title: Anderson & Agarwal/Practicing Safe Computing SPECIAL ISSUE PRACTICING SAFE COMPUTING: A MULTIMETHOD EMPIRICAL EXAMINATION OF HOME COMPUTER USER SECURITY BEHAVIORAL INTENTIONS

abstract: Although firms are expending substantial resources to develop technology and processes that can help safeguard the security of their computing assets, increased attention is being focused on the role people play in maintaining a safe computing environment. Unlike employees in a work setting, home users are not subject to training, nor are they protected by a technical staff dedicated to keeping security software and hardware current. Thus, with over one billion people with access to the Internet, individual home computer users represent a significant point of weakness in achieving the security of the cyber infrastructure. We study the phenomenon of conscientious cybercitizens, defined as individuals who are motivated to take the necessary precautions under their direct control to secure their own computer and the Internet in a home setting. Using a multidisciplinary, phased approach, we develop a conceptual model of the conscientious cybercitizen. We present results from two studies-a survey and an experiment-conducted to understand the drivers of intentions to perform security-related behavior, and the interventions that can positively influence these drivers. In the first study, we use protection motivation theory as the underlying conceptual foundation and extend the theory by drawing upon the public goods literature and the concept of psychological ownership. Results from a survey of 594 home computer users from a wide range of demographic and socio-economic backgrounds suggest that a home computer user's intention to perform security-related behavior is influenced by a combination of cognitive, social, and psychological components. In the second study, we draw upon the concepts of goal framing and self-view to examine how the proximal drivers of intentions to perform security-related behavior identified in the first study can be influenced by appropriate messaging. An experiment with 101 subjects is used to test the research hypotheses. Overall, the two studies shed important new light on creating more conscientious cybercitizens. Theoretical and practical implications of the findings are discussed.


abstract: HELGERUD, J., L. C. ENGEN, U. WISLOFF, and J. HOFF. Aerobic endurance training improves soccer performance.Med. Sci. Sports Exerc., Vol. 33, No. 11, 2001, pp. 1925–1931. Purpose: The aim of the present study was to study the effects of aerobic training on performance during soccer match and soccer specific tests. Methods: Nineteen male elite junior soccer players, age 18.1 0.8 yr, randomly assigned to the training group (N 9) and the control group (N 10) participated in the study. The specific aerobic training consisted of interval training, four times 4 min at 90 –95% of maximal heart rate, with a 3-min jog in between, twice per week for 8 wk. Players were monitored by video during two matches, one before and one after training. Results: In the training group: a) maximal oxygen uptake (V u O2max) increased from 58.1 4.5 mL·kg 1 ·min 1 to 64.3 3.9 mL·kg 1 ·min 1 (P 0.01); b) lactate threshold improved from 47.8 5.3 mL·kg 1 ·min 1 to 55.4 4.1 mL·kg 1 ·min 1 (P 0.01); c) running economy was also improved by 6.7% (P 0.05); d) distance covered during a match increased by 20% in the training group (P 0.01); e) number of sprints increased by 100% (P 0.01); f) number of involvements with the ball increased by 24% (P 0.05); g) the average work intensity during a soccer match, measured as percent of maximal heart rate, was enhanced from 82.7 3.4% to 85.6 3.1% (P 0.05); and h) no changes were found in maximal vertical jumping height, strength, speed, kicking velocity, kicking precision, or quality of passes after the training period. The control group showed no changes in any of the tested parameters. Conclusion: Enhanced aerobic endurance in soccer players improved soccer performance by increasing the distance covered, enhancing work intensity, and increasing the number of sprints and involvements with the ball during a match. Key Words: V u O2max, LACTATE THRESHOLD, RUNNING ECONOMY, SKILL


title: Opioid rotation in patients with cancer pain: A retrospective comparison of dose ratios between methadone, hydromorphone, and morphine

abstract: BACKGROUND ::: When a change of opioid is considered, equianalgesic dose tables are used. These tables generally propose a dose ratio of 5:1 between morphine and hydromorphone. In the case of a change from subcutaneous hydromorphone to methadone, dose ratios ranging from 1:6 to 1:10 are proposed. The purpose of this study was to review the analgesic dose ratios for methadone compared with hydromorphone. ::: ::: ::: METHODS ::: In a retrospective study, 48 cases of medication changes from morphine to hydromorphone, and 65 changes between hydromorphone and methadone were identified. the reason for the change, the analgesic dose, and pain intensity were obtained. ::: ::: ::: RESULTS ::: The dose ratios between morphine and hydromorphone and vice versa were found to be 5.33 and 0.28, respectively (similar to expected results). However, the hydromorphone/methadone ratio was found to be 1.14:1 (5 to 10 times higher than expected). Although the dose ratios of hydromorphone/morphine and vice versa did not change according to a previous opioid dose, the hydromorphone/methadone ratio correlated with total opioid dose (correlation coefficient = 0.41 P < 0.001) and was 1.6 (range, 0.3-14.4) in patients receiving more than 330 mg of hydromorphone per day prior to the change, versus 0.95 (range, 0.2-12.3) in patients receiving ae330 mg of hydromorphone per day (P = 0.023). ::: ::: ::: CONCLUSIONS ::: These results suggest that only partial tolerance develops between methadone and hydromorphone. Methadone is much more potent than previously described and any change should start at a lower equivalent dose.


title: Reciprocal Circuits Linking the Prefrontal Cortex with Dorsal and Ventral Thalamic Nuclei

abstract: Summary Reciprocal interactions between the prefrontal cortex (PFC) and thalamus play a critical role in cognition, but the underlying circuits remain poorly understood. Here we use optogenetics to dissect the specificity and dynamics of cortico-thalamo-cortical networks in the mouse brain. We find that cortico-thalamic (CT) neurons in prelimbic PFC project to both mediodorsal (MD) and ventromedial (VM) thalamus, where layer 5 and 6 inputs activate thalamo-cortical (TC) neurons with distinct temporal profiles. We show that TC neurons in MD and VM in turn make distinct connections in PFC, with MD preferentially and strongly activating layer 2/3 cortico-cortical (CC) neurons. Finally, we assess local connections from superficial CC to deep CT neurons, which link thalamo-cortical and cortico-thalamic networks within the PFC. Together our findings indicate that PFC strongly drives neurons in the thalamus, whereas MD and VM indirectly influence reciprocally connected neurons in the PFC, providing a mechanistic understanding of these circuits.


title: Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch

abstract: Smartwatch is becoming one of the most popular wearable device with many major smartphone manufacturers such as Samsung and Apple releasing their smartwatches recently. Apart from the fitness applications, the smartwatch provides a rich user interface that has enabled many applications like instant messaging and email. Since the smartwatch is worn on the wrist, it introduces a unique opportunity to understand user's arm, hand and possibly finger movements using its accelerometer and gyroscope sensors. Although user's arm and hand gestures are likely to be identified with ease using the smartwatch sensors, it is not clear how much of user's finger gestures can be recognized. In this paper, we show that motion energy measured at the smartwatch is sufficient to uniquely identify user's hand and finger gestures. We identify essential features of accelerometer and gyroscope data that reflect the movements of tendons (passing through the wrist) when performing a finger or a hand gesture. With these features, we build a classifier that can uniquely identify 37 (13 finger, 14 hand and 10 arm) gestures with an accuracy of 98\%. We further extend our gesture recognition to identify the characters written by the user with her index finger on a surface, and show that such finger-writing can also be accurately recognized with nearly 95% accuracy. Our presented results will enable many novel applications like remote control and finger-writing-based input to devices using smartwatch.


title: Weakly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation

abstract: In recent years, deep networks have been successfully applied to model image concepts and achieved competitive performance on many data sets. In spite of impressive performance, the conventional deep networks can be subjected to the decayed performance if we have insufficient training examples. This problem becomes extremely severe for deep networks with powerful representation structure, making them prone to over fitting by capturing nonessential or noisy information in a small data set. In this paper, to address this challenge, we will develop a novel deep network structure, capable of transferring labeling information across heterogeneous domains, especially from text domain to image domain. This weakly-shared Deep Transfer Networks (DTNs) can adequately mitigate the problem of insufficient image training data by bringing in rich labels from the text domain. Specifically, we present a novel architecture of DTNs to translate cross-domain information from text to image. To share the labels between two domains, we will build multiple weakly shared layers of features. It allows to represent both shared inter-domain features and domain-specific features, making this structure more flexible and powerful in capturing complex data of different domains jointly than the strongly shared layers. Experiments on real world dataset will show its competitive performance as compared with the other state-of-the-art methods.


title: Proof Protocol for a Machine Learning Technique Making Longitudinal Predictions in Dynamic Contexts

abstract: We demonstrate a protocol for proving strongly that a black-box machine learning technique robustly predicts the future in dynamic, indefinite contexts. We propose necessary components of the proof protocol and demonstrate results visualizations to support evaluation of the proof components. Components include contemporaneously verifiable discrete predictions, deterministic computability of longitudinal predictions, imposition of realistic costs and domain constraints, exposure to diverse contexts, statistically significant excess benefits relative to a priori benchmarks and Monte Carlo trials, insignificant decay of excess benefits, pathology detection and an extended real-time trial "in the wild." We apply the protocol to a big data machine learning technique deployed since 2011 that finds persistent, exploitable opportunities in many of 41 segments of US financial markets, the existence of which opportunities substantially contradict the Efficient Market Hypothesis.


