title: Quality of life and sexual function in patients with borderline tumors of the ovary. A substudy of the Arbeitsgemeinschaft Gynaekologische Onkologie (AGO) study group ROBOT study

abstract: PURPOSE ::: Borderline tumors of the ovary (BOT) represent an entity of ovarian neoplasms with low malignant potential and excellent prognosis. Mainstay of therapy is surgery including adequate staging and complete tumor resection, possibly inducing long-term side effects, especially in premenopausal women. The aim of this study was to evaluate quality of life (QoL) and sexual function in patients treated for BOT, also with respect to treatment modalities. ::: ::: ::: METHODS ::: This prospective study was part of an ambidirectional multicentric cohort study in patients with BOT in Germany. Patients from seven centers completed three questionnaires after completion of therapy. QoL and sexual function data were correlated with treatment characteristics: surgical approach, lymphadenectomy, and fertility sparing surgery. ::: ::: ::: RESULTS ::: One hundred ten patients returned the questionnaires. Median follow-up was 4.0 years. The majority lived in a relationship (n = 85, 77%, missing n = 4), and 49% (n = 54, missing n = 6) were sexually active. The main reason for sexual inactivity was "no partner" (38%). The women had a mean global health status of 69.8 (SD 22.7; min 0, max 100), with 100 implying perfect health. Mean QoL score was 73.7 (SD 23.3, range 0 to 100). Both scores showed no difference regarding fertility preserving surgery, surgical approach, and lymphadenectomy. Sexually active women had a significantly higher QoL (78.7 vs. 67.4, p = 0.0156) and global health status (75.9 vs. 60.9, p = 0.0013) than inactive patients. ::: ::: ::: CONCLUSIONS ::: In this prospective study, patients who had been diagnosed with BOT had a very good quality of life and global health status. Sexually inactive women stated lack of a partner as the main reason and had an inferior HRQoL compared to sexually active women.


Medical Cyber Physical Systems and Bigdata Platforms


title: Modelling of heat and mass transfer processes in neonatology

abstract: This paper reviews some of our recent applications of computational fluid dynamics (CFD) to model heat and mass transfer problems in neonatology and investigates the major heat and mass transfer mechanisms taking place in medical devices such as incubators and oxygen hoods. This includes novel mathematical developments giving rise to a supplementary model, entitled infant heat balance module, which has been fully integrated with the CFD solver and its graphical interface. The numerical simulations are validated through comparison tests with experimental results from the medical literature. It is shown that CFD simulations are very flexible tools that can take into account all modes of heat transfer in assisting neonatal care and the improved design of medical devices.


title: Switched-Capacitor-Based Dual-Switch High-Boost DC–DC Converter

abstract: A switched-capacitor-based dual-switch dc–dc converter with a high-boost voltage gain is proposed in this paper. The proposed converter can obtain a high-voltage gain with a small duty cycle, which decreases the voltage stress and the conduction loss on the power switches. This paper presents the key waveforms, the operating principles at the continuous conduction mode and the discontinuous conduction mode, and the parameter design. Moreover, a comparison between the proposed converter and other nonisolated converters has been completed. To verify the operating principle, a 200 W prototype is constructed with an input voltage of 25–50 V and an output voltage of 200 V. The simulation and experimental results are shown.


title: Characterization of User Online Dating Behavior and Preference on a Large Online Dating Site

abstract: Online dating sites have become popular platforms for people to look for romantic partners, providing an unprecedented level of access to potential dates that is otherwise not available through traditional means. Characterization of the user online dating behavior helps us to obtain a deep understanding of their dating preference and make better recommendations on potential dates. In this paper we study the user online dating behavior and preference using a large real-world dataset from a major online dating site in China. In particular, we characterize the temporal behavior, message send and reply behavior of users, study how users online dating behaviors correlate with various user attributes, and investigate how users’ actual online dating behaviors deviate from their stated preferences. Our results show that on average a male sends out more messages but receives fewer messages than a female. A female is more likely to be contacted but less likely to reply to a message than a male. The number of messages that a user sends out and receives per week quickly decreases with time, especially for female users. Most messages are replied to within a short time frame with a median delay of around 9 h. Many of the user messaging behaviors align with notions in social and evolutionary psychology: males tend to look for younger females while females place more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. The geographic distance between two users and the photo count of users play an important role in their dating behavior. We show that it is important to differentiate between users’ true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that while both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, there is significant discrepancy between a user’s stated dating preference and his/her actual online dating behavior. We further characterize how users actual dating behavior deviate from their stated preference. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.


title: Traffic Light Recognition for Complex Scene With Fusion Detections

abstract: Traffic light recognition is one of the important tasks in the studies of intelligent transport system. In this paper, a robust traffic light recognition model based on vision information is introduced for on-vehicle camera applications. Our contribution mainly includes three aspects. First, in order to reduce computational redundancy, the aspect ratio, area, location, and context of traffic lights are utilized as prior information, which establishes a task model for traffic light recognition. Second, in order to improve the accuracy, we propose a series of improved methods based on an aggregate channel feature method, including modifying the channel feature for each types of traffic light and establishing a structure of fusion detectors. Third, we introduce a method of inter-frame information analysis, utilizing detection information of previous frame to modify original proposal regions, which makes the accuracy further improved. In the comparison of other traffic light detection algorithms, our model achieves competitive results on the complex scene VIVA data set. Furthermore, an analysis of small target luminous object detection tasks is given.


title: Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems

abstract: In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems. The DLT includes compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization. The main feature of the approach is that the controller design depends only on the measured input/output data of the controlled plant. Analysis and extensive simulations have shown that MFAC guarantees the bounded-input bounded-output stability and the tracking error convergence.


title: Relay Node Deployment Strategies in Heterogeneous Wireless Sensor Networks

abstract: In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multihop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large-scale heterogeneous WSN.


title: Selective Inference for Group-Sparse Linear Models

abstract: We develop tools for selective inference in the setting of group sparsity, including the construction of confidence intervals and p-values for testing selected groups of variables. Our main technical result gives the precise distribution of the magnitude of the projection of the data onto a given subspace, and enables us to develop inference procedures for a broad class of group-sparse selection methods, including the group lasso, iterative hard thresholding, and forward stepwise regression. We give numerical results to illustrate these tools on simulated data and on health record data.


title: Inductive Hashing on Manifolds

abstract: Learning based hashing methods have attracted considerable attention due to their ability to greatly increase the scale at which existing algorithms may operate. Most of these methods are designed to generate binary codes that preserve the Euclidean distance in the original space. Manifold learning techniques, in contrast, are better able to model the intrinsic structure embedded in the original high-dimensional data. The complexity of these models, and the problems with out-of-sample data, have previously rendered them unsuitable for application to large-scale embedding, however. In this work, we consider how to learn compact binary embeddings on their intrinsic manifolds. In order to address the above-mentioned difficulties, we describe an efficient, inductive solution to the out-of-sample data problem, and a process by which non-parametric manifold learning may be used as the basis of a hashing method. Our proposed approach thus allows the development of a range of new hashing techniques exploiting the flexibility of the wide variety of manifold learning approaches available. We particularly show that hashing on the basis of t-SNE [29] outperforms state-of-the-art hashing methods on large-scale benchmark datasets, and is very effective for image classification with very short code lengths.


title: Twitter in disaster mode: opportunistic communication and distribution of sensor data in emergencies

abstract: Recent events (earthquakes, floods, etc.) have shown that users heavily rely on online social networks (OSN) to communicate and organize during disasters and in their aftermath. In this paper, we discuss what features could be added to OSN apps for smart phones -- for the example of Twitter -- to make them even more useful for disaster situations. In particular, we consider cases where the fixed communication infrastructure is partially or totally wiped out and propose to equip regular Twitter apps with a disaster mode. The disaster mode relies on opportunistic communication and epidemic spreading of Tweets from phone to phone. Such "disaster-ready" applications would allow to resume (although limited) communication instantaneously and help distressed people to self-organize until regular communication networks are functioning again, or, temporary emergency communication infrastructure is installed. We argue why we believe that Twitter with its simplicity and versatile features (e.g., retweet and hashtag) is a good platform to support a variety of different situations and present Twimight, our disaster ready Twitter application. In addition, we propose Twimight as a platform for disseminating sensor data providing information such as locations of drinkable water sources. Eventually, we propose to rely on interest matching to scale Twitter hashtag-based searches in an opportunistic environment. The combination of these features make our opportunistic Twitter the ideal emergency kit in situations of disasters. We discuss and define the main implementation and research challenges (both technical and non-technical).


