title: Bandwidth enhancement using periodic LHM Star Fractal Antenna

abstract: This paper represents the design and analysis of Star Fractal Antenna meta-material antennas. They are designed on a defected finite ground. It forms a inductive and capacitive circuit. It has left hand characteristics of meta-material. The fractal concept helps in providing multiband from 5.65GHz to 17.50 GHz with return loss better than −10dB. It resulted in desired gain and many resonant frequency. HFSS v 13 is used for the simulation. This paper presents antennas with high gain and multiband applications.


title: Effects of Narcissism, Leisure Boredom, and Gratifications Sought on User-Generated Content Among Net-Generation Users

abstract: This research identifies the gratifications sought by the net-generation when producing user-generated content (UGC) on the internet. Members of the Net-generation want to vent negative feelings, show affection to their frieds and relatives, be involved in others' lives, and fulfill their need to be recognized. These gratifications were all found to be significantly associated with the users' various levels of participation in UGC (e.g., Facebook, YouTube, blogs, online forums, etc.) What's more, narcissism was predictive of content generation in social networking sites, blogs, and personal webpage, while leisure boredom was significantly linked to expressing views in forums, updating personal website, and participating in consumer reviews. In particular, the results showed the Net-geners who encountered leisure boredom had a higher tendency to seek interaction with friends online. Implications of findings are discussed.


title: SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction

abstract: Keyphrase extraction is a fundamental technique in natural language processing. It enables documents to be mapped to a concise set of phrases that can be used for indexing, clustering, ontology building, auto-tagging and other information organization schemes. Two major families of unsupervised keyphrase extraction algorithms may be characterized as statistical and graph-based. We present a hybrid statistical-graphical algorithm that capitalizes on the heuristics of both families of algorithms and is able to outperform the state of the art in unsupervised keyphrase extraction on several datasets.


title: Exploratory font selection using crowdsourced attributes

abstract: This paper presents interfaces for exploring large collections of fonts for design tasks. Existing interfaces typically list fonts in a long, alphabetically-sorted menu that can be challenging and frustrating to explore. We instead propose three interfaces for font selection. First, we organize fonts using high-level descriptive attributes, such as "dramatic" or "legible." Second, we organize fonts in a tree-based hierarchical menu based on perceptual similarity. Third, we display fonts that are most similar to a user's currently-selected font. These tools are complementary; a user may search for "graceful" fonts, select a reasonable one, and then refine the results from a list of fonts similar to the selection. To enable these tools, we use crowdsourcing to gather font attribute data, and then train models to predict attribute values for new fonts. We use attributes to help learn a font similarity metric using crowdsourced comparisons. We evaluate the interfaces against a conventional list interface and find that our interfaces are preferred to the baseline. Our interfaces also produce better results in two real-world tasks: finding the nearest match to a target font, and font selection for graphic designs.


title: English Language Learners and Academic Achievement: Revisiting the Threshold Hypothesis.

abstract: This nonexperimental study explored the predictive strength of English proficiency levels on academic achievement of middle school students in a sample of 17,470 native English-speaking (NES) students, 558 English language learners (current ELLs), and 500 redesignated fluent English proficient students (former ELLs). Results of multilevel analyses indicated that after controlling for relevant student- and school-level characteristics, former ELLs significantly outperformed current ELL and NES students in reading (effect sizes: 1.07 and 0.52) and mathematics (effect sizes: 0.86 and 0.42). The results support Cummins's (1979, 2000) lower level threshold hypothesis predicting that upon reaching adequate proficiency in the language of schooling and testing, ELLs would no longer experience academic disadvantages. Refinements for the theory and directions for future research are discussed.


title: Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

abstract: Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine , a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.


title: Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

abstract: Accurate remaining useful life (RUL) prediction and state-of-health (SOH) diagnosis are of extreme importance for safety, durability, and cost of energy storage systems based on lithium-ion batteries. It is also a crucial challenge for energy storage systems to predict RUL and diagnose SOH of batteries due to the complicated aging mechanism. In this paper, a novel method for battery RUL prediction and SOH estimation is proposed. First, a novel support vector regression-based battery SOH state-space model is established to simulate the battery aging mechanism, which takes the capacity as the state variable and takes the representative features during a constant-current and constant-voltage protocol as the input variables. The estimated impedance variables are taken as the output due to the correlation between battery capacity and the sum of charge transfer resistance and electrolyte resistance. Second, in order to suppress the measurement noises of current and voltage, a particle filter is employed to estimate the impedance degradation parameters. Furthermore, experiments are conducted to validate the proposed method. The results show that the proposed SOH estimation method can provide an accurate and robustness result. The proposed RUL prediction framework can also ensure an accurate RUL prediction result.


title: A Survey of Random Forest Based Methods for Intrusion Detection Systems

abstract: Over the past decades, researchers have been proposing different Intrusion Detection approaches to deal with the increasing number and complexity of threats for computer systems. In this context, Random Forest models have been providing a notable performance on their applications in the realm of the behaviour-based Intrusion Detection Systems. Specificities of the Random Forest model are used to provide classification, feature selection, and proximity metrics. This work provides a comprehensive review of the general basic concepts related to Intrusion Detection Systems, including taxonomies, attacks, data collection, modelling, evaluation metrics, and commonly used methods. It also provides a survey of Random Forest based methods applied in this context, considering the particularities involved in these models. Finally, some open questions and challenges are posed combined with possible directions to deal with them, which may guide future works on the area.


title: Weakly Supervised Semantic Segmentation Based on Co-segmentation

abstract: Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of pixel-level masks, which involves a large amount of human labour and time for annotation. In contrast, image-level labels are much easier to obtain. In this work, we propose a novel method for weakly supervised semantic segmentation with only image-level labels. The method relies on a large scale co-segmentation framework that can produce object masks for a group of images containing objects belonging to the same semantic class. We first retrieve images from search engines, e.g. Flickr and Google, using semantic class names as queries, e.g. class names in PASCAL VOC 2012. We then use high quality masks produced by co-segmentation on the retrieved images as well as the target dataset images with image level labels to train segmentation networks. We obtain IoU 56.9 on test set of PASCAL VOC 2012, which reaches state of the art performance.


title: Four Perspectives on Human Bias in Visual Analytics

abstract: Visual analytic systems, especially mixed-initiative systems, can steer analytical models and adapt views by making inferences from users’ behavioral patterns with the system. Because such systems rely on incorporating implicit and explicit user feedback, they are particularly susceptible to the injection and propagation of human biases. To ultimately guard against the potentially negative effects of systems biased by human users, we must first qualify what we mean by the term bias. Thus, in this chapter we describe four different perspectives on human bias that are particularly relevant to visual analytics. We discuss the interplay of human and computer system biases, particularly their roles in mixed-initiative systems. Given that the term bias is used to describe several different concepts, our goal is to facilitate a common language in research and development efforts by encouraging researchers to mindfully choose the perspective(s) considered in their work.


title: An automatic irrigation system using ZigBee in wireless sensor network

abstract: Wireless Sensing Technology is widely used everywhere in the current scientific world. As the technology is growing and changing rapidly, Wireless sensing Network (WSN) helps to upgrade the technology. In the research field of wireless sensor networks the power efficient time is a major issue. This problem can be overcome by using the ZigBee technology. The main idea of this is to understand how data travels through a wireless medium transmission using wireless sensor network and monitoring system. This paper design an irrigation system which is automated by using controllable parameter such as temperature, soil moisture and air humidity because they are the important factors to be controlled in PA(Precision Agricultures).


title: Vehicle license plate detection and recognition using non-blind image de-blurring algorithm

abstract: This paper proposes the method of vehicle license plate recognition, which is essential in the field of intelligent transportation system. The purpose of the study is to present a simple and effective vehicle license plate detection and recognition using non-bling image de-blurring algorithm. The sharpness of the edges in an image is restored by the prior information on images. The blue kernel is free of noise while using the non-blind image de-blurring algorithm. This non-blind image de-blurring (NBID) algorithm is involved in the process of removing the optimization difficulties with respect to unknown image and unknown blur. Estimation is carried out for the length of the motion kernel with Radon transform in Fourier domain. The proposed algorithm was tested on different vehicle images and achieved satisfactory results in license plate detection. The experimental results deal with the efficiency and robustness of the proposed algorithm in both synthesized and real images.


title: Mechanisms for the generation of synapse specificity in long-term memory: the implications of a requirement for transcription.

abstract: Since long-term memory (LTM) depends on transcription, signals required for LTM must emanate from the cell body. In some cases, signals from the cell body are not only required, but are also sufficient for LTM. However, It is difficult to reconcile this finding with the need to modify synapses independently. To retain synapse specificity during LTM, a form of memory is required that is independent of transcription, and that produces a synaptic 'mark' which interacts with molecules synthesized in the cell body. To reconcile the sufficiency of transcriptional signals for LTM with the need for synapse specificity, I propose that not all LTM is synapse-specific; activation of transcription presynaptically results in cell-wide LTM, whereas postsynaptic transcriptional activation leads to synapse-specific LTM.


