title: Adaptively Attending to Visual Attributes and Linguistic Knowledge for Captioning

abstract: Visual content description has been attracting broad research attention in multimedia community because it deeply uncovers intrinsic semantic facet of visual data. Most existing approaches formulate visual captioning as machine translation task (i.e., from vision to language) via a top-down paradigm with global attention, which ignores to distinguish visual and non-visual parts during word generation. In this work, we propose a novel adaptive attention strategy for visual captioning, which can selectively attend to salient visual content based on linguistic knowledge. Specifically, we design a key control unit, termed visual gate, to adaptively decide "when" and "what" the language generator attend to during the word generation process. We map all the preceding outputs of language generator into a latent space to derive the representation of sentence structures, which assists the "visual gate" to choose appropriate attention timing. Meanwhile, we employ a bottom-up workflow to learn a pool of semantic attributes for serving as the propositional attention resources. We evaluate the proposed approach on two commonly-used benchmarks, i.e., MSCOCO and MSVD. The experimental results demonstrate the superiority of our proposed approach compared to several state-of-the-art methods.


An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease


title: Classification of adaptive memetic algorithms: a comparative study

abstract: Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.


title: Sparse event detection in wireless sensor networks using compressive sensing

abstract: Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for sparse signals. For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Because of deployment cost, the number of sensors is limited, and due to energy constraint, not all the sensors are turned on all the time. In this paper, the first contribution is to formulate the problem for sparse event detection in wireless sensor networks as a compressive sensing problem. The number of (wake-up) sensors can be greatly reduced to the similar level of the number of sparse events, which is much smaller than the total number of sources. Second, we suppose the event has the binary nature, and employ the Bayesian detection using this prior information. Finally, we analyze the performance of the compressive sensing algorithms under the Gaussian noise. From the simulation results, we show that the sampling rate can reduce to 25% without sacrificing performance. With further decreasing the sampling rate, the performance is gradually reduced until 10% of sampling rate. Our proposed detection algorithm has much better performance than the l 1 -magic algorithm proposed in the literature.


title: How organisations leverage Big Data: a maturity model

abstract: Purpose ::: ::: ::: ::: ::: While it is commonly recognised that Big Data have an immense potential to generate value for business organisations, appropriating value from Big Data and, in particular, Big Data-enabled analytics is still an open issue for many organisations. The purpose of this paper is to develop a maturity model to support organisations in the realisation of the value created by Big Data. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The maturity model is developed following a qualitative approach based on literature analysis and semi-structured interviews with domain experts. The completeness and usefulness of the model is evaluated qualitatively by practitioners, whereas the applicability of the model is evaluated by Big Data maturity assessments in three real-world organisations. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The proposed maturity model is considered exhaustive by domain experts and has helped the three assessed organisations to develop a more critical understanding of the next steps to take. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The maturity model integrates existing industry-developed maturity models into one single coherent Big Data maturity model. The proposed model answers the call for research on Big Data to abstract from technical issues to focus on the business implications of Big Data initiatives.


title: Who, Where, When, and What: A Nonparametric Bayesian Approach to Context-aware Recommendation and Search for Twitter Users

abstract: Micro-blogging services and location-based social networks, such as Twitter, Weibo, and Foursquare, enable users to post short messages with timestamps and geographical annotations. The rich spatial-temporal-semantic information of individuals embedded in these geo-annotated short messages provides exciting opportunity to develop many context-aware applications in ubiquitous computing environments. Example applications include contextual recommendation and contextual search. To obtain accurate recommendations and most relevant search results, it is important to capture users’ contextual information (e.g., time and location) and to understand users’ topical interests and intentions. While time and location can be readily captured by smartphones, understanding user’s interests and intentions calls for effective methods in modeling user mobility behavior. Here, user mobility refers to who visits which place at what time for what activity. That is, user mobility behavior modeling must consider user (Who), spatial (Where), temporal (When), and activity (What) aspects. Unfortunately, no previous studies on user mobility behavior modeling have considered all of the four aspects jointly, which have complex interdependencies. In our preliminary study, we propose the first solution named W4 (short for Who, Where, When, and What) to discover user mobility behavior from the four aspects. In this article, we further enhance W4 and propose a nonparametric Bayesian model named EW4 (short for Enhanced W4). EW4 requires no parameter tuning and achieves better results over W4 in our experiments. Given some of the four aspects of a user (e.g., time), our model is able to infer information of the other aspects (e.g., location and topical words). Thus, our model has a variety of context-aware applications, particularly in contextual search and recommendation. Experimental results on two real-world datasets show that the proposed model is effective in discovering users’ spatial-temporal topics. The model also significantly outperforms state-of-the-art baselines for various tasks including location prediction for tweets and requirement-aware location recommendation.


title: The double-side electroplating and slender ferromagnetic patterns torque-enhancement design for magnetostatic actuator

abstract: This study demonstrates the torque-enhancement design for magnetostaic actuators driven by double-side electroplating ferromagnetic film. The present design has two merits: (1) the backside electroplating of the ferromagnetic film increases the magnetostatic torque, (2) the slender ferromagnetic material patterns with higher length-to-width ratio enhance the magnetostatic torque. The actuator is implemented on SOI wafer, and the process also has two merits, (1) the device-layer act as the cathode to enable simultaneous double-side electroplating, (2) the handle-layer is exploited as the shadow mask to pattern the seedlayer at backside wafer. In applications, a scanner were implemented and characterized. Measurements show a 149% torque enhancement from double-side electroplating design. The vertical slender ferromagnetic material patterns further increase the magnetostatic torque to 211%.


title: Deep Learning for Population Genetic Inference

abstract: Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.


title: An Empirical Study into Annotator Agreement, Ground Truth Estimation, and Algorithm Evaluation

abstract: Although agreement between the annotators who mark feature locations within images has been studied in the past from a statistical viewpoint, little work has attempted to quantify the extent to which this phenomenon affects the evaluation of foreground–background segmentation algorithms. Many researchers utilize ground truth (GT) in experimentation and more often than not this GT is derived from one annotator’s opinion. How does the difference in opinion affects an algorithm’s evaluation? A methodology is applied to four image-processing problems to quantify the interannotator variance and to offer insight into the mechanisms behind agreement and the use of GT. It is found that when detecting linear structures, annotator agreement is very low. The agreement in a structure’s position can be partially explained through basic image properties. Automatic segmentation algorithms are compared with annotator agreement and it is found that there is a clear relation between the two. Several GT estimation methods are used to infer a number of algorithm performances. It is found that the rank of a detector is highly dependent upon the method used to form the GT, and that although STAPLE and LSML appear to represent the mean of the performance measured using individual annotations, when there are few annotations, or there is a large variance in them, these estimates tend to degrade. Furthermore, one of the most commonly adopted combination methods—consensus voting—accentuates more obvious features, resulting in an overestimation of performance. It is concluded that in some data sets, it is not possible to confidently infer an algorithm ranking when evaluating upon one GT.


title: Detailed shape representation with parallax mapping

abstract: Although texture mapping is a common technique for adding apparent surface detail to 3-D objects, it lacks the capability to represent the motion parallax effect. So the mesh to which it is applied limits its realism. In this paper, we propose Parallax Mapping, a simple method to motion parallax effects on a polygon. This method has very fast per-pixel shape representation and can be performed in real-time with current hardware.


title: Joint Parsing and Named Entity Recognition

abstract: For many language technology applications, such as question answering, the overall system runs several independent processors over the data (such as a named entity recognizer, a coreference system, and a parser). This easily results in inconsistent annotations, which are harmful to the performance of the aggregate system. We begin to address this problem with a joint model of parsing and named entity recognition, based on a discriminative feature-based constituency parser. Our model produces a consistent output, where the named entity spans do not conflict with the phrasal spans of the parse tree. The joint representation also allows the information from each type of annotation to improve performance on the other, and, in experiments with the OntoNotes corpus, we found improvements of up to 1.36% absolute F1 for parsing, and up to 9.0% F1 for named entity recognition.