title: Fully Lexicalising CCGbank with Hat Categories

abstract: We introduce an extension to CCG that allows form and function to be represented simultaneously, reducing the proliferation of modifier categories seen in standard CCG analyses. ::: ::: We can then remove the non-combinatory rules CCGbank uses to address this problem, producing a grammar that is fully lexicalised and far less ambiguous. ::: ::: There are intrinsic benefits to full lexicalisation, such as semantic transparency and simpler domain adaptation. The clearest advantage is a 52--88% improvement in parse speeds, which comes with only a small reduction in accuracy.


title: Deterministic edge-preserving regularization in computed imaging

abstract: Many image processing problems are ill-posed and must be regularized. Usually, a roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of edges, which are very important attributes of the image. In this paper, we first give conditions for the design of such an edge-preserving regularization. Under these conditions, we show that it is possible to introduce an auxiliary variable whose role is twofold. First, it marks the discontinuities and ensures their preservation from smoothing. Second, it makes the criterion half-quadratic. The optimization is then easier. We propose a deterministic strategy, based on alternate minimizations on the image and the auxiliary variable. This leads to the definition of an original reconstruction algorithm, called ARTUR. Some theoretical properties of ARTUR are discussed. Experimental results illustrate the behavior of the algorithm. These results are shown in the field of 2D single photon emission tomography, but this method can be applied in a large number of applications in image processing.


title: Searching for explanations: How the Internet inflates estimates of internal knowledge.

abstract: As the Internet has become a nearly ubiquitous resource for acquiring knowledge about the world, questions have arisen about its potential effects on cognition. Here we show that searching the Internet for explanatory knowledge creates an illusion whereby people mistake access to information for their own personal understanding of the information. Evidence from 9 experiments shows that searching for information online leads to an increase in self-assessed knowledge as people mistakenly think they have more knowledge “in the head,” even seeing their own brains as more active as depicted by functional MRI (fMRI) images.


title: Compact Yagi antenna for handheld UHF RFID reader

abstract: This paper describes a compact linearly polarized high gain antenna for a handheld UHF RFID reader. The antenna is a Yagi type structure, with three elements (driver, director, and reflector) which fit into compact footprint (100 mm × 100 mm). The antenna has maximum gain of 6 dBi and VSWR better than 1.3 in approximately 50 MHz band around either European or US RFID bands (865–870 MHz or 902–928 MHz). We describe the antenna structure and present the comparison of simulation results with experimental data.


DISCRETIZED NEURAL NETWORKS


title: Analysing Student Performance using Sparse Data of Core Bachelor Courses

abstract: Curricula for Computer Science degrees are characterized by the strong occupational orientation of the discipline. In the BSc degree structure, with clearly separated CS core studies, learning skills between these and other required studies may vary a lot, showing in student’s overall performance. To analyze such a situation, we apply nonstandard educational data mining techniques on a preprocessed log file of the passed courses. The joint variation of the course grades are studied through correlation analysis while the intrinsic groups of students are created and analyzed using a special clustering technique. Since not all students have attended all the courses, there is a nonstructured sparsity pattern to cope with. Finally, multilayer perceptron neural network with cross-validation based generalization assurance is trained and analyzed using analytic mean sensitivity to explain the nonlinear regression model constructed. Local (within-methods) and global (between-methods) triangulation of different analysis methods is argued to improve the technical soundness of the presented approaches, giving more confidence on our final conclusion that general learning capabilities predict the success of students better than specific IT skills obtained as part of the core studies.


A Practical Perspective on Latent Structured Prediction for Coreference Resolution


title: Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions

abstract: Globalization has triggered a rapid increase in cross-border mergers and acquisitions (M&As). However, research shows that only 17 percent of cross-border M&As create shareholder value. One of the main reasons for this poor track record is top management's lack of attention to nonfinancial aspects (e.g., sociocultural aspects) of M&As. With the rapid growth of Web 2.0 applications, online environmental scanning provides top executives with unprecedented opportunities to tap into collective web intelligence to develop better insights about the sociocultural and political--economic factors that cross-border M&As face. Grounded in Porter's five forces model, one major contribution of our research is the design of a novel due diligence scorecard model that leverages collective web intelligence to enhance M&A decision making. Another important contribution of our work is the design and development of an adaptive business intelligence (BI) 2.0 system underpinned by an evolutionary learning approach, domain-specific sentiment analysis, and business relation mining to operationalize the aforementioned scorecard model for adaptive M&A decision support. With Chinese companies' cross-border M&As as the business context, our experimental results confirm that the proposed adaptive BI 2.0 system can significantly aid decision makers under different M&A scenarios. The managerial implication of our findings is that firms can apply the proposed BI 2.0 technology to enhance their strategic decision making, particularly when making cross-border investments in targeted markets for which private information may not be readily available.


Chapter 13 TEXT MINING IN SOCIAL NETWORKS


title: An Empirical Comparison of Parsing Methods for Stanford Dependencies

abstract: Stanford typed dependencies are a widely desired representation of natural language sentences, but parsing is one of the major computational bottlenecks in text analysis systems. In light of the evolving definition of the Stanford dependencies and developments in statistical dependency parsing algorithms, this paper revisits the question of Cer et al. (2010): what is the tradeoff between accuracy and speed in obtaining Stanford dependencies in particular? We also explore the effects of input representations on this tradeoff: part-of-speech tags, the novel use of an alternative dependency representation as input, and distributional representaions of words. We find that direct dependency parsing is a more viable solution than it was found to be in the past. An accompanying software release can be found at: this http URL


title: On an Electric Scooter With G2V/V2H/V2G and Energy Harvesting Functions

abstract: This paper presents an electric scooter with grid-to-vehicle/vehicle-to-home (V2H)/vehicle-to-grid (V2G) and energy harvesting functions. First, the bilateral dc/dc converter-fed scooter dc motor drive is established. Good driving characteristics are achieved via proper control. In an idle case, the motor drive can be arranged to perform the V2H and V2G discharging operations. A front-end dc/dc converter is developed using the motor-drive-embedded components to establish the boosted and well-regulated dc-link voltage from battery. Then, an H-bridge single-phase inverter is established to yield the ac 110-V/60-Hz source for home appliances or emergency power use. Good output sine-wave voltage waveforms are yielded by the designed robust and proportional-plus-resonant controllers. Moreover, the V2G operation is also applicable. The battery can send power to the utility grid. In addition, a synchronous rectifier-based buck dc/dc converter is designed to establish a universal serial bus with 5-V dc for charging various peripheral devices. Conversely in charging mode, an H-bridge boost switch-mode-rectifier-based charger is formed using the same schematics. Good charging characteristics and line drawn power quality are obtained. As to the photovoltaic (PV) energy harvesting scheme, the energies of roof PV or possible dc sources can be harvested to make the battery auxiliary charging.


title: The impact of atmospheric deposition on the aquatic ecosystem with special emphasis on lake productivity, Newfoundland, Canada

abstract: To document and analyze the ecosystem response to acidification and possible concurrent heavy metal enrichment process, a detailed paleoecological study was conducted in Lake 817, a small, and sensitive, second order lake, in insular Newfoundland. The inferred pH history of the lake was recreated using the region-specific equation relating fossil diatom taxa to pH. The profile demonstrated a fairly stable pH history over the bottom 22 cm of the sediment core with an appreciable pH decline in the upper 5 cm. The decrease of inferred pH began in the 1930's (ca. 1931 to 1944) and the lowest inferred pH value (5.3) was observed in the surface stratum. The decreasing pH trend (0.4 unit; 5.7 to 5.3) between ca. 1931 and 1985 correlated well with changes in sediment chemistry. Major elements (P, Mg, Ca, Na, and K) showed significant decrease in deposition primarily in the upper 3 cm level (ca. 1944). The profiles of selected metals (Fe, Mn, Cr, Cu, Zn, Pb, Co, Hg, As, and Ni) indicated that the deposition of these elements increased noticeably, primarily in the upper 3 cm level. The enrichment of Pb began earlier, about 50 years ago (1931 to 1944). The stratigraphy of fossil pigments (chlorophyll derivatives and total carotenoids) demonstrated a general trend of increasing accumulation upwards in the core while the only significant decrease in pigments was observed in the recent strata (ca. between 1957 to 1985). The most dramatic decrease of primary production occurred during ca. 1957 to 1974 period and this phenomenon was associated with increased levels of heavy metals. It seems that primary production in oligotrophic, acidifying lakes in Newfoundland is controlled more by nutrients loading and the possible toxicological effects of heavy metals, than directly by water acidity.


