title: Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment

abstract: This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment. The source code and the data are shared with the community to further enhance indoor mapping research.


title: The role of prediction algorithms in the MavHome smart home architecture

abstract: The goal of the MavHome project is to create a home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and minimize operation cost. To achieve these goals, the agent must be able to predict the mobility patterns and device usages of the inhabitants. We introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms that are central to home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data.


title: An HMM-based Singing Voice Synthesis System

abstract: Abstract The present paper describes a corpus-based singing voice syn-thesis system based on hidden Markov models (HMMs). Thissystem employs the HMM-based speech synthesis to synthesizesingingvoice. Musical information such aslyrics, tones, durationsis modeled simultaneously in a uniﬁed framework of the context-dependent HMM. It can mimic the voice quality and singing styleof the original singer. Results of a singing voice synthesis exper-iment show that the proposed system can synthesize smooth andnatural-sounding singing voice. Index Terms : singing voice synthesis, HMM, time-lag model. 1. Introduction In recent years, various applications of speech synthesis systemshave been proposed and investigated. Singing voice synthesis isone of the hot topics in this area [1–5]. However, only a fewcorpus-based singing voice synthesis systems which can be con-structed automatically have been proposed.Currently, there are two main paradigms in the corpus-basedspeech synthesis area: sample-based approach and statistical ap-proach. The sample-based approach such as unit selection [6]can synthesize high-quality speech. However, it requires a hugeamountoftrainingdatatorealizevariousvoicecharacteristics. Onthe other hand, the quality of statistical approach such as HMM-basedspeechsynthesis[7]isbuzzybecauseitisbasedonavocod-ingtechnique. However,itissmoothandstable,anditsvoicechar-acteristics can easily be modiﬁed by transforming HMM parame-ters appropriately. For singing voice synthesis, applying the unitselection seems to be difﬁcult because a huge amount of singingspeech which covers vast combinations of contextual factors thataffect singing voice has to be recorded. On the other hand, theHMM-based system can be constructed using a relatively smallamount of training data. From this point of view, the HMM-basedapproach seems to be more suitable for the singing voice synthe-sizer. In the present paper, we apply the HMM-based synthesisapproach to singing voice synthesis.Although the singing voice synthesis system proposed in thepresent paper is quite similar to the HMM-based text-to-speechsynthesissystem[7],therearetwomaindifferencesbetweenthem.In the HMM-based text-to-speech synthesis system, contextualfactors which may affect reading speech (e.g. phonemes, sylla-bles, words, phrases, etc.) are taken into account. However, con-textual factors which may affect singing voice should be different


title: Emotional Expressiveness and Neuroticism: Do They Predict Marital Quality?

abstract: This study examines how neuroticism and emotional expressiveness relate to perceptions of marital quality. Data were gathered from a sample of 197 Israeli couples. Wives scored higher than husbands on neuroticism and emotional expressiveness, but no significant gender differences were found in perceived marital quality. Structural equation models were estimated to examine the effect of both spouses' neuroticism and expressiveness on their own and on their spouse's evaluation of marital quality. Neuroticism was a strong predictor of both spouses' perceived marital quality. Wives' perceived marital quality was positively associated with both their own and their husbands' emotional expressiveness. In contrast, husbands' perceived marital quality was associated neither with their own nor with their wives' expressiveness.


title: Crowd-based positioning of UAVs as access points

abstract: Unmanned Aerial Vehicles (UAVs) can be a cost saving and easy to deploy solution to implement a temporary network infrastructure. They can act as access points in scenarios such as emergency situations, special events, or specific area monitoring. Two main deployment families can be found in the literature. The first one, the location-based family, is based on the fundamental assumption that the network user positions are known. We do believe that this could not suit the most general scenarios. On the other hand, the location-independent family can not be as efficient as the first one. The main idea in this paper is to introduce a new crowd-based family, based on a probabilistic knowledge of user positions. We then propose a self-deployment method built on a Coulomb's law analogy where users and UAVs act as electrical charges. Short range interactions are implemented through network sensing, while long range ones use a crowd-based approach. Some numerical results are depicted, showing the performance of this self-deploying mechanism as well as a comparison with a well-known clustering algorithm.


title: New Direct To Motor Drives

abstract: This paper proposes a simple and effective scheme for Direct Torque Control (DTC) of Brushless DC Motor. Unlike the traditional DTC where both the torque and flux must to be employed in order to deliver the switching table for the inverter, the proposed DTC scheme utilizes only the torque information. By considering the particular operating principle of the motor, the instantaneous torque can be estimated only by using back EMF ratio in an unified differential equation. The simulation results have shown the good performance of the proposed scheme. It can be seen thus a very suitable technique for EV drives which need fast and precise torque response.


title: A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

abstract: The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.


title: An RGB-D based image set classification for robust face recognition from Kinect data

abstract: The paper proposes a method for robust face recognition from low quality Kinect acquired images which have a wide range of variations in head pose, illumination, facial expressions, sunglass disguise and occlusions by hand. Multiple Kinect images of a person are considered as an image set and face recognition from these images is formulated as an RGB-D image set classification problem. The Kinect acquired raw depth data is used for pose estimation and an automatic cropping of the face region. Based upon the estimated poses, the face images of a set are divided into multiple image subsets. An efficient block based covariance matrix representation is proposed to model images in an image subset on Riemannian manifold (Lie group). For classification, SVM models are separately learnt for each image subset on the Lie group of Riemannian manifold and a fusion strategy is introduced to combine results from all image subsets. The proposed technique has been evaluated on a combination of three large data sets containing over 35,000 RGB-D images under challenging conditions. The proposed RGB-D based image set classification incurs low computational cost and achieves an identification rate as high as 99.5%.


title: Systematic Reviews: The Good, the Bad, and the Ugly

abstract: Systematic reviews systematically evaluate and summarize current knowledge and have many advantages over narrative reviews. Meta-analyses provide a more reliable and enhanced precision of effect estimate than do individual studies. Systematic reviews are invaluable for defining the methods used in subsequent studies, but, as retrospective research projects, they are subject to bias. Rigorous research methods are essential, and the quality depends on the extent to which scientific review methods are used. Systematic reviews can be misleading, unhelpful, or even harmful when data are inappropriately handled; meta-analyses can be misused when the difference between a patient seen in the clinic and those included in the meta-analysis is not considered. Furthermore, systematic reviews cannot answer all clinically relevant questions, and their conclusions may be difficult to incorporate into practice. They should be reviewed on an ongoing basis. As clinicians, we need proper methodological training to perform good systematic reviews and must ask the appropriate questions before we can properly interpret such a review and apply its conclusions to our patients. This paper aims to assist in the reading of a systematic review.


title: Head Finalization Reordering for Chinese-to-Japanese Machine Translation

abstract: In Statistical Machine Translation, reordering rules have proved useful in extracting bilingual phrases and in decoding during translation between languages that are structurally different. Linguistically motivated rules have been incorporated into Chinese-to-English (Wang et al., 2007) and English-to-Japanese (Isozaki et al., 2010b) translation with significant gains to the statistical translation system. Here, we carry out a linguistic analysis of the Chinese-to-Japanese translation problem and propose one of the first reordering rules for this language pair. Experimental results show substantially improvements (from 20.70 to 23.17 BLEU) when head-finalization rules based on HPSG parses are used, and further gains (to 24.14 BLEU) were obtained using more refined rules.


title: Reduced Order Modeling of Triple Link Inverted Pendulum Using Particle Swarm Optimization Algorithm

abstract: In recent years, Particle Swarm Optimization has evolved as an effective global optimization algorithm whose dynamics has been inspired from swarming or collaborative behavior of biological populations. In this paper, PSO has been applied to Triple Link Inverted Pendulum model to find its reduced order model by minimization of error between the step responses of higher and reduced order model. Model Order Reduction using PSO algorithm is advantageous due to ease in implementation, higher accuracy and decreased time of computation. The second and third order reduced transfer functions of Triple Link Inverted Pendulum have been computed for comparison. Keywords—Particle Swarm Optimization, Triple Link Inverted Pendulum, Model Order Reduction, Pole Placement technique.