title: A Fully Integrated 60-GHz CMOS Direct-Conversion Doppler Radar RF Sensor With Clutter Canceller for Single-Antenna Noncontact Human Vital-Signs Detection

abstract: This paper presents a 60-GHz CMOS direct-conversion Doppler radar RF sensor with a clutter canceller for single-antenna noncontact human vital-signs detection. A high isolation quasi-circulator (QC) is designed to reduce the transmitting (Tx) power leakage (to the receiver). The clutter canceller performs cancellation for the Tx leakage power (from the QC) and the stationary background reflection clutter to enhance the detection sensitivity of weak vital signals. The integration of the 60-GHz RF sensor consists of the voltage-controlled oscillator, divided-by-2 frequency divider, power amplifier, QC, clutter canceller (consisting of variable-gain amplifier and 360 $^{\circ}$ phase shifter), low-noise amplifier, in-phase/quadrature-phase sub-harmonic mixer, and three couplers. In the human vital-signs detection experimental measurement, at a distance of 75 cm, the detected heartbeat (1–1.3 Hz) and respiratory (0.35–0.45 Hz) signals can be clearly observed with a 60-GHz 17-dBi patch-array antenna. The RF sensor is fabricated in 90-nm CMOS technology with a chip size of ${\hbox{2 mm}}\times {\hbox{2 mm}}$ and a consuming power of 217 mW.


Node . js Paradigms and Benchmarks


title: Review of hardware cost estimation methods, models and tools applied to early phases of space mission planning

abstract: The primary purpose of this paper is to review currently existing cost estimation methods, models, tools and resources applicable to the space sector. While key space sector methods are outlined, a specific focus is placed on hardware cost estimation on a system level, particularly for early mission phases during which specifications and requirements are not yet crystallised, and information is limited. For the space industry, cost engineering within the systems engineering framework is an integral discipline. The cost of any space program now constitutes a stringent design criterion, which must be considered and carefully controlled during the entire program life cycle. A first step to any program budget is a representative cost estimate which usually hinges on a particular estimation approach, or methodology. Therefore appropriate selection of specific cost models, methods and tools is paramount, a difficult task given the highly variable nature, scope as well as scientific and technical requirements applicable to each program. Numerous methods, models and tools exist. However new ways are needed to address very early, pre-Phase 0 cost estimation during the initial program research and establishment phase when system specifications are limited, but the available research budget needs to be established and defined. Due to their specificity, for vehicles such as reusable launchers with a manned capability, a lack of historical data implies that using either the classic heuristic approach such as parametric cost estimation based on underlying CERs, or the analogy approach, is therefore, by definition, limited. ::: ::: This review identifies prominent cost estimation models applied to the space sector, and their underlying cost driving parameters and factors. Strengths, weaknesses, and suitability to specific mission types and classes are also highlighted. Current approaches which strategically amalgamate various cost estimation strategies both for formulation and validation of an estimate, and techniques and/or methods to attain representative and justifiable cost estimates are consequently discussed. Ultimately, the aim of the paper is to establish a baseline for development of a non-commercial, low cost, transparent cost estimation methodology to be applied during very early program research phases at a complete vehicle system level, for largely unprecedented manned launch vehicles in the future. This paper takes the first step to achieving this through the identification, analysis and understanding of established, existing techniques, models, tools and resources relevant within the space sector.


title: Chinese Segmentation with a Word-Based Perceptron Algorithm

abstract: Standard approaches to Chinese word segmentation treat the problem as a tagging task, assigning labels to the characters in the sequence indicating whether the character marks a word boundary. Discriminatively trained models based on local character features are used to make the tagging decisions, with Viterbi decoding finding the highest scoring segmentation. In this paper we propose an alternative, word-based segmentor, which uses features based on complete words and word sequences. The generalized perceptron algorithm is used for discriminative training, and we use a beamsearch decoder. Closed tests on the first and second SIGHAN bakeoffs show that our system is competitive with the best in the literature, achieving the highest reported F-scores for a number of corpora.


title: Development of a Local Prosthetic Limb Using Artificial Intelligence

abstract: Electromyography is a special method for evaluating and recording the electrical activity produced by skeletal muscles. It detects electric potential generated through human brain by movement of the muscle cells. Detected signal can be used further into usable information to drive a control system. This paper presents the techniques to design Electromyography based prototype prosthetic arm, in such a way that can imitate the actions like a real human arm. Current prosthetic devices have limited functionality and are cost prohibitive. Designing a cost effective prosthetic human hand is the major focus of this paper. The design features five individually actuated fingers in addition to movable wrist design, which is unseen in commercial products. This study will serve as a platform for future prosthesis development efforts.


title: Analysis of optimum substream lengths for dual TCP/UDP streaming of HD H.264 video over congested WLANs

abstract: Flexible Dual-TCP/UDP Streaming Protocol (FDSP) is a new method for streaming H.264-encoded High-definition (HD) video over WLANs. FDSP streaming is done in sequential video segments or chunks called substreams. Substreams are typically used in HTTP/TCP streaming for efficient switching between different quality levels of video. However, in FDSP, substream lengths are used to control the amount of TCP data that needs to be sent prior to the playback of that substream. Substream length choice also affects the corresponding amount of UDP data. This paper presents an analysis of substream length modification in the context of FDSP. Our results show that as substream length increases, TCP rebuffering time decreases while the number of TCP rebuffering instances increases. However, our results also show that substream length alone does not have a clear correlation with UDP packet loss.


title: Impact of Pacemaker Failover Configuration on Mean Time to Recovery for Small Cloud Clusters

abstract: In cloud environments High Availability characteristics are established by the usage of failover software (like e.g. HAProxy, Keepalive or Pacemaker). Though these tools enable automatic recovery of cloud services from outages, the recovery can still be very slow if it is not configured adequately. In this paper we developed a "Recovery Time Test" to determine if recovery time depends on configuration of the failover software and how recovery time depends on configuration settings. Another goal of the Recovery Time Test is to determine the factor by which recovery time can be decreased by a given configuration. As proof of concept, we applied the Recovery Time Test to an OpenStack cloud environment which is controlled by the Pacemaker failover software. Pacemaker mean recovery time can take a value between 110 and 160 seconds, if the tool is configured badly. We found that with a proper configuration Pacemaker mean recovery time can be reduced significantly to a value between 15 and 20 seconds.


title: ‘BIG DATA’, THE ‘INTERNET OF THINGS’ AND THE ‘INTERNET OF SIGNS’

abstract: SUMMARY ::: This paper examines the relationship between so-called ‘Big Data’, the ‘Internet of Things’ (the ‘Internet of People and Things,’ and the ‘Internet of Everything’), and the ‘Internet of Signs.’ In particular, we investigate how the ‘things’ in the ‘Internet of Things’ generate ‘Big Data’, and how both are used to generate semiotic ‘signs’. In addition, we analyse the importance of context in and the relationships between ‘Big Data’, the ‘Internet of Things’, and the ‘Internet of Signs’. Copyright © 2013 John Wiley & Sons, Ltd.


title: Optimization of high-order non-uniform QAM constellations

abstract: Spectrally efficient transmission on the physical layer is a crucial attribute of state-of-the-art transmission systems. The latest broadcast and terrestrial point-to-point systems such as DVB-T2, DVB-C2 or LTE use LDPC- or Turbo-codes jointly with QAM constellations to achieve capacity approaching the Shannon limit. However, since the gap between the BICM capacity of conventional uniform QAM constellations and the Shannon limit increases for larger SNR, constellation shaping is required to closely approach the Shannon limit when leaving the low SNR region. We propose non-uniform QAM constellations with modulation orders until 1024k-QAM approaching the Shannon limit up to 0.036 b/s/Hz at 29 dB SNR, corresponding to a short-coming of 0.108 dB. To maintain low-complexity 1-dimensional demapping of each component, the symmetry of the constellations is kept. In addition, we propose quadrant symmetric non-uniform QAM constellations optimized in both dimensions, to improve the performance of the constellations at low SNR.


title: Soft skills in software engineering: A study of its demand by software companies in Uruguay

abstract: Software development requires professionals with knowledge and experience on many different methodologies, tools, and techniques. However, the so-called soft skills, such as interpersonal skills, teamwork, problem solving and customer orientation to name just a few, are as important as, or even more important, than traditional qualifications and technical skills. In this paper we review a set of jobs advertisements offering job positions related to software engineering in order to identify what soft skills are most in demand by software companies in Uruguay. We also compare our findings with the ones reported in other recent studies carried out with data from other countries. This comparison shows that evidence exists about a common set of basic soft skills software companies demand when looking for new staff for software engineering activities.


title: Fetal heart defects: potential and pitfalls of first-trimester detection.