title: Multiclass Boosting: Theory and Algorithms

abstract: The problem of multi-class boosting is considered. A new framework, based on multi-dimensional codewords and predictors is introduced. The optimal set of codewords is derived, and a margin enforcing loss proposed. The resulting risk is minimized by gradient descent on a multidimensional functional space. Two algorithms are proposed: 1) CD-MCBoost, based on coordinate descent, updates one predictor component at a time, 2) GD-MCBoost, based on gradient descent, updates all components jointly. The algorithms differ in the weak learners that they support but are both shown to be 1) Bayes consistent, 2) margin enforcing, and 3) convergent to the global minimum of the risk. They also reduce to AdaBoost when there are only two classes. Experiments show that both methods outperform previous multiclass boosting approaches on a number of datasets.


title: Channel Conflict and Coordination in the E-Commerce Age

abstract: A number of factors, including developments in Internet-based commerce and third-party logistics, have led many companies to consider engaging in direct sales. Such a company may at once be both a supplier to and a direct competitor of any existing reseller partners (e.g., land-based retailers), which can result in “channel conflict.” This can have momentous implications for distribution strategy. ::: ::: To generate managerial insights into this important issue, we develop a model that captures key attributes of such a setting, including various sources of inefficiency. We examine these in detail and identify a number of counterintuitive structural properties. For instance, the addition of a direct channel alongside a reseller channel is not necessarily detrimental to the reseller, given the associated adjustment in the manufacturer's pricing. In fact, both parties can benefit. ::: ::: Finally, we examine ways to adjust the manufacturer-reseller relationship that have been observed in industry. These include changes in wholesale pricing, paying the reseller a commission for diverting customers toward the direct channel, or conceding the demand fulfillment function entirely to the reseller. The latter two schemes could be mutually beneficial in that they achieve a division of labor according to each channel's competitive advantage.


title: CARLsim 3: A user-friendly and highly optimized library for the creation of neurobiologically detailed spiking neural networks

abstract: Spiking neural network (SNN) models describe key aspects of neural function in a computationally efficient manner and have been used to construct large-scale brain models. Large-scale SNNs are challenging to implement, as they demand high-bandwidth communication, a large amount of memory, and are computationally intensive. Additionally, tuning parameters of these models becomes more difficult and time-consuming with the addition of biologically accurate descriptions. To meet these challenges, we have developed CARLsim 3, a user-friendly, GPU-accelerated SNN library written in C/C++ that is capable of simulating biologically detailed neural models. The present release of CARLsim provides a number of improvements over our prior SNN library to allow the user to easily analyze simulation data, explore synaptic plasticity rules, and automate parameter tuning. In the present paper, we provide examples and performance benchmarks highlighting the library's features.


Verification of Railway Interlocking Tables using Coloured Petri Nets


title: Measuring project management’s return on investment

abstract: This paper describes a procedure that can help managers measure their return on investment for project management (PM/ROI). Current PM processes and practices of 38 different companies and government agencies in 4 different industries or application areas were quantitatively examined. A PM benchmarking procedure was developed and used to assess the PM process maturity of these different organizations. A 5-level Berkeley PM process maturity model is introduced. This information is used to measure the relative sophistication and maturity of different organizations and industries.


title: Explore-exploit in top-N recommender systems via Gaussian processes

abstract: We address the challenge of ranking recommendation lists based on click feedback by efficiently encoding similarities among users and among items. The key challenges are threefold: (1) combinatorial number of lists; (2) sparse feedback and (3) context dependent recommendations. We propose the CGPRank algorithm, which exploits prior information specified in terms of a Gaussian process kernel function, which allows to share feedback in three ways: Between positions in a list, between items, and between contexts. Under our model, we provide strong performance guarantees and empirically evaluate our algorithm on data from two large scale recommendation tasks: Yahoo! news article recommendation, and Google books. In our experiments, CGPRank significantly outperforms state-of-the-art multi-armed bandit and learning-to-rank methods, with an 18% increase in clicks.


title: Classification of Heart Disease Dataset using Multilayer Feed forward backpropogation Algorithm

abstract: In this study, for the classification of heart diseases dataset, multilayer feed forward network with backpropogation algorithm is introduced. Artificial Neural Network (ANN) is widely used data mining method to extract patterns. Data mining is the process of automating information discovery .Its main aim is to find relationships in data and to predict outcomes. Classification is one of the important data mining techniques for classifying given set of input data. Many real world problems in various fields can be solved by using classification approach such as business, science, industry and medicine. For analysis the medical data related to Heart diseases is considered and analyzed using artificial neural network (ANN). To perform classification task of medical data, the neural network is trained using back propagation algorithm with momentum. To increase the efficiency of the classification process parallel processing approach is also applies on each neuron in different layers.


A Compiler for 3 D Machine Knitting


title: Expanded cloud plumes hiding Big Data ecosystem

abstract: Today, a paradigm shift is being observed in science, where the focus is gradually shifting away from operation to data, which is greatly influencing the decision making also. The data is being inundated proactively from several sources in various forms; especially social media and in modern data science vocabulary is being recognized as Big Data. Today, Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, X?X}classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term "cloud" in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed in the past couple of years. In sum, growing role of cloud in Big Data ecosystem is extensively investigated.A novel XCLOUDX classification is proposed for cloud-assisted Big Data models.A set of important case studies on cloud Big Data models is discussed.The as-a-Service modality of cloud computing is extensively discussed.IoT, Smart Data, and Smart Data Lakes are briefly discussed as future directions.


title: Detecting blickets: How young children use information about novel causal powers in categorization and induction

abstract: Three studies explored whether and when children could categorize objects on the basis of a novel underlying causal power. To test this we constructed a “blicket detector,” a machine that lit up and played music when certain objects were placed on it. First, 2-, 3- and 4-year-old children saw that an object labeled as a “blicket” would set off the machine. In a categorization task, other objects were demonstrated on the machine. Some set it off and some did not. Children were asked to say which objects were “blickets.” In an induction task, other objects were or were not labeled as “blickets.” Children had to predict which objects would have the causal power to set off the machine. The causal power could conflict with perceptual properties of the object, such as color and shape. In an association task the object was associated with the machine’s lighting up but did not cause it to light up. Even the youngest children sometimes used the causal power to determine the object’s name rather than using its perceptual properties and sometimes used the object’s name rather than its perceptual properties to predict the object’s causal powers. Children rarely categorized the object on the basis of the associated event. Young children also sometimes made interesting memory errors—they incorrectly reported that objects with the same perceptual features had had the same causal power. These studies demonstrate that even very young children will easily and swiftly learn about a new causal power of an object and spontaneously use that information in classifying and naming the object.


THE APPLICATION OF COMPUTATIONAL INTELLIGENCE IN ACCOUNTING AND FINANCIAL ACTIVITIES


title: A Solution for Privacy-Preserving Data Manipulation and Query on NoSQL Database

abstract: Privacy of data owners and query users is vital in modern clouding data management. Many researches have been done on cloud security, but most of them are focused on the privacy of data owners or of query users separately. How to protect the privacy of the data owners and users simulta- neously is a great challenge. In this paper, a solution of data storage and query protocol based on classical homomorphic encryption scheme is given to preserve privacy of both data owners and query users. Our main efforts are put on NoSQL database which is less structural than relational database. Storage and indexing structure on NoSQL database, query protocol are proposed, and algorithms for updating and querying are also given. To implement our solution, Berkley DB, an excellent storage solution for NoSQL database is chosen and data are encrypted/decrypted using Elgamal and Paillier encryption system, using basic Java package. Experiments are done under different parameters in order to achieve better efficiency.


title: Social media analytics: a survey of techniques, tools and platforms

abstract: This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an `explosion' of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing.


