Nearest prototype classifier designs: An experimental study

We compare eleven methods for finding prototypes upon which to base the nearest Ž prototype classifier. Four methods for prototype selection are discussed: Wilson Hart a . condensation error-editing method , and three types of combinatorial search random search, genetic algorithm, and tabu search. Seven methods for prototype extraction are discussed: unsupervised vector quantization, supervised learning vector quantization Ž . with and without training counters , decision surface mapping, a fuzzy version of vector quantization, c-means clustering, and bootstrap editing. These eleven methods can be usefully divided two other ways: by whether they employ preor postsupervision; and by whether the number of prototypes found is user-defined or ‘‘automatic.’’ Generalization error rates of the 11 methods are estimated on two synthetic and two real data sets. Offering the usual disclaimer that these are just a limited set of experiments, we feel confident in asserting that presupervised, extraction methods offer a better chance for success to the casual user than postsupervised, selection schemes. Finally, our calculations do not suggest that methods which find the ‘‘best’’ number of prototypes ‘‘automatically’’ are superior to methods for which the user simply specifies the number of prototypes. 2001 John Wiley & Sons, Inc.


Synthesizing evidence in software engineering research

Synthesizing the evidence from a set of studies that spans many countries and years, and that incorporates a wide variety of research methods and theoretical perspectives, is probably the single most challenging task of performing a systematic review. In this paper, we perform a tertiary review to assess the types and methods of research synthesis in systematic reviews in software engineering. Almost half of the 31 studies included in our review did not contain any synthesis; of the ones that did, two thirds performed a narrative or a thematic synthesis. The results show that, despite the focus on systematic reviews, there is, currently, limited attention to research synthesis in software engineering. This needs to change and a repertoire of synthesis methods needs to be an integral part of systematic reviews to increase their significance and utility for research and practice.


Spectral–Spatial Classification and Shape Features for Urban Road Centerline Extraction

This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.


Automatic Optimization Computational Method for Unconventional S . W . A . T . H . Ships Resistance

The paper illustrates the main theoretical and computational aspects of an automatic computer based procedure for the parametric shape optimization of a particular unconventional hull typology: that for a catamaran S.W.A.T.H. ship. The goal of the integrated computational procedure is to find the best shape of the submerged hulls of a new U.S.V. (Unmanned Surface Vehicle) S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel, in terms of minimum wave pattern resistance. After dealing with the theoretical aspects the papers presents the numerical aspects of the main software module of the automatic procedure, which integrates a parametric generation routine for innovative and unconventional S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel geometry, a multi-objective, globally convergent and constrained, optimization algorithm and a Computational Fluid Dynamic (C.F.D.) solver. The integrated process is able to find the best shape of the submerged hull of the vessel, subject to the total displaced volume constraint. The hydrodynamic computation is carried out by means of a free surface potential flow method and it is addressed to find the value of wave resistance of each hull variant. Results of the application of the described computational procedure are presented for two optimization cases and the obtained best shapes are compared with a conventional one, featuring a typical torpedo-shaped body, proving the effectiveness of the method in reducing the resistance by a considerable extent, in the order of 40 percent. Keywords—S.W.A.T.H., B.E.M., Wave Resistance, Parametric Modeling, Optimization, Genetic Algorithms


BRAD 1.0: Book reviews in Arabic dataset

The availability of rich datasets is a pre-requisite for proposing robust sentiment analysis systems. A variety of such datasets exists in English language. However, it is rare or nonexistent for the Arabic language except for a recent LABR dataset, which consists of a little bit over 63,000 book reviews extracted from. Goodreads. com. We introduce BRAD 1.0, the largest Book Reviews in Arabic Dataset for sentiment analysis and machine language applications. BRAD comprises of almost 510,600 book records. Each record corresponds for a single review and has the review in Arabic language and the reviewer's rating on a scale of 1 to 5 stars. In this paper, we present and describe the properties of BRAD. Further, we provide two versions of BRAD: the complete unbalanced dataset and the balanced version of BRAD. Finally, we implement four sentiment analysis classifiers based on this dataset and report our findings. When training and testing the classifiers on BRAD as opposed to LABR, an improvement rate growth of 46% is reported. The highest accuracy attained is 91%. Our core contribution is to make this benchmark-dataset available and accessible to the research community on Arabic language.


An integrated approach to testing complex systems

The increasing complexity of today’s testing scenarios for complex systems demands an integrated, open, and flexible approach to support the management of the overall test process. “Classical” model-based testing approaches, where a complete and precise formal specification serves as a reference for automatic test generation, are often impractical. Reasons are, on the one hand, the absence of a suitable formal specification. As complex systems are composed of several components, either hardware or software, often pre-built and third party, it is unrealistic to assume that a formal specification exists a priori. On the other hand, a sophisticated test execution environment is needed that can handle distributed test cases. This is because the test actions and observations can take place on different subsystems of the overall system. This thesis presents a novel approach to the integrated testing of complex systems. Our approach offers a coarse grained test environment, realized in terms of a component-based test design on top of a library of elementary but intuitively understandable test case fragments. The relations between the fragments are treated orthogonally, delivering a test design and execution environment enhanced by means of light-weight formal verification methods. In this way we are able to shift the test design issues from total experts of the system and the used test tools to experts of the system’s logic only. We illustrate the practical usability of our approach by means of industrial case studies in two different application domains: Computer Telephony Integrated solutions and Web-based applications. As an enhancement of our integrated test approach we provide an algorithm for generating approximate models for complex systems a posteriori. This is done by optimizing a standard machine learning algorithm according to domain-specific structural properties, i.e. properties like prefix-closeness, input-determinism, as well as independency and symmetries of events. The resulting models can never be exact, i.e. reflect the complete and correct behaviour of the considered system. Nevertheless they can be useful in practice, to represent the cumulative knowledge of the system in a consistent description.


Axial-Ratio-Bandwidth Enhancement of a Microstrip-Line-Fed Circularly Polarized Annular-Ring Slot Antenna

The design of a new microstrip-line-fed wideband circularly polarized (CP) annular-ring slot antenna (ARSA) is proposed. Compared with existing ring slot antennas, the ARSAs designed here possess much larger CP bandwidths. The main features of the proposed design include a wider ring slot, a pair of grounded hat-shaped patches, and a deformed bent feeding microstrip line. The ARSAs designed using FR4 substrates in the L and S bands have 3-dB axial-ratio bandwidths (ARBWs) of as large as 46% and 56%, respectively, whereas the one using an RT5880 substrate in the L band, 65%. In these 3-dB axial-ratio bands, impedance matching with VSWR ≤ 2 is also achieved.


A Practical Approach to Differential Private Learning

Applying differential private learning to real-world data is currently unpractical. Differential privacy (DP) introduces extra hyper-parameters for which no thorough good practices exist, while manually tuning these hyper-parameters on private data results in low privacy guarantees. Furthermore, the exact guarantees provided by differential privacy for machine learning models are not well understood. Current approaches use undesirable post-hoc privacy attacks on models to assess privacy guarantees. To improve this situation, we introduce three tools to make DP machine learning more practical. First, two sanity checks for differential private learning are proposed. These sanity checks can be carried out in a centralized manner before training, do not involve training on the actual data and are easy to implement. Additionally, methods are proposed to reduce the effective number of tuneable privacy parameters by making use of an adaptive clipping bound. Lastly, existing methods regarding large batch training and differential private learning are combined. It is demonstrated that this combination improves model performance within a constant privacy budget.


Privacy as a fuzzy concept: A new conceptualization of privacy for practitioners

• Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain.


TG13 flowchart for the management of acute cholangitis and cholecystitis.