title: Passenger-based predictive modeling of airline no-show rates

abstract: Airlines routinely overbook flights based on the expectation that some fraction of booked passengers will not show for each flight. Accurate forecasts of the expected number of no-shows for each flight can increase airline revenue by reducing the number of spoiled seats (empty seats that might otherwise have been sold) and the number of involuntary denied boardings at the departure gate. Conventional no-show forecasting methods typically average the no-show rates of historically similar flights, without the use of passenger-specific information.We develop two classes of models to predict cabin-level no-show rates using specific information on the individual passengers booked on each flight. The first of these models computes the no-show probability for each passenger, using both the cabin-level historical forecast and the extracted passenger features as explanatory variables. This passenger-level model is implemented using three different predictive methods: a C4.5 decision-tree, a segmented Naive Bayes algorithm, and a new aggregation method for an ensemble of probabilistic models. The second cabin-level model is formulated using the desired cabin-level no-show rate as the response variable. Inputs to this model include the predicted cabin-level no-show rates derived from the various passenger-level models, as well as simple statistics of the features of the cabin passenger population. The cabin-level model is implemented using either linear regression, or as a direct probability model with explicit incorporation of the cabin-level no-show rates derived from the passenger-level model outputs.The new passenger-based models are compared to a conventional historical model, using train and evaluation data sets taken from over 1 million passenger name records. Standard metrics such as lift curves and mean-square cabin-level errors establish the improved accuracy of the passenger-based models over the historical model. All models are also evaluated using a simple revenue model, and it is shown that the cabin-level passenger-based model can produce between 0.4% and 3.2% revenue gain over the conventional model, depending on the revenue-model parameters.


title: Analysing Semantic Flow in Academic Writing

abstract: Many approaches have been proposed for providing feedback in academic writing, however, few of them are visually based. We describe a novel visualisation method for providing feedback to support formative essay assessment. The visualisation method makes use of text mining techniques to provide insight on the semantics of the topics in an essay. We propose that visualisation can be used to mitigate many of the problems associated with the subjectivity of formative essay assessment. The visualisation method involves a process of non-negative matrix factorisation (NMF), to uncover topics in an essay, followed by multidimensional scaling, to map the essay topics to a 2-dimensional representation. We evaluate our approach with a subset of the British Academic Written English corpus of 2761 assignments written by university students.


title: Solidification Reaction Sequence of Co-Rich Nb-Al-Co Alloys

abstract: The freezing reaction sequence of Co-rich Nb-Al-Co ternary alloys with emphasis on the formation of Laves and Heusler phases has been examined. For Co-rich alloys, the solidification reaction sequence is observed as primary freezing of α-Co and CoAl phases, subsequent [Co + C36] and [CoAl + C36] eutectics, and the final ternary eutectic reaction [L → α-Co + C36 + CoAl]. The compositions of solidified α-Co and C36 phases agree with the corresponding vertices of the tie-triangle at the solidus temperatures. When the Nb concentration is over 20 at. pct in Co-rich alloys, the quasi-peritectic reaction [L + Co2AlNb → C36 + CoAl] does not occur as equilibrium prediction. The formation of C36 and CoAl phases occurs through solid precipitation and must be distinguished from a solidification reaction.


title: Planar Microassembly by Parallel Actuation of MEMS Microrobots

abstract: We present designs, theory, and results of fabrication and testing for a novel parallel microrobotic assembly scheme using stress-engineered MEMS microrobots. The robots are 240-280 mum times 60 mum times 7-20 mum in size and can be controlled to dock compliantly together, forming planar structures several times this size. The devices are classified into species based on the design of their steering arm actuators, and the species are further classified as independent if they can be maneuvered independently using a single global control signal. In this paper, we show that microrobot species are independent if the two transition voltages of their steering arms, i.e., the voltages at which the arms are raised or lowered, form a unique pair. We present control algorithms that can be applied to groups of independent microrobot species to direct their motion from arbitrary nondead-lock configurations to desired planar microassemblies. We present designs and fabrication for four independent microrobot species, each with a unique transition voltage. The fabricated microrobots are used to demonstrate directed assembly of five types of planar structures from two classes of initial conditions. We demonstrate an average docking accuracy of 5 mum and use self-aligning compliant interaction between the microrobots to further align and stabilize the intermediate assemblies. The final assemblies match their target shapes on average 96%, by area.


title: Dimethyl sulphoxide addition or withdrawal causes biphasic volume changes and its withdrawal causes t-system vacuolation in skeletal muscle

abstract: This letter addresses a methodological problem regarding the use of dimethyl sulphoxide (DMSO) in physiological solutions that is likely to influence the interpretation of some of the experimental results in a recent article (Lindinger et al. 2011). Indeed, examination of the literature reveals that at least one previous article in this journal may have been affected by the same issue (Yeung et al. 2002), suggesting the need to draw attention to this matter. ::: ::: DMSO is commonly used as a solvent for poorly water-soluble drugs in physiological experimentation. Lindinger et al. (2011) employ it as a solvent for bumetanide, diluting a 20 mm stock solution of bumetanide in DMSO to produce an experimental solution containing 0.14 mm bumetanide and 0.7% DMSO; they also use it as a solvent for calcein-AM at a stated final concentration of 0.256%. The protocols employed do not control for any influence of addition or withdrawal of DMSO. Unfortunately, this is a significant omission for two reasons. First, DMSO is known to be semi-membrane permeant in other tissues and therefore causes biphasic volume changes on both addition and withdrawal (Pfaff et al. 1998). Second, 0.7% DMSO is in fact 98.6 mm, given that pure DMSO has a molecular mass of 78.13 g mol−1 and a density of 1.10 g ml−1 (Sigma-Aldrich catalogue, 2011). ::: ::: In previously unpublished control experiments recording fibre volume changes using xz-plane confocal microscopy of whole Rana temporaria cutaneous pectoris muscles (as described by Ferenczi et al. 2004), I noticed that exposure of muscle fibres to Ringer solution containing 100 mm (0.71%) DMSO produces shrinkage and subsequent volume recovery similar to that seen on addition of 100 mm glycerol, though with a faster time-course of volume recovery (Fig. 1). Subsequent washout of DMSO with normal Ringer solution then produces swelling followed by volume recovery (not shown) and t-system vacuolation even with as little as 0.2% (28 mm) DMSO (Fig. 2), again similar to the influence of glycerol withdrawal. It can be noted that simultaneous withdrawal of ouabain and 1% (140 mm) DMSO has been shown to produce vacuolation of mouse muscle fibres, although this was ascribed to withdrawal of ouabain, rather than DMSO, by the authors (Yeung et al. 2002). ::: ::: ::: ::: Figure 1 ::: ::: A shows a confocal xz-plane image of the full thickness of a whole cutaneous pectoris muscle, bathed in Ringer solution containing lissamine rhodamine (62.5 μg ml−1), obtained from Rana temporaria killed by concussion followed by pithing ... ::: ::: ::: ::: ::: ::: Figure 2 ::: ::: Withdrawal of 0.2% (28 mm) DMSO produces pronounced t-system vacuolation, revealed by confocal microscopy of a fibre within a whole cutaneous pectoris muscle bathed in lissamine rhodamine (62.5 μg ml−1) ::: ::: ::: ::: Unfortunately, these observations are of considerable relevance to any analysis of the experiments described by Lindinger et al. (2011). Thus, for example, their Fig. 4B demonstrates the volume changes resulting from the simultaneous addition of 35% hypertonic Tyrode solution and washout of bumetanide, following a 30 min exposure to isotonic bumetanide-containing Tyrode solution. This implies the simultaneous addition of ∼62 mm NaCl and withdrawal of ∼100 mm DMSO. It may also be noteworthy that Lindinger et al. describe an approximately 50 s delay between solution change and re-initiation of imaging, in which time both DMSO and glycerol addition had produced >10% fibre shrinkage in Fig. 1 here. ::: ::: It is also possible that the calcein-loading procedure, which involved addition and withdrawal of DMSO, produced vacuolation of the muscle fibres. Exposure to hypertonic solutions might then produce devacuolation (Gallagher & Huang, 1997). The volume of the t-system in a vacuolated fibre can increase from its normal value of <0.5% to up to 10–15% (Krolenko & Lucy, 2001), and therefore it is possible that t-system volume changes could significantly influence the density of the intracellular calcein dye used in these experiments. ::: ::: The probable influence of DMSO addition and withdrawal on cell volume and possibly on dye concentration during the experiments of Lindinger et al. (2011) makes analysis of some parts of their results very difficult. To my knowledge, the influence of DMSO addition and withdrawal has not been studied in mammalian muscle. Thus, appropriate control experiments are required to reveal which components, if any, of the observed volume changes were artefacts of changing DMSO concentrations. In addition, experiments involving the addition or withdrawal of pharmacological agents dissolved in a vehicle should maintain a constant level of that vehicle throughout. ::: ::: The addition of such controls to the experiments of Lindinger et al. would be welcome, as the suggestion that the sodium–potassium–chloride cotransporter (NKCC) activity can produce significant regulatory volume increase in skeletal muscle fibres raises interesting questions about membrane potential control. For example, full recovery from exposure to a 125 mm hypertonic solution (Figs 3, 4 and 5 of Lindinger et al. 2011) would, if resulting entirely from NKCC activity, require intracellular Cl− to increase by ∼60 mm. Assuming extracellular [Cl−] is 140 mm in the isotonic solutions and 200 mm in the hypertonic solutions, then a value of 6 mm for intracellular [Cl−] would give a reasonable value of −80 mV for ECl. A 60 mm increase in intracellular [Cl−] would then reduce ECl to just −28 mV. Given the normal high Cl− permeability of skeletal muscle, one might therefore expect such a regulatory volume increase to be accompanied by very significant depolarization unless background ionic permeabilities were greatly changed. ::: ::: In conclusion, experimental design must take into account that addition or withdrawal of DMSO from physiological solutions can influence cell volume and produce vacuolation of skeletal muscle.


