title: A shapelet transform for time series classification

abstract: The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set. A shapelet is a time series subsequence that is identified as being representative of class membership. The original research in this field embedded the procedure of finding shapelets within a decision tree. We propose disconnecting the process of finding shapelets from the classification algorithm by proposing a shapelet transformation. We describe a means of extracting the k best shapelets from a data set in a single pass, and then use these shapelets to transform data by calculating the distances from a series to each shapelet. We demonstrate that transformation into this new data space can improve classification accuracy, whilst retaining the explanatory power provided by shapelets.


title: Constraint-based sequential pattern mining: the pattern-growth methods

abstract: Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our mission well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.


title: Temporal TF-IDF: A High Performance Approach for Event Summarization in Twitter

abstract: In recent years, there has been increased interest in real-world event summarization using publicly accessible data made available through social networking services such as Twitter and Facebook. People use these outlets to communicate with others, express their opinion and commentate on a wide variety of real-world events. Due to the heterogeneity, the sheer volume of text and the fact that some messages are more informative than others, automatic summarization is a very challenging task. This paper presents three techniques for summarizing microblog documents by selecting the most representative posts for real-world events (clusters). In particular, we tackle the task of multilingual summarization in Twitter. We evaluate the generated summaries by comparing them to both human produced summaries and to the summarization results of similar leading summarization systems. Our results show that our proposed Temporal TF-IDF method outperforms all the other summarization systems for both the English and non-English corpora as they lead to informative summaries.


title: Anception: Application Virtualization For Android

abstract: The problem of malware has become significant on Android devices. Library operating systems and application virtualization are both possible solutions for confining malware. Unfortunately, such solutions do not exist for Android. Designing mechanisms for application virtualization is a significant chal- lenge for several reasons: (1) graphics performance is important due to popularity of games and (2) applications with the same UID can share state. This paper presents Anception, the first flexible application virtualization framework for Android. It is imple- mented as a modification to the Android kernel and supports application virtualization that addresses the above requirements. Anception is able to confine many types of malware while supporting unmodified Android applications. Our Anception- based system exhibits up to 3.9% overhead on various 2D/3D benchmarks, and 1.8% overhead on the SunSpider benchmark.


title: Sorafenib treatment in Child–Pugh A and B patients with advanced hepatocellular carcinoma: safety, efficacy and prognostic factors

abstract: Background We aimed to evaluate the safety, efficacy and prognostic impact of baseline and early clinical markers in both Child–Pugh A and B patients with advanced hepatocellular carcinoma (HCC). Methods We prospectively studied 89 Japanese patients with HCC (Child–Pugh A, n = 59; Child–Pugh B, n = 30) who were started with sorafenib between May 2010 and July 2013. Results Frequency of sorafenib-related adverse events was almost similar between Child–Pugh score 5, 6, and 7 patients. The rate of liver dysfunction, including any grade encephalopathy, ≥ grade 3 ascites, or ≥ grade 3 bilirubin increased, in Child–Pugh score ≥8 group was significantly higher than that in the other groups. The median overall survival of Child–Pugh score 5, 6, 7 and ≥8 patients were 14.5, 11.1, 8.7 and 4.6 months, respectively. Patients in Child–Pugh score 6 had significantly longer OS than those in Child–Pugh score 7 (P = 0.049). Multivariate analysis identified macrovascular invasion (MVI), alpha-fetoprotein (AFP), Child–Pugh score and aspartate aminotransferase (AST) as baseline predictors of survival. However, extrahepatic metastasis (EHM) was not a significant prognostic factor. In addition, decrease in AFP level and development of hand–foot skin reaction within 4 weeks after sorafenib initiation were closely associated with favorable survival. Conclusion It is possible that not only Child–Pugh score 5 and 6 but also 7 patients are eligible for future clinical trials with sorafenib or similar drugs. Various survival predictors identified in this study might be considered as stratification factor. Although both MVI and EHM is a phenotype of advanced HCC, MVI should be discriminated from EHM because of the prognostic impact on survival in sorafenib-treated advanced HCC patients.


title: Portable millimeter wave 3D imaging radar

abstract: The paper aims to build a new, portable, and affordable millimeter wave imaging radar. The proposed radar uses rotation movement and applies inverse Radon transform to resolve a 2D image of each range bin. Consequently, 3D imaging can be realized by the radar system. Its resolution is comparable to light-based imaging sensors, such as cameras and LIDAR - light detection and ranging. Because of using a single transceiver element, the new radar imaging system is compact, low cost, and portable. It can be used for detection, security and surveillance systems.


title: A Survey of Classification Techniques in the Area of Big Data

abstract: Big Data concern large-volume, growing data sets that are complex and have multiple autonomous sources. Earlier technologies were not able to handle storage and processing of huge data thus Big Data concept comes into existence. This is a tedious job for users unstructured data. So, there should be some mechanism which classify unstructured data into organized form which helps user to easily access required data. Classification techniques over big transactional database provide required data to the users from large datasets more simple way. There are two main classification techniques, supervised and unsupervised. In this paper we focused on to study of different supervised classification techniques. Further this paper shows a advantages and limitations.


title: Enterprise Architecture Quality Attributes: A Case Study

abstract: Enterprise Architecture (EA) is an approach for facilitating the integration of strategy, business, information systems and technology towards a common goal and mastering organizational complexity through the development and usage of architectural descriptions. The planning and modeling aspect of EA is already fairly well covered in the literature, while the attributes of EA quality have attracted less interest - even though EA quality has been perceived as a prerequisite for realizing its benefits. In this exploratory case study, we identify ten quality attributes for EA products and services, utilizing data collected from 14 EA practitioner interviews. We will fill in several gaps in the existing theory base, propose a list of attributes that increase EA quality, and call for more research.


title: A Fast Radial Symmetry Transform for Detecting Points of Interest

abstract: A new feature detection technique is presented that utilises local radial symmetry to identify regions of interest within a scene. This transform is significantly faster than existing techniques using radial symmetry and offers the possibility of real-time implementation on a standard processor. The new transformis shown to perform well on a wide variety of images and its performance is tested against leading techniques from the literature. Both as a facial feature detector and as a generic region of interest detector the new transformis seen to offer equal or superior performance to contemporary techniques whilst requiring drastically less computational effort.


title: Manga FaceNet: Face Detection in Manga based on Deep Neural Network

abstract: Among various elements of manga, character's face plays one of the most important role in access and retrieval. We propose a DNN-based method to do manga face detection, which is a challenging but relatively unexplored topic. Given a manga page, we first find candidate regions based on the selective search scheme. A deep neural network is then proposed to detect manga faces of various appearance. We evaluate the proposed method based on a large-scale benchmark, and show performance comparison and convincing evaluation results that have rarely done before.


title: Relation between carotid vulnerable plaques and peripheral leukocyte: a case-control study of comparison utilizing multi-parametric contrast-enhanced ultrasound

abstract: BACKGROUND ::: This study evaluates carotid vulnerable plaques using contrast-enhanced ultrasound (CEUS) and explores the relationship between vulnerable plaques and leukocytes. ::: ::: ::: METHODS ::: Sixty-two symptomatic and 54 asymptomatic patients underwent CEUS. The images were analyzed using time-intensity and fitting curves, and peak (PTIC), mean (MTIC), peak (PFC), sharpness (SFC), and area under the curve (AUCFC) were obtained. The relations between CEUS parameters and leukocytes were analyzed. ::: ::: ::: RESULTS ::: In the symptomatic group, total leukocytes and neutrophils were higher, while lymphocyte was decreased; PTIC, MTIC, PFC, SFC, and AUCFC were significantly higher; MTIC and AUCFC were negatively correlated with lymphocytes, and MTIC was positively correlated with neutrophils. Classification and regression tree analysis showed that MTIC at a cutoff of 20.8 and AUCFC at a cutoff of 8.8 resulted in a predictive of acute cerebral infarction, accuracy of 84.3%, sensitivity of 87.1%, and specificity of 81.5%. ::: ::: ::: CONCLUSIONS ::: The variation in the perivascular leucocyte is significantly related to intraplaque inflammatory activities, CEUS is a feasible monitor of intraplaque neovascularization, so CEUS combined with perivascular leucocyte could be helpful as a warning for vulnerable plaques.