title: 43 Gb/s 1.3 $\mu$ m DFB Laser for 40 km Transmission

abstract: We describe the performance of 1.3 $\mu$ m InGaAlAs ridge waveguide multiple quantum-well distributed feedback lasers. We obtained a 3 dB down bandwidth frequency of around 30 GHz by shortening the cavity length of the laser. We demonstrated experimentally that it is possible to obtain clear eye opening at a 43 Gb/s modulation speed after a 40 km single-mode fiber (SMF) transmission using a directly modulated laser. We fabricated a compact transistor outline (TO)-CAN module whose volume was about $1/19$ that of a conventional butterfly package. We successfully obtained clear eye opening for a 40 km SMF transmission at 43 Gb/s with the TO-CAN module.


title: CPU bugs, CPU backdoors and consequences on security

abstract: In this paper, we present the security implications of x86 processor bugs or backdoors on operating systems and virtual machine monitors. We will not try to determine whether the backdoor threat is realistic or not, but we will assume that a bug or a backdoor exists and analyze the consequences on systems. We will show how it is possible for an attacker to implement a simple and generic CPU backdoor in order—at some later point in time—to bypass mandatory security mechanisms with very limited initial privileges. We will explain practical difficulties and show proof of concept schemes using a modified Qemu CPU emulator. Backdoors studied in this paper are all usable from the software level without any physical access to the hardware.


title: A critical chains based distributed multi-project scheduling approach

abstract: In this paper, we discuss the distributed resource constrained multi-project scheduling problem (DRCMPSP) and focus on handling resource conflicts. The resource conflicts among projects are originally caused by precedence constraints and resource constraints. To reflect the influences of both constraints, this paper incorporates the critical chain concept into the distributed problem and introduces a corresponding heuristic strategy. Besides, to deal with large scale instances, an elimination mechanism is proposed, which identifies a few conflict time slots in advance and allocates global resources in those time slots only. Depending upon the heuristics and elimination mechanism, we develop a distributed multi-agent system based algorithm called DMAS/EM. Experiments show that DMAS/EM algorithm can generate satisfactory solutions with low average project delay and significant time savings. Furthermore, we observe that our algorithm provides great scalability with respect to variations in the size of instances processed and the degree of conflicts.


title: Speed-accuracy tradeoff in Fitts' law tasks: on the equivalency of actual and nominal pointing precision

abstract: Pointing tasks in human-computer interaction obey certain speed-accuracy tradeoff rules. In general, the more accurate the task to be accomplished, the longer it takes and vice versa. Fitts' law models the speed-accuracy tradeoff effect in pointing as imposed by the task parameters, through Fitts' index of difficulty (Id) based on the ratio of the nominal movement distance and the size of the target. Operating with different speed or accuracy biases, performers may utilize more or less area than the target specifies, introducing another subjective layer of speed-accuracy tradeoff relative to the task specification. A conventional approach to overcome the impact of the subjective layer of speed-accuracy tradeoff is to use the a posteriori "effective" pointing precision We in lieu of the nominal target width W. Such an approach has lacked a theoretical or empirical foundation. This study investigates the nature and the relationship of the two layers of speed-accuracy tradeoff by systematically controlling both Id and the index of target utilization Iu in a set of four experiments. Their results show that the impacts of the two layers of speed-accuracy tradeoff are not fundamentally equivalent. The use of We could indeed compensate for the difference in target utilization, but not completely. More logical Fitts' law parameter estimates can be obtained by the We adjustment, although its use also lowers the correlation between pointing time and the index of difficulty. The study also shows the complex interaction effect between Id and Iu, suggesting that a simple and complete model accommodating both layers of speed-accuracy tradeoff may not exist.


title: The role of semantics in games and simulations

abstract: Powerful graphics hardware is enabling strong improvements in both the appearance and the complexity of virtual worlds for games and simulations. However, current practices in the design and development of virtual worlds mostly resemble high-tech variants of traditional handcrafts, resulting in increasingly unbearable design costs. In this article we state that an essential key to overcoming these problems lies in the enrichment of object models with several kinds of semantic data. We discuss numerous and promising uses for semantic information in virtual worlds, and show, for many of them, how previous results of recent research can be successfully applied. We also identify the fundamental challenges in this new cross-disciplinary area, and point out a number of open issues lying ahead, including the need for (i) a suitable way of specifying semantic data, providing a powerful vocabulary that is useful and usable for all disciplines involved in game design and development; (ii) a seamless integration of semantic data integrated with procedural generation techniques, in order to provide designers with a new and powerful generation of tools; and (iii) a consistency maintenance among evolving objects in a changeable environment, for which powerful constraint-solving methods will be instrumental. We conclude that, as the expectancy for future games and simulations steadily shifts from improved graphics and appearance towards improved character behavior, plausible realism and coherent gameplay, embedding the game world and its objects with richer semantics is going to play a crucial role. We can therefore expect that, in the near future, increasing research efforts and influential results will be emerging in this new exciting area.


title: Heuristic search viewed as path finding in a graph

abstract: Abstract This paper presents a particular model of heuristic search as a path-finding problem in a directed graph. A class of graph-searching procedures is described which uses a heuristic function to guide search. Heuristic functions are estimates of the number of edges that remain to be traversed in reaching a goal node. A number of theoretical results for this model, and the intuition for these results, are presented. They relate the efficiency of search to the accuracy of the heuristic function. The results also explore efficiency as a consequence of the reliance or weight placed on the heuristics used.


Mind the Traps! Design Guidelines for Rigorous BCI Experiments


title: Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices

abstract: We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing hierarchy, a DDNN can scale up in neural network size and scale out in geographical span. Due to its distributed nature, DDNNs enhance sensor fusion, system fault tolerance and data privacy for DNN applications. In implementing a DDNN, we map sections of a DNN onto a distributed computing hierarchy. By jointly training these sections, we minimize communication and resource usage for devices and maximize usefulness of extracted features which are utilized in the cloud. The resulting system has built-in support for automatic sensor fusion and fault tolerance. As a proof of concept, we show a DDNN can exploit geographical diversity of sensors to improve object recognition accuracy and reduce communication cost. In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.


title: Markov Random Field Texture Models

abstract: We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models. The binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors and ``number of tries'' equal to the number of gray levels, was taken to be the basic model for the analysis. A method of generating samples from the binomial model is given, followed by a theoretical and practical analysis of the method's convergence. Examples show how the parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Natural texture samples were digitized and their parameters were estimated under the Markov random field model. A hypothesis test was used for an objective assessment of goodness-of-fit under the Markov random field model. Overall, microtextures fit the model well. The estimated parameters of the natural textures were used as input to the generation procedure. The synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.


title: A nonlinear disturbance observer for robotic manipulators

abstract: A new nonlinear disturbance observer (NDO) for robotic manipulators is derived in this paper. The global exponential stability of the proposed disturbance observer (DO) is guaranteed by selecting design parameters, which depend on the maximum velocity and physical parameters of robotic manipulators. This new observer overcomes the disadvantages of existing DOs, which are designed or analyzed by linear system techniques. It can be applied in robotic manipulators for various purposes such as friction compensation, independent joint control, sensorless torque control and fault diagnosis. The performance of the proposed observer is demonstrated by the friction estimation and compensation for a two-link robotic manipulator. Both simulation and experimental results show the NDO works well.


title: Peer-to-Peer Membership Management for Gossip-Based Protocols

abstract: Gossip-based protocols for group communication have attractive scalability and reliability properties. The probabilistic gossip schemes studied so far typically assume that each group member has full knowledge of the global membership and chooses gossip targets uniformly at random. The requirement of global knowledge impairs their applicability to very large-scale groups. In this paper, we present SCAMP (Scalable Membership protocol), a novel peer-to-peer membership protocol which operates in a fully decentralized manner and provides each member with a partial view of the group membership. Our protocol is self-organizing in the sense that the size of partial views naturally converges to the value required to support a gossip algorithm reliably. This value is a function of the group size, but is achieved without any node knowing the group size. We propose additional mechanisms to achieve balanced view sizes even with highly unbalanced subscription patterns. We present the design, theoretical analysis, and a detailed evaluation of the basic protocol and its refinements. Simulation results show that the reliability guarantees provided by SCAMP are comparable to previous schemes based on global knowledge. The scale of the experiments attests to the scalability of the protocol.


title: Physiopathology of natural auto-antibodies: the case for regulation.