abstract: Congenital heart defects (CHDs) are the leading cause of infant mortality due to birth defects. In the last 15 years, with the shift in screening for aneuploidies to the first trimester, extensive research has concentrated on early screening and detection of CHDs. Early detailed assessment of the fetal heart requires a high level of expertise in early anomaly scanning and fetal echocardiography. However, the detection of major CHDs at 11-13 weeks is influenced by their association with easily detectable markers, such as the nuchal translucency, ductus venosus blood flow and tricuspid regurgitation, and a policy decision as to the objectives of this scan and the allocation of resources necessary to achieve them. The use of transvaginal ultrasound and newer techniques are likely to improve the detection rate. However, the limitations of fetal echocardiography in the first trimester must be borne in mind, and follow-up at mid-gestational echocardiography is prudent in some cases.


title: The effect of cognitive abilities on information search for tasks of varying levels of complexity

abstract: Although web search engines are designed as one-size-fits-all tools, people do not come in one size, but instead vary across many different attributes. One such attribute is cognitive ability. Because information search is primarily a cognitive activity, understanding the extent to which variations in cognitive abilities impact search behaviors and outcomes is especially important. We describe a study in which we explore how people's cognitive abilities affect their search behaviors and perceptions of workload while conducting search tasks with different levels of complexity. Twenty-one adults from the general public completed this study. We assessed participants' associative memory, perceptual speed, and visualization abilities and also measured workload. To evaluate the relationship between cognitive ability, task complexity and workload, we conducted three separate mixed factor ANOVAs corresponding to each of the abilities. Our results suggest three important trends: (1) associative memory ability had no significant effect on search behavior and workload, (2) visualization ability had a significant effect on search behavior, but not workload, and (3) perceptual speed had a significant effect on search behavior and workload. Specifically, participants with high perceptual speed ability engaged in more search activity in less time and experienced less workload. While the interactions were not significant, the differences were more pronounced for more complex tasks. We also found a significant relationship between task complexity and workload, and task complexity and search behaviors, which corroborates previous research.


title: Toward Game Level Generation from Gameplay Videos

abstract: Algorithms that generate computer game content require game design knowledge. We present an approach to automatically learn game design knowledge for level design from gameplay videos. We further demonstrate how the acquired design knowledge can be used to generate sections of game levels. Our approach involves parsing video of people playing a game to detect the appearance of patterns of sprites and utilizing machine learning to build a probabilistic model of sprite placement. We show how rich game design information can be automatically parsed from gameplay videos and represented as a set of generative probabilistic models. We use Super Mario Bros. as a proof of concept. We evaluate our approach on a measure of playability and stylistic similarity to the original levels as represented in the gameplay videos.


title: Adversarial Meta-Learning

abstract: Meta-learning enables a model to learn from very limited data to undertake a new task. In this paper, we study the general meta-learning with adversarial samples. We present a meta-learning algorithm, ADML (ADversarial Meta-Learner), which leverages clean and adversarial samples to optimize the initialization of a learning model in an adversarial manner. ADML leads to the following desirable properties: 1) it turns out to be very effective even in the cases with only clean samples; 2) it is model-agnostic, i.e., it is compatible with any learning model that can be trained with gradient descent; and most importantly, 3) it is robust to adversarial samples, i.e., unlike other meta-learning methods, it only leads to a minor performance degradation when there are adversarial samples. We show via extensive experiments that ADML delivers the state-of-the-art performance on two widely-used image datasets, MiniImageNet and CIFAR100, in terms of both accuracy and robustness.


title: Targeted Influence Maximization in Social Networks

abstract: Influence maximization (IM) problem asks for a set of k nodes in a given graph G, such that it can reach the largest expected number of remaining nodes in G. Existing methods have either considered that the influence be targeted to meet certain deadline constraint, or be restricted to specific geographical region. However, if an event organizer wants to disseminate some event information on a social platform, s/he would want to select a set of users who can influence the most number of people within the neighborhood of the event location, and this influence should occur before the event takes place. Considering the location and deadline independently may lead to a less than optimal set of users. In this paper, we formalize the problem targeted influence maximization in social networks. We adopt a login model where each user is associated with a login probability and he can be influenced by his neighbors only when he is online. We develop a sampling based algorithm that returns a (1-1/e-e)-approximate solution, as well as an efficient heuristic algorithm that focuses on nodes close to the target location. Experiments on real-world social network datasets demonstrate the effectiveness and efficiency of our proposed method.


An Author’s Guide to Writing Articles and Reviews for Educational Research Review


title: NCBI GEO: archive for functional genomics data sets—10 years on

abstract: A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20 000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.


title: Clinical implications of uterine malformations and hysteroscopic treatment results

abstract: Uterine malformations consist of a group of miscellaneous congenital anomalies of the female genital system. Their mean prevalence in the general population and in the population of fertile women is approximately 4.3%, in infertile patients approximately 3.5% and in patients with recurrent pregnancy losses approximately 13%. Septate uterus is the commonest uterine anomaly with a mean incidence of approximately 35% followed by bicornuate uterus (approximately 25%) and arcuate uterus (approximately 20%). It seems that malformed uterus and especially septate uterus is not an infertility factor in itself. However, it may have a part in the delayed natural conception of women with mainly secondary infertility. On the other hand, patients with uterine malformations seem to have an impaired pregnancy outcome even as early as their first pregnancy. Overall term delivery rates in patients with untreated uterine malformations are only approximately 50% and obstetric complications are more frequent. Unicornuate and didelphys uterus have term delivery rates of approximately 45%, and the pregnancy outcome of patients with untreated bicornuate and septate uterus is also poor with term delivery rates of only approximately 40%. Arcuate uterus is associated with a slightly better but still impaired pregnancy outcome with term delivery rates of approximately 65%. Women who have undergone hysteroscopic septum resection and have been reported in the different series comprise a highly selected group of symptomatic patients with term delivery and live birth rates of only approximately 5%. Hysteroscopic treatment seems to restore an almost normal prognosis for the outcome of their pregnancies with term delivery rates of approximately 75% and live birth rates of approximately 85%. It seems, therefore, that hysteroscopic septum resection can be applied as a therapeutic procedure in cases of symptomatic patients but also as a prophylactic procedure in asymptomatic patients in order to improve their chances for a successful delivery.


title: Endoscopy and the Risk of Venous Thromboembolism: A Case-Control Study

abstract: Background and Study Aims To assess whether there was an association between endoscopy and the risk of venous thromboembolism (VTE). Patients and Methods Retrospective case – control study of patients diagnosed with VTE over a 3-year period. Each was age- and sex-matched to one of three controls who attended an outpatient appointment on the same date as that of the diagnosis of VTE in the patients. Patients who had undergone endoscopy within 90 days of VTE were included. On a second analysis, patients who were hospitalized and those with inflammatory bowel disease or malignancy were excluded. The difference in occurrence of endoscopy between cases and controls was examined using the McNemar test. The risk of VTE occurring following endoscopy was quantified by means of odds ratios. Results Forty-five of 436 patients (10.3 %) had undergone an endoscopy in the VTE group compared with 14 /436 controls (3.2 %; P P = 0.001). When patients with inflammatory bowel disease or malignancy were also excluded, no difference was found between patients undergoing endoscopy and controls (1.92 [0.95, 3.85]; P = 0.07). Ten percent of patients with VTE underwent endoscopy in the 3 months before the diagnosis compared with 3 % of controls ( P Conclusions When those with known risk factors for VTE were excluded, no significant increased risk of VTE was found.


title: Behavioral and Social Influences on Food Choice

abstract: Marion Nestle, PhD, MPH, New York University, New YorkNY (Co-chair); Rena Wing, PhD, University of Pittsburgh, Pittsburgh PA(Co-chair); Learn Birch, PhD, Perm State University, University Park PA; Lorelei DiSogra, PhD, Dole Foods, San Mateo CA; Adam Drewnowski, PhD, University ofMichigan, Ann Arbor MI; Suzette Middleton, PhD, Procter & Gamble, Cincinnati OH; Madeleine Sigman-Grant, PhD, University ofNevada, Cooperative Extension, Las Vegas W, Jeffery Sobal, PhD, Cornell University, Ithaca W, Mary Winston, PhD, American Heart Association, Dallas TX; Christina Economos, PhD, Tufts University, Medford MA*


title: The circulatory system of amphioxus (Branchiostoma floridae)

abstract: The endothelial cell of Amphioxus has an endocytotic capability since both ferritin and horseradish peroxidase, when injected into the endostylar artery, became localized within endothelial cell cytoplasmic vesicles. The endothelial cells also contain vesicles within which the lysosomal enzyme acid phosphatase has been localized. Because of the presence of an endocytotic capability and lysosomes, these cells possess both the means and the method for the acquisition and disposal of exogenous proteins.


title: Knowledge Discovery in Databases (KDD) with Images: A Novel Approach toward Image Mining and Processing Pardeep Kumar

abstract: We are in an age often referred to as the information age. In this information age, because we believe that information leads to power and success, from the technologies such as computers, satellites, etc., we have been collecting tremendous amounts of information. Our ability to analyze and understand massive datasets lags far behind our ability to gather and store data. Image and video data contains abundant, rich information for data miners to explore. On one hand, the rich literature on image and video data analysis will naturally provide many advanced methods that may help mining other kinds of data. On the other hand, recent research on data mining will also provide some new, interesting methods that may benefit image and video data retrieval and analysis. Today, a lot of data is available everywhere but the ability to understand and make use of that data is very less. Whether the context is business, medicine, science or government, the datasets themselves are of little value. What is of value is the knowledge that can be inferred from the data and put to use. We need systems which would analyze the data for us. This paper basically aims to find out important pixels of an image using one of the classification technique named as decision tree (ID-3). Our aim is to separate the important and unimportant pixels of an image using simple rules. Further one of the compression techniques named as Huffman algorithm is applied for image compression. Finally, resultant image is stored with lesser space complexity.