We propose a management strategy for acute cholangitis and cholecystitis according to the severity assessment. For Grade I (mild) acute cholangitis, initial medical treatment including the use of antimicrobial agents may be sufficient for most cases. For non-responders to initial medical treatment, biliary drainage should be considered. For Grade II (moderate) acute cholangitis, early biliary drainage should be performed along with the administration of antibiotics. For Grade III (severe) acute cholangitis, appropriate organ support is required. After hemodynamic stabilization has been achieved, urgent endoscopic or percutaneous transhepatic biliary drainage should be performed. In patients with Grade II (moderate) and Grade III (severe) acute cholangitis, treatment for the underlying etiology including endoscopic, percutaneous, or surgical treatment should be performed after the patient's general condition has been improved. In patients with Grade I (mild) acute cholangitis, treatment for etiology such as endoscopic sphincterotomy for choledocholithiasis might be performed simultaneously, if possible, with biliary drainage. Early laparoscopic cholecystectomy is the first-line treatment in patients with Grade I (mild) acute cholecystitis while in patients with Grade II (moderate) acute cholecystitis, delayed/elective laparoscopic cholecystectomy after initial medical treatment with antimicrobial agent is the first-line treatment. In non-responders to initial medical treatment, gallbladder drainage should be considered. In patients with Grade III (severe) acute cholecystitis, appropriate organ support in addition to initial medical treatment is necessary. Urgent or early gallbladder drainage is recommended. Elective cholecystectomy can be performed after the improvement of the acute inflammatory process. Free full-text articles and a mobile application of TG13 are available via http://www.jshbps.jp/en/guideline/tg13.html.


Disease Trajectory Maps

Medical researchers are coming to appreciate that many diseases are in fact complex, heterogeneous syndromes composed of subpopulations that express different variants of a related complication. Longitudinal data extracted from individual electronic health records (EHR) offer an exciting new way to study subtle differences in the way these diseases progress over time. In this paper, we focus on answering two questions that can be asked using these databases of longitudinal EHR data. First, we want to understand whether there are individuals with similar disease trajectories and whether there are a small number of degrees of freedom that account for differences in trajectories across the population. Second, we want to understand how important clinical outcomes are associated with disease trajectories. To answer these questions, we propose the Disease Trajectory Map (DTM), a novel probabilistic model that learns low-dimensional representations of sparse and irregularly sampled longitudinal data. We propose a stochastic variational inference algorithm for learning the DTM that allows the model to scale to large modern medical datasets. To demonstrate the DTM, we analyze data collected on patients with the complex autoimmune disease, scleroderma. We find that DTM learns meaningful representations of disease trajectories and that the representations are significantly associated with important clinical outcomes.


Wideband H-Plane Horn Antenna Based on Ridge Substrate Integrated Waveguide (RSIW)

A substrate integrated waveguide (SIW) H-plane sectoral horn antenna, with significantly improved bandwidth, is presented. A tapered ridge, consisting of a simple arrangement of vias on the side flared wall within the multilayer substrate, is introduced to enlarge the operational bandwidth. A simple feed configuration is suggested to provide the propagating wave for the antenna structure. The proposed antenna is simulated by two well-known full-wave packages, Ansoft HFSS and CST Microwave Studio, based on segregate numerical methods. Close agreement between simulation results is reached. The designed antenna shows good radiation characteristics and low VSWR, lower than 2.5, for the whole frequency range of 18-40 GHz.


Multiscale Approaches To Music Audio Feature Learning

Content-based music information retrieval tasks are typically solved with a two-stage approach: features are extracted from music audio signals, and are then used as input to a regressor or classifier. These features can be engineered or learned from data. Although the former approach was dominant in the past, feature learning has started to receive more attention from the MIR community in recent years. Recent results in feature learning indicate that simple algorithms such as K-means can be very effective, sometimes surpassing more complicated approaches based on restricted Boltzmann machines, autoencoders or sparse coding. Furthermore, there has been increased interest in multiscale representations of music audio recently. Such representations are more versatile because music audio exhibits structure on multiple timescales, which are relevant for different MIR tasks to varying degrees. We develop and compare three approaches to multiscale audio feature learning using the spherical K-means algorithm. We evaluate them in an automatic tagging task and a similarity metric learning task on the Magnatagatune dataset.


Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy.


SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis

This paper describes our SeemGo system for the task of Aspect Based Sentiment Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditional Random Fields that obtains the F-score of 0.683 for Laptops and 0.791 for Restaurants by exploiting both word-based features and context features. The other three subtasks are solved by the Maximum Entropy model, with the occurrence counts of unigram and bigram words of each sentence as features. The subtask of aspect category detection obtains the best result when applying the Boosting method on the Maximum Entropy model, with the precision of 0.869 for Restaurants. The Maximum Entropy model also shows good performance in the subtasks of both aspect term and aspect category polarity classification.


Machine learning approach for automated screening of malaria parasite using light microscopic images.

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification.


Multi-factor Authentication Security Framework in Cloud Computing

Data Security is the most critical issues in a cloud computing environment. Authentication is a key technology for information security, which is a mechanism to establish proof of identities to get access of information in the system. Traditional password authentication does not provide enough security for information in cloud computing environment to the most modern means of attacks. In this paper, we propose a new multi-factor authentication framework for cloud computing. In this paper the features of various access control mechanisms are discussed and a novel framework of access control is proposed for cloud computing, which provides a multi -step and multifactor authentication of a user. The model proposed is well-organized and provably secure solution of access control for externally hosted applications.


A Hybrid Model to Detect Phishing-Sites Using Supervised Learning Algorithms

Since last decades, online technologies have revolutionized the modern computing world. However, as a result, security threats are increasing rapidly. A huge community is using the online services even from chatting to banking is done via online transactions. Customers of web technologies face various security threats and phishing is one of the most important threat that needs to be address. Therefore, the security mechanism must be enhanced. The attacker uses phishing attack to get victims credential information like bank account number, passwords or any other information by mimicking a website of an enterprise, and the victim is unaware of phishing website. In literature, several approaches have been proposed for detection and filtering phishing attack. However, researchers are still searching for such a solution that can provide better results to secure users from phishing attack. Phishing websites have certain characteristics and patterns and to identify those features can help us to detect phishing. To identify such features is a classification task and can be solved using data mining techniques. In this paper, we are presenting a hybrid model for classification to overcome phishing-sites problem. To evaluate this model, we have used the dataset from UCI repository which contains 30 attributes and 11055 instances. The experimental results showed that our proposed hybrid model outperforms in terms of high accuracy and less error rate.


Top 10 algorithms in data mining

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.


Rapid Object Detection using a Boosted Cascade of Simple Features

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.


An Associative Classification Data Mining Approach for Detecting Phishing Websites

Phishing websites are fake websites that are created by dishonest people to mimic webpages of real websites. Victims of phishing attacks may expose their financial sensitive information to the attacker whom might use this information for financial and criminal activities. Various approaches have been proposed to detect phishing websites, among which, approaches that utilize data mining techniques had shown to be more effective. The main goal of data mining is to analyze a large set of data to identify unsuspected relation and extract understandable useful patterns. Associative Classification (AC) is a promising data mining approach that integrates association rule and classification to build classification models (classifiers). This paper, proposes a new AC algorithm called Phishing Associative Classification (PAC), for detecting phishing websites. PAC employed a novel methodology in construction the classifier which results in generating moderate size classifiers. The algorithm improved the effectiveness and efficiency of a known algorithm called MCAR, by introducing a new prediction procedure and adopting a different rule pruning procedure. The conducted experiments compared PAC with 4 well-known data mining algorithms, these are: covering algorithm (Prism), decision tree (C4.5), associative Classification (CBA) and MCAR. Experiments are performed on a dataset that consists of 1010 website. Each Website is represented using 17 features categorized into 4 sets. The features are extracted from the website contents and URL. The results on each features set show that PAC is either equivalent or more effective than the compared algorithms. When all features are considered, PAC outperformed the compared algorithms and correctly identified 99.31% of the tested websites. Furthermore, PAC produced less number of rules than MCAR, and therefore, is more efficient.