abstract: The cause of autoimmune diseases remains unknown and, as a consequence, disease prediction and prophylaxis are not part of current clinical practice. Many autoimmune syndromes are accompanied by serological evidence of autoimmunity in the form of circulating auto-antibodies (AAb). As normal individuals produce large amounts of AAb, exploring the main differences between such physiologic AAb and those classified as pathogenic may provide the clues needed for new clinical approaches to this group of disorders. Reviewing the differential characteristics of normal and disease-associated autoantibodies, we conclude that the problem will be best tackled if we understand how the organism normally ensures that autoantigen-driven B cell activation does not lead to high titers of autoantibodies and severe autoimmunity. As natural activation of autoreactive B cells occur by both T cell dependent and T cell independent mechanisms, we argue that absence of clonal expansion in normal autoreactive B cells upon activation does not result from lack of appropriate stimulation but, rather, from the presence of negative regulation and suppressive mechanisms.


title: A multi-dimensional ontology-based IoT resource model

abstract: Nowadays researches related to resource management and representation of IoT (Internet of Things) emphasize on making identify regulation or describing device working principle. However they could not represent dynamic feature and multi-dimensional feature of interaction. This paper propose a multi-dimensional ontology-based IoT resource model to solve above defects. For resource management, searching and reasoning, this model divides resource into 4 key concepts: general situation, variable feature, function and enforce. Then this paper classify resource into 5 dimensions. Experimental result shows that when IoT resource contains time feature, location feature and priority feature, resource model works well to accomplish management and searching task. In addition, this model lays a foundation for service model, which is in upper layer of resource model.


title: Ecological Resilience, Biodiversity, and Scale

abstract: We describe existing models of the relationship between species diversity and ecological function, and propose a conceptual model that relates species richness, ecological resilience, and scale. We suggest that species interact with scale-dependent sets of ecological structures and processes that determine functional opportunities. We propose that ecological resilience is generated by diverse, but overlapping, function within a scale and by apparently redundant species that operate at different scales, thereby reinforcing function across scales. The distribution of functional diversity within and across scales enables regeneration and renewal to occur following ecological disruption over a wide range of scales.


title: The role of logic and ontology in language and reasoning

abstract: Natural languages have words for all the operators of first-order logic, modal logic, and many logics that have yet to be invented. They also have words and phrases for everything that anyone has ever discovered, assumed, or imagined. Aristotle invented formal logic as a tool (organon) for analyzing and reasoning about the ontologies implicit in language. Yet some linguists and logicians took a major leap beyond Aristotle: they claimed that there exists a special kind of logic at the foundation of all NLs, and the discovery of that logic would be the key to harnessing their power and implementing them in computer systems. Projects in artificial intelligence developed large systems based on complex versions of logic, yet those systems are fragile and limited in comparison to the robust and immensely expressive natural languages. Formal logics are too inflexible to be the foundation for language; instead, logic and ontology are abstractions from language. This reversal turns many theories about language upside down, and it has profound implications for the design of automated systems for reasoning and language understanding. This article analyzes these issues in terms of Peirce’s semiotics and Wittgenstein’s language games. The resulting analysis leads to a more dynamic, flexible, and extensible basis for ontology and its use in formal and informal reasoning.


title: Processing bulk natural bamboo into a strong and flame-retardant composite material

abstract: Abstract We report a simple and effective approach to process natural bamboo directly into a high-performance composite material with light weight, high tensile strength, high modulus, and low flammability. Our process involves delignification of natural bamboo followed by surface treatment of highly aligned cellulose fibers in the delignified bamboo and finally infiltration of epoxy resin into the porous structure of the surface-treated delignified bamboo. Surface treatment of the highly aligned cellulose fibers by boric acid (BA) under alkaline conditions resulted in the formation of cross-linked structures between the hydroxyl groups of BA and the diol or carboxylic groups of the cellulose backbone. Because the aligned bamboo fibers were well-preserved, the BA-bamboo/epoxy composite showed increases of 234% and 177% in tensile strength and impact strength, respectively, compared to the pristine epoxy. The BA-bamboo/epoxy composite exhibited comparable mechanical properties but significantly improved flame retardancy compared to the delignified bamboo/epoxy composite. Specifically, the limiting oxygen index of the BA-delignified bamboo/epoxy composite was 26.5% larger and the peak heat release rate was 63% smaller than those of the pristine epoxy resin in cone calorimeter measurements. Thermogravimetric analysis indicated that BA promoted charring of the bamboo/epoxy composite, which was responsible for the significantly improved flame retardancy. The simultaneous enhancement of the mechanical properties and flame retardancy makes such bamboo-based composites highly suitable for use as structural materials.


title: The Cell Phone in the Twenty-First Century

abstract: This chapter was originally published in the book Principles of Addiction. The copy attached is provided by Elsevier for the author's benefit and for the benefit of the author's institution, for non-commercial research, and educational use. This includes without limitation use in instruction at your institution, distribution to specific colleagues, and providing a copy to your institution's administrator.


title: Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

abstract: Understanding drugs and their modes of action is a fundamental challenge in systems medicine. Key to addressing this challenge is the elucidation of drug targets, an important step in the search for new drugs or novel targets for existing drugs. Incorporating multiple biological information sources is of essence for improving the accuracy of drug target prediction. In this article, we introduce a novel framework--Similarity-based Inference of drug-TARgets (SITAR)--for incorporating multiple drug-drug and gene-gene similarity measures for drug target prediction. The framework consists of a new scoring scheme for drug-gene associations based on a given pair of drug-drug and gene-gene similarity measures, combined with a logistic regression component that integrates the scores of multiple measures to yield the final association score. We apply our framework to predict targets for hundreds of drugs using both commonly used and novel drug-drug and gene-gene similarity measures and compare our results to existing state of the art methods, markedly outperforming them. We then employ our framework to make novel target predictions for hundreds of drugs; we validate these predictions via curated databases that were not used in the learning stage. Our framework provides an extensible platform for incorporating additional emerging similarity measures among drugs and genes. Supplementary Material is available at www.liebertonline.com/cmb.


title: Bilingualism, biliteracy, and learning to read: interactions among languages and writing systems

abstract: Four groups of children in first grade were compared on early literacy tasks. Children in three of the groups were bilingual, each group representing a different combination of language and writing system, and children in the fourth group were monolingual speakers of English. All the bilingual children used both languages daily and were learning to read in both languages. The children solved decoding and phonological awareness tasks, and the bilinguals completed all tasks in both languages. Initial differences between the groups in factors that contribute to early literacy were controlled in an analysis of covariance, and the results showed a general increment in reading ability for all the bilingual children but a larger advantage for children learning two alphabetic systems. Similarly, bilinguals transferred literacy skills across languages only when both languages were written in the same system. Therefore, the extent of the bilingual facilitation for early reading depends on the relation between the t...


title: BigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms

abstract: The trade-off between language expressiveness and system scalability (E&S) is a well-known problem in RDF stream reasoning. Higher expressiveness supports more complex reasoning logic, however, it may also hinder system scalability. Current research mainly focuses on logical frameworks suitable for stream reasoning as well as the implementation and the evaluation of prototype systems. These systems are normally developed in a centralized setting which suffer from inherent limited scalability, while an in-depth study of applying distributed solutions to cover E&S is still missing. In this paper, we aim to explore the feasibility of applying modern distributed computing frameworks to meet E&S all together. To do so, we first propose BigSR, a technical demonstrator that supports a positive fragment of the LARS framework. For the sake of generality and to cover a wide variety of use cases, BigSR relies on the two main execution models adopted by major distributed execution frameworks: Bulk Synchronous Processing (BSP) and Record-at-A-Time (RAT). Accordingly, we implement BigSR on top of Apache Spark Streaming (BSP model) and Apache Flink (RAT model). In order to conclude on the impacts of BSP and RAT on E&S, we analyze the ability of the two models to support distributed stream reasoning and identify several types of use cases characterized by their levels of support. This classification allows for quantifying the E&S trade-off by assessing the scalability of each type of use case \wrt its level of expressiveness. Then, we conduct a series of experiments with 15 queries from 4 different datasets. Our experiments show that BigSR over both BSP and RAT generally scales up to high throughput beyond million-triples per second (with or without recursion), and RAT attains sub-millisecond delay for stateless query operators.