title: Design and analysis of a low-profile 28 GHz beam steering antenna solution for Future 5G cellular applications

abstract: A first-of-the-kind 28 GHz antenna solution for the upcoming 5G cellular communication is presented in detail. Extensive measurements and simulations ascertain the proposed 28 GHz antenna solution to be highly effective for cellular handsets operating in realistic propagating environments.


title: The effect of social networking websites on positive self-views: An experimental investigation

abstract: Millions of people use social networking sites (SNSs), but it is unclear how these sites shape personality traits and identity. In Experiment 1, college students were randomly assigned to either edit their MySpace page or complete a control task online (interacting with Google Maps). Those who focused on their MySpace page scored significantly higher on the Narcissistic Personality Inventory (NPI) than a control group. In Experiment 2, those who focused on their Facebook page scored significantly higher in general self-esteem, but not narcissism, than a control group. Thus, spending time on SNSs profiles causes young people to endorse more positive self-views, although the specific form this takes depends on the site. Consistent with previous research, narcissism was associated with a larger number of SNSs ''friends'' in both experiments.


title: Minimax Filter: Learning to Preserve Privacy from Inference Attacks

abstract: Preserving privacy of continuous and/or high-dimensional data such as images, videos and audios, can be challenging with syntactic anonymization methods which are designed for discrete attributes. Differential privacy, which provides a more formal definition of privacy, has shown more success in sanitizing continuous data. However, both syntactic and differential privacy are susceptible to inference attacks, i.e., an adversary can accurately infer sensitive attributes from sanitized data. The paper proposes a novel filter-based mechanism which preserves privacy of continuous and high-dimensional attributes against inference attacks. Finding the optimal utility-privacy tradeoff is formulated as a min-diff-max optimization problem. The paper provides an ERM-like analysis of the generalization error and also a practical algorithm to perform the optimization. In addition, the paper proposes an extension that combines minimax filter and differentially-private noisy mechanism. Advantages of the method over purely noisy mechanisms is explained and demonstrated with examples. Experiments with several real-world tasks including facial expression classification, speech emotion classification, and activity classification from motion, show that the minimax filter can simultaneously achieve similar or better target task accuracy and lower inference accuracy, often significantly lower than previous methods.


title: CubeNet: Equivariance to 3D Rotation and Translation

abstract: 3D Convolutional Neural Networks are sensitive to transformations applied to their input. This is a problem because a voxelized version of a 3D object, and its rotated clone, will look unrelated to each other after passing through to the last layer of a network. Instead, an idealized model would preserve a meaningful representation of the voxelized object, while explaining the pose-difference between the two inputs. An equivariant representation vector has two components: the invariant identity part, and a discernable encoding of the transformation. Models that can't explain pose-differences risk"diluting"the representation, in pursuit of optimizing a classification or regression loss function. We introduce a Group Convolutional Neural Network with linear equivariance to translations and right angle rotations in three dimensions. We call this network CubeNet, reflecting its cube-like symmetry. By construction, this network helps preserve a 3D shape's global and local signature, as it is transformed through successive layers. We apply this network to a variety of 3D inference problems, achieving state-of-the-art on the ModelNet10 classification challenge, and comparable performance on the ISBI 2012 Connectome Segmentation Benchmark. To the best of our knowledge, this is the first 3D rotation equivariant CNN for voxel representations.


title: A Communication Goals Model of Online Persuasion

abstract: Online communication media are being used increasingly for attempts to persuade message receivers. This paper presents a theoretical model that predicts outcomes of online persuasion based on the structure of primary and secondary goals message receivers hold toward the communication.


title: How Nervous Systems Evolve in Relation to Their Embodiment: What We Can Learn from Octopuses and Other Molluscs

abstract: Cephalopods such as the octopus show the most advanced behavior among invertebrates, which they accomplish with an exceptionally flexible body plan. In this review I propose that the embodied organization approach, developed by roboticists to design efficient autonomous robots, is useful for understanding the evolution and development of the efficient adaptive interaction of animals with their environment, using the octopus as the leading example. The embodied organization approach explains adaptive behavior as emerging from the continuous dynamical and reciprocal physical and informational interactions between four elements: the controller, the mechanical and the sensory systems and the environment. In contrast to hierarchical organization, in embodied organization, self-organization processes can take part in the emergence of the adaptive properties. I first discuss how the embodiment concept explains covariation of body form, nervous system organization, and level of behavioral complexity using the Mollusca as an example. This is an ideal phylum to test such a qualitative correlation between body/brain/behavior, because they show the greatest variations of body plan within a single phylum. In some cases the covariation of nervous system and body structure seems to arise independently of close phylogenetic relationships. Next, I dwell on the octopus as an ideal model to test the embodiment concept within a single biological system. Here, the unusual body morphology of the octopus exposes the uniqueness of the four components comprising the octopus' embodiment. Considering together the results from behavioral, physiological, anatomical, and motor control research suggests that these four elements mutually influence each other. It is this mutual interactions and self-organization which have led to their unique evolution and development to create the unique and highly efficient octopus embodiment.


title: Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm

abstract: In this paper, we study how to address the sparsity, accuracy and serendipity issues of top-N recommendation with collaborative filtering (CF). Existing studies commonly use rated items (which form only a small section in a rating matrix) or import some additional information (e.g., details about the items and users) to improve the performance of CF. Unlike these methods, we propose a novel notion towards a huge amount of unrated items: serendipity item. By utilizing serendipity items, we propose concise satisfaction and interest injection (CSII), a method that can effectively find interesting, satisfying, and serendipitous items in unrated items. By preventing uninteresting and unsatisfying items to be recommended as top-N items, this concise-but-novel method improves accuracy and recommendation quality (especially serendipity) substantially. Meanwhile, it can address the sparsity and cold-start issues by enriching the rating matrix in CF without additional information. As our method tackles rating matrix before recommendation procedure, it can be applied to most existing CF methods, such as item-based CF, user-based CF and matrix factorization-based CF. Through comprehensive experiments using abundant real-world datasets with LensKit implementation, we successfully demonstrate that our solution improves the performance of existing CF methods consistently and universally. Moreover, comparing with baseline methods, CSII can extract uninteresting items more carefully and cautiously, avoiding potential items inferred by mistake.


title: Topic-driven Multi-type Citation Network Analysis

abstract: In every scientific field, automated citation analysis enables the estimation of importance or reputation of publications and authors. In this paper, we focus on the task of ranking authors. Although previous work has used content-based approaches or citation network link analyses, the combination of the two with topical link analyses is unexplored. Moreover, previous citation analysis applications are typically limited to a graph based on author citations, or a bipartite graph based on author and paper citations. We present in this paper a novel integrated probabilistic model which combines a content-based approach with a multi-type citation network which integrates citations among papers, authors, affiliations and publishing venues in a single model. We further introduce the application of Topical PageRank into citation network link analysis due to the fact that researchers may be experts in different scientific domains. Finally, we describe a heterogenous link analysis of the citation network, exploring the impact of weighting various factors. Comparative experimental results based on data extracted from the ACM digital library show that 1) the multi-type citation graph works better than citation graphs integrating fewer types of entities, 2) the use of Topical PageRank can further improve performance, and 3) Heterogenous PageRank with parameter tuning can work even better than Topical PageRank.