title: Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management

abstract: Notifications can have reduced interruption cost if delivered at moments of lower mental workload during task execution. Cognitive theorists have speculated that these moments occur at subtask boundaries. In this article, we empirically test this speculation by examining how workload changes during execution of goal-directed tasks, focusing on regions between adjacent chunks within the tasks, that is, the subtask boundaries. In a controlled experiment, users performed several interactive tasks while their pupil dilation, a reliable measure of workload, was continuously measured using an eye tracking system. The workload data was extracted from the pupil data, precisely aligned to the corresponding task models, and analyzed. Our principal findings include (i) workload changes throughout the execution of goal-directed tasks; (ii) workload exhibits transient decreases at subtask boundaries relative to the preceding subtasks; (iii) the amount of decrease tends to be greater at boundaries corresponding to the completion of larger chunks of the task; and (iv) different types of subtasks induce different amounts of workload. We situate these findings within resource theories of attention and discuss important implications for interruption management systems.


title: Universal Correspondence Network

abstract: We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations. In contrast to previous CNN-based approaches that optimize a surrogate patch similarity objective, we use deep metric learning to directly learn a feature space that preserves either geometric or semantic similarity. Our fully convolutional architecture, along with a novel correspondence contrastive loss allows faster training by effective reuse of computations, accurate gradient computation through the use of thousands of examples per image pair and faster testing with O(n) feedforward passes for n keypoints, instead of O(n^2) for typical patch similarity methods. We propose a convolutional spatial transformer to mimic patch normalization in traditional features like SIFT, which is shown to dramatically boost accuracy for semantic correspondences across intra-class shape variations. Extensive experiments on KITTI, PASCAL, and CUB-2011 datasets demonstrate the significant advantages of our features over prior works that use either hand-constructed or learned features.


title: Opinion mining for emotions determination

abstract: The rapid growth of the World Wide Web has increased the online communication. The use of social networking sites is one of the approaches for putting views of user. In order to improve the textual methods of communication such as tweets, blogs and chat; it is needed to analyze the emotion of user by studying the input text and emotions of the user. Much of the current work in this area of emotion determination from text has typically focused on detecting the polarity of sentiment (positive/ negative/ neutral).This paper presents a novel approach for emotion estimation from the text entered by user on social networking sites with direct word or indirect emotions like emoji or smiley faces. The work proposed in this paper uses affective words and sentence context analysis methods for emotion determination. Also for helping the users to effectively express their emotion. On the basis of entered emotion by user it produces the result according to highest emotion classification. For designing the system some literature is review which provides the idea about previous work as well as comparison for improvement.


title: A CMOS focal-plane array for heterodyne terahertz imaging

abstract: In this paper we present a focal-plane array (FPA) for heterodyne imaging at 0.65-THz in a low-cost 0.25-µm CMOS process technology. The 3×5 pixel array is fully integrated on chip and consists of differential patch antennas, NMOS square-law mixers, and 43-dB low-IF amplifiers. The NMOS square-law mixers are based on distributed resistive self-mixing and facilitate mixing well beyond the cutoff frequency of the technology. First 0.65-THz heterodyne imaging results are presented.


title: Classifying single trial eeg: Towards brain computer interfacing

abstract: Driven by the progress in the field of single-trial analysis of EEG, there is a growing interest in brain computer interfaces (BCIs), i.e., systems that enable human subjects to control a computer only by means of their brain signals. In a pseudo-online simulation our BCI detects upcoming finger movements in a natural keyboard typing condition and predicts their laterality. This can be done on average 100-230ms before the respective key is actually pressed, i.e., long before the onset of EMG. Our approach is appealing for its short response time and high classification accuracy (>96%) in a binary decision where no human training is involved. We compare discriminative classifiers like Support Vector Machines (SVMs) and different variants of Fisher Discriminant that possess favorable regularization properties for dealing with high noise cases (inter-trial variablity).


title: Zhishi.me: weaving chinese linking open data

abstract: Linking Open Data (LOD) has become one of the most important community efforts to publish high-quality interconnected semantic data. Such data has been widely used in many applications to provide intelligent services like entity search, personalized recommendation and so on. While DBpedia, one of the LOD core data sources, contains resources described in multilingual versions and semantic data in English is proliferating, there is very few work on publishing Chinese semantic data. In this paper, we present Zhishi.me, the first effort to publish large scale Chinese semantic data and link them together as a Chinese LOD (CLOD). More precisely, we identify important structural features in three largest Chinese encyclopedia sites (i.e., Baidu Baike, Hudong Baike, and Chinese Wikipedia) for extraction and propose several data-level mapping strategies for automatic link discovery. As a result, the CLOD has more than 5 million distinct entities and we simply link CLOD with the existing LOD based on the multilingual characteristic of Wikipedia. Finally, we also introduce three Web access entries namely SPARQL endpoint, lookup interface and detailed data view, which conform to the principles of publishing data sources to LOD.


Trisomy 8 Mosaicism in Two Filipino Children


title: On the Robust Control of Buck-Converter DC-Motor Combinations

abstract: The concepts of active disturbance rejection control and flatness-based control are used in this paper to regulate the response of a dc-to-dc buck power converter affected by unknown, exogenous, time-varying load current demands. The generalized proportional integral observer is used to estimate and cancel the time-varying disturbance signals. A key element in the proposed control for the buck converter-dc motor combination is that even if the control input gain is imprecisely known, the control strategy still provides proper regulation and tracking. The robustness of this method is further extended to the case of a double buck topology driving two different dc motors affected by different load torque disturbances. Simulation results are provided.


title: Radial flux and axial flux PM machines analysis for More Electric Engine aircraft applications

abstract: In this paper the advantages, constraints and drawbacks of the aircraft More Electric Engine (MEE) are presented through a comparison between the radial flux and the axial flux Permanent Magnet (PM) machines for four different positions inside the main gas turbine engine. The electromagnetic preliminary designs are performed on the basis of self-consisting dimensioning equations, starting from the constraints of the available volumes. The actual baseline output generated power is assumed to be 300kVA. Taking into account the technical and reliabilities goals imposed by the specific application, in the paper some technological choices characterizing the projects are discussed too. In order to help the designers in the evaluation of the most promising solutions for this application, the comparison between different proposed design solutions is performed on the basis of several specific design indexes.


title: OpLog: a library for scaling update-heavy data structures

abstract: Existing techniques (e.g., RCU) can achieve good multicore scaling for read-mostly data, but for update-heavy data structures only special-purpose techniques exist. This paper presents OpLog, a general-purpose library supporting good scalability for update-heavy data structures. OpLog achieves scalability by logging each update in a low-contention per-core log; it combines logs only when required by a read to the data structure. OpLog achieves generality by logging operations without having to understand them, to ease application to existing data structures. OpLog can further increase performance if the programmer indicates which operations can be combined in the logs. An evaluation shows how to apply OpLog to three update-heavy Linux kernel data structures. Measurements on a 48-core AMD server show that the result significantly improves the performance of the Apache web server and the Exim mail server under certain workloads.


title: Dynamically Constraining Connectionist Networks to Produce Distributed, Orthogonal Representations to Reduce Catastrophic Interference

abstract: It is well known that when a connectionist network is trained on one set of patterns and then attempts to add new patterns to its repertoire, catastrophic interference may result. The use of sparse, orthogonal hidden-layer representations has been shown to reduce catastrophic interference. The author demonstrates that the use of sparse representations may, in certain cases, actually result in worse performance on catastrophic interference. This paper argues for the necessity of maintaining hidden-layer representations that are both as highly distributed and as highly orthogonal as possible. The author presents a learning algorithm, called context-biasing, that dynamically solves the problem of constraining hiddenlayer representations to simultaneously produce good orthogonality and distributedness. On the data tested for this study, context-biasing is shown to reduce catastrophic interference by more than 50% compared to standard backpropagation. In particular, this technique succeeds in reducing catastrophic interference on data where sparse, orthogonal distributions failed to produce any improvement.


title: CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data

abstract: Chain businesses have been dominating the market in many parts of the world. It is important to identify the optimal locations for a new chain store. Recently, numerous studies have been done on chain store location recommendation. These studies typically learn a model based on the features of existing chain stores in the city and then predict what other sites are suitable for running a new one. However, these models do not work when a chain enterprise wants to open business in a new city where there is not enough data about this chain store. To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e.g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city. In particular, CityTransfer is a two-fold knowledge transfer framework based on collaborative filtering, which consists of the transfer rating prediction model, the inter-city knowledge association method and the intra-city semantic extraction method. Experiments using data of chain hotels from four different cities crawled from Ctrip (a popular travel reservation website in China) and the urban characters extracted from several other data sources validate the effectiveness of our approach on store site recommendation.