title: Static analysis of android apps

abstract: ContextStatic analysis exploits techniques that parse program source code or bytecode, often traversing program paths to check some program properties. Static analysis approaches have been proposed for different tasks, including for assessing the security of Android apps, detecting app clones, automating test cases generation, or for uncovering non-functional issues related to performance or energy. The literature thus has proposed a large body of works, each of which attempts to tackle one or more of the several challenges that program analyzers face when dealing with Android apps. ObjectiveWe aim to provide a clear view of the state-of-the-art works that statically analyze Android apps, from which we highlight the trends of static analysis approaches, pinpoint where the focus has been put, and enumerate the key aspects where future researches are still needed. MethodWe have performed a systematic literature review (SLR) which involves studying 124 research papers published in software engineering, programming languages and security venues in the last 5 years (January 2011December 2015). This review is performed mainly in five dimensions: problems targeted by the approach, fundamental techniques used by authors, static analysis sensitivities considered, android characteristics taken into account and the scale of evaluation performed. ResultsOur in-depth examination has led to several key findings: 1) Static analysis is largely performed to uncover security and privacy issues; 2) The Soot framework and the Jimple intermediate representation are the most adopted basic support tool and format, respectively; 3) Taint analysis remains the most applied technique in research approaches; 4) Most approaches support several analysis sensitivities, but very few approaches consider path-sensitivity; 5) There is no single work that has been proposed to tackle all challenges of static analysis that are related to Android programming; and 6) Only a small portion of state-of-the-art works have made their artifacts publicly available. ConclusionThe research community is still facing a number of challenges for building approaches that are aware altogether of implicit-Flows, dynamic code loading features, reflective calls, native code and multi-threading, in order to implement sound and highly precise static analyzers.


title: Answering Clinical Questions with Knowledge-Based and Statistical Techniques

abstract: The combination of recent developments in question-answering research and the availability of unparalleled resources developed specifically for automatic semantic processing of text in the medical domain provides a unique opportunity to explore complex question answering in the domain of clinical medicine. This article presents a system designed to satisfy the information needs of physicians practicing evidence-based medicine. We have developed a series of knowledge extractors, which employ a combination of knowledge-based and statistical techniques, for automatically identifying clinically relevant aspects of MEDLINE abstracts. These extracted elements serve as the input to an algorithm that scores the relevance of citations with respect to structured representations of information needs, in accordance with the principles of evidence-based medicine. Starting with an initial list of citations retrieved by PubMed, our system can bring relevant abstracts into higher ranking positions, and from these abstracts generate responses that directly answer physicians' questions. We describe three separate evaluations: one focused on the accuracy of the knowledge extractors, one conceptualized as a document reranking task, and finally, an evaluation of answers by two physicians. Experiments on a collection of real-world clinical questions show that our approach significantly outperforms the already competitive PubMed baseline.


title: Categorizing the Document Using Multi Class Classification in Data Mining

abstract: Classification is the process of dividing the data into number of groups which are either dependent or independent of each other and each group acts as a class. The task of Classification can be done by using several methods using different types of classifiers. But classification cannot be done easily when it is to be applied on text documents that is: document classification. The main purpose of this paper is to analyze the task multi-class document classification and to learn that how can we achieve high classification accuracy in the context of text documents. Naive Bayes approach is used to deal with the problem of document classification via a deceptively simplistic model: assume all features are independent of one another, and compute the class of a document based on maximal probability. The Naive Bayes approach is applied in Flat (linear) and hierarchical manner for improving the efficiency of classification model. It has been found that Hierarchical Classification technique is more effective then Flat classification. It also performs better in case of multi-label document classification. The dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.


title: The search dashboard: how reflection and comparison impact search behavior

abstract: Most searchers do not know how to use Web search engines as effectively as possible. This is due, in part, to search engines not providing feedback about how search behavior can be improved. Because feedback is an essential part of learning, we created the Search Dashboard, which provides an interface for reflection on personal search behavior. The Dashboard aggregates and presents an individual's search history and provides comparisons with that of archetypal expert profiles. Via a five-week study of 90 Search Dash-board users, we find that users are able to change aspects of their behavior to be more in line with that of the presented expert searchers. We also find that reflection can be beneficial, even without comparison, by changing participants' views about their own search skills, what is possible with search, and what aspects of their behavior may influence search success. Our findings demonstrate a new way for search engines to help users modify their search behavior for positive outcomes.


title: Facial Expression Recognition Using 3D Facial Feature Distances

abstract: In this paper, we propose a novel approach for facial expression analysis and recognition. The proposed approach relies on the distance vectors retrieved from 3D distribution of facial feature points to classify universal facial expressions. Neural network architecture is employed as a classifier to recognize the facial expressions from a distance vector obtained from 3D facial feature locations. Facial expressions such as anger, sadness, surprise, joy, disgust, fear and neutral are successfully recognized with an average recognition rate of 91.3%. The highest recognition rate reaches to 98.3% in the recognition of surprise.


title: Making Sense of Sensemaking 2: A Macrocognitive Model

abstract: For pt.1 see ibid., vol.21, no.4, p. 70-73 (2006). In this paper, we have laid out a theory of sensemaking that might be useful for intelligent systems applications. It's a general, empirically grounded account of sensemaking that goes significantly beyond the myths and puts forward some nonobvious, testable hypotheses about the process. When people try to make sense of events, they begin with some perspective, viewpoint, or framework - however minimal. For now, let's use a metaphor and call this a frame. We can express frames in various meaningful forms, including stories, maps, organizational diagrams, or scripts, and can use them in subsequent and parallel processes. Even though frames define what count as data, they themselves actually shape the data Furthermore, frames change as we acquire data. In other words, this is a two-way street: Frames shape and define the relevant data, and data mandate that frames change in nontrivial ways. We examine five areas of empirical findings: causal reasoning, commitment to hypotheses, feedback and learning, sense-making as a skill, and confirmation bias. In each area the Data/Frame model, and the research it's based on, doesn't align with common beliefs. For that reason, the Data/Frame model cannot be considered a depiction of commonsense views


title: Multimodal Analysis of User-Generated Multimedia Content

abstract: This book presents a summary of the multimodal analysis of user-generated multimedia content (UGC). Several multimedia systems and their proposed frameworks are also discussed. First, improved tag recommendation and ranking systems for social media photos, leveraging both content and contextual information, are presented. Next, we discuss the challenges in determining semantics and sentics information from UGC to obtain multimedia summaries. Subsequently, we present a personalized music video generation system for outdoor user-generated videos. Finally, we discuss approaches for multimodal lecture video segmentation techniques. This book also explores the extension of these multimedia system with the use of heterogeneous continuous streams.


title: Data Governance Maturity Model for Micro Financial Organizations in Peru.

abstract: Micro finance organizations play an important role since they facilitate integration of all social classes to sustained economic growth. Against this background, exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage due to the lack of valuable and quality information for decision-making and process improvement. Data Governance provides a different approach to data management, as seen from the perspective of business assets. In this regard, it is necessary that the organization have the ability to assess the extent to which that management is correct or is generating expected results. This paper proposes a data governance maturity model for micro finance organizations, which frames a series of formal requirements and criteria providing an objective diagnosis. This model was implemented based on the information of a Peruvian micro finance organization. Four domains, out of the seven listed in the model, were evaluated. Finally, after validation of the proposed model, it was evidenced that it serves as a means for identifying the gap between data management and objectives set.


title: MAFIA: A Performance Study of Mining Maximal Frequent Itemsets

abstract: We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effects of individual components of MAFIA, including search space pruning techniques and adaptive compression. We also compare our performance with previous work by running tests on very different types of datasets. Our experiments show that MAFIA performs best when mining long itemsets and outperforms other algorithms on dense data by a factor of three to thirty.


title: Dissociation following traumatic stress: Etiology and treatment.

abstract: We postulate that the cascade ''Freeze-Flight-Fight-Fright-Flag-Faint'' is a coherent sequence of six fear responses that escalate as a function of defense possibilities and proximity to danger during life-threat. The actual sequence of trauma-related response dispositions acted out in an extremely dangerous situation therefore depends on the appraisal of the threat by the organism in relation to her/his own power to act (e.g., age and gender) as well as the perceived characteristics of threat and perpetrator. These reaction patterns provide optimal adaption for particular stages of imminence. Subsequent to the traumatic threats, portions of the experience may be replayed. The actual individual cascade of defense stages a survivor has gone through during the traumatic event will repeat itself every time the fear network, which has evolved peritraumatically, is activated again (i.e., through internal or external triggers or, e.g., during exposure therapy).When a parasympathetically dominated ''shut- down'' was the prominent peri-traumatic response during the traumatic incident, comparable dissociative responses may dominate responding to subsequently experienced threat and may also reappear when the traumatic memory is reactivated. Repeated experience of traumatic stress forms a fear network that can become pathologically detached from contextual cues such as time and location of the danger, a condition which manifests itself as posttraumatic stress disorder (PTSD). Intrusions, for example, can therefore be understood as repetitive displays of fragments of the event, which would then, depending on the dominant physiological response during the threat, elicit a corresponding combination of hyperarousal and dissociation. We suggest that trauma treatment must therefore differentiate between patients on two dimensions: those with peritraumatic sympathetic activation versus those who went down the whole defense cascade, which leads to parasympathetic dominance during the trauma and a corresponding replay of physiological and dissociative responding, when reminded. The differential management of dissociative stages (''fright'' and ''faint'') has important treatment implications.