title: The integration of value-based adoption and expectation-confirmation models: An example of IPTV continuance intention

abstract: Expectation-confirmation theory (ECT) has long been adopted to study continuance intention with respect to various types of products or services. A popular trend in this research stream is examination of the impact of performance confirmation on usefulness (or playfulness) and satisfaction in the context of organizational information system usage or free website access. However, studying the positive attitude of consumers alone is inadequate, especially when access to the products or services is not without cost. That is, costs or sacrifices should be taken into account so as to clarify the antecedents of continuance intention. Based on this idea, this study took the ''net value'' concept from the value-based model and incorporated it into ECT in order to provide a more comprehensive viewpoint. We argue that continuance intention is determined by net value, a thorough comparison of benefits and costs, and satisfaction, which is also a function of net value. After collecting data from 172 IPTV customers, we confirmed all proposed hypotheses. The results show that perceived net value, a function of perceived sacrifices and perceived benefits, is a strong predictor of satisfaction and continuance intention. Discussions and implications for academics and practitioners are also provided.


title: Stock investment decision support for Hong Kong market using RBFNN based candlestick models

abstract: Candlestick pattern, as an efficient method in technical analysis, is widely used in decision support of stock investment. From historical data, there is a no 100% guarantee for a stock price increasing after the appearance of a bullish candlestick pattern. The main aim of this paper is to enhance the prediction ability of Candlestick Patterns using a Multiple Classifier System (MCS) consisting of Radial Basis Function Neural Network (RBFNN) trained by a Localized Generalization Error Model (L-GEM). The RBFNN classifies particular candlestick pattern to be a real bullish candlestick pattern or not based on training with past data. The MCS fusing RBFNN for different patterns makes the final prediction of the stock price trend. In this paper, stock price data of 40 stocks in Hong Kong Hang Seng Component Index is collected to carry out the investment simulation experiment. Experimental result shows that the proposed method yields statistically significant profit when compared with a random investment strategy and candlestick investment without RBFNN.


title: Data mining methods for detection of new malicious executables

abstract: A serious security threat today is malicious executables, especially new, unseen malicious executables often arriving as email attachments. These new malicious executables are created at the rate of thousands every year and pose a serious security threat. Current anti-virus systems attempt to detect these new malicious programs with heuristics generated by hand. This approach is costly and oftentimes ineffective. We present a data mining framework that detects new, previously unseen malicious executables accurately and automatically. The data mining framework automatically found patterns in our data set and used these patterns to detect a set of new malicious binaries. Comparing our detection methods with a traditional signature-based method, our method more than doubles the current detection rates for new malicious executables.


title: The Incidence and Prevalence of Ankle Sprain Injury: A Systematic Review and Meta-Analysis of Prospective Epidemiological Studies

abstract: Background ::: Ankle sprain is one of the most common musculoskeletal injuries, yet a contemporary review and meta-analysis of prospective epidemiological studies investigating ankle sprain does not exist.


title: Algorithms for data warehouse design to enhance decision-making

abstract: On-line analytical processing (OLAP) queries are strongly affected by the amount data needed to be accessed from the disk. Therefore, there is a need to employ techniques that can facilitate efficient execution of these queries. There has been a lot of work to optimize the performance of relational data warehouses. Among the two fragmentation techniques, vertical fragmentation is often considered more complicated than horizontal, it nearly impossible to obtain an optimal solution. Data partitioning concept that has been studied in the context of relational databases aims to reduce query execution time and facilitate the parallel execution of queries. In this paper, we develop a new framework based on genetic algorithm for applying the partitioning technique on relational DW schema (star schema) to minimize the total query execution cost. We develop an analytical cost model for executing a set of OLAP queries on a partitioned star schema. We conduct experiments to evaluate the utility of partitioning in efficiently executing OLAP queries. Finally, we show how partitioning can be used to facilitate parallel execution of OLAP queries.


title: Marijuana intoxication by passive inhalation: documentation by detection of urinary metabolites

abstract: During the course of a study on the effects of chronic, heavy marijuana use, a placebo control subject reported dizziness and nausea, showed tachycardia and conjunctivitis, and was found to have urinary cannabinoid metabolites. Staff members observing the marijuana smokers also complained of similar subjective symptoms. The detection of cannabinoids in the urine of this nonsmoker documents the previously anecdotal concept of the "contact high" and has implications for marijuana research and for precautions that may be necessary should marijuana become legal.


title: Using ITIL to Improve IT Services

abstract: This paper examines the adoption and implementation of the Information Technology Infrastructure Library (ITIL). Specifically, interviews with a CIO, as well as literature from the ITIL Official site and from the practitioner’s journals are consulted in order to determine whether the best practices contained in the ITIL framework may improve the management of information technology (IT) services, as well as assist in promoting the alignment of Business and the IT Function within the organization. A conceptual model is proposed which proposes a two-way relationship between IT and the provision of IT Services, with ITIL positioned as an intervening variable.


title: Prediction of Water Depth From Multispectral Satellite Imagery—The Regression Kriging Alternative

abstract: Bathymetric information is crucial to the study and management of coastal zones. Passive remote sensing provides a cost-effective alternative to acoustic surveys and bathymetric LiDAR techniques. Most previous studies estimated water depth from multispectral imagery in shallow coastal and inland waters by establishing the relationship between image pixel spectral values and known water depth measurements, in which the log-linear inversion model is most widely used. Given a set of known water depth sample points, a bathymetric grid/map can be created by using a spatial interpolation technique. However, when a limited number of water depth sample points are available, the interpolation result is often unsatisfactory for portraying benthic morphology. In this letter, we propose to use the regression kriging (RK) approach to combine the optimal spatial interpolation of kriging with the high-resolution auxiliary information of multispectral imagery for a detailed bathymetric mapping. A case study has been performed to demonstrate and evaluate the performance of the RK method in comparison with ordinary kriging and log-linear inversion methods. It shows that the RK method can produce more accurate water depth estimations than the log-linear inversion method due to the account of the spatial pattern of the modeling residuals. The bathymetric grid created from the RK contains much more spatial details about the ocean floor morphology than that from the ordinary kriging owing to the incorporation of auxiliary information from multispectral satellite imagery.


Physical Causality of Action Verbs in Grounded Language Understanding


title: Openness, fluid intelligence, and crystallized intelligence: Toward an integrative model

abstract: Many studies are concerned with the bivariate relationships between Openness, fluid intelligence (Gf), and crystallized intelligence (Gc). Results suggest an influence of Gf and Openness on Gc. However, the overlap between Gf and Openness is rarely controlled for. Moreover, interaction effects or longitudinal influences are also often neglected. The present two studies aimed to elucidate exactly these interactions and longitudinal influences. Besides a main effect of Gf on Gc, Study 1 (N = 180) revealed an interaction effect between Openness and Gf. Study 2 utilized longitudinal data (N = 172) and identified an effect of Openness on the development of Gf. Gf and Openness predicted Gc 6 years later. A model integrating the results and providing a theoretical framework and outlook is proposed.


title: Expediting scientific data analysis with reorganization of data

abstract: Data producers typically optimize the layout of data files to minimize the write time. In most cases, data analysis tasks read these files in access patterns different from the write patterns causing poor read performance. In this paper, we introduce Scientific Data Services (SDS), a framework for bridging the performance gap between writing and reading scientific data. SDS reorganizes data to match the read patterns of analysis tasks and enables transparent data reads from the reorganized data. We implemented a HDF5 Virtual Object Layer (VOL) plugin to redirect the HDF5 dataset read calls to the reorganized data. To demonstrate the effectiveness of SDS, we applied two parallel data organization techniques: a sort-based organization on a plasma physics data and a transpose-based organization on mass spectrometry imaging data. We also extended the HDF5 data access API to allow selection of data based on their values through a query interface, called SDS Query. We evaluated the execution time in accessing various subsets of data through existing HDF5 Read API and SDS Query.We showed that reading the reorganized data using SDS is up to 55X faster than reading the original data.