title: Adaptive resource discovery in mobile cloud computing

abstract: Mobile cloud computing (MCC) is aimed at integrating mobile devices with cloud computing. It is one of the most important concepts that have emerged in the last few years. Mobile devices, in the traditional agent-client architecture of MCC, only utilize resources in the cloud to enhance their functionalities. However, modern mobile devices have many more resources than before. As a result, researchers have begun to consider the possibility of mobile devices themselves sharing resources. This is called the cooperation-based architecture of MCC. Resource discovery is one of the most important issues that need to be solved to achieve this goal. Most of the existing work on resource discovery has adopted a fixed choice of centralized or flooding strategies. Many improved versions of energy-efficient methods based on both strategies have been proposed by researchers due to the limited battery life of mobile devices. This paper proposes a novel adaptive method of resource discovery from a different point of view to distinguish it from existing work. The proposed method automatically transforms between centralized and flooding strategies to save energy according to different network environments. Theoretical models of both energy consumption and the quality of response information are presented in this paper. A heuristic algorithm was also designed to implement the new adaptive method of resource discovery. The results from simulations demonstrated the effectiveness of the strategy and the efficiency of the proposed heuristic method.


title: The Use of Voice Input to Induce Human Communication with Banking Chatbots

abstract: The use of chatbots is more common in our everyday lives than ever before. However, few studies have been conducted comparing the differences between text- and voice-input modalities of chatbots in the banking industry. In this study, through empirical and survey-based research, users were shown to rate their relationships with the banking chatbot as more helpful and self-validating when they communicate with it by a voice-input modality than by a text-input modality.


title: Explicit kinematic model of the Mitsubishi RV-M1 robot arm

abstract: A complete kinematic model of the 5 DOF-Mitsubishi RV-M1 manipulator is presented in this paper. The forward kinematic model is based on the Modified Denavit-Hartenberg notation, and the inverse one is derived in closed form by fixing the orientation of the tool. A graphical interface is developed using MATHEMATICA software to illustrate the forward and inverse kinematics, allowing student or researcher to have hands-on of virtual graphical model that fully describe both the robot's geometry and the robot's motion in its workspace before to tackle any real task.


title: The rise and fall of High Performance Fortran: an historical object lesson

abstract: High Performance Fortran (HPF) is a high-level data-parallel programming system based on Fortran. The effort to standardize HPF began in 1991, at the Supercomputing Conference in Albuquerque, where a group of industry leaders asked Ken Kennedy to lead an effort to produce a common programming language for the emerging class of distributed-memory parallel computers. The proposed language would focus on data-parallel operations in a single thread of control, a strategy which was pioneered by some earlier commercial and research systems, including Thinking Machines' CM Fortran, Fortran D, and Vienna Fortran. The standardization group, called the High Performance Fortran Forum (HPFF), took a little over a year to produce a language definition that was published in January 1993 as a Rice technical report [50] and, later that same year, as an article in Scientific Programming [49]. The HPF project had created a great deal of excitement while it was underway and the release was initially well received in the community. However, over a period of several years, enthusiasm for the language waned in the United States, although it has continued to be used in Japan. This paper traces the origins of HPF through the programming languages on which it was based, leading up to the standardization effort. It reviews the motivation underlying technical decisions that led to the set of features incorporated into the original language and its two follow-ons: HPF 2 (extensions defined by a new series of HPFF meetings) and HPF/JA (the dialect that was used by Japanese manufacturers and runs on the Earth Simulator). A unique feature of this paper is its discussion and analysis of the technical and sociological mistakes made by both the language designers and the user community:, mistakes that led to the premature abandonment of the very promising approach employed in HPF. It concludes with some lessons for the future and an exploration of the influence of ideas from HPF on new languages emerging from the High Productivity Computing Systems program sponsored by DARPA.


title: An All-Elastomeric Transparent and Stretchable Temperature Sensor for Body-Attachable Wearable Electronics.

abstract: A transparent stretchable (TS) gated sensor array with high optical transparency, conformality, and high stretchability of up to 70% is demonstrated. The TS-gated sensor array has high responsivity to temperature changes in objects and human skin. This unprecedented TS-gated sensor array, as well as the integrated platform of the TS-gated sensor with a transparent and stretchable strain sensor, show great potential for application to wearable skin electronics for recognition of human activity.


The Role Of Self Efficacy As Mediating The Influence Of Family Environment And Social Environment On Student Entrepreneur Interest


title: Functional optimization of gene clusters by combinatorial design and assembly

abstract: Large microbial gene clusters encode useful functions, including energy utilization and natural product biosynthesis, but genetic manipulation of such systems is slow, difficult and complicated by complex regulation. We exploit the modularity of a refactored Klebsiella oxytoca nitrogen fixation (nif) gene cluster (16 genes, 103 parts) to build genetic permutations that could not be achieved by starting from the wild-type cluster. Constraint-based combinatorial design and DNA assembly are used to build libraries of radically different cluster architectures by varying part choice, gene order, gene orientation and operon occupancy. We construct 84 variants of the nifUSVWZM operon, 145 variants of the nifHDKY operon, 155 variants of the nifHDKYENJ operon and 122 variants of the complete 16-gene pathway. The performance and behavior of these variants are characterized by nitrogenase assay and strand-specific RNA sequencing (RNA-seq), and the results are incorporated into subsequent design cycles. We have produced a fully synthetic cluster that recovers 57% of wild-type activity. Our approach allows the performance of genetic parts to be quantified simultaneously in hundreds of genetic contexts. This parallelized design-build-test-learn cycle, which can access previously unattainable regions of genetic space, should provide a useful, fast tool for genetic optimization and hypothesis testing.


title: Pexophagy: The Selective Autophagy of Peroxisomes

abstract: Pichia pastoris and Hanseula polymorpha are methylotrophic yeasts capable of utilizing methanol, as a sole source of carbon and energy. Growth of these yeast species on methanol requires the synthesis of cytosolic and peroxisomal enzymes combined with the proliferation of peroxisomes. Peroxisomes are also abundantly present in the alkane-utilizing yeast Yarrowia lipolytica upon growth of cells on oleic acid. This feature has made these yeast species attractive model systems to dissect the molecular mechanisms controlling peroxisome biogenesis. We have found that upon glucose- or ethanol-induced catabolite inactivation of metabolically superfluous peroxisomes are rapidly and selectively degraded within the vacuole by a process called pexophagy, the selective removal of peroxisomes by autophagy-like processes. Utilizing several genetic screens, we have identified a number of genes that are essential for pexophagy. In this review, we will summarize our current knowledge of the molecular events of pexophagy.


title: On the asymptotic performance of low-complexity decoded LDPC codes with constituent hamming codes

abstract: Hamming code-based LDPC (H-LDPC) block codes are obtained by replacing single parity-check codes in Gallagerpsilas LDPC codes with Hamming constituent codes. This paper investigates the asymptotic error-correcting capabilities of ensembles of random H-LDPC codes, used over the binary symmetric channel and decoded with a low-complexity hard-decision iterative decoding algorithm. The number of required decoding iterations is a logarithmic function of the code length. It is shown that there exist H-LDPC codes for which such an iterative decoding corrects any error pattern with a number of errors that grows linearly with the code length. For various choices of code parameters the results are supported by numerical examples.


title: LogLens: A Real-Time Log Analysis System

abstract: Administrators of most user-facing systems depend on periodic log data to get an idea of the health and status of production applications. Logs report information, which is crucial to diagnose the root cause of complex problems. In this paper, we present a real-time log analysis system called LogLens that automates the process of anomaly detection from logs with no (or minimal) target system knowledge and user specification. In LogLens, we employ unsupervised machine learning based techniques to discover patterns in application logs, and then leverage these patterns along with the real-time log parsing for designing advanced log analytics applications. Compared to the existing systems which are primarily limited to log indexing and search capabilities, LogLens presents an extensible system for supporting both stateless and stateful log analysis applications. Currently, LogLens is running at the core of a commercial log analysis solution handling millions of logs generated from the large-scale industrial environments and reported up to 12096x man-hours reduction in troubleshooting operational problems compared to the manual approach.


title: Metaphors We Think With: The Role of Metaphor in Reasoning

abstract: The way we talk about complex and abstract ideas is suffused with metaphor. In five experiments, we explore how these metaphors influence the way that we reason about complex issues and forage for further information about them. We find that even the subtlest instantiation of a metaphor (via a single word) can have a powerful influence over how people attempt to solve social problems like crime and how they gather information to make "well-informed" decisions. Interestingly, we find that the influence of the metaphorical framing effect is covert: people do not recognize metaphors as influential in their decisions; instead they point to more "substantive" (often numerical) information as the motivation for their problem-solving decision. Metaphors in language appear to instantiate frame-consistent knowledge structures and invite structurally consistent inferences. Far from being mere rhetorical flourishes, metaphors have profound influences on how we conceptualize and act with respect to important societal issues. We find that exposure to even a single metaphor can induce substantial differences in opinion about how to solve social problems: differences that are larger, for example, than pre-existing differences in opinion between Democrats and Republicans.