title: Managing personal health records using meta-data and cloud storage

abstract: Patient generated data or personal clinical data in general is considered an important aspect in improving patient outcomes. However, personal clinical data is difficult to collect and manage due to their distributed nature, i.e., located over multiple places such as doctor's office, radiology center, hospitals, or some clinics, and heterogeneous data types such as text, image, chart, or paper based documents. In case of emergency, this situation makes necessary personal clinical data retrieval almost impossible. In addition, since the amount and types of personal clinical data continue to grow, finding relevant clinical data when needed is getting more difficult if no actions are taken. In response to such scenarios, we propose an approach that manages personal health data by utilizing meta-data for organization and easy retrieval of clinical data and cloud storage for easy access and sharing with caregivers to implement the continuity of care and evidence-based treatment. In case of emergency, we make critical medical information such as current medication and allergies available to relevant caregivers with valid license numbers only.


title: Subdermal neo-umbilicoplasty in abdominoplasty

abstract: Umbilicoplasty is an important surgical procedure in abdominoplasty, regardless of the technique used. An unaesthetic umbilicus often irreversibly affects surgical outcomes. This study describes the experience of our team with the subdermal neo-umbilicoplasty technique and assesses patient satisfaction with the appearance of the new umbilicus. Fifty-eight patients with abdominal deformity underwent abdominoplasty with subdermal neo-umbilicoplasty. Patients were followed up for at least 1 year with photographic documentation, assessment of patient satisfaction, and evaluation of eventual postoperative complications. Postoperative complications included one case of shallow umbilicus, four cases of superficial necrosis, and one case of midline deviation. No patient required surgical revision. There was a high level of patient satisfaction with the natural-looking umbilicus. Subdermal neo-umbilicoplasty resulted in low postoperative complications and provided a new, natural-looking umbilicus without external scars. Level of evidence: Level IV, therapeutic study


abstract: A cloud based and Android supported scalable home automation system is proposed.The mechanism relies on the distributed Google Cloud Platform.Google Cloud Messaging supports the communication infrastructure in the system.The prototype is evaluated based on a list of criteria for an adequate system. In this paper, an Android based home automation system that allows multiple users to control the appliances by an Android application or through a web site is presented. The system has three hardware components: a local device to transfer signals to home appliances, a web server to store customer records and support services to the other components, and a mobile smart device running Android application. Distributed cloud platforms and Google services are used to support messaging between the components. The prototype implementation of the proposed system is evaluated based on the criteria considered after the requirement analysis for an adequate home automation system. The paper presents the outcomes of a survey carried out regarding the properties of home automation systems, and also the evaluation results of the experimental tests conducted with volunteers on running prototype.


title: Efficient and robust pseudonymous authentication in VANET

abstract: Effective and robust operations, as well as security and privacy are critical for the deployment of vehicular ad hoc networks (VANETs). Efficient and easy-to-manage security and privacy-enhancing mechanisms are essential for the wide-spread adoption of the VANET technology. In this paper, we are concerned with this problem; and in particular, how to achieve efficient and robust pseudonym-based authentication. We design mechanisms that reduce the security overhead for safety beaconing, and retain robustness for transportation safety, even in adverse network settings. Moreover, we show how to enhance the availability and usability of privacy-enhancing VANET mechanisms: Our proposal enables vehicle on-board units to generate their own pseudonyms, without affecting the system security.


title: Brain Activation during Human Male Ejaculation

abstract: Brain mechanisms that control human sexual behavior in general, and ejaculation in particular, are poorly understood. We used positron emission tomography to measure increases in regional cerebral blood flow (rCBF) during ejaculation compared with sexual stimulation in heterosexual male volunteers. Manual penile stimulation was performed by the volunteer's female partner. Primary activation was found in the mesodiencephalic transition zone, including the ventral tegmental area, which is involved in a wide variety of rewarding behaviors. Parallels are drawn between ejaculation and heroin rush. Other activated mesodiencephalic structures are the midbrain lateral central tegmental field, zona incerta, subparafascicular nucleus, and the ventroposterior, midline, and intralaminar thalamic nuclei. Increased activation was also present in the lateral putamen and adjoining parts of the claustrum. Neocortical activity was only found in Brodmann areas 7/40, 18, 21, 23, and 47, exclusively on the right side. On the basis of studies in rodents, the medial preoptic area, bed nucleus of the stria terminalis, and amygdala are thought to be involved in ejaculation, but increased rCBF was not found in any of these regions. Conversely, in the amygdala and adjacent entorhinal cortex, a decrease in activation was observed. Remarkably strong rCBF increases were observed in the cerebellum. These findings corroborate the recent notion that the cerebellum plays an important role in emotional processing. The present study for the first time provides insight into which regions in the human brain play a primary role in ejaculation, and the results might have important implications for our understanding of how human ejaculation is brought about, and for our ability to improve sexual function and satisfaction in men.


title: Shoulder Surfing Resistant Virtual Keyboard for Internet Banking

abstract: 2 Abstract: Today the prevalence of online banking is enormous. People prefer to accomplish their financial transactions through the online banking services offered by their banks. This method of accessing is more convenient, quicker and secured. Banks are also encouraging their customers to opt for this mode of e-banking facilities since that result in cost savings for the banks and there is better customer satisfaction. An important aspect of online banking is the precise authentication of users before allowing them to access their accounts. Typically this is done by asking the customers to enter their unique login id and password combination. The success of this authentication relies on the ability of customers to maintain the secrecy of their passwords. Since the customer login to the banking portals normally occur in public environments, the passwords are prone to key logging attacks. To avoid this, virtual keyboards are provided. But virtual keyboards are vulnerable to shoulder surfing based attacks. In this paper, a secured virtual keyboard scheme that withstands such attacks is proposed. Elaborate user studies carried out on the proposed scheme have testified the security and the usability of the proposed approach.


title: Section classification in clinical notes using supervised hidden markov model

abstract: As more and more information is available in the Electronic Health Record in the form of free-text narrative, there is a need for automated tools, which can process and understand such texts. One first step towards the automated processing of clinical texts is to determine the document-level structure of a patient note, i.e., identifying the different sections and mapping them to known section types automatically. This paper considers section mapping as a sequence-labeling problem to 15 possible known section types. Our method relies on a Hidden Markov Model (HMM) trained on a corpus of 9,679 clinical notes from NewYork-Presbyterian Hospital. We compare our method to a state-of-the-art baseline, which ignores the sequential aspect of the sections and considers each section independently of the others in a note. Experiments show that our method outperforms the baseline significantly, yielding 93% accuracy in identifying sections individually and 70% accuracy in identifying all the sections in a note, compared to 70% and 19% for the baseline method respectively.


title: A general framework for benchmarking firewall optimization techniques

abstract: Firewalls are among the most pervasive network security mechanisms, deployed extensively from the borders of networks to end systems. The complexity of modern firewall policies has raised the computational requirements for firewall implementations, potentially limiting the throughput of networks. Administrators currently rely on ad hoc solutions to firewall optimization. To address this problem, a few automatic firewall optimization techniques have been proposed, but there has been no general approach to evaluate the optimality of these techniques. In this paper we present a general framework for rule-based firewall optimization. We give a precise formulation of firewall optimization as an integer programming problem and show that our framework produces optimal reordered rule sets that are semantically equivalent to the original rule set. Our framework considers the complex interactions among the rules in firewall configurations and relies on a novel partitioning of the packet space defined by the rules themselves. For validation, we employ this framework on real firewall rule sets for a quantitative evaluation of existing heuristic approaches. Our results indicate that the framework is general and faithfully captures performance benefits of firewall optimization heuristics.