Using Stories to Teach Human Values to Artificial Agents

Value alignment is a property of an intelligent agent indicating that it can only pursue goals that are beneficial to humans. Successful value alignment should ensure that an artificial general intelligence cannot intentionally or unintentionally perform behaviors that adversely affect humans. This is problematic in practice since it is difficult to exhaustively enumerated by human programmers. In order for successful value alignment, we argue that values should be learned. In this paper, we hypothesize that an artificial intelligence that can read and understand stories can learn the values tacitly held by the culture from which the stories originate. We describe preliminary work on using stories to generate a value-aligned reward signal for reinforcement learning agents that prevents psychotic-appearing behavior.


Supervised and Unsupervised methods to detect Insider Threat from Enterprise Social and Online Activity Data

Insider threat is a significant security risk for organizations, and detection of insider threat is of paramount concern to organizations. In this paper, we attempt to discover insider threat by analyzing enterprise social and online activity data of employees. To this end, we process and extract relevant features that are possibly indicative of insider threat behavior. This includes features extracted from social data including email communication patterns and content, and online activity data such as web browsing patterns, email frequency, and file and machine access patterns. Subsequently, we take two approaches to detect insider threat: (i) an unsupervised approach where we identify statistically abnormal behavior with respect to these features using state-of-the-art anomaly detection methods, and (ii) a supervised approach where we use labels indicating when employees quit the company as a proxy for insider threat activity to design a classifier. We test our approach on a real world data set with artificially injected insider threat events. We obtain a ROC score of 0.77 for the unsupervised approach, and a classification accuracy of 73.4% for the supervised approach. These results indicate that our proposed approaches are fairly successful in identifying insider threat events. Finally, we build a visualization dashboard that enables managers and HR personnel to quickly identify employees with high threat risk scores which will enable them to take suitable preventive measures and limit security risk.


An analytical framework for data stream mining techniques based on challenges and requirements

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques.


BLIS: A Framework for Rapidly Instantiating BLAS Functionality

The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in ISO C99 so that it can be reused and/or reparameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL).
 Replicated Computational Results Certified: This article was subjected to an optional supplementary review process in which the computational results leading to the main conclusions of the paper were independently replicated. A separate report with the results of this process is published in this issue (see: http://dx.doi.org/10.1145/2738033). The certification process is described in Michael Heroux's editorial: http://dx.doi.org/10.1145/2743015.


Video2GIF: Automatic Generation of Animated GIFs from Video

We introduce the novel problem of automatically generating animated GIFs from video. GIFs are short looping video with no sound, and a perfect combination between image and video that really capture our attention. GIFs tell a story, express emotion, turn events into humorous moments, and are the new wave of photojournalism. We pose the question: Can we automate the entirely manual and elaborate process of GIF creation by leveraging the plethora of user generated GIF content? We propose a Robust Deep RankNet that, given a video, generates a ranked list of its segments according to their suitability as GIF. We train our model to learn what visual content is often selected for GIFs by using over 100K user generated GIFs and their corresponding video sources. We effectively deal with the noisy web data by proposing a novel adaptive Huber loss in the ranking formulation. We show that our approach is robust to outliers and picks up several patterns that are frequently present in popular animated GIFs. On our new large-scale benchmark dataset, we show the advantage of our approach over several state-of-the-art methods.


SoilJ : An ImageJ Plugin for the Semiautomatic Processing of Three-Dimensional X-ray Images of Soils

Noninvasive threeand four-dimensional X-ray imaging approaches have proved to be valuable analysis tools for vadose zone research. One of the main bottlenecks for applying X-ray imaging to data sets with a large number of soil samples is the relatively large amount of time and expertise needed to extract quantitative data from the respective images. SoilJ is a plugin for the free and open imaging software ImageJ that aims at automating the corresponding processing steps for cylindrical soil columns. It includes modules for automatic column outline recognition, correction of image intensity bias, image segmentation, extraction of particulate organic matter and roots, soil surface topography detection, as well as morphology and percolation analyses. In this study, the functionality and precision of some key SoilJ features were demonstrated on five different image data sets of soils. SoilJ has proved to be useful for strongly decreasing the amount of time required for image processing of large image data sets. At the same time, it allows researchers with little experience in image processing to make use of X-ray imaging methods. The SoilJ source code is freely available and may be modified and extended at will by its users. It is intended to stimulate further community-driven development of this software.


Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids

Monitoring systems are expected to play a major role in active distribution grids, and the design of the measurement infrastructure is a critical element for an effective operation. The use of any available and newly installed, though heterogeneous, metering device providing more accurate and real-time measurement data offers a new paradigm for the distribution grid monitoring system. In this paper the authors study the meter placement problem for the measurement infrastructure of an active distribution network, where heterogeneous measurements provided by Phasor Measurement Units (PMUs) and other advanced measurement systems such as Smart Metering systems are used in addition to measurements that are typical of distribution networks, in particular substation measurements and a-priori knowledge. This work aims at defining a design approach for finding the optimal measurement infrastructure for an active distribution grid. The design problem is posed in terms of a stochastic optimization with the goal of bounding the overall uncertainty of the state estimation using heterogeneous measurements while minimizing the investment cost. The proposed method is also designed for computational efficiency so to cover a wide set of scenarios.


Relative clustering validity criteria: A comparative overview

Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes of problems. In addition, they may have completely different computational requirements. Then, it is a hard task for the user to choose a specific criterion when he or she faces such a variety of possibilities. For this reason, a relevant issue within the field of clustering analysis consists of comparing the performances of existing validity criteria and, eventually, that of a new criterion to be proposed. In spite of this, the comparison paradigm traditionally adopted in the literature is subject to some conceptual limitations. The present paper describes an alternative, possibly complementary methodology for comparing clustering validity criteria and uses it to make an extensive comparison of the performances of 40 criteria over a collection of 962,928 partitions derived from five well-known clustering algorithms and 1080 different data sets of a given class of interest. A detailed review of the relative criteria under investigation is also provided that includes an original comparative asymptotic analysis of their computational complexities. This work is intended to be a complement of the classic study reported in 1985 by Milligan and Cooper as well as a thorough extension of a preliminary paper by the authors themselves.  2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3:


A multi-resonant gate driver for Very-High-Frequency (VHF) resonant converters

This paper presents the design and implementation of a Very-High-Frequency (VHF) multi-resonant gate drive circuit. The design procedure is greatly simplified compared with some VHF self-oscillating multi-resonant gate drivers presented in previous works. The proposed circuit has the potential to reduce long start-up time required in a self-oscillating resonant gate drive circuit and to better utilize fast transient capability of VHF converters. A prototype resonant gate driver is demonstrated and able to reduce 60 % of the gate driving loss of a 20 MHz 32 W Class-E power amplifier with Si MOSFET.


Automated segmentation of cell nuclei in PAP smear images

In this paper an automated method for cell nucleus segmentation in PAP smear images is presented. The method combines the global knowledge about the cells and nuclei appearance and the local characteristics of the area of the nuclei, in order to achieve an accurate nucleus boundary. Filters and morphological operators in all three channels of a color image result in the determination of the locations of nuclei in the image, even in cases where cell overlapping occurs. The nucleus boundary is determined by a deformable model. The results are very promising, even when images with high degree of overlapping are used.


Two-Stream Neural Networks for Tampered Face Detection

We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swaping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectness of our method.


Orestes: A scalable Database-as-a-Service architecture for low latency

Today, the applicability of database systems in cloud environments is considerably restricted because of three major problems: I) high network latencies for remote/mobile clients, II) lack of elastic horizontal scalability mechanisms, and III) missing abstraction of storage and data models. In this paper, we propose an architecture, a REST/HTTP protocol and a set of algorithms to solve these problems through a Database-as-a-Service middleware called Orestes (Objects RESTfully Encapsulated in Standard Formats). Orestes exposes cloud-hosted NoSQL database systems through a scalable tier of REST servers. These provide database-independent, object-oriented schema design, a client-independent REST-API for database operations, globally distributed caching, cache consistency mechanisms and optimistic ACID transactions. By comparative evaluations we offer empirical evidence that the proposed Database-as-a-Service architecture indeed solves common latency, scalability and abstraction problems encountered in modern cloud-based applications.