title: Theoretical Design of Broadband Multisection Wilkinson Power Dividers With Arbitrary Power Split Ratio

abstract: This paper describes a new approach for the theoretical design of unequal multisection Wilkinson power dividers, using single-layer microstrip lines. Similar to most multisection structures, the presented unequal divider can provide a broadband performance. To provide the design guideline, closed-form formulas are derived for the divider with odd or even number of sections. In each case, various simulations have been conducted to investigate the performance of dividers over the frequency band. For experimental verifications, two 1:2 power dividers with a center frequency of 1 GHz are fabricated, having three and four sections. The simulated and measured results are presented, and good agreements between them are observed.


title: Neanderthal introgression at chromosome 3p21.31 was under positive natural selection in East Asians.

abstract: Studies of the Neanderthal and Denisovan genomes demonstrate archaic hominin introgression in Eurasians. Here, we present evidence of Neanderthal introgression within the chromosome 3p21.31 region, occurring with a high frequency in East Asians (ranging from 49.4% to 66.5%) and at a low frequency in Europeans. We also detected a signal of strong positive selection in this region only in East Asians. Our data indicate that likely candidate targets of selection include rs12488302-T and its associated alleles--among which four are nonsynonymous, including rs35455589-G in HYAL2, a gene related to the cellular response to ultraviolet-B irradiation. Furthermore, suggestive evidence supports latitude-dependent selection, implicating a role of ultraviolet-B. Interestingly, the distribution of rs35455589-G suggests that this allele was lost during the exodus of ancestors of modern Eurasians from Africa and reintroduced to Eurasians from Neanderthals.


title: Network Coding in Cognitive Radio Networks: A Comprehensive Survey

abstract: Network coding (NC) is a technique used for effective and secure communication by improving the network capacity, throughput, efficiency, and robustness. In NC, data packets are encoded by intermediate nodes and are then decoded at the destination nodes. NC has been successfully applied in a variety of networks including relay networks, peer-to-peer networks, wireless networks, cognitive radio networks, and wireless sensor networks. Cognitive radio network (CRN) is an emerging field which exploits the utilization of unused spectrum or white spaces, effectively and efficiently. In CRNs, NC schemes are also applied to maximize the spectrum utilization, as well as to maintain the effective and secure transmission of data packets over the network. In this paper, we provide a comprehensive survey of NC schemes in cognitive radio networks, highlighting the motivations for and the applications of NC in CRNs. We provide typical case studies of NC schemes in CRNs, as well as the taxonomy of NC schemes in CRNs. Finally, we present open issues, challenges, and future research directions related with NC in cognitive radio networks.


A Study of Nurses ' Job Satisfaction : The Relationship to Organizational Commitment , Perceived Organizational Support , Transactional Leadership , Transformational Leadership , and Level of Education


title: Profiling Phishing Emails Based on Hyperlink Information

abstract: In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with who is (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVMto generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information.


title: On the Security of the Automatic Dependent Surveillance-Broadcast Protocol

abstract: Automatic dependent surveillance-broadcast (ADS-B) is the communications protocol currently being rolled out as part of next-generation air transportation systems. As the heart of modern air traffic control, it will play an essential role in the protection of two billion passengers per year, in addition to being crucial to many other interest groups in aviation. The inherent lack of security measures in the ADS-B protocol has long been a topic in both the aviation circles and in the academic community. Due to recently published proof-of-concept attacks, the topic is becoming ever more pressing, particularly with the deadline for mandatory implementation in most airspaces fast approaching. This survey first summarizes the attacks and problems that have been reported in relation to ADS-B security. Thereafter, it surveys both the theoretical and practical efforts that have been previously conducted concerning these issues, including possible countermeasures. In addition, the survey seeks to go beyond the current state of the art and gives a detailed assessment of security measures that have been developed more generally for related wireless networks such as sensor networks and vehicular ad hoc networks, including a taxonomy of all considered approaches.


title: CQSTR: Securing Cross-Tenant Applications with Cloud Containers

abstract: Cloud providers are in a position to greatly improve the trust clients have in network services: IaaS platforms can isolate services so they cannot leak data, and can help verify that they are securely deployed. We describe a new system called CQSTR that allows clients to verify a service's security properties. CQSTR provides a new cloud container abstraction similar to Linux containers but for VM clusters within IaaS clouds. Cloud containers enforce constraints on what software can run, and control where and how much data can be communicated across service boundaries. With CQSTR, IaaS providers can make assertions about the security properties of a service running in the cloud. We investigate implementations of CQSTR on both Amazon AWS and OpenStack. With AWS, we build on virtual private clouds to limit network access and on authorization mechanisms to limit storage access. However, with AWS certain security properties can be checked only by monitoring audit logs for violations after the fact. We modified OpenStack to implement the full CQSTR model with only modest code changes. We show how to use CQSTR to build more secure deployments of the data analytics frameworks PredictionIO, PacketPig, and SpamAssassin. In experiments on CloudLab we found that the performance impact of CQSTR on applications is near zero.


title: P.: Reinforcement learning and reactive search: an adaptive max-sat solver

abstract: This paper investigates Reinforcement Learning (RL) applied to online parameter tuning in Stochastic Local Search (SLS) methods. In particular, a novel application of RL is proposed in the Reactive Tabu Search (RTS) scheme, where the appropriate amount of diversification in prohibition-based local search is adapted in a fast online manner to the characteristics of a task and of the local configuration. The experimental tests demonstrate promising results on Maximum Satisfiability (MAX-SAT) instances when compared with state-of-the-art SLS SAT solvers, such us AdaptNovelty, rSAPS and gNovelty.


title: Fingerprint authentication using geometric features

abstract: Biometric based authentication, particularly for fingerprint authentication systems play a vital role in identifying an individual. The existing fingerprint authentication systems depend on specific points known as minutiae for recognizing an individual. Designing a reliable automatic fingerprint authentication system is still very challenging, since not all fingerprint information is available. Further, the information obtained is not always accurate due to cuts, scars, sweat, distortion and various skin conditions. Moreover, the existing fingerprint authentication systems do not utilize other significant minutiae information, which can improve the accuracy. Various local feature detectors such as Difference-of-Gaussian, Hessian, Hessian Laplace, Harris Laplace, Multiscale Harris, and Multiscale Hessian have been extensively used for feature detection. However, these detectors have not been employed for detecting fingerprint image features. In this article, a versatile local feature fingerprint matching scheme is proposed. The local features are obtained by exploiting these local geometric detectors and SIFT descriptor. This scheme considers local characteristic features of the fingerprint image, thus eliminating the issues caused in existing fingerprint feature based matching techniques. Computer simulations of the proposed algorithm on specific databases show significant improvements when compared to existing fingerprint matchers, such as minutiae matcher, hierarchical matcher and graph based matcher. Computer simulations conducted on the Neurotechnology database demonstrates a very low Equal Error Rate (EER) of 0.8%. The proposed system a) improves the accuracy of the fingerprint authentication system, b) works when the minutiae information is sparse, and c) produces satisfactory matching accuracy in the case when minutiae information is unavailable. The proposed system can also be employed for partial fingerprint authentication.


