title: Physiological and subjective evaluation of a humanerobot object hand-over task

abstract: In the context of task sharing between a robot companion and its human partners, the notions of safe and compliant hardware are not enough. It is necessary to guarantee ergonomic robot motions. Therefore, we have developed Human Aware Manipulation Planner (Sisbot et al., 2010), a motion planner specifically designed for humanerobot object transfer by explicitly taking into account the legibility, the safety and the physical comfort of robot motions. The main objective of this research was to define precise subjective metrics to assess our planner when a human interacts with a robot in an object hand-over task. A second objective was to obtain quantitative data to evaluate the effect of this interaction. Given the short duration, the“relative ease”of theobjecthand-over taskanditsqualitative component,classicalbehavioral measures basedonaccuracyorreactiontimewereunsuitabletocompareourgestures.Inthisperspective,weselected three measurements based on the galvanic skin conductance response, the deltoid muscle activity and the ocular activity. To test our assumptions and validate our planner, an experimental set-up involving Jido, a mobilemanipulator robot, anda seated humanwas proposed. Forthe purpose of the experiment, we have defined three motions that combine different levels of legibility, safety and physical comfort values. After each robot gesture the participants were asked to rate them on a three dimensional subjective scale. It has appeared that the subjective data were in favor of our reference motion. Eventually the three motions elicited different physiological and ocular responses that could be used to partially discriminate them.


title: Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness

abstract: It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.


title: Publish/Subscribe in a mobile enviroment

abstract: A publish/subscribe system dynamically routes and delivers events from sources to interested users, and is an extremely useful communication service when it is not clear in advance who needs what information. In this paper we discuss how a publish/subscribe system can be extended to operate in a mobile environment, where events can be generated by moving sensors or users, and subscribers can request delivery at handheld and/or mobile devices. We describe how the publish/subscribe system itself can be distributed across multiple (possibly mobile) computers to distribute load, and how the system can be replicated to cope with failures, message loss, and disconnections.


Oil Systems Modeling for an Operators ' Training Combined Cycle Plant Simulator


title: A new intra-disk redundancy scheme for high-reliability RAID storage systems in the presence of unrecoverable errors

abstract: Today's data storage systems are increasingly adopting low-cost disk drives that have higher capacity but lower reliability, leading to more frequent rebuilds and to a higher risk of unrecoverable media errors. We propose an efficient intradisk redundancy scheme to enhance the reliability of RAID systems. This scheme introduces an additional level of redundancy inside each disk, on top of the RAID redundancy across multiple disks. The RAID parity provides protection against disk failures, whereas the proposed scheme aims to protect against media-related unrecoverable errors. In particular, we consider an intradisk redundancy architecture that is based on an interleaved parity-check coding scheme, which incurs only negligible I/O performance degradation. A comparison between this coding scheme and schemes based on traditional Reed--Solomon codes and single-parity-check codes is conducted by analytical means. A new model is developed to capture the effect of correlated unrecoverable sector errors. The probability of an unrecoverable failure associated with these schemes is derived for the new correlated model, as well as for the simpler independent error model. We also derive closed-form expressions for the mean time to data loss of RAID-5 and RAID-6 systems in the presence of unrecoverable errors and disk failures. We then combine these results to characterize the reliability of RAID systems that incorporate the intradisk redundancy scheme. Our results show that in the practical case of correlated errors, the interleaved parity-check scheme provides the same reliability as the optimum, albeit more complex, Reed--Solomon coding scheme. Finally, the I/O and throughput performances are evaluated by means of analysis and event-driven simulation.


title: Design and implementation of college Student Information Management System based on Web Services

abstract: As an example of SIMS (Student Information Management System) developed independently by School of Information Science & Engineering of Shandong Normal University, this paper introduces database design, specific realization of each function module and key technologies used in the system. In the. NET environment, using ASP.NET technology, Visual C # and JavaScript as programming language, this system accesses the database of Microsoft SQL Server 2005 with ADO.NET technology, and could be employed by users with high security following the access control mechanism of RBAC(Role-Based Access Control) on Web Services.


title: Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G

abstract: Vehicle-to-everything (V2X), including vehicle- to-vehicle (V2V), vehicle-to-pedestrian (V2P), vehicle-to-infrastructure (V2I), and vehicle-to-network (V2N) communications, improves road safety, traffic efficiency, and the availability of infotainment services. Standardization of Long Term Evolution (LTE)-based V2X has been actively conducted by the Third Generation Partnership Project (3GPP) to provide solutions for V2X communications, and has benefited from the global deployment and fast commercialization of LTE systems. LTE-based V2X was widely used as LTE-V in the Chinese vehicular communications industry, and LTE-based V2X was redefined as LTE V2X in 3GPP standardization progress. In this article, the overview of requirements and use cases in V2X services in 3GPP is presented. The up-to-date standardization of LTE V2X in 3GPP is surveyed. The challenges and detailed design aspects in LTE V2X are also discussed. Meanwhile, the enhanced V2X (eV2X) services and possible 5G solutions are analyzed. Finally, the implementations of LTE V2X are presented with the latest progress in industrial alliances, research, development of prototypes, and field tests.


title: Iris code matching using adaptive Hamming distance

abstract: The most popular metric distance used in iris code matching is Hamming distance. In this paper, we improve the performance of iris code matching stage by applying adaptive Hamming distance. Proposed method works with Hamming subsets with adaptive length. Based on density of masked bits in the Hamming subset, each subset is able to expand and adjoin to the right or left neighbouring bits. The adaptive behaviour of Hamming subsets increases the accuracy of Hamming distance computation and improves the performance of iris code matching. Results of applying proposed method on Chinese Academy of Science Institute of Automation, CASIA V3.3 shows performance of 99.96% and false rejection rate 0.06.


Performance Comparison of OCR Tools


title: Making AES Great Again: The Forthcoming Vectorized AES Instruction

abstract: The introduction of the processor instructions AES-NI and VPCLMULQDQ, that are designed for speeding up encryption, and their continual performance improvements through processor generations, has significantly reduced the costs of encryption overheads. More and more applications and platforms encrypt all of their data and traffic. As an example, we note the world wide proliferation of the use of AES-GCM, with performance dropping down to 0.64 cycles per byte (from ∼23 before the instructions), on the latest Intel processors. This is close to the theoretically achievable performance with the existing hardware support.


title: Virtual synchronous machine

abstract: Demands in the area of electrical energy generation and distribution, as a result of energy policies, are leading to far reaching changes in the structure of the energy supply, which is characterised, on the one hand, by the substitution of conventional power stations by renewable energy generation, a decision which has already been made, and, on the other hand, by the changeover from centralised to decentralised energy generation. From an electrical engineering point of view, a new situation will arise for consumers concerning security of supply and power quality, which calls for further technical measures by the grid operators to ensure that the increasingly stringent supply criteria can be met. This article describes a new power electronics based approach which allows a grid compatible integration of predominantly renewable electricity generators even in weak grids making them appear to be electromechanical synchronous machines. As a consequence, all the proven properties of this type of machine which have so far defined the grid continue to do so, even when integrating photovoltaic or wind energy. These properties include, for instance, interaction between grid and generator as in a remote power dispatch, reaction to transients as well as the full electrical effects of a rotating mass. In addition, this new development can be operated in such a way that it provides primary reserve allowing, from a grid point of view, electricity generators such as wind and PV to be regarded as conventional power stations.


title: Analysis and design of low power nonlinear PFD architectures for a fast locking PLL

abstract: Two novel nonlinear phase frequency detectors (NL-PFD1 and NL-PFD2) designed with the objective of achieving higher gain, eliminating blind zone, reducing dead zone and area are proposed in this paper. Using pass transistor logic in realizing D-flip-flop has resulted in higher gain, area reduction and higher maximum frequency for NL-PFD2. Reset delay and dead zone are also found to be reduced using simple structure. The necessity of reset action is eliminated in both the NL-PFDs as UP and DN signals are never allowed to reach logic high simultaneously and hence, blind zone is completely eliminated in both the cases. Nonlinear Phase-Voltage characteristics of PFD has been modeled using Fourier series and time response of PLL using state space technique and both have been validated by simulation in MATLAB and circuit simulation in Cadence. PLL built with the NL-PFDs are found to provide a lock time of 1.753 μs and reference spur of −56.7 dBc with 180 nm CMOS process while power dissipation in NL-PFD2 is observed to be only 0.27μW.