Estimating Continuous Distributions in Bayesian Classifiers

When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous vari ables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality as sumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experi mental results on a variety of natural and ar tificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparamet ric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.


Semantic web-mining and deep vision for lifelong object discovery

Autonomous robots that are to assist humans in their daily lives must recognize and understand the meaning of objects in their environment. However, the open nature of the world means robots must be able to learn and extend their knowledge about previously unknown objects on-line. In this work we investigate the problem of unknown object hypotheses generation, and employ a semantic web-mining framework along with deep-learning-based object detectors. This allows us to make use of both visual and semantic features in combined hypotheses generation. Experiments on data from mobile robots in real world application deployments show that this combination improves performance over the use of either method in isolation.


TDParse: Multi-target-specific sentiment recognition on Twitter

Existing target-specific sentiment recognition methods consider only a single target per tweet, and have been shown to miss nearly half of the actual targets mentioned. We present a corpus of UK election tweets, with an average of 3.09 entities per tweet and more than one type of sentiment in half of the tweets. This requires a method for multi-target specific sentiment recognition, which we develop by using the context around a target as well as syntactic dependencies involving the target. We present results of our method on both a benchmark corpus of single targets and the multi-target election corpus, showing state-of-the art performance in both corpora and outperforming previous approaches to multi-target sentiment task as well as deep learning models for singletarget sentiment.


Understanding the Factors Driving NFC-Enabled Mobile Payment Adoption: an Empirical Investigation

Though NFC mobile payment offers both convenience and benefits to consumers, its adoption rate is still very low. This study aims to explore the factors determining consumers’ adoption of NFC mobile payment. In this study, building on TAM, an extended adoption intention model is developed by incorporating three sorts of variables into it, namely mobile payment system factors (compatibility and perceived complementarity), user characteristics (mobile payment knowledge), and a risk-related factor (perceived risk). The model is empirically tested with a sample of 377 validated respondents. Compatibility, perceived ease of use and mobile payment knowledge are found to be the factors determining individuals’ intention to use NFC mobile payment. Against our expectations, perceived usefulness and perceived risk do not affect use intention significantly in adopting NFC mobile payments among consumers. We discuss the theoretical and practical implications of these findings, and point out the limitations and need for future research.


Development of a 4-joint 3-DOF robotic arm with anti-reaction force mechanism for a multicopter

In this paper, we propose a design method for the mechanism of a robotic arm suitable for a multicopter. The motion of an arm attached to a multicopter can possibly disturb the multicopter attitude. Our aim is to suppress this attitude disturbance by mechanical methods. In the proposed design method, the arm has the following features for suppressing the disturbance of the multicopter attitude. 1. The robotic arm can adjust its center of gravity (COG). 2. In the simplified model, the sum of the angular momentum of the rotating parts constituting the robot arm is equal to zero. These features can compensate for both the displacement of the COG of the arm and the counter torque generated from rotating parts, which cause the disturbance to the multicopter attitude. Since the disturbance of the multicopter attitude can be suppressed mechanically, the robotic arm does not require a special flight control law. In other words, this robotic arm has the advantage of being attached to a multicopter using a common, off-the-shelf, multipurpose flight controller. Furthermore, we discuss the mass distribution, power transmission method, and the moment of inertia of rotating parts, such as the speed reducer, motor, and arm. Additionally, we fabricate a prototype robotic arm with four joints and three degrees of freedom (DOFs), based on the proposed design method. Finally, the experiments showed that the robotic arm suppressed the disturbance of the multicopter attitude caused by the arm motion.


Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model perspective. Well-known examples of graphical models include Bayesian networks , directed graphs representing a Markov chain, and undirected networks representing a Markov eld. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, diierentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation max-imization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical speciication. This includes versions of linear regression , techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.


The Q-CHAT (Quantitative CHecklist for Autism in Toddlers): a normally distributed quantitative measure of autistic traits at 18-24 months of age: preliminary report.

We report a major revision of the CHecklist for Autism in Toddlers (CHAT). This quantitative CHAT (Q-CHAT) contains 25 items, scored on a 5 point scale (0-4). The Q-CHAT was completed by parents of n = 779 unselected toddlers (mean age 21 months) and n = 160 toddlers and preschoolers (mean age 44 months) with an Autism Spectrum Condition (ASC). The ASC group (mean (SD) = 51.8 (14.3)) scored higher on the Q-CHAT than controls (26.7 (7.8)). Boys in the control group (27.5 (7.8)) scored higher than girls (25.8 (7.7)). The intraclass correlation for test-retest reliability was 0.82 (n = 330). The distribution in the control group was close to normal. Full examination of the clinical validity of the Q-CHAT and test properties is underway.


Leakage Current Elimination of Four-Leg Inverter for Transformerless Three-Phase PV Systems

Eliminating the leakage current is one of the most important issues for transformerless three-phase photovoltaic (PV) systems. In this paper, the leakage current elimination of a three-phase four-leg PV inverter is investigated. With the common-mode loop model established, the generation mechanism of the leakage current is clearly identified. Different typical carrier-based modulation methods and their corresponding common-mode voltages are discussed. A new modulation strategy with Boolean logic function is proposed to achieve the constant common-mode voltage for the leakage current reduction. Finally, the different modulation methods are implemented and tested on the TMS320F28335 DSP +XC3S400 FPGA digital control platform. The experimental results verify the effectiveness of the proposed solution.


Hybrid cryptography mechanism for securing self-organized wireless networks

Nowadays use of wireless technologies tremendously increased due to the continuous development of these technologies in various applications. One of the development in wireless technologies leads to deployment of the networks in which all mobile nodes will communicate via wireless links directly without use of any base station or predefined infrastructural support. These networks are self-organized networks called mobile ad hoc networks (MANETs) and architecture used for these networks will be either stand alone with one or few nodes communicating with base station or distributed. As communication is done through wireless links, these types of networks are more prone to several kinds of attacks. Authentication and encryption acts as first line of defense while an intrusion detection system (IDS) acts as a second line of defense. One of the intrusion detection system used in MANET is Enhanced Adaptive Acknowledgement (EAACK) which is based on acknowledgement packets for detection of malicious activities. This system increases the overhead of network significantly relative to other system such as watchdog. So, in this paper we adopt the hybrid cryptography to reduce the network overhead and enhance the security of networks significantly.


Amazigh Isolated-Word speech recognition system using Hidden Markov Model toolkit (HTK)

This paper aims to build a speaker-independent automatic Amazigh Isolated-Word speech recognition system. Hidden Markov Model toolkit (HTK) that uses hidden Markov Models has been used to develop the system. The recognition vocabulary consists on the Amazigh Letters and Digits. The system has been trained to recognize the Amazigh 10 first digits and 33 alphabets. Mel frequency spectral coefficients (MFCCs) have been used to extract the feature. The training data has been collected from 60 speakers including both males and females. The test-data used for evaluating the system-performance has been collected from 20 speakers. The experimental results show that the presented system provides the overall word-accuracy 80%. The initial results obtained are very satisfactory in comparison with the training database's size, this encourages us to increase system performance to achieve a higher recognition rate.


Identification of Flux Linkage Map of Permanent Magnet Synchronous Machines Under Uncertain Circuit Resistance and Inverter Nonlinearity

This paper proposes a novel scheme for the identification of the whole flux linkage map of permanent magnet synchronous machines, by which the map of dq-axis flux linkages at different load or saturation conditions can be identified by the minimization of a proposed estimation model. The proposed method works on a conventional three-phase inverter based vector control system and the immune clonal based quantum genetic algorithm is employed for the global searching of minimal point. Besides, it is also noteworthy that the influence of uncertain inverter nonlinearity and circuit resistance are cancelled during the modeling process. The proposed method is subsequently tested on two PMSMs and shows quite good performance compared with the finite element prediction results.