title: Two faces of agricultural intensification hanging over aquatic biodiversity: The case of chironomid diversity from farm ponds vs. natural wetlands in a coastal region

abstract: Abstract Increasing agricultural land use and intensification have given rise to the loss and eutrophication of coastal wetlands worldwide. In Mediterranean coastal regions, irrigated agriculture, in turn, has prompted the proliferation of farm ponds which might compensate for wetland loss and degradation if their management regimen results are compatible with biodiversity conservation. Here, we studied regional (γ-), local (α-) and interlocal (β-) diversities of chironomids in coastal wetlands and irrigation ponds from a Mediterranean region, to determine the contribution of each habitat type to regional diversity, and to disentangle which environmental factors, anthropogenic or natural, contributed most to explain diversity patterns. Regional diversity was slightly, but still significantly, higher in natural wetlands than in farm ponds, which can be attributed to the significantly higher β-diversity in natural wetlands, since, despite the much larger surface area of wetlands, both habitat types did not differ in local diversity (α-diversity). In both habitats, however, the contribution of β-diversity to regional diversity was higher compared to that of α-diversity, and the component ‘spatial species turnover’ exceeded that of the component ‘nestedness’ of β-diversity. This, together with an outstanding assemblage complementarity (approx. 50%) between habitat types, emphasizes the vital contribution of farm ponds, together with natural wetlands, to regional diversity. Despite the higher salinity and eutrophication of natural wetlands that tended to reduce diversity in chironomid assemblages, their more heterogeneous shore line likely compensated somewhat for such negative effects. Unlike wetlands, the homogeneous and unvegetated shore of farm ponds, in conjunction with their intensive management, probably induced adverse effects on local and interlocal diversity. Specific recommendations are given in this regards to mitigate impacts and improve the value of both habitats for biodiversity conservation.


MixKMeans: Clustering Question-Answer Archives


title: Reconstructive surgery after female genital mutilation: a prospective cohort study

abstract: Summary Background Women who have undergone female genital mutilation rarely have access to the reconstructive surgery that is now available. Our objective was to assess the immediate and long-term outcomes of this surgery. Methods Between 1998 and 2009, we included consecutive patients with female genital mutilation aged 18 years or older who had consulted a urologist at Poissy-St Germain Hospital, France. We used the WHO classification to prospectively include patients with type II or type III mutilation. The skin covering the stump was resected to reveal the clitoris. The suspensory ligament was then sectioned to mobilise the stump, the scar tissue was removed from the exposed portion and the glans was brought into a normal position. All patients answered a questionnaire at entry about their characteristics, expectations, and preoperative clitoris pleasure and pain, measured on a 5-point scale. Those patients who returned at 1 year for follow-up were questioned about clitoris pain and functionality. We compared data from the 1-year group with the total group of patients who had surgery. Findings We operated on 2938 women with a mean age of 29·2 (SD 7·77 years; age at excision 6·1, SD 3·5 years). Mali, Senegal, and Ivory Coast were the main countries of origin, but 564 patients had undergone female genital mutilation in France. The 1-year follow-up visit was attended by 866 patients (29%). Expectations before surgery were identity recovery for 2933 patients (99%), improved sex life for 2378 patients (81%), and pain reduction for 847 patients (29%). At 1-year follow-up, 363 women (42%) had a hoodless glans, 239 (28%) had a normal clitoris, 210 (24%) had a visible projection, 51 (6%) had a palpable projection, and three (0·4%) had no change. Most patients reported an improvement, or at least no worsening, in pain (821 of 840 patients) and clitoral pleasure (815 of 834 patients). At 1 year, 430 (51%) of 841 women experienced orgasms. Immediate complications after surgery (haematoma, suture failure, moderate fever) were noted in 155 (5%) of the 2938 patients, and 108 (4%) were briefly re-admitted to hospital. Interpretation Reconstructive surgery after female genital mutilation seems to be associated with reduced pain and restored pleasure. It needs to be made more readily available in developed countries by training surgeons. Funding French Urological Association.


title: LA REPETICIÓN ESCOLAR EN EDUCACIÓN SECUNDARIA Y FACTORES DE RIESGO ASOCIADOS

abstract: The high rate of pupils that repeat a grade at school is a concern for the Spanish educational system. Studies show that the highest percentage of repeaters is among students of the first grade of secondary education, with a higher frequency of males. Secondary School coincides with a stage in the life cycle of transformation, facilitating risk behaviors among adolescents, including grade repetition, drug use and certain aggressive behaviors such as bullying. This study aims to examine the main factors associated with repetition, taking into consideration the socio-demographic characteristics (scope, ownership, gender, origin and age), bullying (bully or victim); risk or protective factors for drug use (family, friends or school context), level of general adjustment/maladjustment (personal, social and scholar); dissatisfaction with the home environment and with siblings. The participants, 1,267 students aged from 11 to 18 (M = 14.41; Dt= 1.427), 49.3% males, completed the sociometric test evaluation of violence among school mates Bull-S, the FRIDA questionnaire (Interpersonal Risk Factors for Adolescent Drug Consumption) and the Self-reported Multifactor Child Adaptation Test –TAMAI–. The results indicate that repeating a grade in school is related to variables such as: title of the school, sex, origin, age, the role of the aggressor in bullying, accessibility, family risk factors (family reaction to drug use, family risk, protection activities) for drug consumption as well as maladjustment to school and dissatisfaction with the family environment. These results demonstrate the need to carry out cooperation actions between families and schools and to make resources available that allow teachers to identify and address risk and prevent repetition.


Ghana’s Middle-Income Reality Check Part I: The Economic Dimension


title: A systematic review of psychosocial factors associated with emotional adjustment in in vitro fertilization patients

abstract: BACKGROUND ::: IVF treatment is usually stressful for patients, but individual differences in emotional response do exist. Differences in the stress response may be related to reproductive outcomes as well as to the development of psychiatric problems. This review collates research exploring which psychosocial factors (e.g. personality traits and coping strategies) are associated with the emotional adjustment of IVF patients. The aim is to reveal what is currently known about risk and protective factors for coping with the stress of IVF treatment and where further enquiry would be most beneficial. ::: ::: ::: METHODS ::: The databases, MEDLINE/PUBMED (US National Library of Medicine), PsycINFO (American Psychological Association), Web of Science (Social Sciences Citation Index) and EMbase, were searched from 1978 to September 2012 using relevant key words. All published peer-reviewed studies exploring associations between psychosocial factors and emotional adjustment outcomes were considered for inclusion. ::: ::: ::: RESULTS ::: There were 23 studies identified for review. One-third of the psychosocial factors explored were found to be significantly related to emotional adjustment outcome measures. Neuroticism and the use of escapist coping strategies were positively associated with distress by multiple studies. Social support was negatively associated with distress by several studies. A number of other psychosocial variables appear to be associated with distress, including self-criticism, dependency, situation appraisals and attachment style, but these have only been explored by one or two studies at most. There is a paucity of research using positive emotional outcome measures (e.g. well-being, positive affect, happiness or life satisfaction) to quantify emotional adjustment. ::: ::: ::: CONCLUSIONS ::: Whilst some psychosocial variables appear to be consistently associated with distress for IVF patients, two-thirds of the variables tested to date do not appear to be associated with emotional adjustment. This review highlights key psychosocial factors to assist the identification of patients at high risk of psychological distress. These findings highlight at least two psychological factors that may be amenable to alteration with psychological or educational interventions. Future work should explore whether experimental manipulation of such psychosocial factors can provide effective stress reduction in this clinical context.


title: Consumer attitudes toward and intentions to accept mobile advertising

abstract: Article history: Received March 25, 2015 Received in revised format June 1 2015 Accepted June 27 2015 Available online July 1 2015 The objective of this study was to examine the drivers of consumers’ attitudes towards mobile advertisement. It also sought the relationship between consumers’ attitudes towards mobile advertisement and their willingness to accept mobile advertising. Confirmatory factor analysis was used to assess the measurement model while structural equation was conducted to assess the goodness-fit of the overall model. The findings indicate that entertainment, credibility and personalization had positive effects on consumers’ attitudes toward mobile advertising. Furthermore, the results show that, consumers’ attitude determines their willingness to accept mobile advertising. Growing Science Ltd. All rights reserved. 5 © 201


title: Integration of ePortfolios in learning management systems

abstract: The LMS plays a decisive role in most eLearning environments. Although they integrate many useful tools for managing eLearning activities, they must also be effectively integrated with other specialized systems typically found in an educational environment such as Repositories of Learning Objects or ePortfolio Systems. Both types of systems evolved separately but in recent years the trend is to combine them, allowing the LMS to benefit from using the ePortfolio assessment features. This paper details the most common strategies for integrating an ePortfolio system into an LMS: the data, the API and the tool integration strategies. It presents a comparative study of strategies based on the technical skills, degree of coupling, security features, batch integration, development effort, status and standardization. This study is validated through the integration of two of the most representative systems on each category - respectively Mahara and Moodle.


title: Fall Detection in Homes of Older Adults Using the Microsoft Kinect

abstract: A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.