SAGH: A Social Analysis tool for GitHub


title: Effect of Robotic-Assisted Three-Dimensional Repetitive Motion to Improve Hand Motor Function and Control in Children With Handwriting Deficits: A Nonrandomized Phase 2 Device Trial

abstract: OBJECTIVE. We explored the efficacy of robotic technology in improving handwriting in children with impaired motor skills. ::: ::: METHOD. Eighteen participants had impairments arising from cerebral palsy (CP), autism spectrum disorder (ASD), attention deficit disorder (ADD), attention deficit hyperactivity disorder (ADHD), or other disorders. The intervention was robotic-guided three-dimensional repetitive motion in 15–20 daily sessions of 25–30 min each over 4–8 wk. ::: ::: RESULTS. Fine motor control improved for the children with learning disabilities and those ages 9 or older but not for those with CP or under age 9. All children with ASD or ADHD referred for slow writing speed were able to increase speed while maintaining legibility. ::: ::: CONCLUSION. Three-dimensional, robot-assisted, repetitive motion training improved handwriting fluidity in children with mild to moderate fine motor deficits associated with ASD or ADHD within 10 hr of training. This dosage may not be sufficient for children with CP


title: The adolescent brain

abstract: Adolescence is a developmental period characterized by suboptimal decisions and actions that give rise to an increased incidence of unintentional injuries and violence, alcohol and drug abuse, unintended pregnancy and sexually transmitted diseases. Traditional neurobiological and cognitive explanations for adolescent behavior have failed to account for the nonlinear changes in behavior observed during adolescence, relative to childhood and adulthood. This review provides a biologically plausible conceptualization of the neural mechanisms underlying these nonlinear changes in behavior, as a heightened responsiveness to incentives while impulse control is still relatively immature during this period. Recent human imaging and animal studies provide a biological basis for this view, suggesting differential development of limbic reward systems relative to top-down control systems during adolescence relative to childhood and adulthood. This developmental pattern may be exacerbated in those adolescents with a predisposition toward risk-taking, increasing the risk for poor outcomes.


title: Automatic Segmentation of Skin Cancer Images using Adaptive Color Clustering

abstract: This paper presents the development of an adaptive image segmentation algorithm designed for the identification of the skin cancer and pigmented lesions in dermoscopy images. The key component of the developed algorithm is the Adaptive Spatial K-Means (A-SKM) clustering technique that is applied to extract the color features from skin cancer images. Adaptive-SKM is a novel technique that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The A-SKM has been included in the development of a flexible color-texture image segmentation scheme and the experimental data indicates that the developed algorithm is able to produce accurate segmentation when applied to a large number of skin cancer (melanoma) images.


title: Clover: Compiler Directed Lightweight Soft Error Resilience

abstract: This paper presents Clover, a compiler directed soft error detection and recovery scheme for lightweight soft error resilience. The compiler carefully generates soft error tolerant code based on idempotent processing without explicit checkpoint. During program execution, Clover relies on a small number of acoustic wave detectors deployed in the processor to identify soft errors by sensing the wave made by a particle strike. To cope with DUE (detected unrecoverable errors) caused by the sensing latency of error detection, Clover leverages a novel selective instruction duplication technique called tail-DMR (dual modular redundancy). Once a soft error is detected by either the sensor or the tail-DMR, Clover takes care of the error as in the case of exception handling. To recover from the error, Clover simply redirects program control to the beginning of the code region where the error is detected. The experiment results demonstrate that the average runtime overhead is only 26%, which is a 75% reduction compared to that of the state-of-the-art soft error resilience technique.


title: RankPL: A Qualitative Probabilistic Programming Language

abstract: In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn’s ranking theory. Broadly speaking, RankPL can be used to represent and reason about processes that exhibit uncertainty expressible by distinguishing “normal” from “surprising” events. RankPL allows (iterated) revision of rankings over alternative program states and supports various types of reasoning, including abduction and causal inference. We present the language, its denotational semantics, and a number of practical examples. We also discuss an implementation of RankPL that is available for download.


title: Modula-2 and Oberon

abstract: This is an account of the development of the languages Modula-2 and Oberon. Together with their ancestors ALGOL 60 and Pascal they form a family called Algol-like languages. Pascal (1970) reflected the ideas of structured programming, Modula-2 (1979) added those of modular system design, and Oberon (1988) catered to the object-oriented style. Thus they mirror the essential programming paradigms of the past decades. Here the major language properties are outlined, followed by an account of the respective implementation efforts. The conditions and the environments in which the languages were created are elucidated. We point out that simplicity of design was the most essential guiding principle. Clarity of concepts, economy of features, efficiency and reliability of implementations were its consequences.


title: 3-D SAR Tomography: The Multibaseline Sector Interpolation Approach

abstract: Multibaseline (MB) synthetic aperture radar (SAR) tomography is a promising mode of SAR interferometry, allowing full 3-D imaging of volumetric and layover scatterers in place of a single elevation estimation capability for each SAR cell However, Fourier-based MB SAR tomography is generally affected by unsatisfactory imaging quality due to a typically low number of baselines with irregular distribution. In this paper, we improve the basic elevation focusing technique by reconstructing a set of uniform baselines data exploiting in the interpolation step the ancillary information about the extension of a height sector which contains all the scatterers. This a priori information can be derived from the knowledge of the kind of the observed scenario (e.g., forest or urban). To demonstrate the concept, an imaging enhancement analysis is carried out by simulation.


title: The laboratory test utilization management toolbox

abstract: Efficiently managing laboratory test utilization requires both ensuring adequate utilization of needed tests in some patients and discouraging superfluous tests in other patients. After the difficult clinical decision is made to define the patients that do and do not need a test, a wealth of interventions are available to the clinician and laboratorian to help guide appropriate utilization. These interventions are collectively referred to here as the utilization management toolbox. Experience has shown that some tools in the toolbox are weak and other are strong, and that tools are most effective when many are used simultaneously. While the outcomes of utilization management studies are not always as concrete as may be desired, what data is available in the literature indicate that strong utilization management interventions are safe and effective measures to improve patient health and reduce waste in an era of increasing financial pressure.


title: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space

abstract: We propose an effective color image denoising method that exploits filtering in highly sparse local 3D transform domain in each channel of a luminance-chrominance color space. For each image block in each channel, a 3D array is formed by stacking together blocks similar to it, a process that we call "grouping". The high similarity between grouped blocks in each 3D array enables a highly sparse representation of the true signal in a 3D transform domain and thus a subsequent shrinkage of the transform spectra results in effective noise attenuation. The peculiarity of the proposed method is the application of a "grouping constraint" on the chrominances by reusing exactly the same grouping as for the luminance. The results demonstrate the effectiveness of the proposed grouping constraint and show that the developed denoising algorithm achieves state-of-the-art performance in terms of both peak signal-to-noise ratio and visual quality.


title: Continual Learning with Deep Generative Replay

abstract: Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks.


title: Self-Transcendence and Well-Being in Homeless Adults

abstract: This study examines the relationships of spiritually and physically related variables to well-being among homeless adults. A convenience sample of 61 sheltered homeless persons completed the Spirit...


title: Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

abstract: Personalization technologies offer powerful tools for enhancing the user experience in a wide variety of systems, but at the same time raise new privacy concerns. For example, systems that personalize advertisements according to the physical location of the user or according to the user's friends' search history, introduce new privacy risks that may discourage wide adoption of personalization technologies. This article analyzes the privacy risks associated with several current and prominent personalization trends, namely social-based personalization, behavioral profiling, and location-based personalization. We survey user attitudes towards privacy and personalization, as well as technologies that can help reduce privacy risks. We conclude with a discussion that frames risks and technical solutions in the intersection between personalization and privacy, as well as areas for further investigation. This frameworks can help designers and researchers to contextualize privacy challenges of solutions when designing personalization systems.


title: Object-Oriented Programming Versus Abstract Data Types

abstract: This tutorial collects and elaborates arguments for distinguishing between object-oriented programming and abstract data types. The basic distinction is that object-oriented programming achieves data abstraction by the use of procedural abstraction, while abstract data types depend upon type abstraction. Object-oriented programming and abstract data types can also be viewed as complimentary implementation techniques: objects are centered around the constructors of a data abstraction, while abstract data types are organized around the operations. These differences have consequences relating to extensibility, efficiency, typing, and verification; in many cases the strengths of one paradigm are the weaknesses of the other. Most object-oriented programming languages support aspects of both techniques, not a unification of them, so an understanding of their relative merits is useful in designing programs.


title: Subhallucal Interphalangeal Sesamoiditis: A Rare Cause of Chronic Great Toe Pain

abstract: Subhallucal interphalangeal sesamoiditis presenting as chronic great toe pain is a rarely reported clinical entity, being often overlooked and misdiagnosed for other pathologies. By altering the biomechanics of the movements at great toe, the interphalangeal sesamoid is prone to various painful pathologies like trauma, infection, degeneration, osteonecrosis and inflammation. Imaging plays an important role in narrowing down the differentials and guiding for appropriate therapy. Herein, we present a neglected case of hallucal interphalangeal sesamoiditis presenting as a case of chronic great toe pain and discuss the role of Magnetic resonance imaging (MRI) and Computed tomography (CT) scan in its diagnosis.