title: Big data in global health: improving health in low- and middle-income countries

abstract: Over the last decade, a massive increase in data collection and analysis has occurred in many fields. In the health sector, however, there has been relatively little progress in data analysis and application despite a rapid rise in data production. Given adequate governance, improvements in the quality, quantity, storage and analysis of health data could lead to substantial improvements in many health outcomes. In low- and middle-income countries in particular, the creation of an information feedback mechanism can move health-care delivery towards results-based practice and improve the effective use of scarce resources. We review the evolving definition of big data and the possible advantages of – and problems in – using such data to improve health-care delivery in low- and middle-income countries. The collection of big data as mobile-phone based services improve may mean that development phases required elsewhere can be skipped. However, poor infrastructure may prevent interoperability and the safe use of patient data. An appropriate governance framework must be developed and enforced to protect individuals and ensure that health-care delivery is tailored to the characteristics and values of the target communities.


title: Code obfuscation techniques for metamorphic viruses

abstract: This paper deals with metamorphic viruses. More precisely, it examines the use of advanced code obfuscation techniques with respect to metamorphic viruses. Our objective is to evaluate the difficulty of a reliable static detection of viruses that use such obfuscation techniques. Here we extend Spinellis’ result (IEEE Trans. Inform. Theory, 49(1), 280–284, 2003) on the detection complexity of bounded-length polymorphic viruses to metamorphic viruses. In particular, we prove that reliable static detection of a particular category of metamorphic viruses is an \({\mathcal{NP}}\)-complete problem. Then we empirically illustrate our result by constructing a practical obfuscator which could be used by metamorphic viruses in the future to evade detection.


title: Framework for developing volcanic fragility and vulnerability functions for critical infrastructure

abstract: Volcanic risk assessment using probabilistic models is increasingly desired for risk management, particularly for loss forecasting, critical infrastructure management, land-use planning and evacuation planning. Over the past decades this has motivated the development of comprehensive probabilistic hazard models. However, volcanic vulnerability models of equivalent sophistication have lagged behind hazard modelling because of the lack of evidence, data and, until recently, minimal demand. There is an increasingly urgent need for development of quantitative volcanic vulnerability models, including vulnerability and fragility functions, which provide robust quantitative relationships between volcanic impact (damage and disruption) and hazard intensity. The functions available to date predominantly quantify tephra fall impacts to buildings, driven by life safety concerns. We present a framework for establishing quantitative relationships between volcanic impact and hazard intensity, specifically through the derivation of vulnerability and fragility functions. We use tephra thickness and impacts to key infrastructure sectors as examples to demonstrate our framework. Our framework incorporates impact data sources, different impact intensity scales, preparation and fitting of data, uncertainty analysis and documentation. The primary data sources are post-eruption impact assessments, supplemented by laboratory experiments and expert judgment, with the latter drawing upon a wealth of semi-quantitative and qualitative studies. Different data processing and function fitting techniques can be used to derive functions; however, due to the small datasets currently available, simplified approaches are discussed. We stress that documentation of data processing, assumptions and limitations is the most important aspect of function derivation; documentation provides transparency and allows others to update functions more easily. Following our standardised approach, a volcanic risk scientist can derive a fragility or vulnerability function, which then can be easily compared to existing functions and updated as new data become available. To demonstrate how to apply our framework, we derive fragility and vulnerability functions for discrete tephra fall impacts to electricity supply, water supply, wastewater and transport networks. These functions present the probability of an infrastructure site or network component equalling or exceeding one of four impact states as a function of tephra thickness.


Secure program execution via dynamic information flow tracking


title: Intraoperative Nerve Monitoring Can Reduce Prevalence of Recurrent Laryngeal Nerve Injury in Thyroid Reoperations: Results of a Retrospective Cohort Study

abstract: BackgroundThe prevalence of recurrent laryngeal nerve (RLN) injury is higher in repeat than in primary thyroid operations. The use of intraoperative nerve monitoring (IONM) as an aid in dissection of the scar tissue is believed to minimize the risk of nerve injury. The aim of this study was to examine whether the use of IONM in thyroid reoperations can reduce the prevalence of RLN injury.MethodsThis was a retrospective cohort study of patients who underwent thyroid reoperations with IONM versus with RLN visualization, but without IONM. The database of thyroid surgery was searched for eligible patients (treated in the years 1993–2012). The primary outcomes were transient and permanent RLN injury. Laryngoscopy was used to evaluate and follow RLN injury.ResultsThe study group comprised 854 patients (139 men, 715 women) operated for recurrent goiter (n = 576), recurrent hyperthyroidism (n = 36), completion thyroidectomy for cancer (n = 194) or recurrent thyroid cancer (n = 48), including 472 bilateral and 382 unilateral reoperations; 1,326 nerves at risk (NAR). A group of 306 patients (500 NAR) underwent reoperations with IONM and 548 patients (826 NAR) had reoperations with RLN visualization, but without IONM. Transient and permanent RLN injuries were found respectively in 13 (2.6 %) and seven (1.4 %) nerves with IONM versus 52 (6.3 %) and 20 (2.4 %) nerves without IONM (p = 0.003 and p = 0.202, respectively).ConclusionsIONM decreased the incidence of transient RLN paresis in repeat thyroid operations compared with nerve visualization alone. The prevalence of permanent RLN injury tended to be lower in thyroid reoperations with IONM, but statistical validation of the observed differences requires a sample size of 920 NAR per arm.


title: Actions and Attributes from Wholes and Parts

abstract: We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision. Our part detectors are a deep version of poselets and capture parts of the human body under a distinct set of poses. For the tasks of action and attribute classification, we train holistic convolutional neural networks and show that adding parts leads to top-performing results for both tasks. We observe that for deeper networks parts are less significant. In addition, we demonstrate the effectiveness of our approach when we replace an oracle person detector, as is the default in the current evaluation protocol for both tasks, with a state-of-the-art person detection system.


title: POCO-framework for Pareto-optimal resilient controller placement in SDN-based core networks

abstract: With the introduction of Software Defined Networking (SDN), the concept of an external and optionally centralized network control plane, i.e. controller, is drawing the attention of researchers and industry. A particularly important task in the SDN context is the placement of such external resources in the network. In this paper, we discuss important aspects of the controller placement problem with a focus on SDN-based core networks, including different types of resilience and failure tolerance. When several performance and resilience metrics are considered, there is usually no single best controller placement solution, but a trade-off between these metrics. We introduce our framework for resilient Pareto-based Optimal COntroller-placement (POCO) that provides the operator of a network with all Pareto-optimal placements. The ideas and mechanisms are illustrated using the Internet2 OS3E topology and further evaluated on more than 140 topologies of the Topology Zoo. In particular, our findings reveal that for most of the topologies more than 20% of all nodes need to be controllers to assure a continuous connection of all nodes to one of the controllers in any arbitrary double link or node failure scenario.


title: Implementation and control of grid connected AC-DC-AC power converter for variable speed wind energy conversion system

abstract: 30 kW electrical power conversion system is developed for a variable speed wind turbine system. In the wind energy conversion system (WECS) a synchronous generator converts the mechanical energy into electrical energy. As the voltage and frequency of generator output vary along the wind speed change, a DC-DC boosting chopper is utilized to maintain constant DC link voltage. The input DC current is regulated to follow the optimized current reference for maximum power point operation of turbine system. Line side PWM inverter supply currents into the utility line by regulating the DC link voltage. The active power is controlled by q-axis current whereas the reactive power can be controlled by d-axis current. The phase angle of utility voltage is detected using software PLL (phased locked loop) in d-q synchronous reference frame. Proposed scheme gives a low cost and high quality power conversion solution for variable speed WECS.


title: A Survey on Game-Theoretic Approaches for Intrusion Detection and Response Optimization

abstract: Intrusion Detection Systems (IDS) are key components for securing critical infrastructures, capable of detecting malicious activities on networks or hosts. However, the efficiency of an IDS depends primarily on both its configuration and its precision. The large amount of network traffic that needs to be analyzed, in addition to the increase in attacks’ sophistication, renders the optimization of intrusion detection an important requirement for infrastructure security, and a very active research subject. In the state of the art, a number of approaches have been proposed to improve the efficiency of intrusion detection and response systems. In this article, we review the works relying on decision-making techniques focused on game theory and Markov decision processes to analyze the interactions between the attacker and the defender, and classify them according to the type of the optimization problem they address. While these works provide valuable insights for decision-making, we discuss the limitations of these solutions as a whole, in particular regarding the hypotheses in the models and the validation methods. We also propose future research directions to improve the integration of game-theoretic approaches into IDS optimization techniques.