title: Invited - Things, trouble, trust: on building trust in IoT systems

abstract: The emerging and much-touted Internet of Things (IoT) presents a variety of security and privacy challenges. Prominent among them is the establishment of trust in remote IoT devices, which is typically attained via remote attestation, a distinct security service that aims to ascertain the current state of a potentially compromised remote device. Remote attestation ranges from relatively heavy-weight secure hardware-based techniques, to light-weight software-based ones, and also includes approaches that blend software (e.g., control-flow integrity) and hardware features (e.g., PUFs). In this paper, we survey the landscape of state-of-the-art attestation techniques from the IoT device perspective and argue that most of them have a role to play in IoT trust establishment.


title: Neurocognitive Deficits and Functional Outcome in Schizophrenia: Are We Measuring the “Right Stuff”?

abstract: There has been a surge of interest in the functional consequences of neurocognitive deficits in schizophrenia. The published literature in this area has doubled in the last few years. In this paper, we will attempt to confirm the conclusions from a previous review that certain neurocognitive domains (secondary verbal memory, immediate memory, executive functioning as measured by card sorting, and vigilance) are associated with functional outcome. In addition to surveying the number of replicated findings and tallying box scores of results, we will approach the review of the studies in a more thorough and empirical manner by applying a meta-analysis. Lastly, we will discuss what we see as a key limitation of this literature, specifically, the relatively narrow selection of predictor measures. This limitation has constrained identification of mediating variables that may explain the mechanisms for these relationships.


title: A new “smart parking” system based on optimal resource allocation and reservations

abstract: We propose a new “smart parking” system for an urban environment. The system assigns and reserves an optimal resource (parking space) for a user (driver) based on the user's objective function that combines proximity to destination and parking cost, while also ensuring that the overall parking capacity is efficiently utilized. Our approach solves a Mixed Integer Linear Program (MILP) problem at each decision point in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and subject to random events such as new user requests or parking spaces becoming available. The allocation is updated at the next decision point ensuring that there is no resource reservation conflict and that no user is ever assigned a resource with higher than the current cost function value. Simulated case studies are included based on parking at part of the Boston University campus showing that we can achieve significant improvement over uncontrolled parking processes or state-of-the-art guidance-based systems. We also describe a laboratory setting where this system has been tested in real time.


title: Unsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation

abstract: We introduce a novel unsupervised loss function for learning semantic segmentation with deep convolutional neural nets (ConvNet) when densely labeled training images are not available. More specifically, the proposed loss function penalizes the L1-norm of the gradient of the label probability vector image , i.e. total variation, produced by the ConvNet. This can be seen as a regularization term that promotes piecewise smoothness of the label probability vector image produced by the ConvNet during learning. The unsupervised loss function is combined with a supervised loss in a semi-supervised setting to learn ConvNets that can achieve high semantic segmentation accuracy even when only a tiny percentage of the pixels in the training images are labeled. We demonstrate significant improvements over the purely supervised setting in the Weizmann horse, Stanford background and Sift Flow datasets. Furthermore, we show that using the proposed piecewise smoothness constraint in the learning phase significantly outperforms post-processing results from a purely supervised approach with Markov Random Fields (MRF). Finally, we note that the framework we introduce is general and can be used to learn to label other types of structures such as curvilinear structures by modifying the unsupervised loss function accordingly.


title: Quiet Neighborhoods: Key to Protect Job Performance Predictability

abstract: Interference of nearby jobs has been recently identified as the dominant reason for the high performance variability of parallel applications running on High Performance Computing (HPC) systems. Typically, HPC systems are dynamic with multiple jobs coming and leaving in an unpredictable fashion, sharing simultaneously the system interconnection network. In such environment contention for network resources is causing random stalls in the progress of application execution degrading application and system performance overall. Eliminating job interactions in their neighbourhoods is key for guaranteeing performance predictability of applications. In this paper we are proposing the concept of quiet neighbourhoods that significantly reduce job interactions. Quiet neighbourhoods are created by the system resource manager in two phases. First, multiple virtual network blocks are defined on the top of the physical network resources based on typical workload distributions. Second, newly arriving jobs are allocated in these virtual blocks based on their size.


Support Vector Machine Based Fingerprint Identification


title: Adaptive Web Sites: Automatically Synthesizing Web Pages

abstract: The creation of a complex web site is a thorny problem in user interface design. In IJCAI '97, we challenged the AI community to address this problem by creating adaptive web sites: sites that automatically improve their organization and presentation by mining visitor access data collected in Web server logs. In this paper we introduce our own approach to this broad challenge. Specifically, we investigate the problem of index page synthesis -- the automatic creation of pages that facilitate a visitor's navigation of a Web site.First, we formalize this problem as a clustering problem and introduce a novel approach to clustering, which we call cluster mining: Instead of attempting to partition the entire data space into disjoint clusters, we search for a small number of cohesive (and possibly overlapping) clusters. Next, we present PageGather, a cluster mining algorithm that takes Web server logs as input and outputs the contents of candidate index pages. Finally, we show experimentally that PageGather is both faster (by a factor of three) and more effective than traditional clustering algorithms on this task. Our experiment relies on access logs collected over a month from an actual web site.


title: Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii

abstract: Background:Obesity is associated with increased health risk and has been associated with alterations in bacterial gut microbiota, with mainly a reduction in Bacteroidetes, but few data exist at the genus and species level. It has been reported that the Lactobacillus and Bifidobacterium genus representatives may have a critical role in weight regulation as an anti-obesity effect in experimental models and humans, or as a growth-promoter effect in agriculture depending on the strains.Objectives and methods:To confirm reported gut alterations and test whether Lactobacillus or Bifidobacterium species found in the human gut are associated with obesity or lean status, we analyzed the stools of 68 obese and 47 controls targeting Firmicutes, Bacteroidetes, Methanobrevibacter smithii, Lactococcus lactis, Bifidobacterium animalis and seven species of Lactobacillus by quantitative PCR (qPCR) and culture on a Lactobacillus-selective medium.Findings:In qPCR, B. animalis (odds ratio (OR)=0.63; 95% confidence interval (CI) 0.39–1.01; P=0.056) and M. smithii (OR=0.76; 95% CI 0.59–0.97; P=0.03) were associated with normal weight whereas Lactobacillus reuteri (OR=1.79; 95% CI 1.03–3.10; P=0.04) was associated with obesity.Conclusion:The gut microbiota associated with human obesity is depleted in M. smithii. Some Bifidobacterium or Lactobacillus species were associated with normal weight (B. animalis) while others (L. reuteri) were associated with obesity. Therefore, gut microbiota composition at the species level is related to body weight and obesity, which might be of relevance for further studies and the management of obesity. These results must be considered cautiously because it is the first study to date that links specific species of Lactobacillus with obesity in humans.


title: An improved sentiment analysis of online movie reviews based on clustering for box-office prediction

abstract: With the rapid development of E-commerce, more online reviews for products and services are created, which form an important source of information for both sellers and customers. Research on sentiment and opinion mining for online review analysis has attracted increasingly more attention because such study helps leverage information from online reviews for potential economic impact. The paper discusses applying sentiment analysis and machine learning methods to study the relationship between the online reviews for a movie and the movies box office revenue performance. The paper shows that a simplified version of the sentiment-aware autoregressive model can produce very good accuracy for predicting the box office sale using online review data. Document level sentiment analysis is used which consists of Term Frequency (TF) and Inverse Document Frequency (IDF) values as features along with Fuzzy Clustering which results in positive and negative sentiments. This lead to the creation of a simpler model which could be more efficient to train and use. In addition, a classification model is created using Support Vector Machine (SVM) Classifier for predicting the trend of the box office revenue from the review sentiment.


title: ULTIMATELY FAST ACCURATE SUMMATION

abstract: We present two new algorithms FastAccSum and FastPrecSum, one to compute a faithful rounding of the sum of floating-point numbers and the other for a result “as if” computed in $K$-fold precision. Faithful rounding means the computed result either is one of the immediate floating-point neighbors of the exact result or is equal to the exact sum if this is a floating-point number. The algorithms are based on our previous algorithms AccSum and PrecSum and improve them by up to 25%. The first algorithm adapts to the condition number of the sum; i.e., the computing time is proportional to the difficulty of the problem. The second algorithm does not need extra memory, and the computing time depends only on the number of summands and $K$. Both algorithms are the fastest known in terms of flops. They allow good instruction-level parallelism so that they are also fast in terms of measured computing time. The algorithms require only standard floating-point addition, subtraction, and multiplication in one working precision, for example, double precision.