GAVIANI G.: Context Awareness in Mobile Cultural Heritage Applications


title: Author Identification using Stylometry

abstract: “Every person is unique”, we have been hearing this since ages. Every person has a unique identity, a unique fingerprint, a unique retina and a lot more. These features playa vital role in identification of individuals for security purposes. Unfortunately, when it comes to security of written pieces or words from an individual, these primary unique identities are futile. One cannot identify a writer from a written piece of text on the basis of retina or fingerprint scans, sometimes even the signature can be forged, in such situations for security purposes and intellectual property rights it becomes very important to identify the true author. Stylometry plays an important role in this. Every author has a unique style of writing, measure of this style of writing is called Stylometry. This paper proposes to identify authors from text based on their style of writing. First a data set consisting of articles, short stories and emails will be used to train the system for multiple authors, then a random text would be given to the system to identify the author correctly, if the author predicted by the system is similar to the author claimed then the information is authentic otherwise the author claiming to be the writer is a fraud. For stylometry, over the ages, many features have been focused on, but this paper proposes new features to be used for this purpose. While writing, there are many unconscious styles that are incorporated by the author, these features have been unnoticed till date, but can playa vital role in accurate and fast identification of authors. These features include: ‘intellectual property right’, ‘chapter length’ and frequency of particular words per thousand words. The algorithms used to train the system can be Decision tree, Naive Bayesian or Multilayer Perceptron.


title: Data Burst Statistics and Performance Analysis of Optical Burst Switching Networks with Self-Similar Traffic

abstract: The self-similar model can reflect the Internet traffic property more precisely than Poisson model, the theoretical and simulation results of burst length distribution based on the time threshold assembly mechanism are shown in this paper. Moreover, the simulation result of OBS schedule algorithm under self-similar traffic model is reported. Both the analytic and simulation results show that burst length distribution is related to the length of assembly period; secondly, the assembly mechanism based on time period can reduce the self-similarity of traffic effectively; at last, the schedule algorithms in OBS have different performance based on the self-similar traffic.


Switching Kalman filters


title: Deep Read: A Reading Comprehension System

abstract: This paper describes initial work on Deep Read, an automated reading comprehension system that accepts arbitrary text input (a story) and answers questions about it. We have acquired a corpus of 60 development and 60 test stories of 3rd to 6th grade material; each story is followed by short-answer questions (an answer key was also provided). We used these to construct and evaluate a baseline system that uses pattern matching (bag-of-words) techniques augmented with additional automated linguistic processing (stemming, name identification, semantic class identification, and pronoun resolution). This simple system retrieves the sentence containing the answer 30--40% of the time.


On the clasping organs attaching the hind to the fore wings in Hymenoptera


title: How Does Not Responding to Appetitive Stimuli Cause Devaluation: Evaluative Conditioning or Response Inhibition?

abstract: In a series of 6 experiments (5 preregistered), we examined how not responding to appetitive stimuli causes devaluation. To examine this question, a go/no-go task was employed in which appetitive stimuli were consistently associated with cues to respond (go stimuli), or with cues to not respond (either no-go cues or the absence of cues; no-go stimuli). Change in evaluations of no-go stimuli was compared to change in evaluations of both go stimuli and of stimuli not presented in the task (untrained stimuli). Experiments 1 to 3 show that not responding to appetitive stimuli in a go/no-go task causes devaluation of these stimuli regardless of the presence of an explicit no-go cue. Experiments 4a and 4b show that the devaluation effect of appetitive stimuli is contingent on the percentage of no-go trials; devaluation appears when no-go trials are rare, but disappears when no-go trials are frequent. Experiment 5 shows that simply observing the go/no-go task does not lead to devaluation. Experiment 6 shows that not responding to neutral stimuli does not cause devaluation. Together, these results suggest that devaluation of appetitive stimuli by not responding to them is the result of response inhibition. By employing both go stimuli and untrained stimuli as baselines, alternative explanations are ruled out, and apparent inconsistencies in the literature are resolved. These experiments provide new theoretical insight into the relation between not responding and evaluation, and can be applied to design motor response training procedures aimed at changing people's behavior toward appetitive stimuli.


MetisIDX - From Adaptive to Predictive Data Indexing


title: A Global Perspective on Child Sexual Abuse: Meta-Analysis of Prevalence Around the World

abstract: Our comprehensive meta-analysis combined prevalence figures of childhood sexual abuse (CSA) reported in 217 publications published between 1980 and 2008, including 331 independent samples with a total of 9,911,748 participants. The overall estimated CSA prevalence was 127/1000 in self-report studies and 4/1000 in informant studies. Self-reported CSA was more common among female (180/1000) than among male participants (76/1000). Lowest rates for both girls (113/1000) and boys (41/1000) were found in Asia, and highest rates were found for girls in Australia (215/1000) and for boys in Africa (193/1000). The results of our meta-analysis confirm that CSA is a global problem of considerable extent, but also show that methodological issues drastically influence the self-reported prevalence of CSA.


title: Sex-role concepts and sex typing in childhood as a function of school and home environments.

abstract: The study compared sex-role attitudes and sex-typed reactions of children from "traditional" middle-class schools and homes (stressing socialization toward general standards) with those of children from "modern" middleclass schools and homes (stressing individualized development). Subjects were 105 fourth-grade nine-year-olds. Direct and projective techniques were used to test the hypothesis that conventional attitudes and reactivity would be more characteristic of children from traditional backgrounds. Findings support the hypothesis: unequivocal commitment to own sex role, sex-typed play, aggressive expression in boys, and family orientation in girls were more consistently characteristic of children from traditional backgrounds. Girls from modern backgrounds departed most from conventional expectations. This century has seen a broadening in the range of prevailing attitudes toward social sex roles for men and women and sex-appropriate behavior for boys and girls. Increasingly flexible attitudes have grown up and now


title: Testicular and Epididymal Characteristcs of Rabbit Bucks Fed Diets Containing Cottonseed Cake Supplemented with Carrot (Daucus carota) or Ginger (Zingiber officinale Roscoe)

abstract: This experiment was conducted to investigate the reproductive characteristics of rabbit bucks fed cottonseed cake-based diets supplemented with carrot or ginger. Twenty four (24) cross bred (New Zealand White X Chinchilla) 6-8 weeks old rabbit bucks were involved in the study. The bucks were balanced for weight and allocated to four dietary treatments T1, T2, T3 and T4. Treatment T1 (control) contained soyabean meal (SBM) as the main plant protein source denoted as (SBM or 0% CSC). T2 had 100% SBM replaced with cottonseed cake (CSC), while T3 and T4 had T2 supplemented with carrot and ginger respectively. Except for testicular width, testicular characteristics were not significantly (P>0.05) affected. Testicular width decreased from 2.20cm in the control (T1) to 1.63cm in T2 (100% CSC). Mean epididymal weight and mean epididymal length were significantly (P<0.05) affected with mean epididymal length decreasing from 13.72cm in the control to 8.32cm in T2 (100% CSC). Supplementing T2 with either carrot or ginger increased the epididymal characteristics significantly (P<0.05). It could be concluded from this study that CSC with or without carrot or ginger supplementation may not have adverse effect on testicular characteristics. However CSC depressed epididymal characteristics which were corrected by carrot or ginger supplementation with ginger proving more effective. Keywords : Rabbit buck, Reproductive characteristics, Cottonseed cake, Carrot and Ginger.


title: Optimizing the Soft Tissue Triangle, Alar Margin Furrow, and Alar Ridge Aesthetics: Analysis and Use of the Articulate Alar Rim Graft

abstract: The alar lobule, alar margin, and soft triangle facet are receiving more attention in the literature as critical elements to address both preoperatively and during rhinoplasty. We have found that the use of the articulated alar rim graft (AARG) corrects deficiencies in these areas as well as provides mechanical stability to the external valve. In this article, we describe indications for AARG, describe in detail the procedure for AARG placement, and highlight the transformation AARGs can achieve in two illustrated case studies.


title: A framework for detecting public health trends with Twitter

abstract: Traditional public health surveillance requires regular clinical reports and considerable effort by health professionals to analyze data. Therefore, a low cost alternative is of great practical use. As a platform used by over 500 million users worldwide to publish their ideas about many topics, including health conditions, Twitter provides researchers the freshest source of public health conditions on a global scale. We propose a framework for tracking public health condition trends via Twitter. The basic idea is to use frequent term sets from highly purified health-related tweets as queries into a Wikipedia article index -- treating the retrieval of medically-related articles as an indicator of a health-related condition. By observing fluctuations in frequent term sets and in turn medically-related articles over a series of time slices of tweets, we detect shifts in public health conditions and concerns over time. Compared to existing approaches, our framework provides a general a priori identification of emerging public health conditions rather than a specific illness (e.g., influenza) as is commonly done.


title: Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

abstract: Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. Most existing AU detection works often treat face alignment as a preprocessing and handle the two tasks independently. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared features are learned firstly, and high-level features of face alignment are fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment features and global features for AU detection. Experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for AU detection.