title: Hierarchical Neural Network for Extracting Knowledgeable Snippets and Documents

abstract: In this study, we focus on extracting knowledgeable snippets and annotating knowledgeable documents from Web corpus, consisting of the documents from social media and We-media. Informally, knowledgeable snippets refer to the text describing concepts, properties of entities, or relations among entities, while knowledgeable documents are the ones with enough knowledgeable snippets. These knowledgeable snippets and documents could be helpful in multiple applications, such as knowledge base construction and knowledge-oriented service. Previous studies extracted the knowledgeable snippets using the pattern-based method. Here, we propose the semantic-based method for this task. Specifically, a CNN based model is developed to extract knowledgeable snippets and annotate knowledgeable documents simultaneously. Additionally, a"low-level sharing, high-level splitting"structure of CNN is designed to handle the documents from different content domains. Compared with building multiple domain-specific CNNs, this joint model not only critically saves the training time, but also improves the prediction accuracy visibly. The superiority of the proposed method is demonstrated in a real dataset from Wechat public platform.


title: Topology and Prediction Focused Research on Graph Convolutional Neural Networks

abstract: Important advances have been made using convolutional neural network (CNN) approaches to solve complicated problems in areas that rely on grid structured data such as image processing and object classification. Recently, research on graph convolutional neural networks (GCNN) has increased dramatically as researchers try to replicate the success of CNN for graph structured data. Unfortunately, traditional CNN methods are not readily transferable to GCNN, given the irregularity and geometric complexity of graphs. The emerging field of GCNN is further complicated by research papers that differ greatly in their scope, detail, and level of academic sophistication needed by the reader. The present paper provides a review of some basic properties of GCNN. As a guide to the interested reader, recent examples of GCNN research are then grouped according to techniques that attempt to uncover the underlying topology of the graph model and those that seek to generalize traditional CNN methods on graph data to improve prediction of class membership. Discrete Signal Processing on Graphs (DSPg) is used as a theoretical framework to better understand some of the performance gains and limitations of these recent GCNN approaches. A brief discussion of Topology Adaptive Graph Convolutional Networks (TAGCN) is presented as an approach motivated by DSPg and future research directions using this approach are briefly discussed.


title: Can Pretrained Neural Networks Detect Anatomy?

abstract: Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant. In medical imaging, there is a huge variety of possible imaging modalities and contrasts, where annotated data is usually very scarce. We present two approaches to deal with this challenge. A network pretrained in a different domain with abundant data is used as a feature extractor, while a subsequent classifier is trained on a small target dataset; and a deep architecture trained with heavy augmentation and equipped with sophisticated regularization methods. We test the approaches on a corpus of X-ray images to design an anatomy detection system.


title: A comparative study of neural network models for lexical intent classification

abstract: Domain and intent classification are critical pre-processing steps for many speech understanding and dialog systems, as it allows for certain types of utterances to be routed to particular subsystems. In previous work, we explored many types of neural network (NN) architectures — some feedforward and some recurrent — for lexical intent classification and found that they improved upon more traditional statistical baselines. In this paper we carry out a more comprehensive comparison of NN models including the recently proposed gated recurrent unit network, for two domain/intent classification tasks. Furthermore, whereas the previous work was confined to relatively small and controlled datasets, we now include experiments based on a large set obtained from the Cortana personal assistant application. We compare feedforward, recurrent, and gated — such as LSTM and GRU — networks against each other. On both the ATIS intent task and the much larger Cortana domain classification tasks, gated networks outperform recurrent models, which in turn outperform feedforward networks. Also, we compared standard word vector models against a representation which encodes words as sets of character n-grams to mitigate the out-of-vocabulary problem. We find that in nearly all cases, the standard word vectors outperform character-based word representations. Best results are obtained by linearly combining scores from NN models with log likelihood ratios obtained from N-gram language models.


title: A Systematic Mapping Study of Software Reliability Modeling

abstract: Abstract Context Software Reliability (SR) is a highly active and dynamic research area. Published papers have approached this topic from various and heterogeneous points of view, resulting in a rich body of literature on this topic. The counterpart to this is the considerable complexity of this body of knowledge. Objective The objective of this study is to obtain a panorama and a taxonomy of Software Reliability Modeling (SRM). Method In order to do this, a Systematic Mapping Study (SMS) which analyzes and structures the literature on Software Reliability Modeling has been carried out. Results A total of 972 works were obtained as a result of the Systematic Mapping Study. On the basis of the more than 500 selected primary studies found, the results obtained show an increasing diversity of work. Conclusion Although it was discovered that Software Reliability Growth Models (SRGM) are still the most common modeling technique, it was also found that both the modeling based on static and architectural characteristics and the models based on Artificial Intelligence and automatic learning techniques are increasingly more apparent in literature. We have also observed that most Software Reliability Modeling efforts take place in the Pacific Rim area and in academic environments. Industrial initiatives are as yet marginal, and would appear to be primarily located in the USA.


title: Image completion using planar structure guidance

abstract: We propose a method for automatically guiding patch-based image completion using mid-level structural cues. Our method first estimates planar projection parameters, softly segments the known region into planes, and discovers translational regularity within these planes. This information is then converted into soft constraints for the low-level completion algorithm by defining prior probabilities for patch offsets and transformations. Our method handles multiple planes, and in the absence of any detected planes falls back to a baseline fronto-parallel image completion algorithm. We validate our technique through extensive comparisons with state-of-the-art algorithms on a variety of scenes.


title: Dynamic priority ceilings: A concurrency control protocol for real-time systems

abstract: Real-time systems have stringent deadline requirements for their tasks. To meet the requirements, a real-time system must use scheduling algorithms that ensure a predictable response even in the face of mutually exclusive accesses to critical sections. We present a concurrency control protocol for systems using the earliest deadline first scheduling algorithm. The protocol specifies a dynamic priority ceiling for each critical section which is the earliest deadline of jobs which are currently in or will enter the critical section. Jobs trying to enter a critical section will be blocked if they do not have a priority higher than the priority ceiling of any critical section which is in use. We show that the protocol prevents both deadlock and chained blocking. The schedulability condition and implementation issues of the protocol are also discussed.


Using latent semantic analysis in text summarization and summary evaluation


title: Design And Implementation Of 2 Bit Ternary ALU Slice

abstract: Abstract This paper describes the architecture, design & implementation of 2 bit ternary ALU (T-ALU) slice. The proposed ALU is designed for two -bit operation & can be used for n bit operations by cascading n/2 ALU slices. This ALU is implemented using C-MOS ternary logic gates (T-Gates) for ternary arithmetic & logic circuits. Ternary gates are implemented using enhancement / depletion MOSFET technology, thus proposed ALU is suitable for LSI / VLSI implementation. The designed technique used here requires only two stages i.e . decoder & T-gates, as against three stages i.e. decoder, binary gates & encoder require in conventional ternary logic implementation . Index Terms : Ternary, Unary function, T -Gates, Literal. I. Introduction Alexander [1964] showed that natural base (e= 2.71828) is the most efficient radix for implementation of switching circuits. It seems that most efficient radix for the implementation of digital system is 3 than 2. Ternary logic system, meaning that it has 3 valued switching. Ternary system has several important advantages over binary. It can be summarized as reductions in the interconnections require to implement logic functions, thereby reducing chip area, more information can be transmitted over a given set of lines, lesser memory requirement for a given data length. Besides this serial & some serial-parallel operations can be carried out at higher speed [1][2][3]. Its advantages have been confirmed in the application like memories, communications and digital signal processing etc. [7]. It has been proven that realization & implementation of combinational & sequential function is possible for ternary systems [4][5][6][7]. The implementation is based around bipolar transistors, MOSFETs etc. a basic switching elements, which is refereed to as T-Ga tes [8]. Besides this several authors have proposed reduction techniques to realize ternary functions [9][10][11][12]. In this contribution, we propose ALU capable of performing basic ternary arithmetic & logic operations as mentioned in table 1. We also suggest a scheme that takes the advantage of minimization techniques proposed by [9][11][13] & implemented using T-gates designed for ternary operations. This scheme shows reduction in the number of gate count to implement ternary functions. Firstly we describe the design of 2 bit ALU and then integrate over ALU slice. The organization of paper is: Section II describes basic T-Gate implementation, 2 bit ALU architecture is given in section III, section IV describes 2 bit ALU design and ALU slice design. Experimental results & performance evaluation is given in section V. Finally conclusion is given in section VI. Table 1:Functional Table of T -ALU


title: Electromagnetic Modeling of a Novel Linear Oscillating Actuator

abstract: Single-phase linear oscillating actuators (LOAs) produce short-stroke reciprocating motion, and, thus, have many applications. Feasible pole/tooth number combinations for single-phase tubular moving-magnet LOAs are identified and results from a study on a 2-pole LOA equipped with an E-cored stator, and quasi-Halbach magnetized and radially magnetized magnets, respectively, are presented. It is shown that a quasi-Halbach magnetized LOA, in which the magnets are mounted on a ferromagnetic support tube, results in the best electromagnetic performance. Finally, its predicted thrust force characteristic is validated by measurements on a prototype actuator.