title: The Distributed Computing Paradigms: P2P, Grid, Cluster, Cloud, and Jungle

abstract: The distributed computing is done on many systems to solve a large scale problem. The growing of high-speed broadband networks in developed and developing countries, the continual increase in computing power, and the rapid growth of the Internet have changed the way. In it the society manages information and information services. Historically, the state of computing has gone through a series of platform and environmental changes. Distributed computing holds great assurance for using computer systems effectively. As a result, supercomputer sites and datacenters have changed from providing high performance floating point computing capabilities to concurrently servicing huge number of requests from billions of users. The distributed computing system uses multiple computers to solve large-scale problems over the Internet. It becomes data-intensive and network-centric. The applications of distributed computing have become increasingly wide-spread. In distributed computing, the main stress is on the large scale resource sharing and always goes for the best performance. In this article, we have reviewed the work done in the area of distributed computing paradigms. The main stress is on the evolving area of cloud computing.


title: Balloon Selection: A Multi-Finger Technique for Accurate Low-Fatigue 3D Selection

abstract: Balloon selection is a 3D interaction technique that is modeled after the real world metaphor of manipulating a helium balloon attached to a string. Balloon selection allows for precise 3D selection in the volume above a tabletop surface by using multiple fingers on a multi-touch-sensitive surface. The 3DOF selection tasks is decomposed in part into a 2DOF positioning task performed by one finger on the tabletop in an absolute 2D Cartesian coordinate system and a 1DOF positioning task performed by another finger on the tabletop in a relative 2D polar coordinate system. We have evaluated balloon selection in a formal user study that compared it to two well-known interaction techniques for selecting a static 3D target: a 3DOF tracked wand and keyboard cursor keys. We found that balloon selection was significantly faster than using cursor keys and had a significantly lower error rate than the wand. The lower error rate appeared to result from the user's hands being supported by the tabletop surface, resulting in significantly reduced hand tremor and arm fatigue.


title: Culture and Negotiation

abstract: This article develops a model of how culture affects negotiation processes and outcomes. It begins with a description of negotiation from a Western perspective: confrontational, focused on transactions or the resolution of disputes, evaluated in terms of integrative and distributive outcomes. It proposes that power and information processes are fundamental to negotiations and that one impact of culture on negotiations is through these processes. The cultural value of individualism versus collectivism is linked to goals in negotiation; the cultural value of egalitarianism versus hierarchy is linked to power in negotiation; and the cultural value for high versus low context communication is linked to information sharing in negotiation. The article describes why inter-cultural negotiations pose significant strategic challenges, but concludes that negotiators who are motivated to search for information, and are flexible about how that search is carried out, can reach high-quality negotiated outcomes. Cet arti...


title: Object Extraction and Revision By Image Analysis Using Existing Geodata And . . .

abstract: The paper focuses mainly on extraction of important topographic objects, like buildings and roads, that have received much attention the last decade. As main input data, aerial imagery is considered, although other data, like from laser scanner, SAR and high-resolution satellite imagery, can be also used. After a short review of recent image analysis trends, and strategy and overall system aspects of knowledge-based image analysis, the paper focuses on aspects of knowledge that can be used for object extraction: types of knowledge, problems in using existing knowledge, knowledge representation and management, current and possible use of knowledge, upgrading and augmenting of knowledge. Finally, an overview on commercial systems regarding automated object extraction and use of a priori knowledge is given. In spite of many remaining unsolved problems and need for further research and development, use of knowledge and semi-automation are the only viable alternatives towards development of useful object extraction systems, as some commercial systems on building extraction and 3D city modelling as well as advanced, practically oriented research have shown.


Can Good Principals Keep Teachers in Disadvantaged Schools? Linking Principal Effectiveness to Teacher Satisfaction and Turnover in Hard-to-Staff Environments.


title: The effect of acid accumulation in power-transformer oil on the aging rate of paper insulation

abstract: Utilities need to understand the aging of paper insulation in order to prolong the life of transformers. If the paper becomes severely degraded, it loses its mechanical strength and thus its ability to insulate the windings.


title: ACE: abstracting, characterizing and exploiting peaks and valleys in datacenter power consumption

abstract: Peak power management of datacenters has tremendous cost implications. While numerous mechanisms have been proposed to cap power consumption, real datacenter power consumption data is scarce. To address this gap, we collect power demands at multiple spatial and fine-grained temporal resolutions from the load of geo-distributed datacenters of Microsoft over 6 months. We conduct aggregate analysis of this data, to study its statistical properties. With workload characterization a key ingredient for systems design and evaluation, we note the importance of better abstractions for capturing power demands, in the form of peaks and valleys. We identify and characterize attributes for peaks and valleys, and important correlations across these attributes that can influence the choice and effectiveness of different power capping techniques. With the wide scope of exploitability of such characteristics for power provisioning and optimizations, we illustrate its benefits with two specific case studies.


title: Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services

abstract: Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling. The ancillary services considered are load regulation and spinning reserves. The algorithm is developed to be used by an aggregator, which may be a utility or a third party. This algorithm maximizes profits to the aggregator while providing additional system flexibility and peak load shaving to the utility and low costs of EV charging to the customer. The formulation also takes into account unplanned EV departures during the contract periods and compensates accordingly. Simulations using a hypothetical group of 10 000 commuter EVs in the ERCOT system using different battery replacement costs demonstrate these significant benefits.


title: Saliency-based parameter tuning for tone mapping

abstract: We present a saliency-based parameter tuning algorithm that can optimize the parameters of tone mapping operators automatically by minimizing the saliency distortion caused by the process of tone mapping. The algorithm employs an improved saliency detection model for HDR images, and the saliency distortion is quantified as the Kullback-Leibler divergence between the saliency distributions of the tone mapped images and those of the corresponding HDR images. We show that the minimization can be accomplished by employing an evolution strategy with individuals representing parameter settings and fitness values based on saliency distortion. The effectiveness of our algorithm is demonstrated through experiments using several tone mapping operators and test images.


title: CONSUMPTION SYMBOLS AS CARRIERS OF CULTURE: A STUDY OF JAPANESE AND SPANISH BRAND PERSONALITY CONSTRUCTS

abstract: This research argues that the meaning embedded in consumption symbols, such as commercial brands, can serve to represent and institutionalize the values and beliefs of a culture. We conducted four studies to examine how the symbolic and expressive attributes associated with commercial brands are structured, and determine the degree to which this structure varies across three cultures. Relying on a combined emic-etic approach, we identified indigenous constructs of `brand personality' (Aaker, 1997) in two non-Anglo cultures (Japan and Spain), and compared these dimensions to those previously found in the United States. The results of Studies 1 and 2 revealed a set of brand personality dimensions common to both Japan and the United States (Sincerity, Excitement, Competence, and Sophistication), as well as culture-specific Japanese (Peacefulness) and American (Ruggedness) dimensions. Studies 3 and 4 extended this set of findings to Spain. Results from these studies also identified brand personality dimensions common to both Spain and the United States (Sincerity, Excitement, and Sophistication), plus non-shared Spanish (Passion) and American (Competence and Ruggedness) dimensions. The meaning of the culturally-common and -specific brand personality dimensions is discussed in the context of cross-cultural research on values and affect, globalization issues, and cultural frame shifting.


title: [Sensitization to gallates: review of 46 cases].

abstract: BACKGROUND ::: Since 1947, the cosmetics, pharmaceutical, and food industries have used gallates as antioxidants to prevent the oxidation of unsaturated fats in their products. The aim of this study was to review the cases of sensitization to gallates diagnosed in our department between 1985 and 2006. ::: ::: ::: METHODS ::: All suspected cases of allergic contact dermatitis with patch tests positive for propyl gallate, octyl gallate, and/or dodecyl gallate were retrieved from the database of the allergy section of our dermatology department. ::: ::: ::: RESULTS ::: Forty-six patients had positive patch tests for 1 or more gallates (36 women and 10 men, with a mean age of 42.8 years). The most common presenting complaint was cheilitis (63% of the cases) followed by dermatitis of the hands (28.26%). The most common sensitizing agent was lipstick (54.3%) followed by bakery products (15.2%). The test result was considered clinically relevant in 73.9% of the cases whereas the relevance was unknown in 23.9 %. ::: ::: ::: CONCLUSIONS ::: In our case series, most patients sensitized to gallates attended the clinic due to cheilitis. The main allergen was propyl gallate and the most common mechanism of sensitization was use of lipstick. The most common gallate among bakers was octyl gallate. This agent was considered to be clinically relevant in all cases studied.


title: Adoption of Telemedicine - Challenges and Opportunities

abstract: U.S. health care system is plagued by rising cost and limited access. While the cost of care is increasing faster than the rate of inflation, people living in rural areas have very limited access to quality health care due to a shortage of physicians and facilities in these areas. Information and communication technologies in general and telemedicine in particular offer great promise to extend quality care to underserved rural communities at an affordable cost. However, adoption of telemedicine among the various stakeholders of the health care system has not been very encouraging. Based on an analysis of the extant research literature, this study identifies critical factors that impede the adoption of telemedicine, and offers suggestions to mitigate these challenges.