Towards an Understanding of Stakeholders and Dependencies in the EU GDPR


title: Andromeda: Accurate and scalable security analysis of web applications

abstract: Security auditing of industry-scale software systems mandates automation. Static taint analysis enables deep and exhaustive tracking of suspicious data flows for detection of potential leakage and integrity violations, such as cross-site scripting (XSS), SQL injection (SQLi) and log forging. Research in this area has taken two directions: program slicing and type systems. Both of these approaches suffer from a high rate of false findings, which limits the usability of analysis tools based on these techniques. Attempts to reduce the number of false findings have resulted in analyses that are either (i) unsound, suffering from the dual problem of false negatives, or (ii) too expensive due to their high precision, thereby failing to scale to real-world applications. ::: ::: In this paper, we investigate a novel approach for enabling precise yet scalable static taint analysis. The key observation informing our approach is that taint analysis is a demand-driven problem, which enables lazy computation of vulnerable information flows, instead of eagerly computing a complete data-flow solution, which is the reason for the traditional dichotomy between scalability and precision. We have implemented our approach in Andromeda, an analysis tool that computes data-flow propagations on demand, in an efficient and accurate manner, and additionally features incremental analysis capabilities. Andromeda is currently in use in a commercial product. It supports applications written in Java, .NET and JavaScript. Our extensive evaluation of Andromeda on a suite of 16 production-level benchmarks shows Andromeda to achieve high accuracy and compare favorably to a state-of-the-art tool that trades soundness for precision.


title: Semantic Role Chunking Combining Complementary Syntactic Views

abstract: This paper describes a semantic role labeling system that uses features derived from different syntactic views, and combines them within a phrase-based chunking paradigm. For an input sentence, syntactic constituent structure parses are generated by a Charniak parser and a Collins parser. Semantic role labels are assigned to the constituents of each parse using Support Vector Machine classifiers. The resulting semantic role labels are converted to an IOB representation. These IOB representations are used as additional features, along with flat syntactic chunks, by a chunking SVM classifier that produces the final SRL output. This strategy for combining features from three different syntactic views gives a significant improvement in performance over roles produced by using any one of the syntactic views individually.


title: Mid-arm lymph nodes dissection for melanoma.

abstract: An interval node in the upper limb termed the mid-arm node was recently identified. However, its surgical anatomy remains unclear. We report a patient with metastatic melanoma of the mid-arm node and the epitrochlear node at 10 years after removal of the primary tumour from the forearm and therapeutic axillary lymph node dissection. The mid-arm node is located halfway up the upper arm on the medial intermuscular septum, at the site where the brachial vessels, the median nerve and the ulnar nerve run adjacent to each other. The mid-arm node lies adjacent to the basilic vein where lymphatic vessels ascend and converge. This is the first report regarding the surgical anatomy of the mid-arm node.


title: The optimization of Radial Line Slot Antenna

abstract: A Single-Layered Radial Line Slot Antenna (CL-RLSA) is a efficient planar array antenna for receiving direct broadcast from a satellite(DBS). By arranging the angle and modifying the width and length of slots, this paper proposes a new optimizing single-layered RSLA which can suppress grating lobes, ameliorate return loss and increase the gain. And the article uses a low dielectric constant material, relative permittivity of 1.06. The results of experiment demonstrate the validity of the optimization.


title: Adding noise to the input of a model trained with a regularized objective

abstract: Regularization is a well studied problem in the context of neural networks. It is usually used to improve the generalization performance when the number of input samples is relatively small or heavily contaminated with noise. The regularization of a parametric model can be achieved in different manners some of which are early stopping (Morgan and Bourlard, 1990), weight decay, output smoothing that are used to avoid overfitting during the training of the considered model. From a Bayesian point of view, many regularization techniques correspond to imposing certain prior distributions on model parameters (Krogh and Hertz, 1991). Using Bishop's approximation (Bishop, 1995) of the objective function when a restricted type of noise is added to the input of a parametric function, we derive the higher order terms of the Taylor expansion and analyze the coefficients of the regularization terms induced by the noisy input. In particular we study the effect of penalizing the Hessian of the mapping function with respect to the input in terms of generalization performance. We also show how we can control independently this coefficient by explicitly penalizing the Jacobian of the mapping function on corrupted inputs.


Design-to-Life-Cycle-Cost in Aerospace


title: A 2.4 GHz Hybrid Polyphase Filter Based BFSK Receiver With High Frequency Offset Tolerance for Wireless Sensor Networks

abstract: A low power 2.4 GHz hybrid polyphase filter (PPF) based BFSK receiver with high frequency offset tolerance (FOT) at small modulation indexes (MIs) is presented for medium data rate wireless sensor network applications. A high FOT at low MI is achieved by a frequency-to-energy conversion architecture using PPFs without any frequency correction circuits. Channel selection and interference rejection are performed simultaneously by the PPFs without any extra hardware and power consumption. Furthermore, the proposed hybrid topology of the PPFs provides an improved adjacent channel rejection (ACR) at reduced power. The prototype receiver fabricated in a 0.13-µm CMOS process, including the RF and analog front-ends, consumes 1.97 mW from a 1 V supply. With a data rate of 1 Mb/s, a sensitivity of –84 dBm, a FOT of ${\pm} $ 450 kHz ( ${\pm} $ 180 ppm), and an ACR of 40 dB are achieved for a MI of 2.


title: QUANTITATIVE METHODS IN PSYCHOLOGY A Power

abstract: Abstract One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.


title: Single stage multi product kanban system. optimization and parametric analysis

abstract: In this work a Single-Stage Multi-Product system managed using kanban board has been analyzed. Specifically, the aim is to build experimental curves representing the influence of specific production parameters of a manufacturing plant on designing a Supermarket Pull Kanban, in order to obtain a valuable tool to support strategic decisions. The approach chosen for the representation and the analysis of such system was the simulation on Siman - Arena environment, while OptQuest has been used for the next optimization phase.


A Crow or a Blackbird?: Using True Social Network and Tweeting Behavior to Detect Malicious Entities in Twitter


title: PyMC: Bayesian stochastic modelling in Python

abstract: This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.


title: An Access Control scheme for Big Data processing

abstract: Access Control (AC) systems are among the most critical of network security components. A system's privacy and security controls are more likely to be compromised due to the misconfiguration of access control policies rather than the failure of cryptographic primitives or protocols. This problem becomes increasingly severe as software systems become more and more complex, such as Big Data (BD) processing systems, which are deployed to manage a large amount of sensitive information and resources organized into a sophisticated BD processing cluster. Basically, BD access control requires the collaboration among cooperating processing domains to be protected as computing environments that consist of computing units under distributed AC managements. Many BD architecture designs were proposed to address BD challenges; however, most of them were focused on the processing capabilities of the “three Vs” (Velocity, Volume, and Variety). Considerations for security in protecting BD are mostly ad hoc and patch efforts. Even with some inclusion of security in recent BD systems, a critical security component, AC (Authorization), for protecting BD processing components and their users from the insider attacks, remains elusive. This paper proposes a general purpose AC scheme for distributed BD processing clusters.


title: Connective tissue graft to correct peri-implant soft tissue margin: A clinical report

abstract: This clinical report describes the use of a subepithelial connective tissue graft to recontour a soft tissue margin discrepancy for a single-implant crown in the anterior maxilla. This procedure demonstrates that the use of soft tissue grafts to correct an esthetic deficiency may be a feasible approach to establish new and stable peri-implant soft tissue contours. The patient presented was followed for 18 months.


title: Solar PV powered SRM driven water pumping system using Landsman converter

abstract: This work is primarily focused on the design and development of an efficient and cost effective solar photovoltaic generator (PVG) based water pumping system implying a switched reluctance motor (SRM) drive. The maximum extraction of available power from PVG is attained by introducing an incremental conductance (IC) maximum power point tracking (MPPT) controller with Landsman DC-DC converter as a power conditioning stage. The CCM (continuous conduction mode) operation of Landsman DC-DC converter helps to reduce the current and voltage stress on its components and to realize the DC-DC conversion ratio independent of the load. The efficient utilization of SPV array and limiting the high initial inrush current in the motor drive is the primary concern of a Landsman converter. The inclusion of start-up control algorithm in the motor drive system facilitates the smooth self-starting of an 8/6 SRM drive. A novel approach to regulate the speed of the motor-pump system by controlling the DC link voltage of split capacitors converter helps in eliminating the voltage or current sensors required for speed control of SRM drive. The electronic commutated operation of mid-point converter considerably reduces its switching losses. This topology is designed and modeled in Matlab/Simulink platform and a laboratory prototype is developed to validate its performance under varying environmental conditions.