title: SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities

abstract: SigTur/E-Destination is a Web-based system that provides personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labeled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. The system has been fully designed and implemented in the Science and Technology Park of Tourism and Leisure. The paper presents a numerical evaluation of the correlation between the recommendations and the user's motivations, and a qualitative evaluation performed by end users.


title: What exactly is viral marketing

abstract: An attachment for a tennis racket characterized by a pair of resilient retaining members releasably positioned on the frame of the racket and arranged to receive a tennis ball resting on a court or other surface, and to raise such with upward movement of the racket for subsequent removal and use. With the ready grasping of a tennis ball, the tennis racket attachment eliminates, or at least minimizes, the countless bending and stooping formerly required for tennis ball retrieval during the course of a tennis game.


title: A Neural Network Component for an Intrusion Detection System Herv

abstract: An approach toward user behavior modeling that takes advantage of the properties of neural algorithms is described, and results obtained on preliminary testing of the approach are presented. The basis of the approach is the IDES (Intruder Detection Expert System) which has two components, an expert system looking for evidence of attacks on known vulnerabilities of the system and a statistical model of the behavior of a user on the computer system under surveillance. This model learns the habits a user has when he works with the computer, and raises warnings when the current behavior is not consistent with the previously learned patterns. The authors suggest the time series approach to add broader scope to the model. They therefore feel the need for alternative techniques and introduce the use of a neural network component for modeling user's behavior as a component for the intrusion detection system. >


title: Body Parts Segmentation with Attached Props Using RGB-D Imaging

abstract: People detection is essential in a lot of different systems. Many applications nowadays tend to require people detection to achieve certain tasks. These applications come under many disciplines, such as robotics, ergonomics, biomechanics, gaming and automotive industries. This wide range of applications makes human body detection an active area of research. With the release of depth sensors or RGB-D cameras such as Micosoft Kinect, this area of research became more active, specially with their affordable price. Human body detection requires the adaptation of many scenarios and situations. Various conditions such as occlusions, background cluttering and props attached to the human body require training on custom built datasets. In this paper we present an approach to prepare training datasets to detect and track human body with attached props. The proposed approach uses rigid body physics simulation to create and animate different props attached to the human body. Three scenarios are implemented. In the first scenario the prop is closely attached to the human body, such as a person carrying a backpack. In the second scenario, the prop is slightly attached to the human body, such as a person carrying a briefcase. In the third scenario the prop is not attached to the human body, such as a person dragging a trolley bag. Our approach gives results with accuracy of 93% in identifying both the human body parts and the attached prop in all the three scenarios.


title: Managing Duplicated Code with Linked Editing

abstract: We present linked editing, a novel, lightweight editor-based technique for managing duplicated source code. Linked editing is implemented in a prototype editor called Codelink. We argue that the use of programming abstractions like functions and macros - the traditional solution to duplicated code - has inherent cognitive costs, leading programmers to chronically copy and paste code instead. Our user study compares functional abstraction with linked editing and shows that linked editing can give the benefits of abstraction with orders of magnitude decrease in programming time


title: Interactive Medical Image Segmentation Using Snake and Multiscale Curve Editing

abstract: Image segmentation is typically applied to locate objects and boundaries, and it is an essential process that supports medical diagnosis, surgical planning, and treatments in medical applications. Generally, this process is done by clinicians manually, which may be accurate but tedious and very time consuming. To facilitate the process, numerous interactive segmentation methods have been proposed that allow the user to intervene in the process of segmentation by incorporating prior knowledge, validating results and correcting errors. The accurate segmentation results can potentially be obtained by such user-interactive process. In this work, we propose a novel framework of interactive medical image segmentation for clinical applications, which combines digital curves and the active contour model to obtain promising results. It allows clinicians to quickly revise or improve contours by simple mouse actions. Meanwhile, the snake model becomes feasible and practical in clinical applications. Experimental results demonstrate the effectiveness of the proposed method for medical images in clinical applications.


title: Xscale: Online X-Code RAID-6 Scaling Using Lightweight Data Reorganization

abstract: Disk additions to a RAID-6 storage system can simultaneously increase the I/O parallelism and expand the storage capacity. To regain a balanced load among both old and new disks, RAID-6 scaling requires moving certain data blocks onto newly added disks. Existing approaches to RAID-6 scaling are restricted by preserving a round-robin data distribution, and require migrating all the data, resulting in an expensive cost for RAID-6 scaling. In this paper, we propose Xscale, a new approach to accelerating X-code RAID-6 scaling by using lightweight data reorganization. Xscale minimizes the number of data blocks that require being moved, while maintaining a uniform data distribution across all disks. Furthermore, Xscale eliminates metadata updates while guaranteeing data consistency and data reliability. Compared with the round-robin approach, Xscale reduces the number of blocks to be moved by 63.6-89.5 percent, decreases the reorganization time by 35.62-37.26 percent, and reduces the I/O latency by 23.29-37.74 percent while the scaling programs are running in the background. In addition, there is no penalty in the performance of the data layout after scaling using Xscale, compared with the layouts maintained by other existing scaling approaches.


title: Driving Process Improvement via Comparative Agility Assessment

abstract: Rather than striving to be “perfectly agile, ” some organizations desire to be more agile than their competition and/or the industry. The Comparative Agility™ (CA) assessment tool can be used to aid organizations in determining their relative agility compared with other teams who responded to CA. The results of CA can be used by a team to guide process improvement related to the use of agile software development practices. This paper provides an overview of industry trends in agility based upon 1, 235 CA respondents in a range of domains and geographical locations. Additionally, the paper goes further in depth on the results of four industrial teams who responded to the CA, explaining why their results were relatively high or low based upon experiences with the teams. The paper also discusses the resultant process improvement reactions and plans of these teams subsequent to reviewing their CA results.


title: Tensor Representation in High-Frequency Financial Data for Price Change Prediction

abstract: Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and detect patterns of price change based on transaction records must be available. The multichannel, time-series representation of financial data naturally suggests tensor-based learning algorithms. In this work, we investigate the effectiveness of two multilinear methods for the mid-price prediction problem against other existing methods. The experiments in a large scale dataset which contains more than 4 millions limit orders show that by utilizing tensor representation, multilinear models outperform vector-based approaches and other competing ones.


title: Monitoring of gait performance using dynamic time warping on IMU-sensor data

abstract: In this paper, a novel method for monitoring the changes in gait joint angle trajectories recorded using the low-cost and wearable Inertial Measurement Units (IMU) is presented. The introduced method is based on Dynamic Time Warping (DTW), an algorithm commonly used for evaluating the similarity of two time series which may vary in time and speed. DTW is employed as the measure of distance between two gait trajectories taken in different time instances, which could be used as an intuitive and effective measure for the evaluation of gait performances. The experimental results presented in the paper demonstrate that the proposed method is applicable for clinically relevant applications and is consequently adaptable to patients with diseases characterized with gait disorders and to different walking scenarios. The proposed method was firstly validated by applying the DTW-based measure on gait trajectories of five healthy subjects recorded while simulating different levels of walking disabilities. Then proposed measure was applied to estimate the distance between the “healthy” gait trajectories and gait trajectories of three patients with Parkinson's disease (PD) while performing single-task and dual-task overground walking. Also, the proposed measure was demonstrated as an effective measure for monitoring the changes in gait patterns of a PD patient before and after medication-based treatment. This result indicates potential use of proposed method for effective pharmacological management of PD.


title: Stochastic Gradient Boosting

abstract: Gradient boosting constructs additive regression models by sequentially fitting a simple parameterized function (base learner) to current "pseudo'-residuals by least squares at each iteration. The pseudo-residuals are the gradient of the loss functional being minimized, with respect to the model values at each training data point evaluated at the current step. It is shown that both the approximation accuracy and execution speed of gradient boosting can be substantially improved by incorporating randomization into the procedure. Specifically, at each iteration a subsample of the training data is drawn at random (without replacement) from the full training data set. This randomly selected subsample is then used in place of the full sample to fit the base learner and compute the model update for the current iteration. This randomized approach also increases robustness against overcapacity of the base learner.


title: Named Entity Recognition using FOX

abstract: Unstructured data still makes up an important portion of the Web. One key task towards transforming this unstructured data into structured data is named entity recognition. We demo FOX, the Federated knOwledge eXtraction framework, a highly accurate open-source framework that implements RESTful web services for named entity recognition. Our framework achieves a higher F-measure than state-of-the-art named entity recognition frameworks by combining the results of several approaches through ensemble learning. Moreover, it disambiguates and links named entities against DBpedia by relying on the AGDISTIS framework. As a result, FOX provides users with accurately disambiguated and linked named entities in several RDF serialization formats. We demonstrate the different interfaces implemented by FOX within use cases pertaining to extracting entities from news texts.