title: Patterns of Enterprise Application Architecture

abstract: Preface. Who This Book Is For. Acknowledgements. Colophon. Introduction. Architecture. Enterprise Applications. Kinds of Enterprise Application. Thinking About Performance. Patterns. The Structure of the Patterns. Limitations of These Patterns. I. THE NARRATIVES. 1. Layering. The Evolution of Layers in Enterprise Applications. The Three Principal Layers. Choosing Where to Run Your Layers. 2. Organizing Domain Logic. Making a Choice. Service Layer. 3. Mapping to Relational Databases. Architectural Patterns. The Behavioral Problem. Reading in Data Structural Mapping Patterns. Mapping Relationships. Inheritance. Building the Mapping. Double Mapping. Using Metadata. Database Connections. Some Miscellaneous Points. Further Reading. 4. Web Presentation. View Patterns. Input Controller Patterns. Further Reading. 5. Concurrency (by Martin Fowler and David Rice). Concurrency Problems. Execution Contexts. Isolation and Immutability. Optimistic and Pessimistic Concurrency Control. Preventing Inconsistent Reads. Deadlocks. Transactions. ACID. Transactional Resources. Reducing Transaction Isolation for Liveness. Business and System Transactions. Patterns for Offline Concurrency Control. Application Server Concurrency. Further Reading. 6. Session State. The Value of Statelessness. Session State. Ways to Store Session State. 7. Distribution Strategies. The Allure of Distributed Objects. Remote and Local Interfaces. Where You Have to Distribute. Working with the Distribution Boundary. Interfaces for Distribution. 8. Putting it all Together. Starting With the Domain Layer. Down to the Data Source. Data Source for Transaction Script. Data Source Table Module (125). Data Source for Domain Model (116). The Presentation Layer. Some Technology-Specific Advice. Java and J2EE. .NET. Stored Procedures. Web Services. Other Layering Schemes. II. THE PATTERNS. 9. Domain Logic Patterns. Transaction Script. How It Works. When to Use It. The Revenue Recognition Problem. Example: Revenue Recognition (Java). Domain Model. How It Works. When to Use It. Further Reading. Example: Revenue Recognition (Java). Table Module. How It Works. When to Use It. Example: Revenue Recognition with a Table Module (C#). Service Layer(by Randy Stafford). How It Works. When to Use It. Further Reading. Example: Revenue Recognition (Java). 10. Data Source Architectural Patterns. Table Data Gateway. How It Works. When to Use It. Further Reading. Example: Person Gateway (C#). Example: Using ADO.NET Data Sets (C#). Row Data Gateway. How It Works. When to Use It. Example: A Person Record (Java). Example: A Data Holder for a Domain Object (Java). Active Record. How It Works. When to Use It. Example: A Simple Person (Java). Data Mapper. How It Works. When to Use It. Example: A Simple Database Mapper (Java). Example: Separating the Finders (Java). Example: Creating an Empty Object (Java). 11. Object-Relational Behavioral Patterns. Unit of Work. How It Works. When to Use It. Example: Unit of Work with Object Registration (Java) (by David Rice). Identity Map. How It Works. When to Use It. Example: Methods for an Identity Map (Java). Lazy Load. How It Works. When to Use It. Example: Lazy Initialization (Java). Example: Virtual Proxy (Java). Example: Using a Value Holder (Java). Example: Using Ghosts (C#). 12. Object-Relational Structural Patterns. Identity Field. How It Works. When to Use It. Further Reading. Example: Integral Key (C#). Example: Using a Key Table (Java). Example: Using a Compound Key (Java). Foreign Key Mapping. How It Works. When to Use It. Example: Single-Valued Reference (Java). Example: Multitable Find (Java). Example: Collection of References (C#). Association Table Mapping. How It Works. When to Use It. Example: Employees and Skills (C#). Example: Using Direct SQL (Java). Example: Using a Single Query for Multiple Employees (Java) (by Matt Foemmel and Martin Fowler). Dependent Mapping. How It Works. When to Use It. Example: Albums and Tracks (Java). Embedded Value. How It Works. When to Use It. Further Reading. Example: Simple Value Object (Java). Serialized LOB. How It Works. When to Use It. Example: Serializing a Department Hierarchy in XML (Java). Single Table Inheritance. How It Works. When to Use It. Example: A Single Table for Players (C#). Loading an Object from the Database. Class Table Inheritance. How It Works. When to Use It. Further Reading. Example: Players and Their Kin (C#). Concrete Table Inheritance. How It Works. When to Use It. Example: Concrete Players (C#). Inheritance Mappers. How It Works. When to Use It. 13. Object-Relational Metadata Mapping Patterns. Metadata Mapping. How It Works. When to Use It. Example: Using Metadata and Reflection (Java). Query Object. How It Works. When to Use It. Further Reading. Example: A Simple Query Object (Java). Repository (by Edward Hieatt and Rob Mee). How It Works. When to Use It. Further Reading. Example: Finding a Person's Dependents (Java). Example: Swapping Repository Strategies (Java). 14. Web Presentation Patterns. Model View Controller. How It Works. When to Use It. Page Controller. How It Works. When to Use It. Example: Simple Display with a Servlet Controller and a JSP View (Java). Example: Using a JSP as a Handler (Java). Example: Page Handler with a Code Behind (C#). Front Controller. How It Works. When to Use It. Further Reading. Example: Simple Display (Java). Template View. How It Works. When to Use It. Example: Using a JSP as a View with a Separate Controller (Java). Example: ASP.NET Server Page (C#). Transform View. How It Works. When to Use It. Example: Simple Transform (Java). Two Step View. How It Works. When to Use It. Example: Two Stage XSLT (XSLT). Example: JSP and Custom Tags (Java). Application Controller. How It Works. When to Use It. Further Reading. Example: State Model Application Controller (Java). 15. Distribution Patterns. Remote Facade. How It Works. When to Use It. Example: Using a Java Session Bean as a Remote Facade (Java). Example: Web Service (C#). Data Transfer Object. How It Works. When to Use It. Further Reading. Example: Transferring Information about Albums (Java). Example: Serializing Using XML (Java). 16. Offline Concurrency Patterns. Optimistic Offline Lock (by David Rice). How It Works. When to Use It. Example: Domain Layer with Data Mappers (165) (Java). Pessimistic Offline Lock (by David Rice). How It Works. When to Use It. Example: Simple Lock Manager (Java). Coarse-Grained Lock (by David Rice and Matt Foemmel). How It Works. When to Use It. Example: Shared Optimistic Offline Lock (416) (Java). Example: Shared Pessimistic Offline Lock (426) (Java). Example: Root Optimistic Offline Lock (416) (Java). Implicit Lock (by David Rice). How It Works. When to Use It. Example: Implicit Pessimistic Offline Lock (426) (Java). 17. Session State Patterns. Client Session State. How It Works. When to Use It. Server Session State. How It Works. When to Use It. Database Session State. How It Works. When to Use It. 18. Base Patterns. Gateway. How It Works. When to Use It. Example: A Gateway to a Proprietary Messaging Service (Java). Mapper. How It Works. When to Use It. Layer Supertype. How It Works. When to Use It. Example: Domain Object (Java). Separated Interface. How It Works. When to Use It. Registry. How It Works. When to Use It. Example: A Singleton Registry (Java). Example: Thread-Safe Registry (Java) (by Matt Foemmel and Martin Fowler). Value Object. How It Works. When to Use It. Money. How It Works. When to Use It. Example: A Money Class (Java) (by Matt Foemmel and Martin Fowler). Special Case. How It Works. When to Use It. Further Reading. Example: A Simple Null Object (C#). Plugin (by David Rice and Matt Foemmel). How It Works. When to Use It. Example: An Id Generator (Java). Service Stub (by David Rice). How It Works. When to Use It. Example: Sales Tax Service (Java). Record Set. How It Works. When to Use It. References Index. 0321127420T10162002


title: PLL performance under frequency fluctuation-compliance with standards for distributed generation connected to the grid

abstract: Grid frequency fluctuation from the nominal value is expected by the standards related to distributed generation (DG) and smart grid (SG) systems. The Phase Locked Loop (PLL) estimates a signal synchronized with the fundamental frequency of the grid voltage that will be used by the controllers of the power converter of a DG system. A discrete model for the PLL, including the phase detector harmonic generation, is proposed. Poor low pass filtering of the PLL phase detector output signal can lead to PLL output distortion and slow time response. This paper analyses and compares the operation of moving average (MAV) filters, and two strategies of adaptive MAV filters, proposed in the literature, under frequency variation. The PLL controller and its tuning presented in these papers are also discussed. An improved PLL based on adaptive MAV filter and a better tuned controller is presented. Simulation and experimental results are provided.