Simple and strong : Twisted silver painted nylon artificial muscle actuated by Joule heating

Highly oriented nylon and polyethylene fibres shrink in length when heated and expand in diameter. By twisting and then coiling monofilaments of these materials to form helical springs, the anisotropic thermal expansion has recently been shown to enable tensile actuation of up to 49% upon heating. Joule heating, by passing a current through a conductive coating on the surface of the filament, is a convenient method of controlling actuation. In previously reported work this has been done using highly flexible carbon nanotube sheets or commercially available silver coated fibres. In this work silver paint is used as the Joule heating element at the surface of the muscle. Up to 29% linear actuation is observed with energy and power densities reaching 840 kJ m (528 J kg) and 1.1 kW kg (operating at 0.1 Hz, 4% strain, 1.4 kg load). This simple coating method is readily accessible and can be applied to any polymer filament. Effective use of this technique relies on uniform coating to avoid temperature gradients.


WHY AGENTS? ON THE VARIED MOTIVATIONS FOR AGENT COMPUTING IN THE SOCIAL SCIENCES

The many motivations for employing agent-based computation in the social sciences are reviewed. It is argued that there exist three distinct uses of agent modeling techniques. One such use — the simplest — is conceptually quite close to traditional simulation in operations research. This use arises when equations can be formulated that completely describe a social process, and these equations are explicitly soluble, either analytically or numerically. In the former case, the agent model is merely a tool for presenting results, while in the latter it is a novel kind of Monte Carlo analysis. A second, more commonplace usage of computational agent models arises when mathematical models can be written down but not completely solved. In this case the agent-based model can shed significant light on the solution structure, illustrate dynamical properties of the model, serve to test the dependence of results on parameters and assumptions, and be a source of counter-examples. Finally, there are important classes of problems for which writing down equations is not a useful activity. In such circumstances, resort to agent-based computational models may be the only way available to explore such processes systematically, and constitute a third distinct usage of such models. ∗ Corresponding author address: Robert Axtell, Center on Social and Economic Dynamics, The Brookings Institution, 1775 Massachusetts Ave. NW, Washington, DC 20036; e-mail: raxtell@brook.edu; web: http://www.brook.edu/es/dynamics.


Agent-based modeling: methods and techniques for simulating human systems.

Agent-based modeling is a powerful simulation modeling technique that has seen a number of applications in the last few years, including applications to real-world business problems. After the basic principles of agent-based simulation are briefly introduced, its four areas of application are discussed by using real-world applications: flow simulation, organizational simulation, market simulation, and diffusion simulation. For each category, one or several business applications are described and analyzed.


The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations

Swarm is a multi agent software platform for the simulation of complex adaptive systems In the Swarm system the basic unit of simulation is the swarm a collec tion of agents executing a schedule of actions Swarm supports hierarchical model ing approaches whereby agents can be composed of swarms of other agents in nested structures Swarm provides object oriented libraries of reusable components for build ing models and analyzing displaying and controlling experiments on those models Swarm is currently available as a beta version in full free source code form It requires the GNU C Compiler Unix and X Windows More information about Swarm can be obtained from our web pages http www santafe edu projects swarm Santa Fe Institute Hyde Park Rd Santa Fe NM nelson santafe edu Deere Company John Deere Road Moline IL roger ci deere com Santa Fe Institute Hyde Park Rd Santa Fe NM cgl santafe edu Santa Fe Institute Hyde Park Rd Santa Fe NM manor santafe edu Swarm has been developed at the Santa Fe Institute with the support of Grant No N from the O ce of Naval Research and Grant No N G from the Naval Research Laboratory both acting in cooperation with the Defense Advanced Research Projects Agency Swarm bene ted from earlier support from The Carol O Donnell Foundation Mr and Mrs Michael Grantham and the National Science Foundation SFI also gratefully acknowledges invaluable support from Deere Company for this


From Factors to Actors : Computational Sociology and Agent-Based Modeling

■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach.


Complex suicide by self-stabbing with subsequent drowning in the sea.

The paper presents a unique case of a complex suicide committed by a young man, mostly probably triggered by a disappointment in love. The uniqueness of the suicide lies in the fact that the victim inflicted several deep stab wounds on himself, in the chest and abdomen, while standing partly submerged in the sea and, having done so, he dropped and disappeared in the water. The postmortem examination showed, apart from deep wounds in the trunk, characteristics of drowning that manifested itself in the form of aqueous emphysema of the lungs. Suicide was clearly determined on the basis of the circumstances preceding death, the location, and arrangement of the trunk wounds and the testimony given by a witness of the incident. The circumstances preceding the suicidal act clearly suggest an underlying undiagnosed mental disorder.


An End-to-End Discriminative Approach to Machine Translation

We present a perceptron-style discriminative approach to machine translation in which large feature sets can be exploited. Unlike discriminative reranking approaches, our system can take advantage of learned features in all stages of decoding. We first discuss several challenges to error-driven discriminative approaches. In particular, we explore different ways of updating parameters given a training example. We find that making frequent but smaller updates is preferable to making fewer but larger updates. Then, we discuss an array of features and show both how they quantitatively increase BLEU score and how they qualitatively interact on specific examples. One particular feature we investigate is a novel way to introduce learning into the initial phrase extraction process, which has previously been entirely heuristic.


EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech Synthesis System

We present EMPHASIS, an emotional phoneme-based acoustic model for speech synthesis system. EMPHASIS includes a phoneme duration prediction model and an acoustic parameter prediction model. It uses a CBHG-based regression network to model the dependencies between linguistic features and acoustic features. We modify the input and output layer structures of the network to improve the performance. For the linguistic features, we apply a feature grouping strategy to enhance emotional and prosodic features. The acoustic parameters are designed to be suitable for the regression task and waveform reconstruction. EMPHASIS can synthesize speech in real-time and generate expressive interrogative and exclamatory speech with high audio quality. EMPHASIS is designed to be a multi-lingual model and can synthesize Mandarin-English speech for now. In the experiment of emotional speech synthesis, it achieves better subjective results than other real-time speech synthesis systems.


Tensor-Factorized Neural Networks

The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations. The classification performance using vectorized NN is constrained, because the temporal or spatial information in neighboring ways is disregarded. More parameters are required to learn the complicated data structure. This paper presents a new tensor-factorized NN (TFNN), which tightly integrates TF and NN for multiway feature extraction and classification under a unified discriminative objective. This TFNN is seen as a generalized NN, where the affine transformation in an NN is replaced by the multilinear and multiway factorization for tensor-based NN. The multiway information is preserved through layerwise factorization. Tucker decomposition and nonlinear activation are performed in each hidden layer. The tensor-factorized error backpropagation is developed to train TFNN with the limited parameter size and computation time. This TFNN can be further extended to realize the convolutional TFNN (CTFNN) by looking at small subtensors through the factorized convolution. Experiments on real-world classification tasks demonstrate that TFNN and CTFNN attain substantial improvement when compared with an NN and a convolutional NN, respectively.


Using inaccurate models in reinforcement learning

In the model-based policy search approach to reinforcement learning (RL), policies are found using a model (or "simulator") of the Markov decision process. However, for high-dimensional continuous-state tasks, it can be extremely difficult to build an accurate model, and thus often the algorithm returns a policy that works in simulation but not in real-life. The other extreme, model-free RL, tends to require infeasibly large numbers of real-life trials. In this paper, we present a hybrid algorithm that requires only an approximate model, and only a small number of real-life trials. The key idea is to successively "ground" the policy evaluations using real-life trials, but to rely on the approximate model to suggest local changes. Our theoretical results show that this algorithm achieves near-optimal performance in the real system, even when the model is only approximate. Empirical results also demonstrate that---when given only a crude model and a small number of real-life trials---our algorithm can obtain near-optimal performance in the real system.