Real-time News Recommendations using Apache Spark


title: I Still Know What You Visited Last Summer: Leaking Browsing History via User Interaction and Side Channel Attacks

abstract: History sniffing attacks allow web sites to learn about users' visits to other sites. The major browsers have recently adopted a defense against the current strategies for history sniffing. In a user study with 307 participants, we demonstrate that history sniffing remains feasible via interactive techniques which are not covered by the defense. While these techniques are slower and cannot hope to learn as much about users' browsing history, we see no practical way to defend against them.


title: Label Consistent Quadratic Surrogate model for visual saliency prediction

abstract: Recently, an increasing number of works have proposed to learn visual saliency by leveraging human fixations. However, the collection of human fixations is time consuming and the existing eye tracking datasets are generally small when compared with other domains. Thus, it contains a certain degree of dataset bias due to the large image variations (e.g., outdoor scenes vs. emotion-evoking images). In the learning based saliency prediction literature, most models are trained and evaluated within the same dataset and cross dataset validation is not yet a common practice. Instead of directly applying model learned from another dataset in cross dataset fashion, it is better to transfer the prior knowledge obtained from one dataset to improve the training and prediction on another. In addition, since new datasets are built and shared in the community from time to time, it would be good not to retrain the entire model when new data are added. To address these problems, we proposed a new learning based saliency model, namely Label Consistent Quadratic Surrogate algorithm, which employs an iterative online algorithm to learn a sparse dictionary with label consistent constraint. The advantages of the proposed model are three-folds: (1) the quadratic surrogate function guarantees convergence at each iteration, (2) the label consistent constraint enforces the predicted sparse code to be discriminative, and (3) the online properties enable the proposed algorithm to adapt existing model with new data without retraining. As shown in this work, the proposed saliency model achieves better performance than the state-of-the-art saliency models.


title: Coherent MapDrift Technique

abstract: A new parametric autofocus technique with a high accuracy of flight-parameter estimation dedicated to strip-mode synthetic aperture radar (SAR) systems is presented. Most of the known autofocus techniques require high-reflectivity targets (man-made targets) to obtain a properly focused SAR image. The technique proposed in this paper allows flight parameters to be estimated effectively, even for a low-contrast scene (e.g., forests, fields, small paths, etc.). The autofocus technique is based on well-known MapDrift (MD) principles. The presented technique is a coherent one, which allows flight parameters to be estimated more precisely than in the other well-known parametric technique referred to as classical MD. The presented technique allows flight parameters to be estimated with accuracy that is independent of the initial velocity error. It can be used for real-time processing for both Earth imaging and moving-target indication.


title: Efficient partial shape matching using Smith-Waterman algorithm

abstract: This paper presents an efficient partial shape matching method based on the Smith-Waterman algorithm. For two contours of m and n points respectively, the complexity of our method to find similar parts is only O(mn). In addition to this improvement in efficiency, we also obtain comparable accurate matching with fewer shape descriptors. Also, in contrast to arbitrary distance functions that are used by previous methods, we use a probabilistic similarity measurement, p-value, to evaluate the similarity of two shapes. Our experiments on several public shape databases indicate that our method outperforms state-of-the-art global and partial shape matching algorithms in various scenarios.


title: Nasal Bone in Prenatal Trisomy 21 Screening

abstract: The definitive diagnosis of fetal chromosomal abnormalities is only accomplished through tests that sample fetal tissue. These procedures—amniocentesis, chorionic villus sampling and cordocentesis—are invasive and carry with them the risks of bleeding, rupture of membranes, and even pregnancy loss.


title: The Arabic Knowledge Graph: Opportunities and Challenges

abstract: Semantic Web has brought forth the idea of computing with knowledge, hence, attributing the ability of thinking to machines. Knowledge Graphs represent a major advancement in the construction of the Web of Data where machines are context-aware when answering users' queries. The English Knowledge Graph was a milestone realized by Google in 2012. Even though it is a useful source of information for English users and applications, it does not offer much for the Arabic users and applications. In this paper, we investigated the different challenges and opportunities prone to the life-cycle of the construction of the Arabic Knowledge Graph (AKG) while following some best practices and techniques. Additionally, this work suggests some potential solutions to these challenges. The proprietary factor of data creates a major problem in the way of harvesting this latter. Moreover, when the Arabic data is openly available, it is generally in an unstructured form which requires further processing. The complexity of the Arabic language itself creates a further problem for any automatic or semi-automatic extraction processes. Therefore, the usage of NLP techniques is a feasible solution. Some preliminary results are presented later in this paper. The AKG has very promising outcomes for the Semantic Web in general and the Arabic community in particular. The goal of the Arabic Knowledge Graph is mainly the integration of the different isolated datasets available on the Web. Later, it can be used in both the academic (by providing a large dataset for many different research fields and enhance discovery) and commercial sectors (by improving search engines, providing metadata, interlinking businesses).


Soft Robotics Technologies to Address Shortcomings in Today ’ s Minimally Invasive Surgery : The STIFF-FLOP Approach


title: Internet of things security: A top-down survey

abstract: Abstract Internet of Things (IoT) is one of the promising technologies that has attracted a lot of attention in both industrial and academic fields these years. It aims to integrate seamlessly both physical and digital worlds in one single ecosystem that makes up a new intelligent era of Internet. This technology offers a huge business value for organizations and provides opportunities for many existing applications such as energy, healthcare and other sectors. However, as new emergent technology, IoT suffers from several security issues which are most challenging than those from other fields regarding its complex environment and resources-constrained IoT devices. A lot of researches have been initiated in order to provide efficient security solutions in IoT, particularly to address resources constraints and scalability issues. Furthermore, some technologies related to networking and cryptocurrency fields such as Software Defined Networking (SDN) and Blockchain are revolutionizing the world of the Internet of Things thanks to their efficiency and scalability. In this paper, we provide a comprehensive top down survey of the most recent proposed security and privacy solutions in IoT. We discuss particularly the benefits that new approaches such as blockchain and Software Defined Networking can bring to the security and the privacy in IoT in terms of flexibility and scalability. Finally, we give a general classification of existing solutions and comparison based on important parameters.


title: Integration of metabolomics, lipidomics and clinical data using a machine learning method

abstract: Background ::: The recent pandemic of obesity and the metabolic syndrome (MetS) has led to the realisation that new drug targets are needed to either reduce obesity or the subsequent pathophysiological consequences associated with excess weight gain. Certain nuclear hormone receptors (NRs) play a pivotal role in lipid and carbohydrate metabolism and have been highlighted as potential treatments for obesity. This realisation started a search for NR agonists in order to understand and successfully treat MetS and associated conditions such as insulin resistance, dyslipidaemia, hypertension, hypertriglyceridemia, obesity and cardiovascular disease. The most studied NRs for treating metabolic diseases are the peroxisome proliferator-activated receptors (PPARs), PPAR-α, PPAR-γ, and PPAR-δ. However, prolonged PPAR treatment in animal models has led to adverse side effects including increased risk of a number of cancers, but how these receptors change metabolism long term in terms of pathology, despite many beneficial effects shorter term, is not fully understood. In the current study, changes in male Sprague Dawley rat liver caused by dietary treatment with a PPAR-pan (PPAR-α, −γ, and –δ) agonist were profiled by classical toxicology (clinical chemistry) and high throughput metabolomics and lipidomics approaches using mass spectrometry.


title: Redox- and non-redox-metal-induced formation of free radicals and their role in human disease

abstract: Transition metal ions are key elements of various biological processes ranging from oxygen formation to hypoxia sensing, and therefore, their homeostasis is maintained within strict limits through tightly regulated mechanisms of uptake, storage and secretion. The breakdown of metal ion homeostasis can lead to an uncontrolled formation of reactive oxygen species, ROS (via the Fenton reaction, which produces hydroxyl radicals), and reactive nitrogen species, RNS, which may cause oxidative damage to biological macromolecules such as DNA, proteins and lipids. An imbalance between the formation of free radicals and their elimination by antioxidant defense systems is termed oxidative stress. Most vulnerable to free radical attack is the cell membrane which may undergo enhanced lipid peroxidation, finally producing mutagenic and carcinogenic malondialdehyde and 4-hydroxynonenal and other exocyclic DNA adducts. While redox-active iron (Fe) and copper (Cu) undergo redox-cycling reactions, for a second group of redox-inactive metals such as arsenic (As) and cadmium (Cd), the primary route for their toxicity is depletion of glutathione and bonding to sulfhydryl groups of proteins. While arsenic is known to bind directly to critical thiols, other mechanisms, involving formation of hydrogen peroxide under physiological conditions, have been proposed. Redox-inert zinc (Zn) is the most abundant metal in the brain and an essential component of numerous proteins involved in biological defense mechanisms against oxidative stress. The depletion of zinc may enhance DNA damage by impairing DNA repair mechanisms. Intoxication of an organism by arsenic and cadmium may lead to metabolic disturbances of redox-active copper and iron, with the occurrence of oxidative stress induced by the enhanced formation of ROS/RNS. Oxidative stress occurs when excessive formation of ROS overwhelms the antioxidant defense system, as is maintained by antioxidants such as ascorbic acid, alpha-tocopherol, glutathione (GSH), carotenoids, flavonoids and antioxidant enzymes which include SOD, catalase and glutathione peroxidase. This review summarizes current views regarding the role of redox-active/inactive metal-induced formation of ROS, and modifications to biomolecules in human disease such as cancer, cardiovascular disease, metabolic disease, Alzheimer's disease, Parkinson's disease, renal disease, blood disorders and other disease. The involvement of metals in DNA repair mechanisms, tumor suppressor functions and interference with signal transduction pathways are also discussed.