title: Chemotherapy-induced peripheral neuropathy: Prevention and treatment strategies

abstract: Chemotherapy-induced peripheral neuropathy (CIPN) is a major dose limiting side effect of many commonly used chemotherapeutic agents, including platinum drugs, taxanes, epothilones and vinca alkaloids, and also newer agents such as bortezomib and lenolidamide. Symptom control studies have been conducted looking at ways to prevent or alleviate established CIPN. This manuscript provides a review of studies directed at both of these areas. New evidence strongly suggests that intravenous calcium and magnesium therapy can attenuate the development of oxaliplatin-induced CIPN, without reducing treatment response. Accumulating data suggest that vitamin E may also attenuate the development of CIPN, but more data regarding its efficacy and safety should be obtained prior to its general use in patients. Other agents that look promising in preliminary studies, but need substantiation, include glutamine, glutathione, N-acetylcysteine, oxcarbazepine, and xaliproden. Effective treatment of established CIPN, however, has yet to be found. Lastly, paclitaxel causes a unique acute pain syndrome which has been hypothesised to be caused by neurologic injury. No drugs, to date, have been proven to prevent this toxicity.


title: Modeling Approaches for Prognostics and Health Management of Electronics

abstract: Prognostics and Health Management is an enabling technology with the potential to solve complex reliability problems that are due to complexity in design, manufacturing, and maintenance. There are several different mathematical techniques that can assist in performing prognostics and health management of electronic systems. These techniques can be categorized into statistical reliability, life cycle loads, state estimation, and feature-extraction based models. The selection of the appropriate model depends on the application environment. This paper presents a methodology for selecting the correct model to perform diagnostics and prognostics in electronic systems based on a user's application environment. The model selection method is based on five properties, including usability, accuracy, performance, applicability at the system level, and flexibility of the model. Based on all this information, a comparison is made between the five prognostic models to show the advantages and disadvantages of each. Finally, recommendations are given for selecting the most appropriate model for system fault diagnostics and prognostics of electronics. While this methodology used in this study for analysis of electronic systems, it can be extended to other applications as well.


title: Regional specificity and practice: Dynamic changes in object and spatial working memory

abstract: Working memory (WM) tasks engage a network of brain regions that includes primary, unimodal, and multimodal associative cortices. Little is known, however, about whether task practice influences these types of regions differently. In this experiment, we used event-related fMRI to examine practice-related activation changes in different region types over the course of a scanning session while participants performed a delayed-recognition task. The task contained separate WM processing stages (encoding, maintenance, retrieval) and different materials (object, spatial), which allowed us to investigate the influence of practice on different component processes. We observed significant monotonic decreases, and not increases, in fMRI signal primarily in unimodal and multimodal regions. These decreases occurred during WM encoding and retrieval, but not during maintenance. Finally, regions specific to the type of memoranda (e.g., spatial or object) showed a lesser degree of sensitivity to practice as compared to regions activated by both types of memoranda, suggesting that these regions may be specialized more for carrying out processing within a particular modality than for experience-related flexibility. Overall, these findings indicate that task practice does not have a uniform effect on stages of WM processing, the type of WM memoranda being processed or on different types of brain regions. Instead, regions engaged during WM encoding and retrieval may have greater capacity for functional plasticity than WM maintenance. Additionally, the degree of specialization within brain regions may determine processing efficiency. Unimodal and multimodal regions that participate in both object and spatial processing may be specialized for flexible experience-related change, while those supporting primary sensorimotor processing may operate at optimal efficiency and are less susceptible to practice.


title: Personality from a controlled processing perspective: An fMRI study of neuroticism, extraversion, and self-consciousness

abstract: Although neuroticism has been central to most theories of personality, self-reported neuroticism has had limited success in predicting expected behavioral outcomes. The reason for this may be due, in part, to the imprecision of self-reports. The purpose of this study was to examine the relationship between neural correlates of control systems and neuroticism, extraversion, and self-consciousness. In response to an oddball task, neuroticism was associated with increased dorsal anterior cingulate cortex (dACC) reactivity, typically associated with discrepancy detection, whereas extraversion and self-consciousness were associated with lateral and medial frontoparietal networks, respectively, typically associated with task-focused (lateral) or self-focused (medial) controlled processes. We also examined whether the neural measure of neuroticism would predict a relevant behavioral outcome better than self-reports would. Interoceptive accuracy, an outcome associated with neuroticism, was better accounted for by dACC reactivity (r 2=.74) than by self-reported neuroticism (r 2=.16), suggesting that neural reactivities may provide a more direct measure of personality than self-reports do.


title: A Variable Threshold Voltage CMOS Comparator for Flash Analog to Digital Converter

abstract: This paper presents a variable threshold voltage CMOS comparator for flash analog to digital converter. The proposed comparator has single-ended type of architecture. The comparator is designed and analyzed by Cadence Virtuoso Analog Design Environment using UMC 180nm technology. The proposed comparator consumes peak power of 34.97 μW from 1.8 V power supply. It achieves the power delay product (PDP) of 8 fJ and propagation delay of 230 ps. The designed comparator eliminates the requirement of resistive ladder network for reference voltage generation. This makes it highly suitable in the design of flash analog to digital converter. General Terms Comparator, analog to digital converter, propagation delay


title: Causal memory: definitions, implementation, and programming

abstract: The abstraction of a shared memory is of growing importance in distributed computing systems. Traditional memory consistency ensures that all processes agree on a common order of all operations on memory. Unfortunately, providing these guarantees entails access latencies that prevent scaling to large systems. This paper weakens such guarantees by definingcausal memory, an abstraction that ensures that processes in a system agree on the relative ordering of operations that arecausally related. Because causal memory isweakly consistent, it admits more executions, and hence more concurrency, than either atomic or sequentially consistent memories. This paper provides a formal definition of causal memory and gives an implementation for message-passing systems. In addition, it describes a practical class of programs that, if developed for a strongly consistent memory, run correctly with causal memory.