Distance Metric Learning for Large Margin Nearest Neighbor Classification


Learning by Example: Training Users with High-quality Query Suggestions

The queries submitted by users to search engines often poorly describe their information needs and represent a potential bottleneck in the system. In this paper we investigate to what extent it is possible to aid users in learning how to formulate better queries by providing examples of high-quality queries interactively during a number of search sessions. By means of several controlled user studies we collect quantitative and qualitative evidence that shows: (1) study participants are able to identify and abstract qualities of queries that make them highly effective, (2) after seeing high-quality example queries participants are able to themselves create queries that are highly effective, and, (3) those queries look similar to expert queries as defined in the literature. We conclude by discussing what the findings mean in the context of the design of interactive search systems.


Monolithic CMOS distributed amplifier and oscillator

CMOS implementations for RF applications often employ technology modifications to reduce the silicon substrate loss at high frequencies. The most common techniques include the use of a high-resistivity substrate (/spl rho/>10 /spl Omega/-cm) or silicon-on-insulator (SOI) substrate and precise bondwire inductors. However, these techniques are incompatible with low-cost CMOS manufacture. This design demonstrates use of CMOS with a conventional low-resistivity epi-substrate and on-chip inductors for applications above 10 GHz.


Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration

Fluent and safe interactions of humans and robots require both partners to anticipate the others’ actions. A common approach to human intention inference is to model specific trajectories towards known goals with supervised classifiers. However, these approaches do not take possible future movements into account nor do they make use of kinematic cues, such as legible and predictable motion. The bottleneck of these methods is the lack of an accurate model of general human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motions. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.


Mapping Texts: Combining Text-Mining and Geo-Visualization To Unlock The Research Potential of Historical Newspapers

In this paper, we explore the task of automatic text processing applied to collections of historical newspapers, with the aim of assisting historical research. In particular, in this first stage of our project, we experiment with the use of topical models as a means to identify potential issues of interest for historians. 1 Newspapers in Historical Research Surviving newspapers are among the richest sources of information available to scholars studying peoples and cultures of the past 250 years, particularly for research on the history of the United States. Throughout the nineteenth and twentieth centuries, newspapers served as the central venues for nearly all substantive discussions and debates in American society. By the mid-nineteenth century, nearly every community (no matter how small) boasted at least one newspaper. Within these pages, Americans argued with one another over politics, advertised and conducted economic business, and published articles and commentary on virtually all aspects of society and daily life. Only here can scholars find editorials from the 1870s on the latest political controversies, advertisements for the latest fashions, articles on the latest sporting events, and languid poetry from a local artist, all within one source. Newspapers, in short, document more completely the full range of the human experience than nearly any other source available to modern scholars, providing windows into the past available nowhere else. Despite their remarkable value, newspapers have long remained among the most underutilized historical resources. The reason for this paradox is quite simple: the sheer volume and breadth of information available in historical newspapers has, ironically, made it extremely difficult for historians to go through them page-by-page for a given research project. A historian, for example, might need to wade through tens of thousands of newspaper pages in order to answer a single research question (with no guarantee of stumbling onto the necessary information). Recently, both the research potential and problem of scale associated with historical newspapers has expanded greatly due to the rapid digitization of these sources. The National Endowment for the Humanities (NEH) and the Library of Congress (LOC), for example, are sponsoring a nationwide historical digitization project, Chronicling America, geared toward digitizing all surviving historical newspapers in the United States, from 1836 to the present. This project recently digitized its one millionth page (and they project to have more than 20 million pages within a few years), opening a vast wealth of historical newspapers in digital form. While projects such as Chronicling America have indeed increased access to these important sources, they have also increased the problem of scale that have long prevent scholars from using these sources in meaningful ways. Indeed, without tools and methods capable of handling such large datasets – and thus sifting out meaningful patterns embedded within them – scholars find themselves confined to performing only basic word searches across enormous collections. These simple searches can, indeed, find stray information scattered in unlikely 46 places. Such rudimentary search tools, however, become increasingly less useful to researchers as datasets continue to grow in size. If a search for a particular term yields 4,000,000 results, even those search results produce a dataset far too large for any single scholar to analyze in a meaningful way using traditional methods. The age of abundance, it turns out, can simply overwhelm historical scholars, as the sheer volume of available digitized historical newspapers is beginning to do. In this paper, we explore the use of topic modeling, in an attempt to identify the most important and potentially interesting topics over a given period of time. Thus, instead of asking a historian to look through thousands of newspapers to identify what may be interesting topics, we take a reverse approach, where we first automatically cluster the data into topics, and then provide these automatically identified topics to the historian so she can narrow her scope to focus on the individual patterns in the dataset that are most applicable to her research. Of more utility would be where the modeling would reveal unexpected topics that point towards unusual patterns previously unknown, thus help shaping a scholar’s subsequent research. The topic modeling can be done for any periods of time, which can consist of individual years or can cover several years at a time. In this way, we can see the changes in the discussions and topics of interest over the years. Moreover, pre-filters can also be applied to the data prior to the topic modeling. For instance, since research being done in the History department at our institution is concerned with the “U. S. cotton economy,” we can use the same approach to identify the interesting topics mentioned in the news articles that talk about the issue of “cotton.” 2 Topic Modeling Topic models have been used by Newman and Block (2006) and Nelson (2010) on newspaper corpora to discover topics and trends over time. The former used the probabilistic latent semantic analysis (pLSA) model, and the latter used the latent Dirichlet allocation (LDA) model, a method introduced by Blei et al. (2003). LDA has also been used by Griffiths and Steyvers (2004) to find research topic trends by looking at abstracts of scientific papers. Hall et al. (2008) have similarly applied LDA to discover trends in the computational linguistics field. Both pLSA and LDA models are probabilistic models that look at each document as a mixture of multinomials or topics. The models decompose the document collection into groups of words representing the main topics. See for instance Table 1, which shows two topics extracted from our collection. Topic worth price black white goods yard silk made ladies wool lot inch week sale prices pair suits fine quality state states bill united people men general law government party made president today washington war committee country public york Table 1: Example of two topic groups Boyd-Graber et al. (2009) compared several topic models, including LDA, correlated topic model (CTM), and probabilistic latent semantic indexing (pLSI), and found that LDA generally worked comparably well or better than the other two at predicting topics that match topics picked by the human annotators. We therefore chose to use a parallel threaded SparseLDA implementation to conduct the topic modeling, namely UMass Amherst’s MAchine Learning for LanguagE Toolkit (MALLET) (McCallum, 2002). MALLET’s topic modeling toolkit has been used by Walker et al. (2010) to test the effects of noisy optical character recognition (OCR) data on LDA. It has been used by Nelson (2010) to mine topics from the Civil War era newspaper Dispatch, and it has also been used by Blevins (2010) to examine general topics and to identify emotional moments from Martha Ballards Diary. 3 Dataset Our sample data comes from a collection of digitized historical newspapers, consisting of newspapers published in Texas from 1829 to 2008. Issues are segmented by pages with continuous text containing articles and advertisements. Table 2 provides more information about the dataset. 2 http://mallet.cs.umass.edu/ 1 http://americanpast.richmond.edu/dispatch/ 3 http://historying.org/2010/04/01/ 47 Property Number of titles Number of years Number of issues Number of pages Number of tokens 114 180 32,745 232,567 816,190,453 Table 2: Properties of the newspaper collection 3.1 Sample Years and Categories From the wide range available, we sampled several historically significant dates in order to evaluate topic modeling. These dates were chosen for their unique characteristics (detailed below), which made it possible for a professional historian to examine and evaluate the relevancy of the results. These are the subcategories we chose as samples: • Newspapers from 1865-1901: During this period, Texans rebuilt their society in the aftermath of the American Civil War. With the abolition of slavery in 1865, Texans (both black and white) looked to rebuild their post-war economy by investing heavily in cotton production throughout the state. Cotton was considered a safe investment, and so Texans produced enough during this period to make Texas the largest cotton producer in the United States by 1901. Yet overproduction during that same period impoverished Texas farmers by driving down the market price for cotton, and thus a large percentage went bankrupt and lost their lands (over 50 percent by 1900). As a result, angry cotton farmers in Texas during the 1890s joined a new political party, the Populists, whose goal was to use the national government to improve the economic conditions of farmers. This effort failed by 1896, although it represented one of the largest third-party political revolts in American history. This period, then, was dominated by the rise of cotton as the foundation of the Texas economy, the financial failures of Texas farmers, and their unsuccessful political protests of the 1890s as cotton bankrupted people across the state. These are the issues we would expect to emerge as important topics from newspapers in this category. This dataset consists of 52,555 pages over 5,902 issues. • Newspapers from 1892: This was the year of the formation of the Populist Party, which a large portion of Texas farmers joined for the U. S. presidential election of 1892. The Populists sought to have the U. S. federal government become actively involved in regulating the economy in places like Texas (something never done before) in order to prevent cotton farmers from going further into debt. In the 1892 election, the Populists did surprisingly well (garnering about 10 percent of the vote nationally) and won a full 23 percent of the vote in Texas. This dataset consists of 1,303 pages over 223 issue