Anticipating the Future by Constructing Human Activities using Object Affordances


title: Automated ontology construction for unstructured text documents

abstract: Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.


A New Calibration Method for SAR Analog-to-Digital Converters Based on All Digital


sHype: Secure Hypervisor Approach to Trusted Virtualized Systems


title: Fuzzy Cognitive Maps Tool for Scenario Analysis and Pattern Classification

abstract: After 30 years of research, challenges and solutions, Fuzzy Cognitive Maps (FCMs) have become a suitable knowledgebased methodology for modeling and simulation. This technique is especially attractive when modeling systems that are characterized by ambiguity, complexity and non-trivial causality. FCMs are well-known due to the transparency achieved during modeling tasks. The literature reports successful studies related to the modeling of complex systems using FCMs. However, the situation is not the same when it comes to software implementations where domain experts can design FCM-based systems, run simulations or perform more advanced experiments. The existing implementations are not proficient in providing many options to adjust essential parameters during the modeling steps. The gap between the theoretical advances and the development of accurate, transparent and sound FCM-based systems advocates for the creation of more complete and flexible software products. Therefore, the goal of this paper is to introduce FCM Expert, a software tool for fuzzy cognitive modeling oriented to scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters defining the model, optimize the network topology and improve the system convergence without losing information. On the other hand, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.


title: A Semantic Web Based Architecture for Assessment Analytics

abstract: Learning analytics has attracted a remarkable attention in the last years due to its potential to support and improve the learning experience. The first step of the learning analytics process is the educational data collection, therefore it is highly significant to track and collect the big educational data generated (learning data, assessment data, communication data etc). Assessment data represent an important proportion of the educational data generated. When we focus on assessment, we can launch a new source of data that can be analyzed and hence contribute to the improvement of the field of learning analytics. In this paper, we will focus on assessment data by proposing an assessment analytics architecture. This architecture uses the semantic web technologies and focus essentially on analyzing the set of assessment activities, the assessment result and the assessment context. These assessment data are conceived and annotated via an ontological model, inspired from the xAPI data model, which enable performing logic rules for supporting inference mechanisms related to assessment analytics and especially concerning the concrete level of the assessment actor and the level of difficulty of the assessment object.


title: GNSS/INS based estimation of air data and wind vector using flight maneuvers

abstract: We develop a GNSS/INS based algorithm to estimate air data (angle-of-attack and sideslip angle) and wind vector in real time. Our work uses observability analysis to demonstrate the feasibility of the model-free synthetic air data estimation. In addition, we show that certain canonical flight maneuvers, defined by the aircraft's orientation and airspeed, result in a high degree of observability for the air data parameters and the wind velocity vector estimates. Furthermore, a lower bound on the average time of wind variation can be derived from the analysis. Finally, the GNSS/INS-based algorithm is tested to estimate these parameters using simulation data. Results of simulation are consistent with the observability analysis.


title: A young root-specific gene (ArMY2) from horseradish encoding a MYR II myrosinase with kinetic preference for the root-specific glucosinolate gluconasturtiin.

abstract: Abstract The pungent taste of horseradish is caused by isothiocyanates which are released from glucosinolates by myrosinases. These enzymes are encoded by genes belonging to one of two subfamilies, termed MYR I and MYR II, respectively. A MYR II-type myrosinase gene was identified for the first time in horseradish. The gene termed ArMY 2 was only expressed in young roots. A full-length cDNA encoding a myrosinase termed ArMy2 was isolated and heterologously expressed in Pichia pastoris . The recombinant His-tagged enzyme was characterized biochemically. Substrate affinity was 5 times higher towards gluconasturtiin than towards sinigrin. Gluconasturtiin was found to be the most abundant glucosinolate in young horseradish roots while sinigrin dominated in storage roots and leaves. This indicates that a specialized glucosinolate–myrosinase defense system might be active in young roots.


title: Periodicity data mining in time series using Suffix Arrays

abstract: This research paper focuses on data mining in time series and its applications on financial data. Data-mining attempts to analyze time series and extract valuable information about pattern periodicity, which might be concealed by substantial amounts of unformatted, random information. Such information, however, is of great importance as it can be used to forecast future behavior. In this paper, a new methodology is introduced aiming to utilize Suffix Arrays in data mining instead of the commonly used data structure Suffix Trees. Although Suffix Arrays, normally, require high storage capacity, the algorithm proposed allows them to be constructed in linear time. The methodology is also extended to detect repeated patterns in time series with time complexity of. This, combined with the capability of external storage, creates a critical advantage, for an overall efficient data mining and analysis regarding the construction of time series data structure and periodicity detection. The test results, presented below demonstrate the applicability and effectiveness of the proposed technique.


DAG-Structured Long Short-Term Memory for Semantic Compositionality


title: Feedback effects between similarity and social influence in online communities

abstract: A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings. We develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. We find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. We also consider the relative value of similarity and social influence in modeling future behavior. For instance, to predict the activities that an individual is likely to do next, is it more useful to know the current activities of their friends, or of the people most similar to them?


12 SYSTEM DYNAMICS : SYSTEMIC FEEDBACK MODELING FOR POLICY ANALYSIS


title: Detecting malicious tweets in trending topics using a statistical analysis of language

abstract: Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody's lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34K trending topics and 20million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-of-the-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.


title: Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

abstract: Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort (BBC, 2015; Lomas, 2015). We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of various extra-linguistic features in conjunction with character n-grams for hatespeech detection. We also present a dictionary based the most indicative words in our data.


title: Yield and speed optimization of a latch-type voltage sense amplifier

abstract: A quantitative yield analysis of a latch-type voltage sense amplifier with a high-impedance differential input stage is presented. It investigates the impact of supply voltage, input DC level, transistor sizing, and temperature on the input offset voltage. The input DC level turns out to be most significant. Also, an analytical expression for the sensing delay is derived which shows low sensitivity on the input DC bias voltage. A figure of merit indicates that an input dc level of 0.7 V/sub DD/ is optimal regarding speed and yield. Experimental results in 130-nm CMOS technology confirm that the yield can be significantly improved by lowering the input DC voltage to about 70% of the supply voltage. Thereby, the offset standard deviation decreases from 19 to 8.5 mV without affecting the delay.


title: A Review on Internet of Things for Defense and Public Safety

abstract: The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies (i.e., data-driven applications or embedded automation and intelligent adaptive systems) to revolutionize modern warfare and provide beneﬁts similar to those in industry. It identiﬁes scenarios where Defense and Public Safety (PS) could leverage better commercial IoT capabilities to deliver greater survivability to the warﬁghter or ﬁrst responders, while reducing costs and increasing operation efﬁciency and effectiveness. This article reviews the main tactical requirements and the architecture, examining gaps and shortcomings in existing IoT systems across the military ﬁeld and mission-critical scenarios. The review characterizes the open challenges for a broad deployment and presents a research roadmap for enabling an affordable IoT for defense and PS.