Tools of the Mind: A Case Study of Implementing the Vygotskian Approach


title: The impact of participation in virtual brand communities on consumer trust and loyalty

abstract: Purpose – The importance of virtual brand communities is growing day by day as a result of consumers increasingly using online tools to contact fellow consumers in order to get information on which to base their decisions. For this reason, this work aims to explore some of the effects of participation in a virtual brand community on consumer behaviour.Design/methodology/approach – The paper proposes the positive effects of participation in a virtual community on both consumer trust and loyalty to the product, brand or organisation around which the community is developed. In addition, it also proposes a positive effect of trust on consumer loyalty. After the validations of measurement scales, the hypotheses are contrasted through structural modelling.Findings – The data, obtained through a web survey using members of several free software virtual communities, show that participation in the activities carried out in a virtual community may foster consumer trust and loyalty to the mutual interest of the comm...


title: Novel TPMS sensing chip with pressure sensor embedded in accelerometer

abstract: This study reports the design architecture to embed piezoresistive pressure sensor into accelerometer (PinG sensor) on a single chip by using the cavity-SOI process. The monolithic sensing chip can find various applications such as tire pressure monitoring system (TPMS), etc. The merits of the presented design includes significant chip size reduction by integrating the diaphragm of pressure sensor into the proof-mass of accelerometer, as well as better manufacturability through combining piezoresistor (PZR) process and CSOI process. Preliminary tests demonstrated the feasibility of detecting both pressure and acceleration using the presented PinG sensor. This design architecture is also applicable for other sensors integration.


title: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model

abstract: This paper presents a method of learning reconfigurable hierarchical And-Or models to integrate context and occlusion for car detection. The And-Or model represents the regularities of car-to-car context and occlusion patterns at three levels: (i) layouts of spatially-coupled N cars, (ii) single cars with different viewpoint-occlusion configurations, and (iii) a small number of parts. The learning process consists of two stages. We first learn the structure of the And-Or model with three components: (a) mining N-car contextual patterns based on layouts of annotated single car bounding boxes, (b) mining the occlusion configurations based on the overlapping statistics between single cars, and (c) learning visible parts based on car 3D CAD simulation or heuristically mining latent car parts. The And-Or model is organized into a directed and acyclic graph which leads to the Dynamic Programming algorithm in inference. In the second stage, we jointly train the model parameters (for appearance, deformation and bias) using Weak-Label Structural SVM. In experiments, we test our model on four car datasets: the KITTI dataset [11], the street parking dataset [19], the PASCAL VOC2007 car dataset [7], and a self-collected parking lot dataset. We compare with state-of-the-art variants of deformable part-based models and other methods. Our model obtains significant improvement consistently on the four datasets.


title: Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control

abstract: This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack , consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.


Evaluating the Predictive Accuracy of Association Football Forecasting Systems


title: Smelling of Odorous Sex Hormone-like Compounds Causes Sex-Differentiated Hypothalamic Activations in Humans

abstract: Abstract The anatomical pathways for processing of odorous stimuli include the olfactory nerve projection to the olfactory bulb, the trigeminal nerve projection to somatosensory and insular cortex, and the projection from the accessory olfactory bulb to the hypothalamus. In the majority of tetrapods, the sex-specific effects of pheromones on reproductive behavior is mediated via the hypothalamic projection. However, the existence of this projection in humans has been regarded as improbable because humans lack a discernable accessory olfactory bulb. Here, we show that women smelling an androgen-like compound activate the hypothalamus, with the center of gravity in the preoptic and ventromedial nuclei. Men, in contrast, activate the hypothalamus (center of gravity in paraventricular and dorsomedial nuclei) when smelling an estrogen-like substance. This sex-dissociated hypothalamic activation suggests a potential physiological substrate for a sex-differentiated behavioral response in humans.


title: A Hybrid Rogue Access Point Protection Framework for Commodity Wi-Fi Networks

abstract: We develop a practical and comprehensive hybrid rogue access point (AP) detection framework for commodity Wi- Fi networks. It is the first scheme that combines the distributed wireless media surveillance and the centralized wired end socket level traffic "fingerprinting" The former is designed not only to detect various types of rogue APs, but also to discover suspicious activities so as to prevent the adversaries from turning victim APs into rogue devices. Moreover, the socket level traffic fingerprinting helps our frame work to achieve a finer granularity on rogue AP detection among the existing schemes. This framework has the following nice properties: i) it requires neither specialized hardware nor modification to existing standards; ii) the proposed mechanism greatly improves the rogue AP detection probability so that network resilience is improved; iii) it provides a cost-effective solution to Wi-Fi network security enhancement by incorporating free but mature software tools; iv) it can protect the network from adversaries capable of using customized equipment and/or violating the IEEE 802.11 standard; v) its open architecture allows extra features to be easily added on in the future. Our analysis and evaluation demonstrate that this hybrid rogue AP protection framework is capable of reliably revealing rogue devices and preempting potential attacks.


“ FPGA Implementation of Intelligent Traffic Signal Controller Based On Neuro-Fuzzy System ”


title: Effects of four psychological primary colors on GSR, heart rate and respiration rate.

abstract: Judges selected from ISCC-NBS patches those colors which were most representative of red, yellow, green, and blue. These colors were presented for 1 min. each with GSR, heart rate, and respiration ...


title: Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

abstract: In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM) field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multivehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various large-scale experiments that have been carried out until now and discuss the remaining challenges and future orientations.


title: A Survey of Blockchain Applications in Different Domains

abstract: Blockchains have received much attention recently since they provide decentralized approaches to the creation and management of value. Many banks, Internet companies, car manufacturers, and even governments worldwide have incorporated or started considering blockchains to improve the security, scalability, and efficiency of their services. In this paper, we survey blockchain applications in different areas. These areas include cryptocurrency, healthcare, advertising, insurance, copyright protection, energy, and societal applications. Our work provides a timely summary for individuals and organizations interested in blockchains. We envision our study to motivate more blockchain applications.


title: Compound Control for Electro-hydraulic Positioning Servo System Based on Dynamic Inverse Model

abstract: Electro-hydraulic positioning servo system is a typical nonlinear system,and usually suffersuncertain external disturbances which make the conventional controller fail to meet the high performance requirements.A compound controller based on the dynamic inverse model of system is proposed,which includes a feed-forward controller and a robust controller to improve system control accuracy and robustness respectively.The rules of robust controller are presented and online parameter estimation is given for the compound controller to adapt the system nonlinear characteristics.A case study is presented,and experimental results show that the system nonlinear parameters are estimated accurately and the proposed compound control can handle the external disturbances effectively and upgrade the tracking performance of the hydraulic system.


Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks


title: Semi-supervised Density-Based Clustering

abstract: Most of the effort in the semi-supervised clustering literature was devoted to variations of the K-means algorithm. In this paper we show how background knowledge can be used to bias a partitional density-based clustering algorithm. Our work describes how labeled objects can be used to help the algorithm detecting suitable density parameters for the algorithm to extract density-based clusters in specific parts of the feature space. Considering the set of constraints estabilished by the labeled dataset we show that our algorithm, called SSDBSCAN, automatically finds density parameters for each natural cluster in a dataset. Four of the most interesting characteristics of SSDBSCAN are that (1) it only requires a single, robust input parameter, (2) it does not need any user intervention, (3) it automaticaly finds the noise objects according to the density of the natural clusters and (4) it is able to find the natural cluster structure even when the density among clusters vary widely. The algorithm presented in this paper is evaluated with artificial and real-world datasets, demonstrating better results when compared to other unsupervised and semi-supervised density-based approaches.


title: A 300-MS/s, 1.76-ps-Resolution, 10-b Asynchronous Pipelined Time-to-Digital Converter With on-Chip Digital Background Calibration in 0.13-µm CMOS

abstract: This paper presents an asynchronous pipelined all-digital 10-b time-to-digital converter (TDC) with fine resolution, good linearity, and high throughput. Using a 1.5-b/stage pipeline architecture, an on-chip digital background calibration is implemented to correct residue subtraction error in the seven MSB stages. An asynchronous clocking scheme realizes pipeline operation for higher throughput. The TDC was implemented in standard 0.13-μm CMOS technology and has a maximum throughput of 300 MS/s and a resolution of 1.76 ps with a total conversion range of 1.8 ns. The measured DNL and INL were 0.6 LSB and 1.9 LSB, respectively.


title: Design and Experimental Results of a 300-kHz Synthetic Aperture Sonar Optimized for Shallow-Water Operations

abstract: The design of a shallow-water synthetic aperture sonar (SAS) requires an understanding of key system and environmental issues. The main factors that limit SAS performance are as follows: micronavigation accuracy, where micronavigation is defined as the problem of estimating the acoustic path lengths to allow the focusing of the aperture; multipath effects; and target view angle changes. All of them can degrade shadow classification performance. Micronavigation accuracy is successfully addressed by the gyrostabilized displaced phase center antenna technique, which combines data-driven motion estimates with external attitude sensors. Multipath effects in shallow water are effectively countered by narrow vertical beams. Shadow blur induced by view angle changes is mitigated by increasing the center frequency, to reduce the SAS integration length while still maintaining the desired resolution and by designing the system with a minimum grazing angle of about 6deg to reduce shadow length. The combination of these factors led to the choice of a 300-kHz center frequency and of a multipath mitigation scheme that uses multiple vertical beams. Experimental results obtained with a sonar incorporating these features have produced SAS images with 1.6 cm times 5 cm resolution in range times cross-range and shadow contrast in excess of 5 dB, at ranges of up to 170 m in 20 m of water.