Building an Intelligent Assistant for Digital Forensics

Software tools designed for disk analysis play a critical role today in digital forensics investigations. However, these digital forensics tools are often difficult to use, usually task specific, and generally require professionally trained users with IT backgrounds. The relevant tools are also often open source requiring additional technical knowledge and proper configuration. This makes it difficult for investigators without some computer science background to easily conduct the needed disk analysis. In this dissertation, we present AUDIT, a novel automated disk investigation toolkit that supports investigations conducted by non-expert (in IT and disk technology) and expert investigators. Our system design and implementation of AUDIT intelligently integrates open source tools and guides non-IT professionals while requiring minimal technical knowledge about the disk structures and file systems of the target disk image. We also present a new hierarchical disk investigation model which leads AUDIT to systematically examine the disk in its totality based on its physical and logical structures. AUDIT’s capabilities as an intelligent digital assistant are evaluated through a series of experiments comparing it with a human investigator as well as against standard benchmark disk images.


ELDEN: Improved Entity Linking Using Densified Knowledge Graphs

Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG). Degree of connectivity of an entity in the KG directly affects an EL system’s ability to correctly link mentions in text to the entity in KG. This causes many EL systems to perform well for entities well connected to other entities in KG, bringing into focus the role of KG density in EL. In this paper, we propose Entity Linking using Densified Knowledge Graphs (ELDEN). ELDEN is an EL system which first densifies the KG with co-occurrence statistics from a large text corpus, and then uses the densified KG to train entity embeddings. Entity similarity measured using these trained entity embeddings result in improved EL. ELDEN outperforms stateof-the-art EL system on benchmark datasets. Due to such densification, ELDEN performs well for sparsely connected entities in the KG too. ELDEN’s approach is simple, yet effective. We have made ELDEN’s code and data publicly available.


What Do Stroke Patients Look for in Game-Based Rehabilitation

Stroke is one of the most common causes of physical disability, and early, intensive, and repetitive rehabilitation exercises are crucial to the recovery of stroke survivors. Unfortunately, research shows that only one third of stroke patients actually perform recommended exercises at home, because of the repetitive and mundane nature of conventional rehabilitation exercises. Thus, to motivate stroke survivors to engage in monotonous rehabilitation is a significant issue in the therapy process. Game-based rehabilitation systems have the potential to encourage patients continuing rehabilitation exercises at home. However, these systems are still rarely adopted at patients' places. Discovering and eliminating the obstacles in promoting game-based rehabilitation at home is therefore essential. For this purpose, we conducted a study to collect and analyze the opinions and expectations of stroke patients and clinical therapists. The study is composed of 2 parts: Rehab-preference survey - interviews to both patients and therapists to understand the current practices, challenges, and expectations on game-based rehabilitation systems; and Rehab-compatibility survey - a gaming experiment with therapists to elaborate what commercial games are compatible with rehabilitation. The study is conducted with 30 outpatients with stroke and 19 occupational therapists from 2 rehabilitation centers in Taiwan. Our surveys show that game-based rehabilitation systems can turn the rehabilitation exercises more appealing and provide personalized motivation for various stroke patients. Patients prefer to perform rehabilitation exercises with more diverse and fun games, and need cost-effective rehabilitation systems, which are often built on commodity hardware. Our study also sheds light on incorporating the existing design-for-fun games into rehabilitation system. We envision the results are helpful in developing a platform which enables rehab-compatible (i.e., existing, appropriately selected) games to be operated on commodity hardware and brings cost-effective rehabilitation systems to more and more patients' home for long-term recovery.


Deep Sparse Subspace Clustering

In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC). Regularized by the unit sphere distribution assumption for the learned deep features, DSSC can infer a new data affinity matrix by simultaneously satisfying the sparsity principle of SSC and the nonlinearity given by neural networks. One of the appealing advantages brought by DSSC is: when original real-world data do not meet the class-specific linear subspace distribution assumption, DSSC can employ neural networks to make the assumption valid with its hierarchical nonlinear transformations. To the best of our knowledge, this is among the first deep learning based subspace clustering methods. Extensive experiments are conducted on four real-world datasets to show the proposed DSSC is significantly superior to 12 existing methods for subspace clustering.


Semi-Supervised Learning for Relation Extraction

This paper proposes a semi-supervised learning method for relation extraction. Given a small amount of labeled data and a large amount of unlabeled data, it first bootstraps a moderate number of weighted support vectors via SVM through a co-training procedure with random feature projection and then applies a label propagation (LP) algorithm via the bootstrapped support vectors. Evaluation on the ACE RDC 2003 corpus shows that our method outperforms the normal LP algorithm via all the available labeled data without SVM bootstrapping. Moreover, our method can largely reduce the computational burden. This suggests that our proposed method can integrate the advantages of both SVM bootstrapping and label propagation.


Sequence-to-Sequence RNNs for Text Summarization

In this work, we cast text summarization as a sequence-to-sequence problem and apply the attentional encoder-decoder RNN that has been shown to be successful for Machine Translation (Bahdanau et al. (2014)). Our experiments show that the proposed architecture significantly outperforms the state-of-the art model of Rush et al. (2015) on the Gigaword dataset without any additional tuning. We also propose additional extensions to the standard architecture, which we show contribute to further improvement in performance.


Research on the Forecast of Shared Bicycle Rental Demand Based on Spark Machine Learning Framework

In recent years, the shared bicycle project has developed rapidly. In use of shared bicycles, a great deal of user riding information is recorded. How to extract effective knowledge from these vast amounts of information, how to use this knowledge to improve the shared bicycle system, and how to improve the user experience, are problems to solve. Citi Bike is selected as the research target. Data on Citi Bike’s user historical behavior, weather information, and holiday information are collected from three different sources, and converted into appropriate formats for model training. Spark MLlib is used to construct three different predictive models, advantages and disadvantages of different forecasting models are compared. Some techniques are used to enhance the accuracy of random forests model. The experimental results show that the root mean square error RMSE of the final model is reduced from 305.458 to 243.346.


W-Band CMOS on-chip energy harvester and rectenna

This paper presents the first fully on-chip integrated energy harvester and rectenna at the W-Band in 65nm CMOS technology. The designs are based on a 1-stage Dickson voltage multiplier. The rectenna consists of an on-chip integrated dipole antenna with a reflector underneath the substrate to enhance the directivity and realized gain. The energy harvester and rectenna achieve a power conversion efficiency of 10% and 2% respectively at 94GHz. The stand-alone harvester occupies only 0.0945mm2 including pads, while the fully integrated rectenna occupies a minimal chip area of 0.48mm2.


Spurious group differences due to head motion in a diffusion MRI study

Diffusion-weighted MRI (DW-MRI) has become a popular imaging modality for probing the microstructural properties of white matter and comparing them between populations in vivo. However, the contrast in DW-MRI arises from the microscopic random motion of water molecules in brain tissues, which makes it particularly sensitive to macroscopic head motion. Although this has been known since the introduction of DW-MRI, most studies that use this modality for group comparisons do not report measures of head motion for each group and rely on registration-based correction methods that cannot eliminate the full effects of head motion on the DW-MRI contrast. In this work we use data from children with autism and typically developing children to investigate the effects of head motion on differences in anisotropy and diffusivity measures between groups. We show that group differences in head motion can induce group differences in DW-MRI measures, and that this is the case even when comparing groups that include control subjects only, where no anisotropy or diffusivity differences are expected. We also show that such effects can be more prominent in some white-matter pathways than others, and that they can be ameliorated by including motion as a nuisance regressor in the analyses. Our results demonstrate the importance of taking head motion into account in any population study where one group might exhibit more head motion than the other.