title: A Novel Tri-State Binary Phase Detector

abstract: In this paper a phase detector is introduced which has a similar phase detector response as the Alexander phase detector. Both the Alexander and proposed phase detector are analyzed with respect to their robustness. The analysis shows that the novel phase detector is more robust against process non-idealities than the Alexander, with a 75% reduction in the variation of static phase offsets. The proposed phase detector also consumes less power and requires less area. A CDR circuit which implements the proposed phase detector was designed and fabricated in a 0.18mum six metal layer standard CMOS process. The fabricated CDR circuit can lock to pseudo-random bit sequences (PRBS) up to 231 - 1 at data rates from 5 - 6.25Gb/s. For a PRBS of 231 - 1 at 6.25Gb/s the measured rms jitter and peak-to-peak jitter were 1.7ps and 11ps.


title: From Individual to Relational Strategies: Transforming a Manual-Based Psycho-Educational Course at School.

abstract: The present article focuses on 13-year-old girls' meaning-making processes during participation in a manual-based psycho-educational course at school. Drawing on childhood studies and ethnographic investigations of subjectivity, the author explores how the course is realized in practice. The analysis, based on video-recordings of 13 classes, shows that the girls and the teachers, through collective transformation, move the focus from individual potential problems to relational issues. The results demonstrate the weakness of using manual-based educational courses, and indicate that cognitive methods for dealing with negative thoughts could be replaced by exercises designed to deal with interaction and strengthening the individual.


title: Adsorption of Chromium (VI) From Aqueous Solution onto Bio-Organic Waste in Batch Reactors - An Equilibrium Study

abstract: The presence of heavy metals in water supplies and wastewater threatens the environment and the health of humans. The adsorption of chromium (VI) onto cowdung ash, a bio-organic waste was investigated in a batch reactor under two different conditions, namely, initial metal ion concentration and adsorbent dosages. For the five different initial metal ion concentrations such as 500, 600, 800, 900, 1000 mg/L, the steady state values of chromium removal efficiency were 100, 83.33, 88.09, 94.3 and 96 %, respectively, using 20 g of cowdung ash under shaking at the end of 3 rd h. The equilibrium of the process was found to fit into the two


title: Design and Finite-Element Analysis of Interior Permanent Magnet Synchronous Motor With Flux Barriers

abstract: The paper presents a finite-element method-based design and analysis of interior permanent magnet synchronous motor with flux barriers (IPMSMFB). Various parameters of IPMSMFB rotor structure were taken into account at determination of a suitable rotor construction. On the basis of FEM analysis the rotor of IPMSMFB with three-flux barriers was built. Output torque capability and flux weakening performance of IPMSMFB were compared with performances of conventional interior permanent magnet synchronous motor (IPMSM), having the same rotor geometrical dimensions and the same stator construction. The predicted performance of conventional IPMSM and IPMSMFB was confirmed with the measurements over a wide-speed range of constant output power operation.


The Next Step for Multi-Document Summarization: A Heterogeneous Multi-Genre Corpus Built with a Novel Construction Approach


title: Control Strategies Based on Symmetrical Components for Grid-Connected Converters Under Voltage Dips

abstract: Low-voltage ride-through (LVRT) requirements demand wind-power plants to remain connected to the network in presence of grid-voltage dips. Most dips present positive-, negative-, and zero-sequence components. Hence, regulators based on symmetrical components are well suited to control grid-connected converters. A neutral-point-clamped topology has been considered as an active front end of a distributed power-generation system, following the trend of increasing power and voltage levels in wind-power systems. Three different current controllers based on symmetrical components and linear quadratic regulator have been considered. The performance of each controller is evaluated on LVRT requirement fulfillment, grid-current balancing, maximum grid-current value control, and oscillating power flow. Simulation and experimental results show that all three controllers meet LVRT requirements, although different system performance is found for each control approach. Therefore, controller selection depends on the system constraints and the type of preferred performance features.


title: Chemical Similarity Searching

abstract: This paper reviews the use of similarity searching in chemical databases. It begins by introducing the concept of similarity searching, differentiating it from the more common substructure searching, and then discusses the current generation of fragment-based measures that are used for searching chemical structure databases. The next sections focus upon two of the principal characteristics of a similarity measure: the coefficient that is used to quantify the degree of structural resemblance between pairs of molecules and the structural representations that are used to characterize molecules that are being compared in a similarity calculation. New types of similarity measure are then compared with current approaches, and examples are given of several applications that are related to similarity searching.


title: Dehazing Using Color-Lines

abstract: Photographs of hazy scenes typically suffer having low contrast and offer a limited visibility of the scene. This article describes a new method for single-image dehazing that relies on a generic regularity in natural images where pixels of small image patches typically exhibit a 1D distribution in RGB color space, known as color-lines. We derive a local formation model that explains the color-lines in the context of hazy scenes and use it for recovering the scene transmission based on the lines' offset from the origin. The lack of a dominant color-line inside a patch or its lack of consistency with the formation model allows us to identify and avoid false predictions. Thus, unlike existing approaches that follow their assumptions across the entire image, our algorithm validates its hypotheses and obtains more reliable estimates where possible. In addition, we describe a Markov random field model dedicated to producing complete and regularized transmission maps given noisy and scattered estimates. Unlike traditional field models that consist of local coupling, the new model is augmented with long-range connections between pixels of similar attributes. These connections allow our algorithm to properly resolve the transmission in isolated regions where nearby pixels do not offer relevant information. An extensive evaluation of our method over different types of images and its comparison to state-of-the-art methods over established benchmark images show a consistent improvement in the accuracy of the estimated scene transmission and recovered haze-free radiances.


title: A Probabilistic Concept Annotation for IT Service Desk Tickets

abstract: Ticket annotation and search has become an important research subject in the IT service desk delivery. Millions of tickets are created yearly to address business users' IT related problems. In IT service desk management, it is critical to first capture the pain points for a group of tickets to determine root cause; secondly, to obtain the respective distributions in order to layout the priority of addressing these pain points. An advanced ticket analytics system utilizes a combination of topic modeling and clustering to address the above issues and the integration of these features into information architecture will allow for a wider distribution of this technology and progress to a remarkable financial impact for IT industry. Topic modeling has been used to extract topics from given documents; each topic is represented by unigram distributions. However, it is not clear how to interpret the results. Due to the inadequacy to render top concepts, in this paper, we propose a probabilistic framework, which integrates topic models, POS tags, query expansion and so on, for the practical challenge. The rigorously empirical experiments demonstrate the consistent and utility performance of the proposed method on real datasets.


title: A Vignette-based Method for Improving Cybersecurity Talent Management through Cyber Defense Competition Design

abstract: The preliminary findings are reported from a four-year study of cybersecurity competency assessment and development achieved through the design of cyber defense competitions. The first year of the study focused on identifying the abilities that should indicate aptitude to perform well in the areas of operational security testing and advanced threat response. A recently developed method for Job Performance Modeling (JPM) is applied which uses vignettes -- critical incident stories -- to guide the elicitation of a holistic description of mission-critical roles grounded in the latest tactics, techniques and protocols defining the current state-of-the-art, or ground truth, in cyber defense. Implications are drawn for design of scoring engines and achievement of game balance in cyber defense competitions as a talent management system.


title: Implementation of Jacobi iterative solver in verilog HDL

abstract: There are many engineering simulations and scientific computing applications where large linear system of equations are to be solved. With the generous development in scientific computing the use of large linear system of equations also increases. There are many solvers for solving these equations. Jacobi is one of the important solvers for solving linear system of equations. Jacobi method is more efficient when implemented in a pipelined manner in an FPGA and it also shows more parallelism In this paper hardware based implementation of Jacobi solver is discussed. The main idea of Jacobi to obtain preference is its computation that can be trivially done in parallel.


title: Effective text classification using multi-level fuzzy neural network

abstract: Nowadays, large volumes of text data are being produced in real time due to expansion of communication. It is necessary to organize this data for exploitation and extraction of useful information. Text classification based on the topic is one of the efficient solutions to this problem. Efficient algorithms are applied for text classification if they address high dimensional data. In this paper, a novel neural network classifier is adapted to classify the texts and compared to some similar techniques. To this end, firstly preprocessing is conducted on the words, which includes five steps. After that, the feature extraction phase is applied consisting of three steps, with the Principle Component Analysis (PCA) method used for dimensionality reduction. Finally, a supervised learning method, namely the Multi-Level Fuzzy min-max neural network classifier (MLF), is used for text classification. The simulation results of the experiments, compared to three other principle methods, indicate that MLF method has a high accuracy of 94% for the Reuters-21578 dataset and 95% for the 20 newsgroup dataset. In addition, the execution time of the proposed method is less than other supervised learning algorithms.