title: Action Elimination and Stopping Conditions for Reinforcement Learning

abstract: We incorporate statistical confidence intervals in both the multi-armed bandit and the reinforcement learning problems. In the bandit problem we show that given n arms, it suffices to pull the arms a total of O((n/e2)log(1/δ)) times to find an e-optimal arm with probability of at least 1-δ. This bound matches the lower bound of Mannor and Tsitsiklis (2004) up to constants. We also devise action elimination procedures in reinforcement learning algorithms. We describe a framework that is based on learning the confidence interval around the value function or the Q-function and eliminating actions that are not optimal (with high probability). We provide a model-based and a model-free variants of the elimination method. We further derive stopping conditions guaranteeing that the learned policy is approximately optimal with high probability. Simulations demonstrate a considerable speedup and added robustness over e-greedy Q-learning.


title: Methods: Using Three-Dimensional Culture (Spheroids) as an In Vitro Model of Tumour Hypoxia

abstract: Regions of hypoxia in tumours can be modelled in vitro in 2D cell cultures with a hypoxic chamber or incubator in which oxygen levels can be regulated. Although this system is useful in many respects, it disregards the additional physiological gradients of the hypoxic microenvironment, which result in reduced nutrients and more acidic pH. Another approach to hypoxia modelling is to use three-dimensional spheroid cultures. In spheroids, the physiological gradients of the hypoxic tumour microenvironment can be inexpensively modelled and explored. In addition, spheroids offer the advantage of more representative modelling of tumour therapy responses compared with 2D culture. Here, we review the use of spheroids in hypoxia tumour biology research and highlight the different methodologies for spheroid formation and how to obtain uniformity. We explore the challenge of spheroid analyses and how to determine the effect on the hypoxic versus normoxic components of spheroids. We discuss the use of high-throughput analyses in hypoxia screening of spheroids. Furthermore, we examine the use of mathematical modelling of spheroids to understand more fully the hypoxic tumour microenvironment.


title: Text classification method based on self-training and LDA topic models

abstract: A novel text classification method for learning from very small labeled set.The method uses a text representation based on the LDA topic model.Self-training is used to enlarge labeled set from unlabeled instances.A model for setting methods parameters for any document collection is proposed. Supervised text classification methods are efficient when they can learn with reasonably sized labeled sets. On the other hand, when only a small set of labeled documents is available, semi-supervised methods become more appropriate. These methods are based on comparing distributions between labeled and unlabeled instances, therefore it is important to focus on the representation and its discrimination abilities. In this paper we present the ST LDA method for text classification in a semi-supervised manner with representations based on topic models. The proposed method comprises a semi-supervised text classification algorithm based on self-training and a model, which determines parameter settings for any new document collection. Self-training is used to enlarge the small initial labeled set with the help of information from unlabeled data. We investigate how topic-based representation affects prediction accuracy by performing NBMN and SVM classification algorithms on an enlarged labeled set and then compare the results with the same method on a typical TF-IDF representation. We also compare ST LDA with supervised classification methods and other well-known semi-supervised methods. Experiments were conducted on 11 very small initial labeled sets sampled from six publicly available document collections. The results show that our ST LDA method, when used in combination with NBMN, performed significantly better in terms of classification accuracy than other comparable methods and variations. In this manner, the ST LDA method proved to be a competitive classification method for different text collections when only a small set of labeled instances is available. As such, the proposed ST LDA method may well help to improve text classification tasks, which are essential in many advanced expert and intelligent systems, especially in the case of a scarcity of labeled texts.


A Probabilistic Approach toFeature Selection - A Filter Solution


Meshing Roundtable ( IMR 25 ) Discretized Riemannian Delaunay triangulations


title: Multi-Block ADMM for Big Data Optimization in Modern Communication Networks

abstract: In this paper, we review the parallel and distributed optimization algorithms based on the alternating direction method of multipliers (ADMM) for solving "big data" optimization problems in modern communication networks. We first introduce the canonical formulation of the large-scale optimization problem. Next, we describe the general form of ADMM and then focus on several direct extensions and sophisticated modifications of ADMM from $2$-block to $N$-block settings to deal with the optimization problem. The iterative schemes and convergence properties of each extension/modification are given, and the implementation on large-scale computing facilities is also illustrated. Finally, we numerate several applications in communication networks, such as the security constrained optimal power flow problem in smart grid networks and mobile data offloading problem in software defined networks (SDNs).


title: A new classification scheme of science fields and subfields designed for scientometric evaluation purposes

abstract: A two-level hierarchic system of fields and subfields of the sciences, social sciences and arts & humanities is proposed. The system was specifically designed for scientometric (evaluation) purposes with the ultimate goal of classifying every single document into a well-defined category. This goal was achieved using a three-step iterative process. The basic concepts and some preliminary results are presented.


Discovery of Precursors to Adverse Events using Time Series Data.


title: Enterprise cloud service architectures

abstract: As a new computing paradigm, cloud computing has received a lot of attention from enterprises and has being integrated or applied to enterprise architectures. This paper surveys the state of the art of enterprise cloud service architectures. Specifically, this paper discusses enterprise cloud service architectural requirements, design approaches, architectural styles, emerging cloud service platforms, applications and related challenges in an enterprise context. This paper also identifies research trends and opportunities for researchers and practitioners in this fast moving field.


title: A METRICS SUITE FOR OBJECT ORIENTED DESIGN

abstract: Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a new suite of metrics for OO design. Metrics developed in previous research, while contributing to the field's understanding of software development processes, have generally been subject to serious criticisms, including the lack of a theoretical base. Following Wand and Weber (1989), the theoretical base chosen for the metrics was the ontology of Bunge (1977). Six design metrics are developed, and then analytically evaluated against Weyuker's (1988) proposed set of measurement principles. An automated data collection tool was then developed and implemented to collect an empirical sample of these metrics at two field sites in order to demonstrate their feasibility and suggest ways in which managers may use these metrics for process improvement. >


title: E‐government and developing countries: an overview

abstract: E‐governance is more than just a government website on the Internet. The strategic objective of e‐governance is to support and simplify governance for all parties; government, citizens and businesses. The use of ICTs can connect all three parties and support processes and activities. In other words, in e‐governance electronic means support and stimulate good governance. Therefore, the objectives of e‐governance are similar to the objectives of good governance. Good governance can be seen as an exercise of economic, political, and administrative authority to better manage affairs of a country at all levels. It is not difficult for people in developed countries to imagine a situation in which all interaction with government can be done through one counter 24 hours a day, 7 days a week, without waiting in lines. However to achieve this same level of efficiency and flexibility for developing countries is going to be difficult. The experience in developed countries shows that this is possible if governments ar...


title: Knowledge distillation across ensembles of multilingual models for low-resource languages

abstract: This paper investigates the effectiveness of knowledge distillation in the context of multilingual models. We show that with knowledge distillation, Long Short-Term Memory(LSTM) models can be used to train standard feed-forward Deep Neural Network (DNN) models for a variety of low-resource languages. We then examine how the agreement between the teacher's best labels and the original labels affects the student model's performance. Next, we show that knowledge distillation can be easily applied to semi-supervised learning to improve model performance. We also propose a promising data selection method to filter un-transcribed data. Then we focus on knowledge transfer among DNN models with multilingual features derived from CNN+DNN, LSTM, VGG, CTC and attention models. We show that a student model equipped with better input features not only learns better from the teacher's labels, but also outperforms the teacher. Further experiments suggest that by learning from each other, the original ensemble of various models is able to evolve into a new ensemble with even better combined performance.


title: Wait for It: Determinants of Pull Request Evaluation Latency on GitHub

abstract: The pull-based development model, enabled by git and popularised by collaborative coding platforms like Bit Bucket, Gitorius, and GitHub, is widely used in distributed software teams. While this model lowers the barrier to entry for potential contributors (since anyone can submit pull requests to any repository), it also increases the burden on integrators (i.e., Members of a project's core team, responsible for evaluating the proposed changes and integrating them into the main development line), who struggle to keep up with the volume of incoming pull requests. In this paper we report on a quantitative study that tries to resolve which factors affect pull request evaluation latency in GitHub. Using regression modeling on data extracted from a sample of GitHub projects using the Travis-CI continuous integration service, we find that latency is a complex issue, requiring many independent variables to explain adequately.


title: Semantic Matching of Web Services Capabilities

abstract: The Web is moving from being a collection of pages toward a collection of services that interoperate through the Internet. The first step toward this interoperation is the location of other services that can help toward the solution of a problem. In this paper we claim that location of web services should be based on the semantic match between a declarative description of the service being sought, and a description of the service being offered. Furthermore, we claim that this match is outside the representation capabilities of registries such as UDDI and languages such as WSDL.We propose a solution based on DAML-S, a DAML-based language for service description, and we show how service capabilities are presented in the Profile section of a DAML-S description and how a semantic match between advertisements and requests is performed.


title: A Wideband Power Amplifier MMIC Utilizing GaN on SiC HEMT Technology

abstract: The design and performance of a wideband power amplifier MMIC suitable for electronic warfare (EW) systems and other wide bandwidth applications is presented. The amplifier utilizes dual field plate 0.25-mum GaN on SiC device technology integrated into the three metal interconnect (3MI) process flow. Experimental results for the MMIC at 30V power supply operation demonstrate greater than 10 dB of small signal gain, 9 W to 15 W saturated output power and 20% to 38% peak power added efficiency over a 1.5 GHz to 17 GHz bandwidth.