Tap water versus sterile saline solution in the colonisation of skin wounds.

Irrigating wounds with tap water does not increase colonisation, but controlled studies are required for further evidence. Microbial colonisation was assessed in skin wounds, before and after irrigation with tap water, and was compared with irrigation using 0·9% sodium chloride sterile solution. The study included 120 subjects with chronic, traumatic, vascular, pressure or neuropathic wounds. A total of 60 wounds were randomly assigned to be irrigated with tap water (tap water group) and another 60 to be irrigated with 0·9% sodium chloride sterile solution (saline group), at a pressure of 0·46-0·54 PSI. Samples were collected from the centre of each wound using Levine's technique, before and after irrigation, and cultivated in thioglycollate, hypertonic mannitol agar, eosin methylene blue (EMB) agar, blood agar and Sabouraud agar at 37°C for 72 hours. There was concordance (kappa test) and discordance (McNemar test) regarding the count of positive and/or negative samples before and after irrigation in each group. The proportion of reduction of positive samples was similar for both groups in all cultures. Colony-forming unit count before and after irrigation was similar in both groups and in all cultures, except for the culture in hypertonic mannitol agar from the tap water group, for which the count was lower after irrigation (Wilcoxon z = 2·05, P = 0·041). It is concluded that skin wound irrigation with tap water leads to further reduction of Gram-positive bacteria compared with 0·9% sodium chloride sterile solution, with no difference in colonisation of haemolytic bacteria, Gram-negative bacteria and fungi.


Analysis of level design 'push & pull' within 21 games

This paper investigates the differences between 3D level designs in 21 popular games. We have developed a framework to analyze 3D level designs based on patterns extracted from level designers and game play sessions. We then use these patterns to analyze several game play sessions. Results of this analysis reveal methods by which designers push and pull players through levels. We discuss an analysis of these patterns in terms of three level affordance configurations, combat, environmental resistance, and mixed goals in 21 different games using one walkthrough play session per game. By looking at the variety of games, we can further explore the level similarities and differences between games.


Spinal Instability Neoplastic Score (SINS): Reliability Among Spine Fellows and Resident Physicians in Orthopedic Surgery and Neurosurgery

Study Design
Reliability analysis.

Objectives
The Spinal Instability Neoplastic Score (SINS) was developed for assessing patients with spinal neoplasia. It identifies patients who may benefit from surgical consultation or intervention. It also acts as a prognostic tool for surgical decision making. Reliability of SINS has been established for spine surgeons, radiologists, and radiation oncologists, but not yet among spine surgery trainees. The purpose of our study is to determine the reliability of SINS among spine residents and fellows, and its role as an educational tool.

Methods
Twenty-three residents and 2 spine fellows independently scored 30 de-identified spine tumor cases on 2 occasions, at least 6 weeks apart. Intraclass correlation coefficient (ICC) measured interobserver and intraobserver agreement for total SINS scores. Fleiss's kappa and Cohen's kappa analysis evaluated interobserver and intraobserver agreement of 6 component subscores (location, pain, bone lesion quality, spinal alignment, vertebral body collapse, and posterolateral involvement of spinal elements).

Results
Total SINS scores showed near perfect interobserver (0.990) and intraobserver (0.907) agreement. Fleiss's kappa statistics revealed near perfect agreement for location; substantial for pain; moderate for alignment, vertebral body collapse, and posterolateral involvement; and fair for bone quality (0.948, 0.739, 0.427, 0.550, 0.435, and 0.382). Cohen's kappa statistics revealed near perfect agreement for location and pain, substantial for alignment and vertebral body collapse, and moderate for bone quality and posterolateral involvement (0.954, 0.814, 0.610, 0.671, 0.576, and 0.561, respectively).

Conclusions
The SINS is a reliable and valuable educational tool for spine fellows and residents learning to judge spinal instability.


MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations

Author co-citation analysis (ACA) is a well-known and frequently-used method to exhibit the academic researchers and the professional field sketch according to co-citation relationships between authors in an article set. However, visualizing subtle examination is limited because only author co-citation information is required in ACA. The proposed method, called modified author co-citation analysis (MACA), exploits author co-citation relationship, citations published time, citations published carriers, and citations keywords, to construct MACA-based co-citation matrices. According to the results of our experiments: (1) MACA shows a good clustering result with more delicacy and more clearness; (2) more information involved in co-citation analysis performs good visual acuity; (3) in visualization of co-citation network produced by MACA, the points in different categories have far more distance, and the points indicating authors in the same category are closer together. As a result, the proposed MACA is found that more detailed and subtle information of a knowledge domain analyzed can be obtained, compared to ACA.


Probabilistic Inference Using Markov Chain Monte Carlo Methods

Probabilistic inference is an attractive approach to uncertain reasoning and em pirical learning in arti cial intelligence Computational di culties arise however because probabilistic models with the necessary realism and exibility lead to com plex distributions over high dimensional spaces Related problems in other elds have been tackled using Monte Carlo methods based on sampling using Markov chains providing a rich array of techniques that can be applied to problems in arti cial intelligence The Metropolis algorithm has been used to solve di cult problems in statistical physics for over forty years and in the last few years the related method of Gibbs sampling has been applied to problems of statistical inference Concurrently an alternative method for solving problems in statistical physics by means of dynamical simulation has been developed as well and has recently been uni ed with the Metropolis algorithm to produce the hybrid Monte Carlo method In computer science Markov chain sampling is the basis of the heuristic optimization technique of simulated annealing and has recently been used in randomized algorithms for approximate counting of large sets In this review I outline the role of probabilistic inference in arti cial intelligence present the theory of Markov chains and describe various Markov chain Monte Carlo algorithms along with a number of supporting techniques I try to present a comprehensive picture of the range of methods that have been developed including techniques from the varied literature that have not yet seen wide application in arti cial intelligence but which appear relevant As illustrative examples I use the problems of probabilistic inference in expert systems discovery of latent classes from data and Bayesian learning for neural networks


Bayesian Learning for Neural Networks


Generalization by Weight-Elimination with Application to Forecasting

Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates.


A case study of user participation in the information systems development process

There are many in the information systems discipline who believe that user participation is necessary for successful systems development. However, it has been suggested that this belief is neither grounded in theory nor substantiated by research data. This may indicate that researchers have not addressed fully the underlying complexity of the concept. If so, this is indicative of a deficiency in understanding user participation in information systems development as it occurs in organizations. In order to enhance the extant understanding of participative information systems development, the present study adopts a qualitative, case-based approach to research so as to provide an in-depth description of the complex social nature of the phenomenon as manifested in one organization. The results of the study illustrate that a high degree of direct and indirect user participation did not guarantee the successful implementation and use of information systems in the organization studied. Such participatory development practices did, however, result in the development of systems that adequately captured user requirements and hence satisfied user informational needs. It was clear that despite the perceived negative impact, which the new systems would have on user work-related roles and activities, the existence of an organization-wide participative policy, and associated participative structures, coupled with a favorable organization culture, generated a participatory development climate that was conducive to the successful development of information systems, while not guaranteeing it. That said, the central conclusion of this study was that user dissatisfaction with developed systems centered on the poor management of change in the organization.


Spatial Data Mining Features between General Data Mining

Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-trivial patterns information or knowledge from large spatial datasets.Extracting interesting and useful patterns from spatial datasets must be more difficult than extracting the corresponding patterns from traditional numeric or categorical data due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation.Emphasized overviewed the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning,resource discovery and other areas.