title: Structural mechanics for electromagnetic railguns

abstract: The objective of this investigation is to explore potential designs and appropriate materials for a lightweight fieldable electromagnetic railgun. Lessons learned from the previous launcher designs are reviewed and discussed from a mechanical point of view. Analyses are performed with a conceptually workable gun bore to illustrate the requirement of a fieldable gun construction. Both static and dynamic responses of the railgun are discussed. The study provides a fundamental understanding for developing a fieldable railgun in terms of mechanics and material requirement.


title: Using Semantic Roles to Improve Question Answering

abstract: Shallow semantic parsing, the automatic identification and labeling of sentential constituents, has recently received much attention. Our work examines whether semantic role information is beneficial to question answering. We introduce a general framework for answer extraction which exploits semantic role annotations in the FrameNet paradigm. We view semantic role assignment as an optimization problem in a bipartite graph and answer extraction as an instance of graph matching. Experimental results on the TREC datasets demonstrate improvements over state-of-the-art models.


title: Dynamic Projection Mapping onto Deforming Non-Rigid Surface Using Deformable Dot Cluster Marker

abstract: Dynamic projection mapping for moving objects has attracted much attention in recent years. However, conventional approaches have faced some issues, such as the target objects being limited to rigid objects, and the limited moving speed of the targets. In this paper, we focus on dynamic projection mapping onto rapidly deforming non-rigid surfaces with a speed sufficiently high that a human does not perceive any misalignment between the target object and the projected images. In order to achieve such projection mapping, we need a high-speed technique for tracking non-rigid surfaces, which is still a challenging problem in the field of computer vision. We propose the Deformable Dot Cluster Marker (DDCM), a novel fiducial marker for high-speed tracking of non-rigid surfaces using a high-frame-rate camera. The DDCM has three performance advantages. First, it can be detected even when it is strongly deformed. Second, it realizes robust tracking even in the presence of external and self occlusions. Third, it allows millisecond-order computational speed. Using DDCM and a high-speed projector, we realized dynamic projection mapping onto a deformed sheet of paper and a T-shirt with a speed sufficiently high that the projected images appeared to be printed on the objects.


title: Dynamic load balancing without packet reordering

abstract: Dynamic load balancing is a popular recent technique that protects ISP networks from sudden congestion caused by load spikes or link failures. Dynamic load balancing protocols, however, require schemes for splitting traffic across multiple paths at a fine granularity. Current splitting schemes present a tussle between slicing granularity and packet reordering. Splitting traffic at the granularity of packets quickly and accurately assigns the desired traffic share to each path, but can reorder packets within a TCP flow, confusing TCP congestion control. Splitting traffic at the granularity of a flow avoids packet reordering but may overshoot the desired shares by up to 60% in dynamic environments, resulting in low end-to-end network goodput Contrary to popular belief, we show that one can systematically split a single flow across multiple paths without causing packet reordering. We propose FLARE, a new traffic splitting algorithm that operates on bursts of packets, carefully chosen to avoid reordering. Using a combination of analysis and trace-driven simulations, we show that FLARE attains accuracy and responsiveness comparable to packet switching without reordering packets. FLARE is simple and can be implemented with a few KB of router state


title: Longitudinal Study of Automatic Face Recognition

abstract: The two underlying premises of automatic face recognition are uniqueness and permanence. This paper investigates the permanence property by addressing the following: Does face recognition ability of state-of-the-art systems degrade with elapsed time between enrolled and query face images? If so, what is the rate of decline w.r.t. the elapsed time? While previous studies have reported degradations in accuracy, no formal statistical analysis of large-scale longitudinal data has been conducted. We conduct such an analysis on two mugshot databases, which are the largest facial aging databases studied to date in terms of number of subjects, images per subject, and elapsed times. Mixed-effects regression models are applied to genuine similarity scores from state-of-the-art COTS face matchers to quantify the population-mean rate of change in genuine scores over time, subject-specific variability, and the influence of age, sex, race, and face image quality. Longitudinal analysis shows that despite decreasing genuine scores, 99% of subjects can still be recognized at 0.01% FAR up to approximately 6 years elapsed time, and that age, sex, and race only marginally influence these trends. The methodology presented here should be periodically repeated to determine age-invariant properties of face recognition as state-of-the-art evolves to better address facial aging.


title: Design and analysis of TEM horn antenna for GPR applications

abstract: Ground penetrating radar (GPR) is a technique used to probe the ground. This technique is positioned top among all existing geophysical techniques. Applications of GPR are enormous and are ranging from planetary exploration to the detection of buried mines. In GPR to get high resolution and to get the wave traveled through deeper distance inside the earth, the transmitting antenna (Tx) must transmit the narrow width pulse signal into the ground where objects are hidden. This thesis presents the work on the design of TEM horn antenna. For this application A Compact Double-Ridged Horn Antenna from the family of horn antennas with an ultra-wide band (0.827–4.5)GHz is designed & simulated. Both the time domain and frequency domain requirements are satisfied by this antenna. This antenna is simulated using Computer simulation technology (CST) microwave studio. This antenna is test simulated in time domain solver with feeding via 50 Ω SMA and also tested in both thermal solver & mechanical solver to check for its malfunction. The variation of characteristic impedance across the antenna structure is gradually varied from 50 Ω to 377 Ω. In this thesis we will go through the steps how the lower range frequencies can be achieved with TEM horn & in what way the Linear TEM horn is transformed to Compact Double-Ridged Horn Antenna. This thesis also includes an explanatory part on both theoretical & simulated version of Linear TEM horn antenna, Double ridged horn antenna, Compact Double-Ridged Horn Antenna & Double-Ridged Horn Antenna in GPR scenario along with the simulation results.


title: A pedagogical framework for mobile learning: Categorizing educational applications of mobile technologies into four types

abstract: Instructional designers and educators recognize the potential of mobile technologies as a learning tool for students and have incorporated them into the distance learning environment. However, little research has been done to categorize the numerous examples of mobile learning in the context of distance education, and few instructional design guidelines based on a solid theoretical framework for mobile learning exist. In this paper I compare mobile learning (m-learning) with electronic learning (e-learning) and ubiquitous learning (u-learning) and describe the technological attributes and pedagogical affordances of mobile learning presented in previous studies. I modify transactional distance (TD) theory and adopt it as a relevant theoretical framework for mobile learning in distance education. Furthermore, I attempt to position previous studies into four types of mobile learning: 1) high transactional distance socialized m-learning, 2) high transactional distance individualized m-learning, 3) low transactional distance socialized m-learning, and 4) low transactional distance individualized m-learning. As a result, this paper can be used by instructional designers of open and distance learning to learn about the concepts of mobile learning and how mobile technologies can be incorporated into their teaching and learning more effectively.


title: Compiler-directed run-time monitoring of program data access

abstract: Accurate run-time analysis has been expensive for complex programs, in part because most methods perform on all a data. Some applications require only partial reorganization. An example of this is off-loading infrequently used data from a mobile device. Complete monitoring is not necessary because not all accesses can reach the displaced data. To support partial monitoring, this paper presents a framework that includes a source-to-source C compiler and a run-time monitor. The compiler inserts run-time calls, which invoke the monitor during execution. To be selective, the compiler needs to identify relevant data and their access. It needs to analyze both the content and the location of monitored data. To reduce run-time overhead, the system uses a source-level interface, where the compiler transfers additional program information to reduce the workload of the monitor. The paper describes an implementation for general C programs. It evaluates different levels of data monitoring and their application on an SGI workstation and an Intel PC.


title: Characterization and detection of noise in clustering

abstract: Abstract A concept of ‘Noise Cluster’ is introduced such that noisy data points may be assigned to the noise class. The approach is developed for objective functional type ( K -means or fuzzy K -means) algorithms, and its ability to detect ‘good’ clusters amongst noisy data is demonstrated. The approach presented is applicable to a variety of fuzzy clustering algorithms as well as regression analysis.


title: Clustering Approaches for Financial Data Analysis: a Survey

abstract: Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, confidence of expected return, etc. Banking and financial institutes have applied different data mining techniques to enhance their business performance. Among these techniques, clustering has been considered as a significant method to capture the natural structure of data. However, there are not many studies on clustering approaches for financial data analysis. In this paper, we evaluate different clustering algorithms for analysing different financial datasets varied from time series to transactions. We also discuss the advantages and disadvantages of each method to enhance the understanding of inner structure of financial datasets as well as the capability of each clustering method in this context.


title: A simple amplitude detector-based demodulator for resolver converters

abstract: This paper presents a simple method based on sinusoidal-amplitude detector for realizing the resolver-signal demodulator. The proposed demodulator consists of two full-wave rectifiers, two ±unity-gain amplifiers, and two sinusoidal-amplitude detectors with control switches. Two output voltages are proportional to sine and cosine envelopes of resolver-shaft angle without low-pass filter. Experimental results demonstrating characteristic of the proposed circuit are included.