title: Validation of a Malay Version of the Smartphone Addiction Scale among Medical Students in Malaysia

abstract: INTRODUCTION ::: This study was initiated to determine the psychometric properties of the Smart Phone Addiction Scale (SAS) by translating and validating this scale into the Malay language (SAS-M), which is the main language spoken in Malaysia. This study can distinguish smart phone and internet addiction among multi-ethnic Malaysian medical students. In addition, the reliability and validity of the SAS was also demonstrated. ::: ::: ::: MATERIALS AND METHODS ::: A total of 228 participants were selected between August 2014 and September 2014 to complete a set of questionnaires, including the SAS and the modified Kimberly Young Internet addiction test (IAT) in the Malay language. ::: ::: ::: RESULTS ::: There were 99 males and 129 females with ages ranging from 19 to 22 years old (21.7±1.1) included in this study. Descriptive and factor analyses, intra-class coefficients, t-tests and correlation analyses were conducted to verify the reliability and validity of the SAS. Bartlett's test of sphericity was significant (p <0.01), and the Kaiser-Mayer-Olkin measure of sampling adequacy for the SAS-M was 0.92, indicating meritoriously that the factor analysis was appropriate. The internal consistency and concurrent validity of the SAS-M were verified (Cronbach's alpha = 0.94). All of the subscales of the SAS-M, except for positive anticipation, were significantly related to the Malay version of the IAT. ::: ::: ::: CONCLUSIONS ::: This study developed the first smart phone addiction scale among medical students. This scale was shown to be reliable and valid in the Malay language.


title: Virtual engineering at work: the challenges for designing mechatronic products

abstract: The product race has become an innovation race, reconciling challenges of branding, performance, time to market and competitive pricing while complying with ecological, safety and legislation constraints. The answer lies in “smart” products of high complexity, relying on heterogeneous technologies and involving active components. To keep pace with this evolution and further accelerate the design cycle, the design engineering process must be rethought. The paper presents a mechatronic simulation approach to achieve this goal. The starting point is the current virtual prototyping paradigm that is widely adopted and that continues to improve in terms of model complexity, accuracy, robustness and automated optimization. Two evolutions are discussed. A first one is the extension to multi-physics simulation answering the design needs of the inherent multi-disciplinarity of “intelligent” products. Integration of thermal, hydraulic, mechanical, haptic and electrical functions requires simulation to extend beyond the traditional CAD-FEM approach, supporting the use of system, functional and perception models. The second evolution is the integration of control functions in the products. Where current industrial practice treats mechanical system design and control design as different design loops, this paper discusses their integration in a model-based design process at all design stages, turning concepts such as software-in-the-loop and hardware-in-the-loop into basic elements of an industrial design approach. These concepts are illustrated by a number of automotive design engineering cases, which demonstrate that the combined use of perception, geometric and system models allows to develop innovative solutions for the active safety, low-emission and high-comfort performance of next-generation vehicles. This process in turn poses new challenges to the design in terms of the specification and validation of such innovative products, including their failure modes and fault-tolerant behaviour. This will imply adopting a model-based system engineering approach that is currently already common practice in software engineering.


title: Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain

abstract: This paper addresses the cooperative localization and visual mapping problem with multiple heterogeneous robots. The approach is designed to deal with the challenging large semi-structured outdoors environments in which aerial/ground ensembles are to evolve. We propose the use of heterogeneous visual landmarks, points and line segments, to achieve effective cooperation in such environments. A large-scale SLAM algorithm is generalized to handle multiple robots, in which a global graph maintains the relative relationships between a series of local sub-maps built by the different robots. The key issue when dealing with multiple robots is to find the link between them, and to integrate these relations to maintain the overall geometric consistency; the events that introduce these links on the global graph are described in detail. Monocular cameras are considered as the primary extereoceptive sensor. In order to achieve the undelayed initialization required by the bearing-only observations, the well-known inverse-depth parametrization is adopted to estimate 3D points. Similarly, to estimate 3D line segments, we present a novel parametrization based on anchored Plucker coordinates, to which extensible endpoints are added. Extensive simulations show the proposed developments, and the overall approach is illustrated using real-data taken with a helicopter and a ground rover.


title: Automatic gamma correction based on average of brightness

abstract: Preprocessing is essential stage in image processing because of limitation of imaging device or inappropriate environmental light. This paper presents a preprocessing technique for estimating the amount of gamma correction in the absence of any information or knowledge about environmental light and imaging device. The basic approach exploits the amount of gamma correction based on average brightness. The amount of gamma correction is then estimated by a power which transports average of brightness to center of histogram .


title: From Noise Modeling to Blind Image Denoising

abstract: Traditional image denoising algorithms always assume the noise to be homogeneous white Gaussian distributed. However, the noise on real images can be much more complex empirically. This paper addresses this problem and proposes a novel blind image denoising algorithm which can cope with real-world noisy images even when the noise model is not provided. It is realized by modeling image noise with mixture of Gaussian distribution (MoG) which can approximate large varieties of continuous distributions. As the number of components for MoG is unknown practically, this work adopts Bayesian nonparametric technique and proposes a novel Low-rank MoG filter (LR-MoG) to recover clean signals (patches) from noisy ones contaminated by MoG noise. Based on LR-MoG, a novel blind image denoising approach is developed. To test the proposed method, this study conducts extensive experiments on synthesis and real images. Our method achieves the state-of the-art performance consistently.


title: Healing of hymenal injuries in prepubertal and adolescent girls: a descriptive study.

abstract: OBJECTIVE ::: The objective of this study was to identify the healing process and outcome of hymenal injuries in prepubertal and adolescent girls. ::: ::: ::: METHODS ::: This multicenter, retrospective project used photographs to document the healing process and outcome of hymenal trauma that was sustained by 239 prepubertal and pubertal girls whose ages ranged from 4 months to 18 years. ::: ::: ::: RESULTS ::: The injuries that were sustained by the 113 prepubertal girls consisted of 21 accidental or noninflicted injuries, 73 secondary to abuse, and 19 "unknown cause" injuries. All 126 pubertal adolescents were sexual assault victims. The hymenal injuries healed at various rates and except for the deeper lacerations left no evidence of the previous trauma. Abrasions and "mild" submucosal hemorrhages disappeared within 3 to 4 days, whereas "marked" hemorrhages persisted for 11 to 15 days. Only petechiae and blood blisters proved to be "markers" for determining the approximate age of an injury. Petechiae resolved within 48 hours in the prepubertal girls and 72 hours in the adolescents. A blood blister was detected at 34 days in an adolescent. As lacerations healed, their observed depth became shallower and their configuration smoothed out. Of the girls who sustained "superficial," "intermediate," or "deep" lacerations, 15 of 18 prepubertal girls had smooth and continuous appearing hymenal rims, whereas 24 of 41 adolescents' hymens had a normal, "scalloped" appearance and 30 of 34 had no disruption of continuity on healing. The final "width" of a hymenal rim was dependent on the initial depth of the laceration. No scar tissue formation was observed in either group of girls. ::: ::: ::: CONCLUSIONS ::: The hymenal injuries healed rapidly and except for the more extensive lacerations left no evidence of a previous injury. There were no significant differences in the healing process and the outcome of the hymenal injuries in the 2 groups of girls.


title: A Stretchable and Highly Sensitive Graphene-Based Fiber for Sensing Tensile Strain, Bending, and Torsion

abstract: Wearable sensors are increasingly finding their way into applications of kinesthetic sensing, personal health monitoring, and smart prosthetics/robotics. A graphene-based composite fiber sensor with a "compression spring" structure is fabricated, featuring the ability of detecting multiple kinds of deformation. This fiber sensor is integrated into wearable sensors for monitoring human activities and intricate movements of robotics successfully.


title: Demonstration of a Semi-Autonomous Hybrid Brain–Machine Interface Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic

abstract: To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocortico-graphic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p <; 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.


title: Landmark classification in large-scale image collections

abstract: With the rise of photo-sharing websites such as Facebook and Flickr has come dramatic growth in the number of photographs online. Recent research in object recognition has used such sites as a source of image data, but the test images have been selected and labeled by hand, yielding relatively small validation sets. In this paper we study image classification on a much larger dataset of 30 million images, including nearly 2 million of which have been labeled into one of 500 categories. The dataset and categories are formed automatically from geotagged photos from Flickr, by looking for peaks in the spatial geotag distribution corresponding to frequently-photographed landmarks. We learn models for these landmarks with a multiclass support vector machine, using vector-quantized interest point descriptors as features. We also explore the non-visual information available on modern photo-sharing sites, showing that using textual tags and temporal constraints leads to significant improvements in classification rate. We find that in some cases image features alone yield comparable classification accuracy to using text tags as well as to the performance of human observers.


