug problems and are complementary to each other. For each category, we survey the motivation, key issues, solutions, and the current state of the art. In addition, we summarize the classical benchmarks widely used in previous empirical studies and the contribution of active research groups. Finally, some future research directions on concurrency bugs are recommended. We believe this survey would be useful for concurrency programmers and researchers.


title: APE: an annotation language and middleware for energy-efficient mobile application development

abstract: Energy-efficiency is a key concern in continuously-running mobile applications, such as those for health and context monitoring. Unfortunately, developers must implement complex and customized power-management policies for each application. This involves the use of complex primitives and writing error-prone multithreaded code to monitor hardware state. To address this problem, we present APE, an annotation language and middleware service that eases the development of energy-efficient Android applications. APE annotations are used to demarcate a power-hungry code segment whose execution is deferred until the device enters a state that minimizes the cost of that operation. The execution of power-hungry operations is coordinated across applications by the APE middleware. Several examples show the expressive power of our approach. A case study of using APE annotations in a real mobile sensing application shows that annotations can cleanly specify a power management policy and reduce the complexity of its implementation. An empirical evaluation of the middleware shows that APE introduces negligible overhead and equals hand-tuned code in energy savings, in this case achieving 63.4% energy savings compared to the case when there is no coordination.


title: Semi-supervised graph clustering: a kernel approach

abstract: Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We first show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective (Dhillon et al., in Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004a). A recent theoretical connection between weighted kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For graph data, this result leads to algorithms for optimizing several new semi-supervised graph clustering objectives. For vector data, the kernel approach also enables us to find clusters with non-linear boundaries in the input data space. Furthermore, we show that recent work on spectral learning (Kamvar et al., in Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2003) may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current state-of-the-art semi-supervised algorithms on both vector-based and graph-based data sets.