How Coarsening Simplifies Matching-Based Causal Inference Theory ∗


title: An innovative algorithm for train detection

abstract: Train detection is a very important research issue affecting vehicles and line safety. Currently, the European Train Control System ETCS (a signalling, control and train protection system) Level 1 and 2 provide the train localization functionalities by using track circuits and/or axle counter systems: the problem of these solutions is represented by the high cost of track circuit and axle counter installation and of the related equipment management. This paper presents an innovative train detection algorithm, able to perform the train localization, by estimating its speed, crossing time instants and axle number. The aim of the proposed solution is to use the same processing approach to evaluate all these quantities, starting from the knowledge of the vertical loads on the sleepers directly measured on the track. The inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. A suitable model of railway vehicle and track has been also developed to test the algorithm when experimental data are not available. The railway vehicle chosen as benchmark is the Manchester Wagon, implemented in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed in order to verify the performance of the proposed algorithm, under different operative conditions. The research has been carried out in cooperation with Ansaldo STS and ECM.


title: Multi-Instance Deep Learning: Discover Discriminative Local Anatomies for Bodypart Recognition

abstract: In general image recognition problems, discriminative information often lies in local image patches. For example, most human identity information exists in the image patches containing human faces. The same situation stays in medical images as well. “Bodypart identity” of a transversal slice—which bodypart the slice comes from—is often indicated by local image information, e.g., a cardiac slice and an aorta arch slice are only differentiated by the mediastinum region. In this work, we design a multi-stage deep learning framework for image classification and apply it on bodypart recognition. Specifically, the proposed framework aims at: 1) discover the local regions that are discriminative and non-informative to the image classification problem, and 2) learn a image-level classifier based on these local regions. We achieve these two tasks by the two stages of learning scheme, respectively. In the pre-train stage, a convolutional neural network (CNN) is learned in a multi-instance learning fashion to extract the most discriminative and and non-informative local patches from the training slices. In the boosting stage, the pre-learned CNN is further boosted by these local patches for image classification. The CNN learned by exploiting the discriminative local appearances becomes more accurate than those learned from global image context. The key hallmark of our method is that it automatically discovers the discriminative and non-informative local patches through multi-instance deep learning. Thus, no manual annotation is required. Our method is validated on a synthetic dataset and a large scale CT dataset. It achieves better performances than state-of-the-art approaches, including the standard deep CNN.


title: Hematometrocolpos in a Pubescent Girl with Abdominal Pain

abstract: Hematometrocolpos is a rare congenital abnormality of the female urogenital system that leadsto an imperforate hymen and subsequent retrograde menstruation. We present the case of a14-year-old female patient who presented to the emergency department with amenorrhea andabdominal pain, and was found to have an imperforate hymen and hematometrocolpos on transabdominalpoint-of-care ultrasound. It is important for emergency physicians to consider thisdiagnosis in pubescent female patients presenting with abdominal pain, as missed diagnosis canlead to infertility and other complications.


title: Optimization of a lens-patch antenna for radar sensor applications

abstract: In this paper we propose a dielectric lens antenna configuration for radar sensor applications in order to overcome the difficulties related to interconnection and feeder losses of antenna array needed to increase the directivity of radar system. Here the directivity is increased by a single curvature dielectric lens collimating the radiation from a single patch. The resulting radiation and matching properties are compared with a patch array, showing better efficiency and bandwidth


title: Performance Analysis of Touch-Interaction Behavior for Active Smartphone Authentication

abstract: The increasing use of touchscreen smartphones to access sensitive and privacy data has given rise to the need of secure and usable authentication technique. Smartphone users have their own unique behavioral characteristics when performing touch operations. These personal characteristics are reflected on different rhythm, strength, and angle preferences of touch-interaction behavior. This paper investigates the reliability and applicability on the usage of users’ touch-interaction behavior for active authentication on smartphones. For each common type of touch operations, both static and dynamic features are extracted and analyzed for fine-grained characterization of users’ touch behavior. Classification techniques (nearest neighbor, neural network, support vector machine, and random forest) are applied to the feature space for performing the task of active authentication. Analyses are conducted using data from around 134 900 touch operations of 71 participants in real-world scenarios, and the authentication performance is evaluated across various types of touch operations, varying operation lengths, different application tasks, and different application scenarios. The extensive experimental results are included to show that touch-interaction behavior exhibits sufficient discriminability and stability among smartphone users for active authentication, and achieves equal-error rates between 1.72% and 9.01% for different types of touch operations with the operation length of 11; the authentication accuracies improve when having long observation or small timespan between the training and testing phases, and express more reliably and stably in a specific task than in the free task. We also discuss a number of avenues for additional research that we believe are necessary to advance the state-of-the-art in this area.


title: Providing Clarity on Big Data Technologies: A Structured Literature Review

abstract: The success of big data projects depends heavily on the ability to decide which technological expertise and architectures are required in such a context. However, there is confusion in scientific discourse regarding the systemization of big data technologies which exacerbates this problem. Therefore, in this paper, a structured literature review is conducted to assess the current state of the art and give an overview about big data technology classifications. It can be stated that only very limited approaches for classifications exist, which are partially incomplete or address only single aspects of big data technologies, such as the used database type. Conducting this literature review, investigations have also been made to derive possible starting points for extending existing or developing new classification approaches, which can form the basis of a future big data classification framework. Such a concept could be used to improve the decision-support for planning and managing big data projects. However, a common understanding would require unambiguous definitions of the term technology and various other expressions, which are used in the literature as a synonym today.


title: Backpropagation Applied to Handwritten Zip Code Recognition

abstract: The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.


title: Machine learning techniques in the education process of students of economics

abstract: Nowadays, in the period of the digitalization and knowledge economy development, majority of activities result in the increase of data that should be captured. In each area of business, there is an increasing urge to extract the knowledge from data in a timely manner in order to be able to make shifts that ensure a competitive advantage. Thus, the knowledge of methods and techniques of big data processing is currently a tool of a modern economist. Courses oriented to how to use machine learning tools in the economic practice should become a part of a study plan at universities of economics and business. In our paper, we propose the concept of teaching of machine learning techniques using software Weka as a possible solution to integrate this issue into the study plan based on the example of problem-based learning.


title: Simultaneous coordinated tuning of PSS and FACTS damping controllers in large power systems

abstract: This work deals with the simultaneous coordinated tuning of the flexible AC transmission systems (FACTS) power oscillation damping controller and the conventional power system stabilizer (PSS) controllers in multi-machine power systems. Using the linearized system model and the parameter-constrained nonlinear optimization algorithm, interactions among FACTS controller and PSS controllers are considered. Furthermore, the parameters of the damping controllers are optimized simultaneously. Simulation results of multi-machine power system validate the efficiency of this approach. The proposed method is effective for the tuning of multi-controllers in large power systems.


title: Long-term retention and overshadowing of proximal and distal cues following habituation in an object exploration task

abstract: The object displacement task is a popular tool used to investigate spatial learning and memory. However, little attention has previously been given to long-term retention of spatial information following habituation to this task. Furthermore, the role of both proximal and distal cues in this type of passive retention of spatial information is controversial. In Study 1, we examined habituation in the object displacement task across 4 days and examined reactivity to spatial change 7 days post-acquisition. We found that rats habituated rapidly to the environment and retained this environment for the 7 days. Furthermore, this experiment shows that both proximal and distal spatial cues are important in the encoding of the environment during object displacement learning task. In Study 2, we examined the effect of overshadowing and demonstrate that proximal visual spatial cues can overshadow distal spatial cues if a conflict arises between both set.


title: Methodological Review: The Technology Acceptance Model: Its past and its future in health care

abstract: Increasing interest in end users' reactions to health information technology (IT) has elevated the importance of theories that predict and explain health IT acceptance and use. This paper reviews the application of one such theory, the Technology Acceptance Model (TAM), to health care. We reviewed 16 data sets analyzed in over 20 studies of clinicians using health IT for patient care. Studies differed greatly in samples and settings, health ITs studied, research models, relationships tested, and construct operationalization. Certain TAM relationships were consistently found to be significant, whereas others were inconsistent. Several key relationships were infrequently assessed. Findings show that TAM predicts a substantial portion of the use or acceptance of health IT, but that the theory may benefit from several additions and modifications. Aside from improved study quality, standardization, and theoretically motivated additions to the model, an important future direction for TAM is to adapt the model specifically to the health care context, using beliefs elicitation methods.