title: SIMD-oriented fast Mersenne twister: A 128-bit pseudorandom number generator

abstract: Mersenne Twister (MT) is a widely-used fast pseudorandom number generator (PRNG) with a long period of 219937 - 1, designed 10 years ago based on 32-bit operations. In this decade, CPUs for personal computers have acquired new features, such as Single Instruction Multiple Data (SIMD) operations (i.e., 128-bit operations) and multi-stage pipelines. Here we propose a 128-bit based PRNG, named SIMD-oriented Fast Mersenne Twister (SFMT), which is analogous to MT but making full use of these features. Its recursion fits pipeline processing better than MT, and it is roughly twice as fast as optimised MT using SIMD operations. Moreover, the dimension of equidistribution of SFMT is better than MT.


title: BD-tree: output-sensitive collision detection for reduced deformable models

abstract: We introduce the Bounded Deformation Tree, or BD-Tree, which can perform collision detection with reduced deformable models at costs comparable to collision detection with rigid objects. Reduced deformable models represent complex deformations as linear superpositions of arbitrary displacement fields, and are used in a variety of applications of interactive computer graphics. The BD-Tree is a bounding sphere hierarchy for output-sensitive collision detection with such models. Its bounding spheres can be updated after deformation in any order, and at a cost independent of the geometric complexity of the model; in fact the cost can be as low as one multiplication and addition per tested sphere, and at most linear in the number of reduced deformation coordinates. We show that the BD-Tree is also extremely simple to implement, and performs well in practice for a variety of real-time and complex off-line deformable simulation examples.


title: Design and Analysis of a Soft-Fingered Hand With Contact Feedback

abstract: This paper presents a novel approach to the fabrication of a soft robotic hand with contact feedback for grasping delicate objects. Each finger has a multilayered structure, consisting of a main structure and sensing elements. The main structure includes a softer layer much thicker than a stiffer layer. The gripping energy of the fingers is generated from the elastic energy of the prestretched softer layers, and controlled by simple tendon strings pulled/released by a single actuation. Due to the prestretching and the difference in moduli among layers, the shape/posture of the fingers in stable state is similar to that of soft fingers actuated by pressurization. Then, we were able to design a soft-fingered hand for different applications by changing the morphological shape of layers. In addition, the hand includes a soft, flexible, and stretchable sensing element for the detection of contact and applied force, which can also be used in other designs of soft fingers. We assessed the ability of the proposed soft hand to grasp food products with feedback of contact location. The experimental results showed that the proposed hand could safely grasp light and delicate objects, such as fruits, and that it could possibly distinguish among objects based on feedback from sensors. The design proposed in this paper may give rise to other soft hand designs, along with the possibility of using morphological imbalanced deformation of multilayered structures in soft robotics research.


title: Recurrent Saliency Transformation Network: Incorporating Multi-stage Visual Cues for Small Organ Segmentation

abstract: We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach, which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. ::: This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.


title: Once More with Feeling: Supportive Responses to Social Sharing on Facebook

abstract: Life is more than cat pictures. There are tough days, heartbreak, and hugs. Under what contexts do people share these feelings online, and how do their friends respond? Using millions of de-identified Facebook status updates with poster-annotated feelings (e.g., “feeling thankful” or “feeling worried”), we examine the magnitude and circumstances in which people share positive or negative feelings and characterize the nature of the responses they receive. We find that people share greater proportions of both positive and negative emotions when their friend networks are smaller and denser. Consistent with social sharing theory, hearing about a friendâ s troubles on Facebook causes friends to reply with more emotional and supportive comments. Friendsâ comments are also more numerous and longer. Posts with positive feelings, on the other hand, receive more likes, and their comments have more positive language. Feelings that relate to the posterâ s self worth, such as “feeling defeated,” “feeling unloved,” or “feeling accomplished” amplify these effects.


title: Bayesian-Networks-Based Misuse and Anomaly Prevention System

abstract: Network Intrusion Detection Systems (NIDS) aim at preventing network attacks and unauthorised remote use of computers. More accurately, depending on the kind of attack it targets, an NIDS can be oriented to detect misuses (by defining all possible attacks) or anomalies (by modelling legitimate behaviour and detecting those that do not fit on that model). Still, since their problem knowledge is restricted to possible attacks, misuse detection fails to notice anomalies and vice versa. Against this, we present here ESIDEDepian, the first unified misuse and anomaly prevention system based on Bayesian Networks to analyse completely network packets, and the strategy to create a consistent knowledge model that integrates misuse and anomaly-based knowledge. Finally, we evaluate ESIDE-Depian against well-known and new attacks showing how it outperforms a well-established industrial NIDS.


title: Simulating Murder: The Aversion to Harmful Action

abstract: Diverse lines of evidence point to a basic human aversion to physically harming others. First, we demonstrate that unwillingness to endorse harm in a moral dilemma is predicted by individual differences in aversive reactivity, as indexed by peripheral vasoconstriction. Next, we tested the specific factors that elicit the aversive response to harm. Participants performed actions such as discharging a fake gun into the face of the experimenter, fully informed that the actions were pretend and harmless. These simulated harmful actions increased peripheral vasoconstriction significantly more than did witnessing pretend harmful actions or to performing metabolically-matched non-harmful actions. This suggests that the aversion to harmful actions extends beyond empathic concern for victim harm. Together, these studies demonstrate a link between the body and moral decision making processes.


title: Using small screen space more efficiently

abstract: This paper describes techniques for maximizing the efficient use of small screen space by combining delayed response with semi-transparency of control objects ("widgets") and on-screen text. Most research on the limitations of small display screens has focused on methods for optimizing concurrent display of text and widgets at the same level of transparency (that is, both are equally opaque). Prior research which proposes that widgets may be made semi-transparent is promising, but it does not, we feel, adequately address problems associated with user interaction with text that is partially obscured by the widgets. In this paper, we will propose that a variable delay in the response of overlapping widgets and text improves the effectiveness of the semi-transparent widget/text model. Our conclusions are based on usability studies of a prototype of an online newspaper that combined transparency and delayed-response techniques.


title: Practical attribute-based encryption: Outsourcing decryption, attribute revocation and policy updating

abstract: Attribute-Based Encryption (ABE) offers fine-grained access control policy over encrypted data, and thus applicable in cloud storage to provide authorized data privacy. However, there are some issues that should be solved before deploying ABE in practice. Firstly, as the heavy decryption cost grows with the complexity of access policy, an ABE with outsourcing decryption is preferred to relieve user's computation cost. Secondly, when user's attributes are altered, it is required for ABE supporting attribute revocation to change user's access privilege timely and effectively. Thirdly, in the case of access control policy changed by data owner, policy updating requirement must be met in designing ABE. Therefore, a practical ABE scheme is proposed which can solve aforementioned issues simultaneously. In order to support flexible number of attributes, our scheme also achieves large universe and multiple attribute authorities. The security and performance of the proposed scheme are discussed, followed by extensive experiments to demonstrate its effectiveness and practicability.


title: A 8.125–15.625 Gb/s SerDes using a sub-sampling ring-oscillator phase-locked loop

abstract: The paper describes a 8.125-15.625 Gbps medium-reach SerDes macro for use in a networking memory system. The SerDes employs a sub-sampling ring-oscillator phase-locked loop to obtain a large frequency range with low jitter performance. In addition, the transmitter uses a modified hybrid output driver and a multi-step duty-cycle corrector. The receiver uses a BER-based calibration loop to find the set of parameters that maximizes the receiver voltage margin. The transmitter output achieves 160fs RMS jitter and 10.9ps total jitter at 15.625 Gbps with 140fs duty-cycle distortion.


title: Decay theory of immediate memory: From Brown (1958) to today (2014)

abstract: This work takes a historical approach to discussing Brown's (1958) paper, “Some Tests of the Decay Theory of Immediate Memory”. This work was and continues to be extremely influential in the field of forgetting over the short term. Its primary importance is in establishing a theoretical basis to consider a process of fundamental importance: memory decay. Brown (1958) established that time-based explanations of forgetting can account for both memory capacity and forgetting of information over short periods of time. We discuss this view both in the context of the intellectual climate at the time of the paper's publication and in the context of the modern intellectual climate. The overarching theme we observe is that decay is as controversial now as it was in the 1950s and 1960s.


title: Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion

abstract: Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).


title: Capturing natural hand articulation

abstract: Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of liner manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.