title: Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)

abstract: Bibliometrics are important evaluation tools to the scientific production. A bibliometric study was used to evaluate researches about credit risk and bankruptcy. Credit support technique studies on economic and social orders are relevant in this knowledge field. Therefore, the aim of the current study is to identify and describe the application of multivariate data analysis techniques to credit risk and bankruptcy scenarios. The herein presented data were collected in publications indexed to Thomson Reuters' Web of Science database between 1968 and 2014. The results corroborate information in the literature and in previous bibliometric reviews, as well as highlight other indications regarding the construction and development of research fields. Since the 1990's, the neural networks became relevant due to their increased use as study object in publications. However, both the discriminant analysis (J Finance 23(4):589---609, 1968. doi:10.2307/2978933) and the logistic regression (J Account Res 18(1):109---131, 1980. doi:10.2307/2490395) are still often used in researches, fact that shows the tendency to find articles using more than one technique or hybrid models, artificial intelligence techniques and complex computer systems. This field appears to be multidisciplinary in journals and Web Science databases involving the business and economy, operational research, management, mathematics, data processing, engineering and statistics fields. Another relevant discovery was the increased number of publications about this subject launched right after the 2008 crisis.


title: Large-scale Interactive Recommendation with Tree-structured Policy Gradient

abstract: Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.


title: Comparison of implicit and explicit feedback from an online music recommendation service

abstract: Explicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user preference model presents a number of challenges but can also overcome the problems associated with each other. In order to build an effective RS on combination of both types of feedback, we need to have comparative data allowing an understanding of the computation of user preferences. In this paper, we provide an overview of the differentiating characteristics of explicit and implicit feedback using datasets mined from Last.fm, an online music station and recommender service. The datasets consisted of explicit positive feedback (by loving tracks) and implicit feedback which is inherently positive (the number of times a track is played). Rather than relying on just one type of feedback, we present techniques for extracting user preferences from both. In order to compare and contrast the performances of these techniques, we carried out experiments using the Taste recommender system engine and the Last.fm datasets. Our results show that implicit and explicit positive feedback complements each other, with similar performances despite their different characteristics.


title: Simulation of Fractional-Order Low-Pass Filters

abstract: The attenuation of standard analog low-pass filters corresponds to a multiple value of decibels per octave. This quantified value is related to the order of the filter. The issue addressed here is concerned with the extension of integer orders to non integer orders, such that the attenuation of a low-pass filter can be continuously adjusted. Fractional differential systems are known to provide such asymptotic behaviors and many results about their simulation are available. But even for a fixed cutoff frequency, their combination does not generate an additive group with respect to the order and they involve stability problems. In this paper, a class of low-pass filters with orders between 0 (the filter is a unit gain) and 1 (standard one-pole filter) is defined to restore these properties. These infinite dimensional filters are not fractional differential but admit some well-posed representations into weighted integrals of standard one-pole filters. Based on this, finite dimensional approximations are proposed and recast into the framework of state-space representations. A special care is given to reduce the computational complexity, through the dimension of the state. In practice, this objective is reached for the complete family, without damaging the perceptive quality, with dimension 13. Then, an accurate low-cost digital version of this family is built in the time-domain. The accuracy of the digital filters is verified on the complete range of parameters (cutoff frequencies and fractional orders). Moreover, the stability is guaranteed, even for time-varying parameters. As an application, a plugin has been implemented which provides a new audio tool for tuning the cutoff frequency and the asymptotic slope in a continuous way. As a very special application, choosing a one-half order combined with a low cutoff frequency (20 Hz or less), the filter fed with a white noise provides a pink noise generator.


title: Unsupervised Perceptual Rewards for Imitation Learning

abstract: Reward function design and exploration time are arguably the biggest obstacles to the deployment of reinforcement learning (RL) agents in the real world. In many real-world tasks, designing a suitable reward function takes considerable manual engineering and often requires additional and potentially visible sensors to be installed just to measure whether the task has been executed successfully. Furthermore, many interesting tasks consist of multiple steps that must be executed in sequence. Even when the final outcome can be measured, it does not necessarily provide useful feedback on these implicit intermediate steps or sub-goals. ::: To address these issues, we propose leveraging the abstraction power of intermediate visual representations learned by deep models to quickly infer perceptual reward functions from small numbers of demonstrations. We present a method that is able to identify the key intermediate steps of a task from only a handful of demonstration sequences, and automatically identify the most discriminative features for identifying these steps. This method makes use of the features in a pre-trained deep model, but does not require any explicit sub-goal supervision. The resulting reward functions, which are dense and smooth, can then be used by an RL agent to learn to perform the task in real-world settings. To evaluate the learned reward functions, we present qualitative results on two real-world tasks and a quantitative evaluation against a human-designed reward function. We also demonstrate that our method can be used to learn a complex real-world door opening skill using a real robot, even when the demonstration used for reward learning is provided by a human using their own hand. To our knowledge, these are the first results showing that complex robotic manipulation skills can be learned directly and without supervised labels from a video of a human performing the task.


title: Framing as a Theory of Media Effects.

abstract: Research on framing is characterized by theoretical and empirical vagueness. This is due, in part, to the lack of a commonly shared theoretical model underlying framing research. Conceptual problems translate into operational problems, limiting the comparability of instruments and results. In this paper I systematize the fragmented approaches to framing in political communication and integrate them into a comprehensive model. I classify previous approaches to framing research along two dimensions: the type of frame examined (media frames vs. audience frames) and the way frames are operationalized (independent variable or dependent variable). I develop a process model of framing, identifying four key processes that should be addressed in future research: frame building, frame setting, individual-level processes of framing, and a feedback loop from audiences to journalists.


title: Simulation of Impulse Voltage Generator and Impulse Testing of Insulator using MATLAB Simulink

abstract: The main objectives of this work are of two folds: the first is the design of impulse voltage gen- erator and impulse testing of a standard disc type insulator experimentally. The second is the development of MATLAB Simulink model of the above mentioned experimental setup. Investigation shows that this soft- ware is very efficient in studying the effect of parameter changes in the design to obtain the desired impulse voltages and wave shapes from an impulse voltage generator for high voltage applications.


title: Practical Incentive Mechanisms for IoT-Based Mobile Crowdsensing Systems

abstract: Powerful smart terminals with rich set of embedded sensors promote the development of the Internet of Things (IoTs). Mobile crowdsensing systems (MCSs) can be formed by these mobile smart terminals from IoTs to collect and exchange data. The main idea of MCSs is to outsource sensing tasks (collecting data) to various mobile devices which are carried by people or vehicles. The design of incentive mechanisms in MCSs is one of the hottest current research topics. However, most of the existing studies focus on maximizing the utilities or social welfare while neglecting the practical requirements of MCSs surveillance applications. In this paper, we discuss the importance of fairness and unconsciousness of MCS surveillance applications. Then, we propose offline and online incentive mechanisms with fair task scheduling based on the proportional share allocation rules . Furthermore, to have more sensing tasks done over time dimension, we relax the truthfulness and unconsciousness property requirements and design a ( $\varepsilon $ , $\mu $ )-unconsciousness online incentive mechanism. Real map data are used to validate these proposed incentive mechanisms through extensive simulations.


title: Leukocyte telomere length in mastocytosis: Correlations with depression and perceived stress

abstract: BACKGROUND ::: Mastocytosisis a rare disease associated with chronic symptoms related to mast cell mediator release. Patients with mastocytosis display high level of negative emotionality such as depression and stress sensibility. Brain mast cells are mainly localized in the diencephalon, which is linked to emotion regulatory systems. Negative emotionality has been shown to be associated with telomere shortening. Taken together these observations led us to hypothesize that mast cells activity could be involved in both negative emotionality and telomere shortening in mastocytosis. ::: ::: ::: OBJECTIVE ::: To demonstrate a possible relationship between negative emotionality in mastocytosis and leukocytes telomere length. ::: ::: ::: METHODS ::: Leukocyte telomere length and telomerase activity were measured among mastocytosis patients and were correlated with perceived stress and depression assessed by the Beck Depression Inventory revised and the Perceived Stress Scale. ::: ::: ::: RESULTS ::: Mild-severe depression scores were frequent (78.9%) as well as high perceived stress (42.11%). Telomere length was correlated to perceived stress (r=0.77; p=0.0001) but not to depression in our population. Patients displaying Wild-type KIT significantly presented higher perceived stress levels. Patients with the D816VC KIT mutation who had high perceived stress scores displayed significantly shorter telomere but not if they had high depression scores. ::: ::: ::: CONCLUSION ::: These findings suggest that high perceived stress in mastocytosis could accelerate the rate of leukocytes telomere shortening. Since mastocytosis is, by definition, a mast cell mediated disease; these cells could be involved in this phenomenon. Mechanistic causal relationships between these parameters need to be investigated.