title: Unsupervised graph-based topic labelling using dbpedia

abstract: Automated topic labelling brings benefits for users aiming at analysing and understanding document collections, as well as for search engines targetting at the linkage between groups of words and their inherent topics. Current approaches to achieve this suffer in quality, but we argue their performances might be improved by setting the focus on the structure in the data. Building upon research for concept disambiguation and linking to DBpedia, we are taking a novel approach to topic labelling by making use of structured data exposed by DBpedia. We start from the hypothesis that words co-occuring in text likely refer to concepts that belong closely together in the DBpedia graph. Using graph centrality measures, we show that we are able to identify the concepts that best represent the topics. We comparatively evaluate our graph-based approach and the standard text-based approach, on topics extracted from three corpora, based on results gathered in a crowd-sourcing experiment. Our research shows that graph-based analysis of DBpedia can achieve better results for topic labelling in terms of both precision and topic coverage.


title: Hyperprobe: towards virtual machine extrospection

abstract: In a virtualized environment, it is not difficult to retrieve guest OS information from its hypervisor. However, it is very challenging to retrieve information in the reverse direction, i.e., retrieve the hypervisor information from within a guest OS, which remains an open problem and has not yet been comprehensively studied before. In this paper, we take the initiative and study this reverse information retrieval problem. In particular, we investigate how to determine the host OS kernel version from within a guest OS. We observe that modern commodity hypervisors introduce new features and bug fixes in almost every new release. Thus, by carefully analyzing the seven-year evolution of Linux KVM development (including 3485 patches), we can identify 19 features and 20 bugs in the hypervisor detectable from within a guest OS. Building on our detection of these features and bugs, we present a novel framework called Hyperprobe that for the first time enables users in a guest OS to automatically detect the underlying host OS kernel version in a few minutes. We implement a prototype of Hyperprobe and evaluate its effectiveness in five real world clouds, including Google Compute Engine (a.k.a. Google Cloud), HP Helion Public Cloud, ElasticHosts, Joyent Cloud, and CloudSigma, as well as in a controlled testbed environment, all yielding promising results.


title: The LFM-1b Dataset for Music Retrieval and Recommendation

abstract: We present the LFM-1b dataset of more than one billion music listening events created by more than 120,000 users of Last.fm. Each listening event is characterized by artist, album, and track name, and further includes a timestamp. On the (anonymous) user level, basic demographics and a selection of more elaborate user descriptors are included. The dataset is foremost intended for benchmarking in music information retrieval and recommendation. To facilitate experimentation in a straightforward manner, it also includes a precomputed user-item-playcount matrix. In addition, sample Python scripts showing how to load the data and perform efficient computations are provided. An implementation of a simple collaborative filtering recommender rounds off the code package. We discuss in detail the LFM-1b dataset's acquisition, availability, statistics, and content, and place it in the context of existing datasets. We also showcase its usage in a simple artist recommendation task, whose results are intended to serve as baseline against which more elaborate techniques can be assessed. The two unique features of the dataset in comparison to existing ones are (i) its substantial size and (ii) a wide range of additional user descriptors that reflect their music taste and consumption behavior.


title: Attack and Flee: Game-Theory-Based Analysis on Interactions Among Nodes in MANETs

abstract: In mobile ad hoc networks, nodes have the inherent ability to move. Aside from conducting attacks to maximize their utility and cooperating with regular nodes to deceive them, malicious nodes get better payoffs with the ability to move. In this paper, we propose a game theoretic framework to analyze the strategy profiles for regular and malicious nodes. We model the situation as a dynamic Bayesian signaling game and analyze and present the underlining connection between nodes' best combination of actions and the cost and gain of the individual strategy. Regular nodes consistently update their beliefs based on the opponents' behavior, while malicious nodes evaluate their risk of being caught to decide when to flee. Some possible countermeasures for regular nodes that can impact malicious nodes' decisions are presented as well. An extensive analysis and simulation study shows that the proposed equilibrium strategy profile outperforms other pure or mixed strategies and proves the importance of restricting malicious nodes' advantages brought by the flee option.


title: A proposal for microgrids control architecture as aggregator

abstract: This paper presents a review of microgrid control architectures, and it proposes a decentralized control architecture for multiple microgrids operating as a power aggregator in demand response. This architecture consists of four control levels. The interrelationships of the hierarchical levels in the proposed architecture are described, and the operation models of control architectures for each microgrid and for multiple microgrids operating as aggregator are presented. Finally, some comments about the proposal are presented.


title: Similarity Measures for Categorical Data: A Comparative Evaluation

abstract: Measuring similarity or distance between two entities is a key step for several data mining and knowledge discovery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Several data-driven similarity measures have been proposed in the literature to compute the similarity between two categorical data instances but their relative performance has not been evaluated. In this paper we study the performance of a variety of similarity measures in the context of a specific data mining task: outlier detection. Results on a variety of data sets show that while no one measure dominates others for all types of problems, some measures are able to have consistently high performance.


title: Lessons from a real world evaluation of anti-phishing training

abstract: Prior laboratory studies have shown that PhishGuru, an embedded training system, is an effective way to teach users to identify phishing scams. PhishGuru users are sent simulated phishing attacks and trained after they fall for the attacks. In this current study, we extend the PhishGuru methodology to train users about spear phishing and test it in a real world setting with employees of a Portuguese company. Our results demonstrate that the findings of PhishGuru laboratory studies do indeed hold up in a real world deployment. Specifically, the results from the field study showed that a large percentage of people who clicked on links in simulated emails proceeded to give some form of personal information to fake phishing websites, and that participants who received PhishGuru training were significantly less likely to fall for subsequent simulated phishing attacks one week later. This paper also presents some additional new findings. First, people trained with spear phishing training material did not make better decisions in identifying spear phishing emails compared to people trained with generic training material. Second, we observed that PhishGuru training could be effective in training other people in the organization who did not receive training messages directly from the system. Third, we also observed that employees in technical jobs were not different from employees with non-technical jobs in identifying phishing emails before and after the training. We conclude with some lessons that we learned in conducting the real world study.


title: Enhancing K-Means using class labels

abstract: Clustering is a relevant problem in machine learning where the main goal is to locate meaningful partitions of unlabeled data. In the case of labeled data, a related problem is supervised clustering, where the objective is to locate class-uniform clusters. Most current approaches to supervised clustering optimize a score related to cluster purity with respect to class labels. In particular, we present Labeled K-Means LK-Means, an algorithm for supervised clustering based on a variant of K-Means that incorporates information about class labels. LK-Means replaces the classical cost function of K-Means by a convex combination of the joint cost associated to: i A discriminative score based on class labels, and ii A generative score based on a traditional metric for unsupervised clustering. We test the performance of LK-Means using standard real datasets and an application for object recognition. Moreover, we also compare its performance against classical K-Means and a popular K-Medoids-based supervised clustering method. Our experiments show that, in most cases, LK-Means outperforms the alternative techniques by a considerable margin. Furthermore, LK-Means presents execution times considerably lower than the alternative supervised clustering method under evaluation.


title: Treatment of a Pigmented Hypertrophic Scar by Low-Level Laser Therapy (LLLT): a Case Report

abstract: A hypertrophic scar is defined as an excess healing response that is a dilemma for physicians. Several therapies are available: intralesional corticosteroids, topical treatments, cryotherapy, surgery, radiation, silicone gel dressing and laser therapy.Pulsed-dye, Nd-Yag and CO2 lasers have been used for treatment of keloids and hypertrophic scars but recurrence is common. Recently Low-Level Laser Therapy(aluminum-gallium-arsenide (AlGaAs) Diode 980nm, red light (Mustung, KLO4,Helium Neon 630 nm) and blue light LED lasers have been used for closure of wounds. The aim of this report is to show the effectiveness of these lasers for the treatment of a hypertrophic scar on the forearm of a 40 year-old woman due to burning by gas explosion.


title: Imperceptible depth shifts for touch interaction with stereoscopic objects

abstract: While touch technology has proven its usability for 2D interaction and has already become a standard input modality for many devices, the challenges to exploit its applicability with stereoscopically rendered content have barely been studied. In this paper we exploit the properties of the visual perception to allow users to touch stereoscopically displayed objects when the input is constrained to a 2D surface. Therefore, we have extended and generalized recent evaluations on the user's ability to discriminate small induced object shifts while reaching out to touch a virtual object, and we propose a practical interaction technique, the attracting shift technique, suitable for numerous application scenarios where shallow depth interaction is sufficient. In addition, our results indicate that slight object shifts during touch interaction make the virtual scene appear perceptually more stable compared to a static scene. As a consequence, applications have to manipulate the virtual objects to make them appear static for the user.