title: A Robust and Scalable Peer-to-Peer Gossiping Protocol

abstract: The newscast model is a general approach for communication in large agent-based distributed systems. The two basic services—membership management and information dissemination—are implemented by the same epidemic-style protocol. In this paper we present the newscast model and report on experiments using a Java implementation. The experiments involve communication in a large, wide-area cluster computer. By analysis of the outcome of the experiments we demonstrate that the system indeed shows the scalability and dependability properties predicted by our previous theoretical and simulation results.


title: Magnify-Net for Multi-Person 2D Pose Estimation

abstract: We propose a novel method for multi-person 2D pose estimation. Our model zooms in the image gradually, which we refer to as the Magnify-Net, to solve the bottleneck problem of mean average precision (mAP) versus pixel error. Moreover, we squeeze the network efficiently by an inspired design that increases the mAP while saving the processing time. It is a simple, yet robust, bottom-up approach consisting of one stage. The architecture is designed to detect the part position and their association jointly via two branches of the same sequential prediction process, resulting in a remarkable performance and efficiency rise. Our method outcompetes the previous state-of-the-art results on the challenging COCO key-points task and MPII Multi-Person Dataset.


title: VISA: visual subspace clustering analysis

abstract: To gain insight into today's large data resources, data mining extracts interesting patterns. To generate knowledge from patterns and benefit from human cognitive abilities, meaningful visualization of patterns are crucial. Clustering is a data mining technique that aims at grouping data to patterns based on mutual (dis)similarity. For high dimensional data, subspace clustering searches patterns in any subspace of the attributes as patterns are typically obscured by many irrelevant attributes in the full space. For visual analysis of subspace clusters, their comparability has to be ensured. Existing subspace clustering approaches, however, lack interactive visualization and show bias with respect to the dimensionality of subspaces. In this work, dimensionality unbiased subspace clustering and a novel distance function for subspace clusters are proposed. We suggest two visualization techniques that allow users to browse the entire subspace clustering, to zoom into individual objects, and to analyze subspace cluster characteristics in-depth. Bracketing of different parameter settings enable users to immediately see the effect of parameters on their data and hence to choose the best clustering result for further analysis. Usage of user analysis for feedback to the subspace clustering algorithm directly improves the subspace clustering. We demonstrate our visualization techniques on real world data and confirm results through additional accuracy measurements and comparison with existing subspace clustering algorithms.


title: Deep Reinforcement Learning: An Overview

abstract: We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions. ::: Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant update.


Explore Efficient Data Organization for Large Scale Graph Analytics and Storage


title: General Video Game Level Generation

abstract: This paper presents a framework and an initial study in general video game level generation, the problem of generating levels for not only a single game but for any game within a specified range. While existing level generators are tailored to a particular game, this new challenge requires generators to take into account the constraints and affordances of games that might not even have been designed when the generator was constructed. The framework presented here builds on the General Video Game AI framework (GVG-AI) and the Video Game Description Language (VGDL), in order to reap synergies from research activities connected to the General Video Game Playing Competition. The framework will also form the basis for a new track of this competition. In addition to the framework, the paper presents three general level generators and an empirical comparison of their qualities.


title: Traceability of vector network analyzer measurements

abstract: Measurement traceability is NOT just a requirement in ISO quality standards. It is a necessity to ensure the accuracy of any measurement system. For vector network analyzers (VNA), the traceability of S-parameter measurements is not obvious. The latest generation of VNA is also capable of making measurements beyond S-parameters. This paper will present the latest developments in VNA measurement traceability chain, the key traceable parameters, calibration and verification standard traceability and some verification techniques and results.


title: Starting simple: adding value to static visualisation through simple interaction

abstract: Interactive visualisation has been one of the most exciting areas in HCI over recent years. The key term here is 'interactive', and in this paper we assert that virtually any static representation can become more powerful by the addition of simple interactive elements. This is demonstrated by adding interactivity to standard representations including stacked histograms, pie charts and scatter plots. We show how adding interactivity can help resolve many of the trade-offs inherent in static visualisations by allowing multiple options to be available and most importantly for them to be interactively related. Many years of creativity and effort have been invested in traditional generic and bespoke visualisations. Adding interactivity leverages this accumulated experience, but also adds an extra dimension.


title: Design considerations of RFID based baggage handling system, a literature review

abstract: The use of RFID based baggage handling system has widely spread all over the world. This brings a successfully decreasing in the amount of baggage mishandling. SITA 2017 reported that during 10 years until 2016 there is increasing of airline passengers, but decreasing in the amount of mishandled baggage. Many people believe that this great achievement was caused by IT implementation in baggage handling system in airport, including the massive use of RFID for baggage identification tool. Indonesia as one of the countries with the largest population had a significant increasing amount of air passengers and their baggage, that is 15% from 2015 to 2016. Until now this huge number of air baggage is stil manually handled, so Indonesia is facing a serious problem of handling the huge amount of baggage that keep growing. This paper is a preliminary literature study to iniate the development of RFID based baggage handling system in Indonesia. The research question is what is design considerations to develop RFID based baggage handling system.


title: Low power design of the next-generation High Efficiency Video Coding

abstract: This paper provides a comprehensive analysis of the computational complexity, power consumption, temperature, and memory access behavior for the next-generation High Efficiency Video Coding (HEVC) standard. We highlight the associated design challenges and present several low-power algorithmic and architectural techniques for developing power-efficient HEVC-based multimedia system. We explore the interplay between the algorithms and architectures to provide high power efficiency while leveraging the application-specific knowledge and video content characteristics.


title: Building Stronger Ties With Alumni Through Facebook to Increase Volunteerism and Charitable Giving

abstract: This research explores how strength of network ties, as fostered by the use of a social network site, Facebook, (a) influences alumni attitudes toward volunteering for and making charitable gifts to their alma mater, and (b) fortifies consistency between attitude and behavior. After exploratory interviews and participant observation, a survey of 3,085 alumni was conducted for hypothesis testing. Structural equation modeling analysis revealed: First, active participation in Facebook groups positively predicted strength of network ties along 2 dimensions: frequency of communication and emotional closeness. Second, both dimensions of tie strength influenced actual behavior, albeit via different routes. The paper also contributes to attitude change research in showing that strength of network ties can help ensure consistencies between attitude and behavior.


title: Synapse at CAp 2017 NER challenge: Fasttext CRF

abstract: We present our system for the CAp 2017 NER challenge which is about named entity recognition on French tweets. Our system leverages unsupervised learning on a larger dataset of French tweets to learn features feeding a CRF model. It was ranked first without using any gazetteer or structured external data, with an F-measure of 58.89\%. To the best of our knowledge, it is the first system to use fasttext embeddings (which include subword representations) and an embedding-based sentence representation for NER.


title: Accelerating cine phase-contrast flow measurements usingk-t BLAST andk-t SENSE

abstract: Conventional phase-contrast velocity mapping in the ascending aorta was combined with k-t BLAST and k-t SENSE. Up to 5.3-fold net acceleration was achieved, enabling single breathhold acquisitions. A standard phase-contrast (PC) sequence with interleaved acquisition of the velocity-encoded segments was modified to collect data in 2 stages, a high-resolution undersampled and a low-resolution fully sampled training stage. In addition, a modification of the k-t reconstruction strategy was tested. This strategy, denoted as “plug-in,” incorporates data acquired in the training stage into the final reconstruction for improved data consistency, similar to conventional keyhole. “k-t SENSE plug-in” was found to provide best image quality and most accurate flow quantification. For this strategy, at least 10 training profiles are required to yield accurate stroke volumes (relative deviation <5%) and good image quality. In vivo 2D cine velocity mapping was performed in 6 healthy volunteers with 30 –32 cardiac phases (spatial resolution 1.3 1.3 8 –10 mm 3 , temporal resolution of 18 –38 ms), yielding relative stroke volumes of 106 18% (mean 2*SD) and 112 15% for 3.8 and 5.3 net accelerations, respectively. In summary, k-t BLAST and k-t SENSE are promising approaches that permit significant scan-time reduction in PC velocity mapping, thus making high-resolution breath-held flow quantification possible. Magn Reson Med 54:1430 –1438, 2005. © 2005 Wiley


title: Supporting attribute-based access control with ontologies

abstract: In highly open systems like the Internet, attribute-based access control (ABAC) has proven its appropriateness. The specification and maintenance of ABAC policies however has turned out to be complex and error-prone, especially if heterogeneous attribute schemes are involved. Here, the arising semantic Web can contribute to a solution. This paper presents an approach based on an extension of the established XACML standard. It simplifies the policies by providing an ontology-based attribute management facility.


title: Automatic virtual machine configuration for database workloads

abstract: Virtual machine monitors are becoming popular tools for the deployment of database management systems and other enterprise software. In this article, we consider a common resource consolidation scenario in which several database management system instances, each running in a separate virtual machine, are sharing a common pool of physical computing resources. We address the problem of optimizing the performance of these database management systems by controlling the configurations of the virtual machines in which they run. These virtual machine configurations determine how the shared physical resources will be allocated to the different database system instances. We introduce a virtualization design advisor that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offline. Furthermore, runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation and to handle changes in the workload. To estimate the effect of a particular resource allocation on workload performance, we use the query optimizer in a new what-if mode. We have implemented our approach using both PostgreSQL and DB2, and we have experimentally evaluated its effectiveness using DSS and OLTP workloads.