title: Efficient and Robust Compressed Sensing Using Optimized Expander Graphs

abstract: Expander graphs have been recently proposed to construct efficient compressed sensing algorithms. In particular, it has been shown that any n-dimensional vector that is k-sparse can be fully recovered using O(klogn) measurements and only O(klogn) simple recovery iterations. In this paper, we improve upon this result by considering expander graphs with expansion coefficient beyond 3 / 4 and show that, with the same number of measurements, only O(k) recovery iterations are required, which is a significant improvement when n is large. In fact, full recovery can be accomplished by at most 2k very simple iterations. The number of iterations can be reduced arbitrarily close to k, and the recovery algorithm can be implemented very efficiently using a simple priority queue with total recovery time O(nlog(n/k))). We also show that by tolerating a small penalty on the number of measurements, and not on the number of recovery iterations, one can use the efficient construction of a family of expander graphs to come up with explicit measurement matrices for this method. We compare our result with other recently developed expander-graph-based methods and argue that it compares favorably both in terms of the number of required measurements and in terms of the time complexity and the simplicity of recovery. Finally, we will show how our analysis extends to give a robust algorithm that finds the position and sign of the k significant elements of an almost k-sparse signal and then, using very simple optimization techniques, finds a k-sparse signal which is close to the best k-term approximation of the original signal.


title: The Modem Nation: A First Study on Twitch.TV Social Structure and Player/Game Relationships

abstract: With millions of users and intense interactions among them, Twitch.TV has become the largest online gaming community in history. So far there is not much research studying this new merging and prosperous online community. In this paper, we examine various entities, such as steamers and gaming franchises, in Twitch and how they interact with each other. Using Twitch's API services, we first collect data about Twitch entities and then use this data to answer two interesting questions: i) what game franchises attract similar audiences, and ii) how connected/segregated are different predominate subcultures of gamers. To capture the structural relationships, we classify game streamers into three types and develop two novel community visualization methods. These methods help to gain insight into the connectivity and trends of this online community. Our research uncovers valuable information on better understanding gaming communities.


title: A Survey and Classification of Controlled Natural Languages

abstract: What is here called controlled natural language CNL has traditionally been given many different names. Especially during the last four decades, a wide variety of such languages have been designed. They are applied to improve communication among humans, to improve translation, or to provide natural and intuitive representations for formal notations. Despite the apparent differences, it seems sensible to put all these languages under the same umbrella. To bring order to the variety of languages, a general classification scheme is presented here. A comprehensive survey of existing English-based CNLs is given, listing and describing 100 languages from 1930 until today. Classification of these languages reveals that they form a single scattered cloud filling the conceptual space between natural languages such as English on the one end and formal languages such as propositional logic on the other. The goal of this article is to provide a common terminology and a common model for CNL, to contribute to the understanding of their general nature, to provide a starting point for researchers interested in the area, and to help developers to make design decisions.


title: 6LoWPAN Stacks: A Survey

abstract: Wireless Sensor Network (WSN) is one of the key technologies of 21st century, while it is a very active and challenging research area. It seems that in the next coming year, thanks to 6LoWPAN, these wireless micro-sensors will be embedded in everywhere, because 6LoWPAN enables P2P connection between wireless nodes over IPv6. Nowadays different implementations of 6LoWPAN stacks are available so it is interesting to evaluate their performance in term of memory footprint and compliant with the RFC4919 and RFC4944. In this paper, we present a survey on the state-of-art of the current implementation of 6LoWPAN stacks such as uIP/Contiki, SICSlowpan, 6lowpancli, B6LoWPAN, BLIP, NanoStack and Jennic's stack. The key features of all these 6LoWPAN stacks will be established. Finally, we discuss the evolution of the current implementations of 6LoWPAN stacks.


title: Multi-physics analysis of a high torque density motor for electric racing cars

abstract: This paper presents a newly developed high torque density motor design for electric racing cars. An interior permanent magnet motor with a flux-concentration configuration is proposed. The 18slots/16poles motor has pre-formed tooth wound coils, rare-earth magnets type material, whilst employing a highly efficient cooling system with forced oil convection through the slot and forced air convection in the airgap. Losses are minimized either by using special materials, i.e. non-oriented thin gage, laminated steel or special construction, i.e. magnet segmentation or twisted wires. The thermal behavior of the motor is modelled and tested using Le Mans racing typical driving cycle. Several prototypes have been built and tested to validate the proposed configuration.


title: Learning and Predicting Moving Object Trajectory: a piecewise trajectory segment approach

abstract: This paper presents an approach to predict future motion of a moving object based on its past movement. This approach is capable of learning object movement in an open environment, which is one of the limitions in some prior works. The proposed approach exploits the similarities of short-term movement behaviors by modeling a trajectory as concatenation of short segments. These short segments are assumed to be noisy realizations of latent segments. The transitions between the underlying latent segments are assumed to follow a Markov model. This predictive model was applied to two real-world applications and yielded favorable performance on both tasks.


title: The Truth and Nothing but the Truth: Multimodal Analysis for Deception Detection

abstract: We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks, feeding that to a Support Vector Machine to test deceit or truth prediction. We then try out a method to incorporate utterance-based fusion of visual and lexical analysis, using string based matching.


title: Multiresolution PID control of Brushless DC motor in fuel cell electric vehicles

abstract: The exploitation of wavelet theory in a control structure of a Brushless DC (BLDC) motor integrated in a Fuel Cell Electric Vehicle is proposed. The controlling structure, consisted of two interrelated multiresolution PID (MRPID) controllers, regulates: a) BLDC motor torque through DC current and b) rotor speed. The proposed structure is deployed on an algorithm that exploits inherit wavelet filtering capabilities for improving control dynamics and stability over a wide operational range. Full system (FCEV) performance is assessed for both cases of MRPID and conventional PID controlling structures via simulation in MATLAB/Simulink environment.


Deep Shape from Silhouettes


title: A Parking Occupancy Detection Algorithm Based on AMR Sensor

abstract: Recently, with the explosive increase of automobiles in cities, parking problems are serious and even worsen in many cities. This paper proposes a new algorithm for parking occupancy detection based on the use of anisotropic magnetoresistive sensors. Parking occupancy detection is abstracted as binary pattern recognition problem. According to the status of the parking space, the recognition result contains two categories: vacant and occupied. The feature extraction method of the parking magnetic signal is proposed. In addition, the classification criteria are derived based on the distance discriminate analysis method. Eighty-two sensor nodes are deployed on the roadside parking spaces. By running the system for six months, we observed that the accuracy rate of the proposed parking occupancy detection algorithm is better than 98%.


Transfer Learning via Learning to Transfer


title: Gain Enhancement of Beam Scanning Substrate Integrated Waveguide Slot Array Antennas Using a Phase-Correcting Grating Cover

abstract: In this paper, planar beam scanning substrate integrated waveguide (SIW) slot leaky-wave antennas (LWAs) are proposed for gain enhancement using a metallic phase correction grating cover. Unlike conventional Fabry-Perot (FP) cavity antennas, the proposed antenna is fed by the SIW beam scanning LWA instead of a feeding antenna. The beam scanning angle range is enlarged by meandering the entire feeding structure and the gain is enhanced by a metallic grating cover acting as a 1-D lens. Two kinds of the gratings with different metal strip parameters are designed and analyzed. The proposed antennas operating at the center frequency of 25.45 GHz are designed and experimentally verified for an automotive collision avoidance radar with a gain enhancement of about 4 ~ 6 dB. The proposed SIW LWAs have the advantages of high gain, low profile, easy fabrication and beam-scanning capability good for millimeter wave radar applications.


title: Extended phase diagrams for the ternary solutions H2O-NaCl-glycerol and H2O-NaCl-hydroxyethylstarch (HES) determined by DSC

abstract: Abstract Phase transformations occurring in the solutions H 2 O-NaCl-glycerol and H 2 O-NaCl-HES under nonequilibrium cooling and heating conditions were examined using the DSC method. Mainly, glass transition, devitrification, eutectic melting, and primary ice melting were evaluated to yield data for the stability of the amorphous phases as well as for the composition inside the residual solution which remains after crystallization. It was found that for a lot of initial compositions and cooling/heating conditions used in cryobiology only primary ice crystallization occurs. Therefore the description of the ternary phase-change behavior may become even more simple for nonequilibrium conditions than in the case of assumed equilibrium. The results were used to modify and extend the equilibrium phase diagrams to make them applicable to problems with boundary conditions which exclude thermodynamic equilibrium and cause formation of metastable phases. The application of the phase diagrams to obtain information about ice fraction and concentration during the cooling/heating process is demonstrated.


title: Unsupervised Feature Selection for Multi-View Data in Social Media

abstract: The explosive popularity of social media produces mountains of high-dimensional data and the nature of social media also determines that its data is often unlabelled, noisy and partial, presenting new challenges to feature selection. Social media data can be represented by heterogeneous feature spaces in the form of multiple views. In general, multiple views can be complementary and, when used together, can help handle noisy and partial data for any single-view feature selection. These unique challenges and properties motivate us to develop a novel feature selection framework to handle multi-view social media data. In this paper, we investigate how to exploit relations among views to help each other select relevant features, and propose a novel unsupervised feature selection framework, MVFS, for multiview social media data. We systematically evaluate the proposed framework in multi-view datasets from social media websites and the results demonstrate the effectiveness and potential of MVFS.