The Hungarian method for the assignment problem


title: Analysis and Design of a Low-Cost Dual-Band Compact Circularly Polarized Antenna for GPS Application

abstract: A compact dual-coupled shorting probe (DCSP)-loaded patch antenna for dual-band global positioning system (GPS) application is presented. Due to the use of shorting loaded technique, the overall size of the antenna is only $\frac{{\lambda_0}} {8}\cdot 8 \times \frac{{\lambda_0}} {8}\cdot 8 \times \frac{{\lambda_0}} {19}$ , and high-permittivity substrate is replaced by low-cost F4b substrate. The DCSP technique that this communication proposed enables this antenna to resonate at L1/L2 GPS bands. Simulated and measured voltage standing-wave ratio (VSWR) is below 1.5 over both the bands, and boresight RHCP gain is 4.49 and 4.12 dBic over a large ground plane with 41 and 57 MHz 3-dB gain bandwidth at the L2 and L1 band, respectively. Therefore, the proposed antenna is a good candidate for low-cost, dual-band, and compact patch antenna.


title: Correlates of Young Women’s Interest in Obtaining Cosmetic Surgery

abstract: This study examined predictors of young women’s interest in obtaining cosmetic surgery. The sample investigated was comprised of 101 college undergraduates residing in the northeastern U.S. (M age = 19.99, SD = 4.79). Participants’ weight status (M BMI = 24.52, SD = 5.69), body dissatisfaction, internalization of media messages, reports of physical appearance teasing, and interest in obtaining cosmetic surgery were assessed. Results indicated that all of the predictors examined were correlated with women’s interest in cosmetic surgery. Further, body dissatisfaction mediated the relations between weight status, internalization of media messages, reports of teasing, and women’s interest in cosmetic surgery. Findings are discussed in terms of the importance of understanding cosmetic surgery trends and young women’s susceptibility to body dissatisfaction.


title: A comparative study between RadViz and Star Coordinates

abstract: RadViz and star coordinates are two of the most popular projection-based multivariate visualization techniques that arrange variables in radial layouts. Formally, the main difference between them consists of a nonlinear normalization step inherent in RadViz. In this paper we show that, although RadViz can be useful when analyzing sparse data, in general this design choice limits its applicability and introduces several drawbacks for exploratory data analysis. In particular, we observe that the normalization step introduces nonlinear distortions, can encumber outlier detection, prevents associating the plots with useful linear mappings, and impedes estimating original data attributes accurately. In addition, users have greater flexibility when choosing different layouts and views of the data in star coordinates. Therefore, we suggest that analysts and researchers should carefully consider whether RadViz's normalization step is beneficial regarding the data sets' characteristics and analysis tasks.


title: Energy-efficient positioning for smartphones using Cell-ID sequence matching

abstract: Many emerging location-aware applications require position information. However, these applications rarely use celltower-based localization because of its inaccuracy, preferring instead to use the more energy-hungry GPS. In this paper, we present CAPS, a Cell-ID Aided Positioning System. CAPS leverages near-continuous mobility and the position history of a user to achieve significantly better accuracy than the celltower-based approach, while keeping energy overhead low. CAPS is designed based on the insight that users exhibit consistency in routes traveled, and that cell-ID transition points that the user experiences can, on a frequently traveled route, uniquely identify position. To this end, CAPS uses a cell-ID sequence matching technique to estimate current position based on the history of cell-ID and GPS position sequences that match the current cell-ID sequence. We have implemented CAPS on Android-based smartphones and have extensively evaluated it at different locations, and for different platforms and carriers. Our evaluation results show that CAPS can save more than 90% of the energy spent by the positioning system compared to the case where GPS is always used, while providing reasonably accurate position information with errors less than 20% of the celltower-based scheme.


title: Phishing email detection technique by using hybrid features

abstract: Phishing emails is growing at an alarming rate ::: in this few years. It has caused tremendous financial losses ::: to internet users. Phishing techniques getting more advance ::: everyday and this has created great challenge to the existing ::: anti-phishing techniques. Hence, in this paper, we proposed to ::: detect phishing emails through hybrids features. The hybrid ::: features consist of content-based, URL-based, and behaviorbased ::: features. Based on a set of 500 phishing emails and ::: 500 legitimate emails, the proposed method achieved overall ::: accuracy of 97.25% and error rate of 2.75%. This promising ::: result verify the effectiveness of the proposed hybrid features ::: in detecting phishing email.


title: Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification

abstract: Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy.


title: SRL4ORL: Improving Opinion Role Labelling using Multi-task Learning with Semantic Role Labeling

abstract: For over 12 years, machine learning is used to extract opinion-holder-target structures from text to answer the question: Who expressed what kind of sentiment towards what?. However, recent neural approaches do not outperform the state-of-the-art feature-based model for Opinion Role Labelling (ORL). We suspect this is due to the scarcity of labelled training data and address this issue using different multi-task learning techniques with a related task which has substantially more data, i.e. Semantic Role Labelling (SRL). Despite difficulties of the benchmark MPQA corpus, we show that indeed the ORL model benefits from SRL knowledge.


title: Inertia Weight strategies in Particle Swarm Optimization

abstract: Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.


title: Indexing Fingerprint Databases Based on Multiple Features

abstract: In a fingerprint identification system, a person is identified only by his fingerprint. To accomplish this, a database is searched by matching all entries to the given fingerprint. However, the maximum size of the database is limited, since each match takes some amount of time and has a small probability of error. A solution to this problem is to reduce the number of fingerprints that have to be matched. This is achieved by extracting features from the fingerprints and first matching the fingerprints that have the smallest feature distance to the query fingerprint. Using this indexing method, modern systems are able to search databases up to a few hundred fingerprints. In this paper, three possible fingerprint indexing features are discussed: the registered directional field estimate, FingerCode and minutiae triplets. It is shown that indexing schemes that are based on these features, are able to search a database more effectively than a simple linear scan. Next, a new indexing scheme is constructed that is based on advanced methods of combining these features. It is shown that this scheme results in a considerably better performance than the schemes that are based on the individual features or on more naive methods of combining the features, thus allowing much larger fingerprint databases to be searched.


title: Action Search: Learning to Search for Human Activities in Untrimmed Videos

abstract: Traditional approaches for action detection use trimmed data to learn sophisticated action detector models. Although these methods have achieved great success at detecting human actions, we argue that huge information is discarded when ignoring the process, through which this trimmed data is obtained. In this paper, we propose Action Search, a novel approach that mimics the way people annotate activities in video sequences. Using a Recurrent Neural Network, Action Search can efficiently explore a video and determine the time boundaries during which an action occurs. Experiments on the THUMOS14 dataset reveal that our model is not only able to explore the video efficiently but also accurately find human activities, outperforming state-of-the-art methods.


title: A Hierarchical Algorithm for Extreme Clustering

abstract: Many modern clustering methods scale well to a large number of data points, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy, incremental algorithm for hierarchical clustering that scales to both massive N and K---a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time.