title: Detection of Moving Cast Shadows for Object Segmentation

abstract: To prevent moving shadows being misclassified as moving objects or parts of moving objects, this paper presents an explicit method for detection of moving cast shadows on a dominating scene background. Those shadows are generated by objects moving between a light source and the background. Moving cast shadows cause a frame difference between two succeeding images of a monocular video image sequence. For shadow detection, these frame differences are detected and classified into regions covered and regions uncovered by a moving shadow. The detection and classification assume plane background and a nonnegligible size and intensity of the light sources. A cast shadow is detected by temporal integration of the covered background regions while subtracting the uncovered background regions. The shadow detection method is integrated into an algorithm for two-dimensional (2-D) shape estimation of moving objects from the informative part of the description of the international standard ISO/MPEG-4. The extended segmentation algorithm compensates first apparent camera motion. Then, a spatially adaptive relaxation scheme estimates a change detection mask for two consecutive images. An object mask is derived from the change detection mask by elimination of changes due to background uncovered by moving objects and by elimination of changes due to background covered or uncovered by moving cast shadows. Results obtained with MPEG-4 test sequences and additional sequences show that the accuracy of object segmentation is substantially improved in presence of moving cast shadows. Objects and shadows are detected and tracked separately.


title: A note on the evaluation of generative models

abstract: Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided.


title: Mulval: A logic-based network security analyzer

abstract: To determine the security impact software vulnerabilities have on a particular network, one must consider interactions among multiple network elements. For a vulnerability analysis tool to be useful in practice, two features are crucial. First, the model used in the analysis must be able to automatically integrate formal vulnerability specifications from the bug-reporting community. Second, the analysis must be able to scale to networks with thousands of machines. ::: ::: We show how to achieve these two goals by presenting MulVAL, an end-to-end framework and reasoning system that conducts multihost, multistage vulnerability analysis on a network. MulVAL adopts Datalog as the modeling language for the elements in the analysis (bug specification, configuration description, reasoning rules, operating-system permission and privilege model, etc.). We easily leverage existing vulnerability-database and scanning tools by expressing their output in Datalog and feeding it to our MulVAL reasoning engine. Once the information is collected, the analysis can be performed in seconds for networks with thousands of machines. ::: ::: We implemented our framework on the Red Hat Linux platform. Our framework can reason about 84% of the Red Hat bugs reported in OVAL, a formal vulnerability definition language. We tested our tool on a real network with hundreds of users. The tool detected a policy violation caused by software vulnerabilities and the system administrators took remediation measures.


Shepherding Model Evolution in Model-Driven Development


title: Multi-class cost-sensitive boosting with p-norm loss functions

abstract: We propose a family of novel cost-sensitive boosting methods for multi-class classification by applying the theory of gradient boosting to p-norm based cost functionals. We establish theoretical guarantees including proof of convergence and convergence rates for the proposed methods. Our theoretical treatment provides interpretations for some of the existing algorithms in terms of the proposed family, including a generalization of the costing algorithm, DSE and GBSE-t, and the Average Cost method. We also experimentally evaluate the performance of our new algorithms against existing methods of cost sensitive boosting, including AdaCost, CSB2, and AdaBoost.M2 with cost-sensitive weight initialization. We show that our proposed scheme generally achieves superior results in terms of cost minimization and, with the use of higher order p-norm loss in certain cases, consistently outperforms the comparison methods, thus establishing its empirical advantage.


title: A Newborn Girl with Unusual Genitalia

abstract: Anewborn female infant presented to the outpatient pediatric office for an initial weight visit on her third day of life. She was born full term, appropriate for gestational age, via spontaneous vaginal delivery to a 21-year-old mother. The mother was blood type A+, HIV negative, rubella immune, hepatitis B surface antigen negative, rapid plasma reagin nonreactive, and Group B streptococcus positive. The mother received antepartum antibiotics prior to delivery. The baby’s APGAR scores were 8 at 1 minute and 9 at 5 minutes. The baby did well in the newborn nursery, and had been breast-feeding and voiding and stooling several times per day. It was noted in the nursery that she had an unusual appearance of her labia. On examination, the baby was awake and appropriate for age. She had no dysmorphic features, and her palate was intact. There was no heart murmur. Her abdomen was soft and nontender, with no organomegaly or mass. Her umbilical stump was still attached. Her genitourinary examination revealed normal labia majora. There appeared to be mucosal tissue from the fossa navicularis extending posteriorally through the posterior fourchette ending just anterior to the anal opening. There was no clitoromegaly. There was a sleeve hymen. The anal sphincter surrounded the anal opening normally (Figure 1).


title: A novel control concept for high-efficiency power conversion with the bidirectional non-inverting buck-boost converter

abstract: This paper presents a novel control concept for the bidirectional non-inverting buck-boost converter to achieve extreme high-efficiency power conversion. The duty cycle, duty ratios and time delays of the PWM control signals for the switches are modulated depending on operating points to minimize the power losses in the converter. A 3kW prototype of the converter using SiC MOSFETs has been designed and tested for the verification of the control concept. Measurement results show that the prototype achieves power efficiency between 97.79% and 99.60% over a power range “input voltage from 300V to 500V, output voltage from 200V to 600V and output current from 1A to 5A”.


title: Handover decision using fuzzy MADM in heterogeneous networks

abstract: In the next generation heterogeneous wireless networks, a user with a multi-interface terminal may have a network access from different service providers using various technologies. It is believed that handover decision is based on multiple criteria as well as user preference. Various approaches have been proposed to solve the handover decision problem, but the choice of decision method appears to be arbitrary and some of the methods even give disputable results. In this paper, a new handover criteria is introduced along with a new handover decision strategy. In addition, handover decision is identified us a fuzzy multiple attribute decision making (MADM) problem, and fuzzy logic is applied to deal with the imprecise information of some criteria and user preference. After a systematic analysis of various fuzzy MADM methods, a feasible approach is presented. In the end, examples are provided illustrating the proposed methods and the sensitivity of the methods is also analysed.


title: Linux containers networking: Performance and scalability of kernel modules

abstract: Linux container virtualisation is gaining momentum as lightweight technology to support cloud and distributed computing. Applications relying on container architectures might at times rely on inter-container communication, and container networking solutions are emerging to address this need. Containers can be networked together as part of an overlay network, or with actual links from the container to the network via kernel modules. Most overlay solutions are not quite production ready yet; on the other hand kernel modules that can link a container to the network are much more mature. We benchmarked three kernel modules: veth, macvlan and ipvlan, to quantify their respective raw TCP and UDP performance and scalability. Our results show that the macvlan kernel module outperforms all other solutions in raw performance. All kernel modules seem to provide sufficient scalability to be deployed effectively in multi-containers environments.


title: A defected ground structure for isolation enhancement in a printed MIMO antenna system

abstract: In this work, a novel defected ground structure (DGS) based isolation technique is proposed for printed antennas. The DGS is capable of improving the isolation of a dual band antenna especially for the low bands (800MHz). A 4-shaped MIMO antenna system is used to test the performance of the isolation structure. The proposed method relies on a modified dumbbell shaped DGS to improve the isolation for high band (2440-2980). The primary rectangles of the dumbbell are loaded with a spiral to cover the low band (803-823MHz). The minimum isolation for the low band and high band was approximately 17dB and 9dB respectively. A minimum isolation improvement of 14dB and 4dB was observed for the low band and high band respectively compared to the original MIMO antenna system under test without such a structure.


title: Two-Stage Enhancement Scheme for Low-Quality Fingerprint Images by Learning From the Images

abstract: Fingerprint authentication for content protection in the human-machine systems, cybernetics, and computational intelligence is very popular. Because of the complex input contexts, low-quality input fingerprint images always exist with cracks and scars, dry skin, or poor ridges and valley contrast ridges. Usually, fingerprint images are enhanced by one stage in either the spatial or the frequency domain. However, the enhanced performances are not satisfactory because of the complicated ridge structures that are affected by unusual input contexts. In this paper, we propose a novel and effective two-stage enhancement scheme in both the spatial domain and the frequency domain by learning from the underlying images. To remedy the ridge areas and enhance the contrast of the local ridges, we first enhance the fingerprint image in the spatial domain with a spatial ridge-compensation filter by learning from the images. With the help of the first step, the second-stage filter, i.e., a frequency bandpass filter that is separable in the radial- and angular-frequency domains, is employed. It is noted that the parameters of the bandpass filters are learnt from both the original image and the first-stage enhanced image instead of acquiring from the original image solely. It enhances the fingerprint image significantly because of the fast and sharp attenuation of the filter in both the radial and the angular-frequency domains. Experimental results show that our proposed algorithm is able to handle various input image contexts and achieves better results compared with some state-of-the-art algorithms over public databases, and to improve the performances of fingerprint-authentication systems.