title: Hypermedia-driven restful service composition

abstract: Representational State Transfer (REST) services are gaining momentum as a lightweight approach for the provision of services on the Web. Unlike WSDL-based services, in REST the set of operations is reduced, standardized, with well known semantics, and changes the resource's state. Few attempts have been proposed to support composition models for REST, they are mainly operation-centric and fail to acknowledge the hypermedia nature of REST, that is, clients must inspect the served resource state and choose the link to follow from there. We explore RESTful service composition as it is driven by the hypermedia net that is dynamically created while a client interacts with a server resulting in a light-weight approach. We based our proposal on a hypermedia-centric REST service description, the Resource Linking Language (ReLL) and Petri Nets as a mechanism for describing the machine-client navigation.


title: Determinants of credit card delinquency and bankruptcy: Macroeconomic factors

abstract: In this paper, we examine how county unemployment rates affect consumers' delinquency and bankruptcy behavior by focusing on the credit card market. In particular, after controlling for credit supply and shocks like divorce and health coverage we investigate whether consumer propensity for delinquency and bankruptcy changes with respect to the macroeconomic fluctuations across counties. Our results show that county unemployment rates significantly influence delinquency. Copyright Academy of Economics and Finance 2003


title: News Credibility Evaluation on Microblog with a Hierarchical Propagation Model

abstract: Benefiting from its openness, collaboration and real-time features, Micro blog has become one of the most important news communication media in modern society. However, it is also filled with fake news. Without verification, such information could spread promptly through social network and result in serious consequences. To evaluate news credibility on Micro blog, we propose a hierarchical propagation model. We detect sub-events within a news event to describe its detailed aspects. Thus, for a news event, a three-layer credibility network consisting of event, sub-events and messages can represent it from different scale and reveal vital information for credibility evaluation. After linking these entities with their semantic and social associations, the credibility value of each entity is propagated on this network to achieve the final evaluation result. By formulating this propagation process as a graph optimization problem, we provide a globally optimal solution with an iterative algorithm. Experiments conducted on two real-world datasets show that the proposed model boosts the accuracy by more than 6% and the F-score by more than 16% over a baseline method.


title: Towards Dynamically Crafted Affective Experiences Through Emotional Response Player Modelling

abstract: Designing adaptive games for individual emotional experiences is an elaborate task due to the necessary dedicated hardware, complex signal processing and unique emotional bonds each player may forge with the game. The research work I propose focuses on developing a novel affective interaction scheme where players’ emotional reactions are learned by a computational model that leverages this information to influence their future emotional states. To this end I aim at interpreting the causality relations between physiologically measured emotional response variations and their eliciting events. I then plan using these reactions to build each player’s individual emotional reaction model, which will guide the game engine’s logic on how to plan future game events, in order to elicit a target affective experience. Ultimately, I expect this technique to allow game designers to focus on defining high-level rules for generating their envisioned affective gameplay experience, rather than in manually tuning it in a repetitive and time-consuming fashion.


title: Experiments and analysis of lcc’s two qa systems over trec2004

abstract: In 2004, LCC has participated to TREC QA with two systems; PowerAnswer 2 and PALANTIR. The goal was to study the contribution of new features such as: (1) an improved passage retrieval module based on the Lucene IR system, (2) multiple QA strategies with voting across strategies, (3) the use of text semantic relations, (4) a new method for extracting lexical chains, (5) new logic prover reasoning strategies, (6) anaphora and ellipsis resolution, (7) indexing based on named entities, (8) question similarity analysis, and others. The paper presents the architectures of these two systems, an error analysis, and summary of results.


title: Collaborative Augmented Reality in Education: A Review

abstract: Globalization and innovation in technology have led to the extensive use of the latest technology in almost every sector, and education is no exception. Different technologies have been employed in various disciplines within the educational sector. Studies have shown that technology can enhance teaching and learning experiences. Augmented Reality (AR) is a new technology with vast potentials and great pedagogical value that offers new methods for education. AR enables the overlaying of computer-generated virtual information into the real environment in real time. Thus, researchers believed that the AR has provided new opportunities for designing engaging learning environments. Although the AR may improve educational outcomes, the main factor is to understand the process of designing the AR to support learning activities. Thus, various instructional strategies such as collaborative learning, were considered when designing an AR learning environment. Collaborative learning permits students to engage with other students and the educational content at the same time, resulting in a deeper understanding and higher motivation. Because educational research concerning collaborative AR is still in its infancy, this paper intends to review the literatures concerning collaborative AR, its previous usages and its potential in educational context.


title: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.

abstract: Abstract Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754 ± 0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836 ± 0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779 ± 0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations.


title: Manifold alignment using Procrustes analysis

abstract: In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semi-supervised alignment" in that it results in a mapping that is defined everywhere - when used with a suitable dimensionality reduction method - rather than just on the training data points. We describe and evaluate our approach both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our method including cross-lingual information retrieval and transfer learning in Markov decision processes are presented.


title: Neonatal Abstinence Syndrome and High School Performance

abstract: BACKGROUND AND OBJECTIVES: Little is known of the long-term, including school, outcomes of children diagnosed with Neonatal abstinence syndrome (NAS) ( International Statistical Classification of Disease and Related Problems [10th Edition], Australian Modification, P96.1). METHODS: Linked analysis of health and curriculum-based test data for all children born in the state of New South Wales (NSW), Australia, between 2000 and 2006. Children with NAS ( n = 2234) were compared with a control group matched for gestation, socioeconomic status, and gender ( n = 4330, control) and with other NSW children ( n = 598 265, population) for results on the National Assessment Program: Literacy and Numeracy, in grades 3, 5, and 7. RESULTS: Mean test scores (range 0–1000) for children with NAS were significantly lower in grade 3 (359 vs control: 410 vs population: 421). The deficit was progressive. By grade 7, children with NAS scored lower than other children in grade 5. The risk of not meeting minimum standards was independently associated with NAS (adjusted odds ratio [aOR], 2.5; 95% confidence interval [CI], 2.2–2.7), indigenous status (aOR, 2.2; 95% CI, 2.2–2.3), male gender (aOR, 1.3; 95% CI, 1.3–1.4), and low parental education (aOR, 1.5; 95% CI, 1.1–1.6), with all P s CONCLUSIONS: A neonatal diagnostic code of NAS is strongly associated with poor and deteriorating school performance. Parental education may decrease the risk of failure. Children with NAS and their families must be identified early and provided with support to minimize the consequences of poor educational outcomes.


title: On reproducibility and traceability of simulations

abstract: Reproducibility of experiments is the pillar of a rigorous scientific approach. However, simulation-based experiments often fail to meet this fundamental requirement. In this paper, we first revisit the definition of reproducibility in the context of simulation. Then, we give a comprehensive review of issues that make this highly desirable feature so difficult to obtain. Given that experimental (in-silico) science is only one of the many applications of simulation, our analysis also explores the needs and benefits of providing the simulation reproducibility property for other kinds of applications. Coming back to scientific applications, we give a few examples of solutions proposed for solving the above issues. Finally, going one step beyond reproducibility, we also discuss in our conclusion the notion of traceability and its potential use in order to improve the simulation methodology.


title: Solar energy empowered 5G cognitive metro-cellular networks

abstract: Harvesting energy from natural (solar, wind, vibration, etc.) and synthesized (microwave power transfer) sources is envisioned as a key enabler for realizing green wireless networks. Energy efficient scheduling is one of the prime objectives in emerging cognitive radio platforms. To that end, in this article we present a comprehensive framework to characterize the performance of a cognitive metro-cellular network empowered by solar energy harvesting. The proposed model allows designers to capture both the spatial and temporal dynamics of the energy field and the mobile user traffic. A new definition for the “energy outage probability” metric, which characterizes the self-sustainable operation of the base stations under energy harvesting, is proposed, and the process for quantifying is described with the help of a case study for various UK cities. It is shown that the energy outage probability is strongly coupled with the path-loss exponent, required quality of service, and base station and user density. Moreover, the energy outage probability varies both on a daily and yearly basis depending on the solar geometry. It is observed that even in winter, BSs can run for three to six hours without any purchase of energy from the power grid by harvesting instantaneous energy.