title: Statement Types in Legal Argument.

abstract: © 2016 The authors and IOS Press. All rights reserved. In this paper we present an overview of the process of argumentation with legal cases, from evidence to verdict. We identify the various different types of statement involved in the various stages, and describe how the various types relate to one another. In particular we show how we can obtain the legally accepted facts which form the basis for consideration of the law governing the cases from facts about the world. We also explain how we can determine which particular facts are relevant. In so doing we bring together several important pieces of AI and Law research and clarify their relationships.


title: Cyclopia and proboscis - the extreme end of holoprosencephaly.

abstract: Holoprosencephaly (HPE), a major congenital abnormality in brain development is characterized by the absence or incomplete cleavage of prosencephalon into separate hemispheres, with cyclopia as the extreme manifestation of HPE, presenting as a failure of embryonic prosencephalon to properly divide the orbits of the eye in two cavities. We report the case of a 15-year-old pregnant patient, who delivered a 34-week living fetus with alobar HPE, cyclopia and proboscis. The patient did not have any routine scans during pregnancy; her first obstetrical exam was performed at 29 weeks of gestation (WG), when a prenatal ultrasound found a fetus with alobar HPE, cyclopia, proboscis, polydactyly and single umbilical artery. Despite adequate medical and genetic counseling, the patient and her legal representative refused further investigations - magnetic resonance imaging and genetic testing. She was admitted to the hospital at 34 WG for premature rupture of membranes, with clear amniotic fluid. Twenty-four hours later, she delivered vaginally a living male fetus, weighing 1995 g. Macroscopic examination revealed umbilical cord with two vessels, fetal proboscis, cyclopia, low implanted ears, bilateral polydactyly of the upper limbs, spina bifida occulta in the sacral region. The newborn lived for 40 minutes. Microscopy of the eyeball revealed choroid, ciliary body and conjunctiva structures, with no identification of the retina, and no evidence of the optic nerve in the fragments obtained from the optic chiasm region. This case underlines the importance of early obstetrical examinations during pregnancy and raises concerns about the ethics of allowing therapeutic termination of pregnancy after 24 WG in selected cases.


title: Probabilistic Group Recommendation Model for Crowdfunding Domains

abstract: Crowdfunding has gained a widespread popularity by fueling the creative minds of entrepreneurs. Not only has it democratized the funding of startups, it has also bridged the gap between the venture capitalists and the entrepreneurs by providing a plethora of opportunities for people seeking to invest in new business ventures. Nonetheless, despite the huge success of the crowdfunding platforms, not every project reaches its funding goal. One of the main reasons for a project's failure is the difficulty in establishing a linkage between it's founders and those investors who are interested in funding such projects. A potential solution to this problem is to develop recommendation systems that suggest suitable projects to crowdfunding investors by capturing their interests. In this paper, we explore Kickstarter, a popular reward-based crowdfunding platform. Being a highly heterogeneous platform, Kickstarter is fuelled by a dynamic community of people who constantly interact with each other before investing in projects. Therefore, the decision to invest in a project depends not only on the preference of individuals, but also on the influence of groups that a person belongs and the on-going status of the projects. In this paper, we propose a probabilistic recommendation model, called CrowdRec, that recommends Kickstarter projects to a group of investors by incorporating the on-going status of projects, the personal preference of individual members, and the collective preference of the group . Using a comprehensive dataset of over 40K crowdfunding groups and 5K projects, we show that our model is effective in recommending projects to groups of Kickstarter users.


title: Online Resource Scheduling Under Concave Pricing for Cloud Computing

abstract: With the booming growth of cloud computing industry, computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different IaaS service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers' resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker may help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Our simulation shows that ROSA achieves a competitive ratio close to the theoretical lower bound under a special case cost function. Trace driven simulation using Google cluster data demonstrates that ROSA is superior to the conventional online scheduling algorithms in terms of cost saving.


A Bayesian Classification Model for Fraud Detection over ATM Platforms


title: CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision

abstract: Knowledge of 3D properties of objects is a necessity in order to build effective computer vision systems. However, lack of large scale 3D datasets can be a major constraint for datadriven approaches in learning such properties. We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets. A novel differentiable projection module, called ‘CAPNet’, is introduced to obtain such 2D masks from a predicted 3D point cloud. The key idea is to model the projections as a continuous approximation of the points in the point cloud. To overcome the challenges of sparse projection maps, we propose a loss formulation termed ‘affinity loss’ to generate outlierfree reconstructions. We significantly outperform the existing projection based approaches on a large-scale synthetic dataset. We show the utility and generalizability of such a 2D supervised approach through experiments on a real-world dataset, where lack of 3D data can be a serious concern. To further enhance the reconstructions, we also propose a test stage optimization procedure to obtain reconstructions that display high correspondence with the observed input image.


Phenolic components of smoked meat products


title: MIX: Multi-Channel Information Crossing for Text Matching

abstract: Short Text Matching plays an important role in many natural language processing tasks such as information retrieval, question answering, and conversational system. Conventional text matching methods rely on predefined templates and rules, which are not applicable to short text with limited numebr of words and limit their ability to generalize to unobserved data. Many recent efforts have been made to apply deep neural network models to natural language processing tasks, which reduces the cost of feature engineering. In this paper, we present the design of Multi-Channel Information Crossing , a multi-channel convolutional neural network model for text matching, with additional attention mechanisms from sentence and text semantics. MIX compares text snippets at varied granularities to form a series of multi-channel similarity matrices, which are crossed with another set of carefully designed attention matrices to expose the rich structures of sentences to deep neural networks. We implemented MIX and deployed the system on Tencent's Venus distributed computation platform. Thanks to carefully engineered multi-channel information crossing, evaluation results suggest that MIX outperforms a wide range of state-of-the-art deep neural network models by at least 11.1% in terms of the normalized discounted cumulative gain (NDCG@3), on the English WikiQA dataset. Moreover, we also performed online A/B tests with real users on the search service of Tencent QQ Browser. Results suggest that MIX raised the number of clicks on the returned results by 5.7%, due to an increased accuracy in query-document matching, which demonstrates the superior performance of MIX in production environments.


title: Multi-sensor fusion for reduced uncertainty in autonomous mobile robot docking and recharging

abstract: The power management system for autonomous mobile robots is an important issue for keeping robots in their long-time functionality. Recharging is necessary before the power of the robot has exhausted. In this paper, we propose a multi-sensor fusing method using intensity and range data fusion with covariance intersection approach to locate the robot pose while performing the docking for recharging. An artificial landmark is employed as a visual cue on a docking station in order to recognize the location by using inverse perspective projection. At the same time, the range data acquired by laser range finder are modeled as multiple line segments which are the hypothetical walls in the environment. Then the geometrical relationship between the robot and the docking station is estimated much more precise by using covariance intersection approach. We have demonstrated the success of the proposed algorithms through experimental results.


title: APPLICANTS' STRATEGIC USE OF EXTREME OR MIDPOINT RESPONSES WHEN FAKING PERSONALITY TESTS.

abstract: Faking, the intentional distortion of answers to personality tests, is likely a complex process. In particular, participants in previous research have mentioned that they used different kind of strategies to appear more hirable, including systematically more extreme or more midpoint responses. However, quantitative evidence is still lacking. An experiment was conducted in which 327 students (173 women, 153 men, 1 not indicated; M age = 22.1 yr., SD=2.8) were randomly assigned to two groups. Hypothetical job advertisements primed the participants into believing that the hiring company preferred a person with either a “strong” (Strong Character group) or a “well-balanced” character (Well-balanced Character group). The participants filled out 40 items that were chosen from four established questionnaires as neither socially desirable nor undesirable. The responses to these items were used to calculate two extreme response measures and one midpoint response measure. The Strong Character group used extreme sco...