title: Word and Phrase Translation with word2vec

abstract: Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements. The information captured by these embeddings can be exploited for bilingual translation by learning a transformation matrix that allows matching relative positions across two monolingual vector spaces. This method aims to identify high-quality candidates for word and phrase translation more cost-effectively from unlabeled data. ::: This paper expands the scope of previous attempts of bilingual translation to four languages (English, German, Spanish, and French). It shows how to process the source data, train a neural network to learn the high-dimensional embeddings for individual languages and expands the framework for testing their quality beyond the English language. Furthermore, it shows how to learn bilingual transformation matrices and obtain candidates for word and phrase translation, and assess their quality.


title: Net Locality: Why Location Matters in a Networked World

abstract: Provides an introduction to the new theory of Net Locality and the profound effect on individuals and societies when everything is located or locatable.Describes net locality as an emerging form of location awareness central to all aspects of digital media, from mobile phones, to Google Maps, to location-based social networks and games, such as Foursquare and facebook.Warns of the threats these technologies, such as data surveillance, present to our sense of privacy, while also outlining the opportunities for pro-social developments.Provides a theory of the web in the context of the history of emerging technologies, from GeoCities to GPS, Wi-Fi, Wiki Me, and Google Android.


title: ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly

abstract: Matrix completion and approximation are popular tools to capture a user's preferences for recommendation and to approximate missing data. Instead of using low-rank factorization we take a drastically different approach, based on the simple insight that an additive model of co-clusterings allows one to approximate matrices efficiently. This allows us to build a concise model that, per bit of model learned, significantly beats all factorization approaches in matrix completion. Even more surprisingly, we find that summing over small co-clusterings is more effective in modeling matrices than classic co-clustering, which uses just one large partitioning of the matrix. Following Occam's razor principle, the fact that our model is more concise and yet just as accurate as more complex models suggests that it better captures the latent preferences and decision making processes present in the real world. We provide an iterative minimization algorithm, a collapsed Gibbs sampler, theoretical guarantees for matrix approximation, and excellent empirical evidence for the efficacy of our approach. We achieve state-of-the-art results for matrix completion on Netflix at a fraction of the model complexity.


A Learning Analytics Approach for Student Performance Assessment


title: Omnidirectional Vision Based Topological Navigation

abstract: In this work we present a novel system for autonomous mobile robot navigation. With only an omnidirectional camera as sensor, this system is able to build automatically and robustly accurate topologically organised environment maps of a complex, natural environment. It can localise itself using such a map at each moment, including both at startup (kidnapped robot) or using knowledge of former localisations. The topological nature of the map is similar to the intuitive maps humans use, is memory-efficient and enables fast and simple path planning towards a specified goal. We developed a real-time visual servoing technique to steer the system along the computed path. ::: ::: A key technology making this all possible is the novel fast wide baseline feature matching, which yields an efficient description of the scene, with a focus on man-made environments.


title: A Survival Modeling Approach to Biomedical Search Result Diversification Using Wikipedia

abstract: In this paper, we propose a survival modeling approach to promoting ranking diversity for biomedical information retrieval. The proposed approach concerns with finding relevant documents that can deliver more different aspects of a query. First, two probabilistic models derived from the survival analysis theory are proposed for measuring aspect novelty. Second, a new method using Wikipedia to detect aspects covered by retrieved documents is presented. Third, an aspect filter based on a two-stage model is introduced. It ranks the detected aspects in decreasing order of the probability that an aspect is generated by the query. Finally, the relevance and the novelty of retrieved documents are combined at the aspect level for reranking. Experiments conducted on the TREC 2006 and 2007 Genomics collections demonstrate the effectiveness of the proposed approach in promoting ranking diversity for biomedical information retrieval. Moreover, we further evaluate our approach in the Web retrieval environment. The evaluation results on the ClueWeb09-T09B collection show that our approach can achieve promising performance improvements.


title: A trigonometric velocity estimator using a resolver sensor in drive system applications

abstract: Resolver sensors are widely used in the industry to measure the rotor angle position in high performance drive applications. Different methods are employed to estimate the velocity by measuring the rotor angle position. A novel trigonometric velocity estimator is described in this paper that has a simple structure for implementation and there is no need for parameter tuning. The proposed method is practically verified in a permanent magnet motor drive system. The results confirm that the proposed trigonometric velocity estimator has a good dynamic and steady state response subjected to different speed command profiles.


title: Online shopping acceptance model – a critical survey of consumer factors in online shopping

abstract: Since the late 1990s, online shopping has taken off as an increasing number of consumers purchase increasingly diversified products on the Internet. Given that how to attract and retain consumers is critical to the success of online retailers, research on the antecedents of consumer acceptance of online shopping has attracted widespread attention. There has yet to be a holistic view of online shopping acceptance from the perspective of consumers. In this research, we conducted an extensive survey of extant related studies and synthesized their findings into a reference model called OSAM (Online Shopping Acceptance Model) to explain consumer acceptance of online shopping. Our literature survey reveals that a myriad of factors have been examined in the context of online shopping and mixed results on those factors have been reported. The proposed model helps reconcile conflicting findings, discover recent trends in this line of research, and shed light on future research directions.


title: Automatically selecting shots for action movie trailers

abstract: Movie trailers, or previews, are an important method of advertising movies. They are extensively shown before movies in cinemas, as well as on television and increasingly, over the Internet. Making a trailer is a creative process, in which a number of shots from a movie are selected in order to entice a viewer in to paying to see the full movie. Thus, the creation of these trailers is an integral part in the promotion of a movie. Action movies in particular rely on trailers as a form of advertising as it is possible to show short, exciting portions of an action movie, which are likely to appeal to the target audience. This paper presents an approach which automatically selects shots from action movies in order to assist in the creation of trailers. A set of audiovisual features are extracted that aim to model the characteristics of shots typically present in trailers, and a support vector machine is utilised in order to select the relevant shots. The approach taken is not particularly novel but the results show that the process may be used in order to ease the trailer creation process or to facilitate the creation of variable length, or personalised trailers.


title: Suspended load path tracking control based on zonotopic state estimation using a tilt-rotor UAV

abstract: This work addresses the problem of load transportation by a tilt-rotor unmanned aerial vehicle (UAV). In the literature, the suspended load's dynamics are often regarded as external disturbances affecting the UAV, or are handled implicitly in the equations of motion of the system, not allowing direct control of its coordinates. This work presents the formulation of the whole-body system dynamics from the load's perspective, in which the suspended load's dynamics appear explicitly in the equations of motion, while the aircraft's are handled implicitly. A nonlinear state-space representation of the system is obtained, with the load's position and orientation among the system states, allowing state-feedback strategies to directly control these variables. In real applications, available sensors provide information about the UAV's coordinates, thus the states of the present approach may not be directly measured. Then, this work also proposes a state estimation strategy, based on a set-membership, zonotopic technique, for which it is assumed that measurements are noisy and have different sampling times. Finally, a discrete-time, mixed H 2 /H ∞ controller with pole placement constraints, is designed for path tracking control of the suspended load with constant disturbances rejection, and feedback from the estimated states. Simulation results are presented in order to validate the proposed approach.


title: Enhanced shielded-gate trench MOSFETs for high-frequency, high-efficiency computing power supply applications

abstract: Shielded-gate trench-MOSFETs yield superior performance compared to conventional gate trench devices by allowing higher doping density in the drift region and providing a `shielding effect' for the gate by placing an intermediate electrode between gate and drain. However, further design optimizations can be done for a shielded-gate trench-MOSFET to improve performance parameters particularly suited for next-generation high-frequency computing power supply applications and they have been outlined in this article. Channel length and threshold voltage optimization, substrate thinning and intrinsic gate resistance reduction (by layout enhancements) have been discussed along with their impact on device footprint reduction. Further, effects of these design optimizations on the power loss and efficiency of a high-frequency switching converter have been demonstrated through experimental characterizations.


title: Fault diagnosis based on deep learning

abstract: As representation scheme can severely limit the window by which the system observes its world, deep learning for fault diagnosis is put forward in this paper. It is a real time online scheme that can enhance the accuracy of detection, classification and prediction, and efficient for incipient faults that cannot be detected by traditional statistic technology. A stacked sparse auto encoder is used to learn the deep architectures of fault data to minimize the loss of information. Experiment results show that the proposed method not only improves the divisibility between faults and normal process, but also exhibits a better performance on the accuracy of fault classification for the chemical benchmark, Tennessee Eastman Process (TEP) data.


title: Construction and evaluation of a robust multifeature speech/music discriminator

abstract: We report on the construction of a real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input. We have examined 13 features intended to measure conceptually distinct properties of speech and/or music signals, and combined them in several multidimensional classification frameworks. We provide extensive data on system performance and the cross-validated training/test setup used to evaluate the system. For the datasets currently in use, the best classifier classifies with 5.8% error on a frame-by-frame basis, and 1.4% error when integrating long (2.4 second) segments of sound.