title: Neuroinflammation in Alzheimer's disease

abstract: Summary Increasing evidence suggests that Alzheimer's disease pathogenesis is not restricted to the neuronal compartment, but includes strong interactions with immunological mechanisms in the brain. Misfolded and aggregated proteins bind to pattern recognition receptors on microglia and astroglia, and trigger an innate immune response characterised by release of inflammatory mediators, which contribute to disease progression and severity. Genome-wide analysis suggests that several genes that increase the risk for sporadic Alzheimer's disease encode factors that regulate glial clearance of misfolded proteins and the inflammatory reaction. External factors, including systemic inflammation and obesity, are likely to interfere with immunological processes of the brain and further promote disease progression. Modulation of risk factors and targeting of these immune mechanisms could lead to future therapeutic or preventive strategies for Alzheimer's disease.


title: Organizational factors to support knowledge management and innovation

abstract: Purpose – The purpose of this paper is to analyze how organizational factors such as cultural values, leadership and human resource (HR) practices influence knowledge exploration and exploitation practices and innovation via an empirical study. Design/methodology/approach – From the knowledge-based view of the firm, six hypotheses were established and statistically tested in a sample of 111 Spanish companies belonging to innovative industries. Survey methodology was used with the aim of gathering data regarding knowledge management (KM) practices and certain, related organizational aspects in firms. Findings – This paper provides evidence of a moderating effect of knowledge-centered culture, knowledge-oriented leadership and knowledge-centered HR practices in the relationship between knowledge exploration and exploitation practices and innovation outcomes of companies. In line with previous literature, it is suggested that although KM practices are important on their own for innovation purposes, when certain enablers – organizational factors to overcome human barriers to KM – are properly established, the innovation capacity of the firm can be more successfully exploited. Research limitations/implications – The research is limited to high rate innovation industries. Future studies will include other industries and a more diverse sample of firms. Practical implications – The results of this study suggest that managers should place attention on knowledge exploration and exploitation practices along with several organizational enablers in order to achieve high levels of innovation results for the company. Originality/value – This paper provides new empirical evidence on the relationships between KM, organizational elements such as culture, leadership, HR practices, and innovation in a large sample of firms. To date, the empirical research of these relations has been mainly limited to descriptive case studies and there is thus a lack of empirical evidence with large samples of firms.


title: Analysing the Semantic Change Based on Word Embedding

abstract: This paper intend to present an approach to analyse the change of word meaning based on word embedding, which is a more general method to quantize words than before. Through analysing the similar words and clustering in different period, semantic change could be detected. We analysed the trend of semantic change through density clustering method called DBSCAN. Statics and data visualization is also included to make the result more clear. Some words like ‘gay’, ‘mouse’ are traced as case to prove this approach works. At last, we also compared the context words and similar words on semantic presentation and proved the context words worked better.


title: Cultural variations in emotions: a review.

abstract: The psychological and anthropological literature on cultural variations in emotions is reviewed. The literature has been interpreted within the framework of a cognitive-process model of emotions. Both cross-cultural differences and similarities were identified in each phase of the emotion process; similarities in 1 phase do not necessarily imply similarities in other phases. Whether cross-cultural differences or similarities are found depends to an important degree on the level of description of the emotional phenomena. Cultural differences in emotions appear to be due to differences in event types or schemas, in culture-specific appraisal propensities, in behavior repertoires, or in regulation processes. Differences in taxonomies of emotion words sometimes reflect true emotion differences like those just mentioned, but they may also just result from differences in which emotion-process phase serves as the basis for categorization.


title: Variational Recurrent Neural Machine Translation

abstract: Partially inspired by successful applications of variational recurrent neural networks, we propose a novel variational recurrent neural machine translation (VRNMT) model in this paper. Different from the variational NMT, VRNMT introduces a series of latent random variables to model the translation procedure of a sentence in a generative way, instead of a single latent variable. Specifically, the latent random variables are included into the hidden states of the NMT decoder with elements from the variational autoencoder. In this way, these variables are recurrently generated, which enables them to further capture strong and complex dependencies among the output translations at different timesteps. In order to deal with the challenges in performing efficient posterior inference and large-scale training during the incorporation of latent variables, we build a neural posterior approximator, and equip it with a reparameterization technique to estimate the variational lower bound. Experiments on Chinese-English and English-German translation tasks demonstrate that the proposed model achieves significant improvements over both the conventional and variational NMT models.


title: Profiting from technological innovation : Implications for integration , collaboration , licensing and public policy

abstract: AbstractThis paper attempts to explain why innovating firms often fail to obtain significant economic returns from an innovation, while customers, imitators and other industry participants benefit. Business strategy — particularly as it relates to the firm's decision to integrate and collaborate — is shown to be an important factor. The paper demonstrates that when imitation is easy, markets don't work well, and the profits from innovation may accrue to the owners of certain complementary assets, rather than to the developers of the intellectual property. This speaks to the need, in certain cases, for the innovating firm to establish a prior position in these complementary assets. The paper also indicates that innovators with new products and processes which provide value to consumers may sometimes be so ill positioned in the market that they necessarily will fail. The analysis provides a theoretical foundation for the proposition that manufacturing often matters, particularly to innovating nations. Innovating firms without the requisite manufacturing and related capacities may die, even though they are the best at innovation. Implications for trade policy and domestic economic policy are examined.


title: Decision tree and instance-based learning for label ranking

abstract: The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.


Spatial-semantics : how users derive shape from information space


title: Optimisation of composite boat hulls using first principles and design rules

abstract: The design process is becoming increasingly complex with designers balancing societal, environmental and political issues. Composite materials are attractive to designers due to excellent strength to weight ratio, low corrosion and ability to be tailored to the application. One problem with composite materials can be the low stiffness that they exhibit and as such for many applications they are stiffened. These stiffened structures create a complex engineering problem by which they must be designed to have the lowest cost and mass and yet withstand loads. This paper therefore examines the way in which rapid assessment of stiffened boat structures can be performed for the concept design stage. Navier grillage method is combined with genetic algorithms to produce panels optimised for mass and cost. These models are constrained using design rules, in this case ISO 12215 and Lloyd's Register Rules for Special Service Craft. The results show a method that produces a reasonable stiffened structure rapidly that could be used in advanced concept design or early detailed design to reduce design time.


title: A comprehensive review of knowledge mapping techniques

abstract: Knowledge mapping is one of the most popular techniques used to identify knowledge in organizations. Using knowledge mapping techniques; a large and complex set of knowledge resources can be acquired and navigated more easily. Knowledge mapping has attracted the senior managers' attention as an assessment tool in recent years and is expected to measure deep conceptual understanding and allow experts in organizations to characterize relationships between concepts within a domain visually. Here the very critical issue is how to identify and choose an appropriate knowledge mapping technique. This paper aims to explore the different types of knowledge mapping techniques and give a general idea of their target contexts to have the way for choosing the appropriate map. It attempts to illustrate which techniques are appropriate, why and where they can be applied, and how these mapping techniques can be managed. The paper is based on the comprehensive review of papers on knowledge mapping techniques. In addition, this paper attempts to further clarify the differences among these knowledge mapping techniques and the main purpose for using each. Eventually, it is recommended that experts must understand the purpose for which the map is being developed before proceeding to activities related to any knowledge management dimensions; in order to the appropriate knowledge mapping technique.


title: Beyond AMT: An Analysis of Crowd Work Platforms

abstract: While Amazon's Mechanical Turk (AMT) helped launch the paid crowd work industry eight years ago, many new vendors now offer a range of alternative models. Despite this, little crowd work research has explored other platforms. Such near-exclusive focus risks letting AMT's particular vagaries and limitations overly shape our understanding of crowd work and the research questions and directions being pursued. To address this, we present a cross-platform content analysis of seven crowd work platforms. We begin by reviewing how AMT assumptions and limitations have influenced prior research. Next, we formulate key criteria for characterizing and differentiating crowd work platforms. Our analysis of platforms contrasts them with AMT, informing both methodology of use and directions for future research. Our cross-platform analysis represents the only such study by researchers for researchers, intended to further enrich the diversity of research on crowd work and accelerate progress.


title: DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction

abstract: As urban crimes (e.g., burglary and robbery) negatively impact our everyday life and must be addressed in a timely manner, predicting crime occurrences is of great importance for public safety and urban sustainability. However, existing methods do not fully explore dynamic crime patterns as factors underlying crimes may change over time. In this paper, we develop a new crime prediction framework--DeepCrime, a deep neural network architecture that uncovers dynamic crime patterns and carefully explores the evolving inter-dependencies between crimes and other ubiquitous data in urban space. Furthermore, our DeepCrime framework is capable of automatically capturing the relevance of crime occurrences across different time periods. In particular, our DeepCrime framework enables predicting crime occurrences of different categories in each region of a city by i) jointly embedding all spatial, temporal, and categorical signals into hidden representation vectors, and ii) capturing crime dynamics with an attentive hierarchical recurrent network. Extensive experiments on real-world datasets demonstrate the superiority of our framework over many competitive baselines across various settings.


title: Learning with Random Learning Rates

abstract: Hyperparameter tuning is a bothersome step in the training of deep learning models. One of the most sensitive hyperparameters is the learning rate of the gradient descent. We present the 'All Learning Rates At Once' (Alrao) optimization method for neural networks: each unit or feature in the network gets its own learning rate sampled from a random distribution spanning several orders of magnitude. This comes at practically no computational cost. Perhaps surprisingly, stochastic gradient descent (SGD) with Alrao performs close to SGD with an optimally tuned learning rate, for various architectures and problems. Alrao could save time when testing deep learning models: a range of models could be quickly assessed with Alrao, and the most promising models could then be trained more extensively. This text comes with a PyTorch implementation of the method, which can be plugged on an existing PyTorch model: https://github.com/leonardblier/alrao.