title: Manifold-Manifold Distance with application to face recognition based on image set

abstract: In this paper, we address the problem of classifying image sets, each of which contains images belonging to the same class but covering large variations in, for instance, viewpoint and illumination. We innovatively formulate the problem as the computation of Manifold-Manifold Distance (MMD), i.e., calculating the distance between nonlinear manifolds each representing one image set. To compute MMD, we also propose a novel manifold learning approach, which expresses a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrating the distances between pair of subspaces respectively from one of the involved manifolds. The proposed MMD method is evaluated on the task of Face Recognition based on Image Set (FRIS). In FRIS, each known subject is enrolled with a set of facial images and modeled as a gallery manifold, while a testing subject is modeled as a probe manifold, which is then matched against all the gallery manifolds by MMD. Identification is achieved by seeking the minimum MMD. Experimental results on two public face databases, Honda/UCSD and CMU MoBo, demonstrate that the proposed MMD method outperforms the competing methods.


title: Open domain question answering system using cognitive computing

abstract: Technology has advanced so much that in recent times use internet search engines as primary medium of reference for understanding of any concept and knowledge level doubts. In this paper the main idea is to present an architecture which is the new way of searching for the information present on the internet and it is based on the new era of computing i.e. cognitive computing. Cognitive computing resolves the situation where ambiguity and uncertainty is present and attempts to mimic the mechanism of human brain. Recent information requirements need an efficient mechanism which should be capable of interacting with users in a more natural way. Our system is intended to manage large amount of textual data and mining suitable knowledge. Thus resulting in a new level of refinement to search results for the user by providing exact answer to the natural language query that are specified by users. Our system will be helpful to the users because system is able to provide succinct answers which do not require end users to wade through a huge amount of data present on the internet. This paper briefly summarizes the characteristics of cognitive computing and how it is implemented in our open domain question answering system developed using open source software.


title: Heuristic shortest path algorithms for transportation applications: state of the art

abstract: There are a number of transportation applications that require the use of a heuristic shortest path algorithm rather than one of the standard, optimal algorithms. This is primarily due to the requirements of some transportation applications where shortest paths need to be quickly identified either because an immediate response is required (e.g., in-vehicle route guidance systems) or because the shortest paths need to be recalculated repeatedly (e.g., vehicle routing and scheduling). For this reason a number of heuristic approaches have been advocated for decreasing the computation time of the shortest path algorithm. This paper presents a survey review of various heuristic shortest path algorithms that have been developed in the past. The goal is to identify the main features of different heuristic strategies, develop a unifying classification framework, and summarize relevant computational experience.


title: Evaluating the Impact of Binning 2D Scalar Fields

abstract: The expressiveness principle for visualization design asserts that a visualization should encode all of the available data, and only the available data, implying that continuous data types should be visualized with a continuous encoding channel. And yet, in many domains binning continuous data is not only pervasive, but it is accepted as standard practice. Prior work provides no clear guidance for when encoding continuous data continuously is preferable to employing binning techniques or how this choice affects data interpretation and decision making. In this paper, we present a study aimed at better understanding the conditions in which the expressiveness principle can or should be violated for visualizing continuous data. We provided participants with visualizations employing either continuous or binned greyscale encodings of geospatial elevation data and compared participants' ability to complete a wide variety of tasks. For various tasks, the results indicate significant differences in decision making, confidence in responses, and task completion time between continuous and binned encodings of the data. In general, participants with continuous encodings were faster to complete many of the tasks, but never outperformed those with binned encodings, while performance accuracy with binned encodings was superior to continuous encodings in some tasks. These findings suggest that strict adherence to the expressiveness principle is not always advisable. We discuss both the implications and limitations of our results and outline various avenues for potential work needed to further improve guidelines for using continuous versus binned encodings for continuous data types.


title: An integrated Principal Component Analysis and multi-objective mathematical programming approach to agile supply chain network design under uncertainty

abstract: The design of agile supply chain networks has attracted more attention in recent years according to the competitive business environment. Further, due to high degree of uncertainty in agile supply chains (SCs), developing robust and efficient decision making tools are of interest. In this study, an integrated approach based on principal component analysis (PCA) and multi-objective possibilistic mixed integer programming (MOPMIP) approaches is proposed to optimally design agile supply chain network under uncertainty. The PCA method is used for ranking and filtering the suppliers, constituting the first layer of the supply chain, based on agility criteria. The proposed MOPMIP model is employed to construct the agile supply chain network under uncertainty. In the proposed MOPMIP model, three objective functions including 1) total costs minimization, 2) total delivery time minimization and 3) maximization of flexibility are considered. An interactive fuzzy solution approach is used to solve the proposed MOPMILP model. Two numerical examples, is conducted to evaluate the performance and efficiency of the proposed integrated approach for agile supply chain network design under uncertainty.


title: Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

abstract: In recent years, state-of-the-art methods in computer vision have utilized increasingly deep convolutional neural network architectures (CNNs), with some of the most successful models employing hundreds or even thousands of layers. A variety of pathologies such as vanishing/exploding gradients make training such deep networks challenging. While residual connections and batch normalization do enable training at these depths, it has remained unclear whether such specialized architecture designs are truly necessary to train deep CNNs. In this work, we demonstrate that it is possible to train vanilla CNNs with ten thousand layers or more simply by using an appropriate initialization scheme. We derive this initialization scheme theoretically by developing a mean field theory for signal propagation and by characterizing the conditions for dynamical isometry, the equilibration of singular values of the input-output Jacobian matrix. These conditions require that the convolution operator be an orthogonal transformation in the sense that it is norm-preserving. We present an algorithm for generating such random initial orthogonal convolution kernels and demonstrate empirically that they enable efficient training of extremely deep architectures.


Automatic Remote-sensing Image Registration Using SURF


title: Prediction of helpful reviews using emotions extraction

abstract: Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful reviews, can reduce information processing time and save effort. To create such an interface in real time, we need reliable prediction algorithms to classify and predict new reviews which have not been voted but are potentially helpful. So far such helpfulness prediction algorithms have benefited from structural aspects, such as the length and readability score. Since emotional words are at the heart of our written communication and are powerful to trigger listeners' attention, we believe that emotional words can serve as important parameters for predicting helpfulness of review text. ::: ::: Using GALC, a general lexicon of emotional words associated with a model representing 20 different categories, we extracted the emotionality from the review text and applied supervised classification method to derive the emotion-based helpful review prediction. As the second contribution, we propose an evaluation framework comparing three different real-world datasets extracted from the most well-known product review websites. This framework shows that emotion-based methods are outperforming the structure-based approach, by up to 9%.


title: A survey and comparative study of data deduplication techniques

abstract: Increase in enormous amount of digital data needs more storage space, which in turn significantly increases the cost of backup and its performance. Traditional backup solutions do not provide any inherent capability to prevent duplicate data from being backed up. Backup of duplicate data significantly increases the backup time and unnecessary consumption of resources. Data Deduplication plays an important role in eliminating this redundant data and reducing the storage consumption. Its main intend is how to reduce more duplicates efficiently, removing them at high speed and to achieve good duplicate removal ratio. Many mechanisms have been proposed to meet these objectives. It has been observed that achieving one objective may affect the other. In this paper we classify and review the existing deduplication techniques. We also present evaluation of deduplication algorithms by measuring their performance in terms of complexity and efficiency with unstructured files. We propose an efficient means to achieve high deduplication ratio with minimum backup window. We also indicate some key issues need to focus in the future work.


title: Collaborative 20 Questions for Target Localization

abstract: We consider the problem of 20 questions with noise for multiple players under the minimum entropy criterion in the setting of stochastic search, with application to target localization. Each player yields a noisy response to a binary query governed by a certain error probability. First, we propose a sequential policy for constructing questions that queries each player in sequence and refines the posterior of the target location. Second, we consider a joint policy that asks all players questions in parallel at each time instant and characterize the structure of the optimal policy for constructing the sequence of questions. This generalizes the single player probabilistic bisection method for stochastic search problems. Third, we prove an equivalence between the two schemes showing that, despite the fact that the sequential scheme has access to a more refined filtration, the joint scheme performs just as well on average. Fourth, we establish convergence rates of the mean-square error and derive error exponents. Finally, we obtain an extension to the case of unknown error probabilities. This framework provides a mathematical model for incorporating a human in the loop for active machine learning systems.


title: Actor-Oriented Design of Scientific Workflows

abstract: Scientific workflows are becoming increasingly important as a unifying mechanism for interlinking scientific data management, analysis, simulation, and visualization tasks. Scientific workflow systems are problem-solving environments, supporting scientists in the creation and execution of scientific workflows. While current systems permit the creation of executable workflows, conceptual modeling and design of scientific workflows has largely been neglected. Unlike business workflows, scientific workflows are typically highly data-centric naturally leading to dataflow-oriented modeling approaches. We first develop a formal model for scientific workflows based on an actor-oriented modeling and design approach, originally developed for studying models of complex concurrent systems. Actor-oriented modeling separates two modeling concerns: component communication (dataflow) and overall workflow coordination (orchestration). We then extend our framework by introducing a novel hybrid type system, separating further the concerns of conventional data modeling (structural data type) and conceptual modeling (semantic type). In our approach, semantic and structural mismatches can be handled independently or simultaneously, and via different types of adapters, giving rise to new methods of scientific workflow design.