A Statistical Approach To Anaphora Resolution


title: What modifiable factors are associated with cessation intentions among smoking youth

abstract: Abstract Purpose To examine how smoking behaviour and beliefs, physical activity and weight, and social influences are associated with intentions to quit smoking among youth. Methods This cross-sectional study used self-reported data collected from 26,379 grade 9 to 12 students in Ontario, Canada. Results Intending to quit smoking was positively associated with students believing that they could quit smoking, being moderately active, and having smoking friends. Male smokers were less likely to intend to quit smoking than female smokers. Intending to quit smoking in the next 30 days was positively associated with students believing that they could quit smoking and being highly active, and negatively associated with being overweight and having three or more smoking friends. Conclusion These findings provide insight for researchers and practitioners interested in understanding the factors which may promote smoking cessation among smoking school-aged populations.


title: Development of fluxless chip-on-wafer bonding process for 3DIC chip stacking with 30μm pitch lead-free solder micro bumps and reliability characterization

abstract: 3D IC integration can be accomplished by using the approaches such as chip-on-chip, chip-on-wafer and wafer-on-wafer integration. The scheme of chip-on-chip shows the advantages of high flexibility and yield during assembly process. However, low fabrication throughput has been a major issue. When compared to chip-on-chip integrations, wafer-on-wafer may provide the higher manufacturing throughput but its overall yield will be limited by accumulative yield. Also, good chips are forced to bond on bad chip. Therefore, the development of chip-on-wafer integration may be a better scheme for 3D chip stacking. In this study, a high-yield and fluxless chip-on-wafer bonding process with 30μm pitch lead-free solder micro bump interconnection were demonstrated and the reliability of micro joint was also evaluated. Test chip adopted in this study had more than 3000 micro bumps with a diameter of 18μm and a pitch of 30μm. Sn2.5Ag solder material was electroplated on Cu/Ni under bump metallurgy (UBM) of both the test chip and 200mm wafer. To achieve the purpose of fluxless chip-on-wafer bonding, the plasma pre-treatment was applied to both the test chips and bonded wafer. The thermo-compression bonding method with the gap control capability was also carried out. In this work, the fluxless thermo-compression bonding having more than 90% CoW yield had been accomplished. The factor of Sn thickness was found to evidently influence the CoW yield. With the optimized plasma treatment parameters and bonding conditions, a well-aligned and robust joining of micro bump without using flux could be obtained. The results of reliability test revealed that the introduction of underfill could apparently enhance the reliability performance of micro joint under mechanical evaluation. Also, the solder micro bump joint showed excellent electromigration resistance when compared to standard flip chip bump under current stress of 5×10−4 A/cm2 at an ambient temperature of 150°C.


title: Polymorphism of DNA conformation inside the bacteriophage capsid.

abstract: Double-stranded DNA bacteriophage genomes are packaged into their icosahedral capsids at the highest densities known so far (about 50 % w:v). How the molecule is folded at such density and how its conformation changes upon ejection or packaging are fascinating questions still largely open. We review cryo-TEM analyses of DNA conformation inside partially filled capsids as a function of the physico-chemical environment (ions, osmotic pressure, temperature). We show that there exists a wide variety of DNA conformations. Strikingly, the different observed structures can be described by some of the different models proposed over the years for DNA organisation inside bacteriophage capsids: either spool-like structures with axial or concentric symmetries, or liquid crystalline structures characterised by a DNA homogeneous density. The relevance of these conformations for the understanding of DNA folding and unfolding upon ejection and packaging in vivo is discussed.


title: A Blocker-Tolerant, Noise-Cancelling Receiver Suitable for Wideband Wireless Applications

abstract: A new wideband receiver architecture is proposed that employs two separate passive-mixer-based downconversion paths, which enables noise cancelling, but avoids voltage gain at blocker frequencies. This approach significantly relaxes the trade-off between noise, out-of-band linearity and wideband operation. The resulting prototype in 40 nm is functional from 80 MHz to 2.7 GHz and achieves a 2 dB noise figure, which only degrades to 4.1 dB in the presence of a 0 dBm blocker.


Happiness Questionnaire : a compact scale for the measurement of psychological well-being


Psychonautics : A Model and Method for Exploring the Subjective Effects of Psychoactive Drugs Written by Russell Newcombe Thursday , 16 December 1999 00 : 00 Psychonautics : A Model and Method for Exploring the Subjective Effects of Psychoactive Drugs


title: Ultra-Efficient Processing In-Memory for Data Intensive Applications

abstract: Recent years have witnessed a rapid growth in the domain of Internet of Things (IoT). This network of billions of devices generates and exchanges huge amount of data. The limited cache capacity and memory bandwidth make transferring and processing such data on traditional CPUs and GPUs highly inefficient, both in terms of energy consumption and delay. However, many IoT applications are statistical at heart and can accept a part of inaccuracy in their computation. This enables the designers to reduce complexity of processing by approximating the results for a desired accuracy. In this paper, we propose an ultra-efficient approximate processing in-memory architecture, called APIM, which exploits the analog characteristics of non-volatile memories to support addition and multiplication inside the crossbar memory, while storing the data. The proposed design eliminates the overhead involved in transferring data to processor by virtually bringing the processor inside memory. APIM dynamically configures the precision of computation for each application in order to tune the level of accuracy during runtime. Our experimental evaluation running six general OpenCL applications shows that the proposed design achieves up to 20× performance improvement and provides 480× improvement in energy-delay product, ensuring acceptable quality of service. In exact mode, it achieves 28× energy savings and 4.8× speed up compared to the state-of-the-art GPU cores.


title: End-to-end Sound Source Separation Conditioned on Instrument Labels

abstract: Can we perform an end-to-end sound source separation (SSS) with a variable number of sources using a deep learning model? This paper presents an extension of the Wave-U-Net model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument labels at the bottleneck of the Wave-U-Net and show its effect on the separation results. This approach can be further extended to other types of conditioning such as audio-visual SSS and score-informed SSS.


title: Lifted Proximal Operator Machines

abstract: We propose a new optimization method for training feedforward neural networks. By rewriting the activation function as an equivalent proximal operator, we approximate a feedforward neural network by adding the proximal operators to the objective function as penalties, hence we call the lifted proximal operator machine (LPOM). LPOM is block multiconvex in all layer-wise weights and activations. This allows us to use block coordinate descent to update the layer-wise weights and activations. Most notably, we only use the mapping of the activation function itself, rather than its derivative, thus avoiding the gradient vanishing or blow-up issues in gradient based training methods. So our method is applicable to various non-decreasing Lipschitz continuous activation functions, which can be saturating and non-differentiable. LPOM does not require more auxiliary variables than the layer-wise activations, thus using roughly the same amount of memory as stochastic gradient descent (SGD) does. Its parameter tuning is also much simpler. We further prove the convergence of updating the layer-wise weights and activations and point out that the optimization could be made parallel by asynchronous update. Experiments on MNIST and CIFAR-10 datasets testify to the advantages of LPOM.


title: CitNetExplorer: A new software tool for analyzing and visualizing citation networks

abstract: We present CitNetExplorer, a new software tool for analyzing and visualizing citation networks of scientific publications. CitNetExplorer can for instance be used to study the development of a research field, to delineate the literature on a research topic, and to support literature reviewing. We first introduce the main concepts that need to be understood when working with CitNetExplorer. We then demonstrate CitNetExplorer by using the tool to analyze the scientometric literature and the literature on community detection in networks. Finally, we discuss some technical details on the construction, visualization, and analysis of citation networks in CitNetExplorer.


title: EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis

abstract: Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical properties and to the existence of maximum-likelihood estimators based on expectation-maximization (EM). In this paper we propose a new mixture model that associates a weight with each observed point. We introduce the weighted-data Gaussian mixture and we derive two EM algorithms. The first one considers a fixed weight for each observation. The second one treats each weight as a random variable following a gamma distribution. We propose a model selection method based on a minimum message length criterion, provide a weight initialization strategy, and validate the proposed algorithms by comparing them with several state of the art parametric and non-parametric clustering techniques. We also demonstrate the effectiveness and robustness of the proposed clustering technique in the presence of heterogeneous data, namely audio-visual scene analysis.