The Impact of Computer Animation Learning Toward Students Academic Performance


title: Introduction to Artificial Neural Network (ANN) Methods: What They Are and How to Use Them*.

abstract: Basic concepts of ANNs together with three most widely used ANN learning strategies (error back-propagation, Kohonen, and counterpropagation) are explained and discussed. In order to show how the explained methods can be applied to chemical problems, one simple example, the classification and the prediction of the origin of different olive oil samples, each represented by eigtht fatty acid concentrations, is worked out in detail.


title: Tiered Forensic Methodology Model for Digital Field Triage by Non-Digital Evidence Specialists

abstract: Due to budgetary constraints and the high level of training required, digital forensic analysts are in short supply in police forces the world over. This inevitably leads to a prolonged time taken between an investigator sending the digital evidence for analysis and receiving the analytical report back. In an attempt to expedite this procedure, various process models have been created to place the forensic analyst in the field conducting a triage of the digital evidence. By conducting triage in the field, an investigator is able to act upon pertinent information quicker, while waiting on the full report.The work presented as part of this paper focuses on the training of front-line personnel in the field triage process, without the need of a forensic analyst attending the scene. The premise has been successfully implemented within regular/non-digital forensics, i.e., crime scene investigation. In that field, front-line members have been trained in specific tasks to supplement the trained specialists. The concept of front-line members conducting triage of digital evidence in the field is achieved through the development of a new process model providing guidance to these members. To prove the model's viability, an implementation of this new process model is presented and evaluated. The results outlined demonstrate how a tiered response involving digital evidence specialists and non-specialists can better deal with the increasing number of investigations involving digital evidence.


title: Changing Fashion Cultures

abstract: The paper presents a novel concept that analyzes and visualizes worldwide fashion trends. Our goal is to reveal cutting-edge fashion trends without displaying an ordinary fashion style. To achieve the fashion-based analysis, we created a new fashion culture database (FCDB), which consists of 76 million geo-tagged images in 16 cosmopolitan cities. By grasping a fashion trend of mixed fashion styles,the paper also proposes an unsupervised fashion trend descriptor (FTD) using a fashion descriptor, a codeword vetor, and temporal analysis. To unveil fashion trends in the FCDB, the temporal analysis in FTD effectively emphasizes consecutive features between two different times. In experiments, we clearly show the analysis of fashion trends and fashion-based city similarity. As the result of large-scale data collection and an unsupervised analyzer, the proposed approach achieves world-level fashion visualization in a time series. The code, model, and FCDB will be publicly available after the construction of the project page.


title: Practical Issues In Automatic Documentation Generation

abstract: PLANDoc, a system under joint development by Columbia and Bellcore, documents the activity of planning engineers as they study telephone routes. It takes as input a trace of the engineer's interaction with a network planning tool and produces 1--2 page summary. In this paper, we describe the user needs analysis we performed and how it influenced the development of PLANDoc. In particular, we show how it pinpointed the need for a sublanguage specification, allowing us to identify input messages and to characterize the different sentence paraphrases for realizing them. We focus on the systematic use of conjunction in combination with paraphrase that we developed for PLANDoc, which allows for the generation of summaries that are both concise-avoiding repetition of similar information, and fluent-avoiding repetition of similar phrasing.


title: Autonomous Evolution of Topographic Regularities in Artificial Neural Networks

abstract: Looking to nature as inspiration, for at least the past 25 years, researchers in the field of neuroevolution (NE) have developed evolutionary algorithms designed specifically to evolve artificial neural networks (ANNs). Yet the ANNs evolved through NE algorithms lack the distinctive characteristics of biological brains, perhaps explaining why NE is not yet a mainstream subject of neural computation. Motivated by this gap, this letter shows that when geometry is introduced to evolved ANNs through the hypercube-based neuroevolution of augmenting topologies algorithm, they begin to acquire characteristics that indeed are reminiscent of biological brains. That is, if the neurons in evolved ANNs are situated at locations in space (i.e., if they are given coordinates), then, as experiments in evolving checkers-playing ANNs in this letter show, topographic maps with symmetries and regularities can evolve spontaneously. The ability to evolve such maps is shown in this letter to provide an important advantage in generalization. In fact, the evolved maps are sufficiently informative that their analysis yields the novel insight that the geometry of the connectivity patterns of more general players is significantly smoother and more contiguous than less general ones. Thus, the results reveal a correlation between generality and smoothness in connectivity patterns. They also hint at the intriguing possibility that as NE matures as a field, its algorithms can evolve ANNs of increasing relevance to those who study neural computation in general.


On the SPARQL Direct Semantics Entailment Regime for OWL 2 QL


title: Fraud Type and Auditor Litigation: An Analysis of SEC Accounting and Auditing Enforcement Releases

abstract: This study examines whether certain types of financial reporting fraud result in a higher likelihood of litigation against independent auditors. We expect that auditors are more likely to be judged responsible for failing to detect commonly occurring frauds or those that stem from fictitious transactions. We examine companies with SEC Accounting and Auditing Enforcement Releases and designate whether each fraud present in their financial statements is common and/or arises from fictitious transactions. We then examine whether these types of fraud are related to auditor litigation in analyses that control for various client, auditor and case characteristics. Our results provide some support for our two primary hypotheses-auditors are more likely to be sued when the financial statement frauds are of a common variety or when the frauds arise from fictitious transactions.


title: Morphine or Oxycodone for Cancer-Related Pain? A Randomized, Open-Label, Controlled Trial

abstract: CONTEXT ::: There is wide interindividual variation in response to morphine for cancer-related pain; 30% of patients do not have a good therapeutic outcome. Alternative opioids such as oxycodone are increasingly being used, and opioid switching has become common clinical practice. ::: ::: ::: OBJECTIVES ::: To compare clinical response to oral morphine vs. oral oxycodone when used as first-line or second-line (after switching) treatment in patients with cancer-related pain. ::: ::: ::: METHODS ::: In this prospective, open-label, randomized, controlled trial (ISRCTN65155201) with a selected crossover phase, patients with cancer-related pain were randomized to receive either oral morphine or oxycodone as first-line treatment. Dose was individually titrated until the patient reported adequate pain control. Patients who did not respond to the first-line opioid (either because of inadequate analgesia or unacceptable adverse effects) were switched to the alternative opioid. ::: ::: ::: RESULTS ::: Two hundred patients were recruited. On intention-to-treat analysis (n = 198, morphine 98, oxycodone 100), there was no significant difference between the numbers of patients responding to morphine (61/98 = 62%) or oxycodone (67/100 = 67%) when used as a first-line opioid. Similarly, there was no significant difference in subsequent response when patients were switched to either morphine (8/12 = 67%) or oxycodone (11/21 = 52%). Per-protocol analysis demonstrated a 95% response rate when both opioids were available. There was no difference in adverse reaction scores between morphine and oxycodone either in first-line responders or nonresponders. ::: ::: ::: CONCLUSION ::: In this population, there was no difference between analgesic response or adverse reactions to oral morphine and oxycodone when used as a first- or second-line opioid. These data provide evidence to support opioid switching to improve outcomes.


title: Efficient Object Identification with Passive RFID Tags

abstract: Radio frequency identification systems with passive tags are powerful tools for object identification. However, if multiple tags are to be identified simultaneously, messages from the tags can collide and cancel each other out. Therefore, multiple read cycles have to be performed in order to achieve a high recognition rate. For a typical stochastic anti-collision scheme, we show how to determine the optimal number of read cycles to perform under a given assurance level determining the acceptable rate of missed tags. This yields an efficient procedure for object identification. We also present results on the performance of an implementation.


Social Media : Marketing Public Relations ’ New Best


title: Convergence of translation memory and statistical machine translation

abstract: We present two methods that merge ideas from statistical machine translation (SMT) and translation memories (TM). We use a TM to retrieve matches for source segments, and replace the mismatched parts with instructions to an SMT system to fill in the gap. We show that for fuzzy matches of over 70%, one method outperforms both SMT and TM baselines.