title: PERFORMANCE FUNCTIONS AND REINFORCEMENT LEARNING FOR TRADING SYSTEMS AND PORTFOLIOS

abstract: We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or training via a supervised learning algorithm using labelled trading data, we train our systems using recurrent reinforcement learning (RRL) algorithms. The performance functions that we consider for reinforcement learning are profit or wealth, economic utility, the Sharpe ratio and our proposed differential Sharpe ratio. The trading and portfolio management systems require prior decisions as input in order to properly take into account the effects of transactions costs, market impact, and taxes. This temporal dependence on system state requires the use of reinforcement versions of standard recurrent learning algorithms. We present empirical results in controlled experiments that demonstrate the efficacy of some of our methods for optimizing trading systems and portfolios. For a long/short trader, we find that maximizing the differential Sharpe ratio yields more consistent results than maximizing profits, and that both methods outperform a trading system based on forecasts that minimize MSE. We find that portfolio traders trained to maximize the differential Sharpe ratio achieve better risk-adjusted returns than those trained to maximize profit. Finally, we provide simulation results for an S&P 500/TBill asset allocation system that demonstrate the presence of out-of-sample predictability in the monthly S&P 500 stock index for the 25 year period 1970 through 1994. Copyright © 1998 John Wiley & Sons, Ltd.


title: SAD and SSE implementation for HEVC encoder on DSP TMS320C6678

abstract: High Efficiency Video Coding is the latest video standard aiming to replace H264/AVC standard by improving significantly the coding efficiency and the compression performance which allows HEVC to be mostly suitable for high-definition videos for multimedia applications. However, the encoding process requires a high computational complexity that needs to be alleviated. Hence, the paper proposes a software implementation of HEVC encoder and an optimized architecture on single core DSP TMS320C6678 to perform the rate distortion optimization (RDO) for mode decision procedure. The goal is to use single instruction multiple data (SIMD) operations and data level parallelism in order to optimize the Sum of Absolute Differences (SAD) and Sum Square Error (SSE) engines. The performance of the proposed implementation shows more than 88% improvement in terms of cycle cost for the distortion functions computation and the encoding speed of the proposed optimized HEVC encoder is accelerated by approximately 24% compared to the HEVC reference model (HM12.0) software with slight loss of coding efficiency.


title: Mineable or messy? Assessing the quality of macro-level tourism information derived from social media

abstract: Social media are a primary means for travelers to connect with each other and plan trips. They can also help tourism suppliers (e.g., by providing relevant information), thus overcoming the shortcomings of traditional information sources. User-generated content from social media has already been used in many studies as a primary information source. However, the quality of information derived thus far remains largely unclear. This study assesses the quality of macro-level information on the spatio-temporal distribution of tourism derived from online travel reviews in social media in terms of completeness, timeliness, and accuracy. We found that information quality increased from 2000 to 2009 as online travel reviews increasingly covered more countries, became available earlier than statistics reported by the United Nations World Tourism Organization (UNWTO), were highly correlated with the UNWTO statistics. We conclude that social media are a good information source for macro-level spatio-temporal tourism information and could be used, for example, to estimate tourism figures.


title: Deep permutations: Deep convolutional neural networks and permutation-based indexing

abstract: The activation of the Deep Convolutional Neural Networks hidden layers can be successfully used as features, often referred as Deep Features, in generic visual similarity search tasks.


title: Single-output recurrent neural networks for sentence binary classification

abstract: We report several experiments on using Recurrent Neural Networks (RNNs) for sentence binary classification task. In terms of sentence classification, RNNs have an important advantage compared to well-known traditional machine learning models (e.g. SVM and Maximum Entropy), in which it can naturally take into account neighboring information between contiguous words. In addition, to perform binary classification task, we employed Single-Output RNNs (SORNNs) which only consists of a single output layer located in the last time step. The output layer itself is a vector consisting of two units (since we perform binary classification), in which each unit corresponds to a single label. Our results showed that SORNN achieved better performance than other traditional machine learning models, such as SVM, Maximum Entropy, and Naive Bayes, which have been widely used for sentence classification.


title: A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera

abstract: This paper proposes and validates a real-time on-road vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows.


title: Taking steps: the influence of a walking technique on presence in virtual reality

abstract: This article presents an interactive technique for moving through an immersive virtual environment (or “virtual reality”). The technique is suitable for applications where locomotion is restricted to ground level. The technique is derived from the idea that presence in virtual environments may be enhanced the stronger the match between proprioceptive information from human body movements and sensory feedback from the computer-generated displays. The technique is an attempt to simulate body movements associated with walking. The participant “walks in place” to move through the virtual environment across distances greater than the physical limitations imposed by the electromagnetic tracking devices. A neural network is used to analyze the stream of coordinates from the head-mounted display, to determine whether or not the participant is walking on the spot. Whenever it determines the walking behavior, the participant is moved through virtual space in the direction of his or her gaze. We discuss two experimental studies to assess the impact on presence of this method in comparison to the usual hand-pointing method of navigation in virtual reality. The studies suggest that subjective rating of presence is enhanced by the walking method provided that participants associate subjectively with the virtual body provided in the environment. An application of the technique to climbing steps and ladders is also presented.


title: A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning

abstract: Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others.


title: A Data Mining Approach to the Diagnosis of Tuberculosis by Cascading Clustering and Classification

abstract: In this paper, a methodology for the automated detection and classification of Tuberculosis(TB) is presented. Tuberculosis is a disease caused by mycobacterium which spreads through the air and attacks low immune bodies easily. Our methodology is based on clustering and classification that classifies TB into two categories, Pulmonary Tuberculosis(PTB) and retroviral PTB(RPTB) that is those with Human Immunodeficiency Virus (HIV) infection. Initially K-means clustering is used to group the TB data into two clusters and assigns classes to clusters. Subsequently multiple different classification algorithms are trained on the result set to build the final classifier model based on K-fold cross validation method. This methodology is evaluated using 700 raw TB data obtained from a city hospital. The best obtained accuracy was 98.7% from support vector machine (SVM) compared to other classifiers. The proposed approach helps doctors in their diagnosis decisions and also in their treatment planning procedures for different categories.


title: Querying graph databases

abstract: Graph databases have gained renewed interest in the last years, due to their applications in areas such as the Semantic Web and Social Networks Analysis. We study the problem of querying graph databases, and, in particular, the expressiveness and complexity of evaluation for several general-purpose navigational query languages, such as the regular path queries and its extensions with conjunctions and inverses. We distinguish between two semantics for these languages. The first one, based on simple paths, easily leads to intractability in data complexity, while the second one, based on arbitrary paths, allows tractable evaluation for an expressive family of languages. We also study two recent extensions of these languages that have been motivated by modern applications of graph databases. The first one allows to treat paths as first-class citizens, while the second one permits to express queries that combine the topology of the graph with its underlying data.


title: Acne Scars: Pathogenesis, Classification and Treatment

abstract: Acne has a prevalence of over 90% among adolescents and persists into adulthood in approximately 12%–14% of cases with psychological and social implications. Possible outcomes of the inflammatory acne lesions are acne scars which, although they can be treated in a number of ways, may have a negative psychological impact on social life and relationships. The main types of acne scars are atrophic and hypertrophic scars. The pathogenesis of acne scarring is still not fully understood, but several hypotheses have been proposed. There are numerous treatments: chemical peels, dermabrasion/microdermabrasion, laser treatment, punch techniques, dermal grafting, needling and combined therapies for atrophic scars: silicone gels, intralesional steroid therapy, cryotherapy, and surgery for hypertrophic and keloidal lesions. This paper summarizes acne scar pathogenesis, classification and treatment options.


title: An MOS transistor model for analog circuit design

abstract: This paper presents a physically based model for the metal-oxide-semiconductor (MOS) transistor suitable for analysis and design of analog integrated circuits. Static and dynamic characteristics of the MOS field-effect transistor are accurately described by single-piece functions of two saturation currents in all regions of operation. Simple expressions for the transconductance-to-current ratio, the drain-to-source saturation voltage, and the cutoff frequency in terms of the inversion level are given. The design of a common-source amplifier illustrates the application of the proposed model.