title: Cross-Modal Transfer Learning for HEp-2 Cell Classification Based on Deep Residual Network

abstract: Accurate Human Epithelial-2 (HEp-2) cell image classification plays an important role in the diagnosis of many autoimmune diseases. However, the traditional approach requires experienced experts to artificially identify cell patterns, which extremely increases the workload and suffer from the subjective opinion of physician. To address it, we propose a very deep residual network (ResNet) based framework to automatically recognize HEp-2 cell via cross-modal transfer learning strategy. We adopt a residual network of 50 layers (ResNet-50) that are substantially deep to acquire rich and discriminative feature. Compared with typical convolutional network, the main characteristic of residual network lie in the introduction of residual connection, which can solve the degradation problem effectively. Also, we use a cross-modal transfer learning strategy by pre-training the model from a very similar dataset (from ICPR2012 to ICPR2016-Task1). Our proposed framework achieves an average class accuracy of 95.63% on ICPR2012 HEp-2 dataset and a mean class accuracy of 96.87% on ICPR2016-Task1 HEp-2 dataset, which outperforms the traditional methods.


title: A New Adaptive Video SRR Algorithm With Improved Robustness to Innovations

abstract: In this paper, a new video super-resolution reconstruction (SRR) method with improved robustness to outliers is proposed. Although the regularized least mean squares (R-LMSs) are one of the SRR algorithms with the best reconstruction quality for its computational cost, and is naturally robust to registration inaccuracies, its performance is known to degrade severely in the presence of innovation outliers. By studying the proximal point cost function representation of the R-LMS iterative equation, a better understanding of its performance under different situations is attained. Using statistical properties of typical innovation outliers, a new cost function is then proposed and two new algorithms are derived, which present improved robustness to outliers while maintaining computational costs comparable with that of R-LMS. The Monte–Carlo simulation results illustrate that the proposed method outperforms the traditional and regularized versions of LMS, and is competitive with state-of-the-art SRR methods at a much smaller computational cost.


title: 3D Texture Recognition Using Bidirectional Feature Histograms

abstract: Textured surfaces are an inherent constituent of the natural surroundings, therefore efficient real-world applications of computer vision algorithms require precise surface descriptors. Often textured surfaces present not only variations of color or reflectance, but also local height variations. This type of surface is referred to as a 3D texture. As the lighting and viewing conditions are varied, effects such as shadowing, foreshortening and occlusions, give rise to significant changes in texture appearance. Accounting for the variation of texture appearance due to changes in imaging parameters is a key issue in developing accurate 3D texture models. The bidirectional texture function (BTF) is observed image texture as a function of viewing and illumination directions. In this work, we construct a BTF-based surface model which captures the variation of the underlying statistical distribution of local structural image features, as the viewing and illumination conditions are changed. This 3D texture representation is called the bidirectional feature histogram (BFH). Based on the BFH, we design a 3D texture recognition method which employs the BFH as the surface model, and classifies surfaces based on a single novel texture image of unknown imaging parameters. Also, we develop a computational method for quantitatively evaluating the relative significance of texture images within the BTF. The performance of our methods is evaluated by employing over 6200 texture images corresponding to 40 real-world surface samples from the CUReT (Columbia-Utrecht reflectance and texture) database. Our experiments produce excellent classification results, which validate the strong descriptive properties of the BFH as a 3D texture representation.


title: A Survey on Visual Analytics of Social Media Data

abstract: The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, has received considerable attention in recent years. Many visual analytics methods have been proposed across disciplines to understand large-scale structured and unstructured social media data. This objective, however, also poses significant challenges for researchers to obtain a comprehensive picture of the area, understand research challenges, and develop new techniques. In this paper, we present a comprehensive survey to characterize this fast-growing area and summarize the state-of-the-art techniques for analyzing social media data. In particular, we classify existing techniques into two categories: gathering information and understanding user behaviors. We aim to provide a clear overview of the research area through the established taxonomy. We then explore the design space and identify the research trends. Finally, we discuss challenges and open questions for future studies.


title: Third-party punishment and social norms

abstract: We examine the characteristics and relative strength of third-party sanctions in a series of experiments. We hypothesize that egalitarian distribution norms and cooperation norms apply in our experiments, and that third parties, whose economic payoff is unaffected by the norm violation, may be willing to enforce these norms although the enforcement is costly for them. Almost two-thirds of the third parties indeed punished the violation of the distribution norm and their punishment increased the more the norm was violated. Likewise, up to roughly 60% of the third parties punished violations of the cooperation norm. Thus, our results show that the notion of strong reciprocity extends to the sanctioning behavior of ‘‘unaffected’’ third parties. In addition, these experiments suggest that thirdparty punishment games are powerful tools for studying the characteristics and the content of social norms. Further experiments indicate that second parties, whose economic payoff is reduced by the norm violation, punish the violation much more strongly than do third parties.


title: Direct Prediction of 3D Body Poses from Motion Compensated Sequences

abstract: We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Previous approaches typically compute candidate poses in individual frames and then link them in a post-processing step to resolve ambiguities. By contrast, we directly regress from a spatio-temporal volume of bounding boxes to a 3D pose in the central frame. We further show that, for this approach to achieve its full potential, it is essential to compensate for the motion in consecutive frames so that the subject remains centered. This then allows us to effectively overcome ambiguities and improve upon the state-of-the-art by a large margin on the Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation benchmarks.


title: Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

abstract: Background ::: Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. ::: ::: ::: Methods ::: Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. ::: ::: ::: Results ::: Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. ::: ::: ::: Conclusions ::: We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.


title: Skin Barrier Defects in Atopic Dermatitis

abstract: Atopic dermatitis (AD) is a chronic inflammatory skin condition with complex etiology that is dependent upon interactions between the host and the environment. Acute skin lesions exhibit the features of a Th2-driven inflammatory disorder, and many patients are highly atopic. The skin barrier plays key roles in immune surveillance and homeostasis, and in preventing penetration of microbial products and allergens. Defects that compromise the structural integrity or else the immune function of the skin barrier play a pivotal role in the pathogenesis of AD. This article provides an overview of the array of molecular building blocks that are essential to maintaining healthy skin. The basis for structural defects in the skin is discussed in relation to AD, with an emphasis on filaggrin and its genetic underpinnings. Aspects of innate immunity, including the role of antimicrobial peptides and proteases, are also discussed.


title: Modelling and Performance Evaluation of Railway transport Systems using P-timed Petri Nets

abstract: The regular increase in the number of passengers makes the management of transportation systems more and more complex. Railway transport requires specific needs. Indeed, many decision and optimization problems occur from the planning phase to the implementation phase. Railway transport networks can be considered as discrete event systems with time constraints. The time factor is a critical parameter, since it includes dates and schedules to be respected in order to avoid overlaps, delays and collisions between trains. The uncertainties affect the service and the availability of transportation resources and, consequently, the planned transport schedule. Petri nets have been recognized as powerful modelling and analysis tools for discrete event systems with time constraints. Consequently, they are suitable for railway transport systems. This article is devoted to the modelling, analysis and performance evaluation of the railway transport network system of the Sahel Tunisia in order to help operators to design efficient and robust schedules.


title: Challenges for Nutrition Recommender Systems

abstract: Obesity or being overweight in general often leads to other more severe diseases. With more than half of the population in the west- ern countries being overweight or even obese, systems are developed to help users make healthier food choices by recommending healthier recipes or food items according to the user's needs or likes and dislikes. But there are many challenges designing such systems. In this paper we try to iden- tify the difficulties encountered and discuss ways to deal withthem.


title: Managing Context Information for Adaptive Security in IoT Environments

abstract: Pervasive computing is becoming a reality due to the rise of the so-called Internet of Things (IoT). In this paradigm, everyday and physical objects are being equipped with capabilities to detect and communicate information they receive from their environment, turning them into smart objects. However, such entities are usually deployed on environments with changing and dynamic conditions, which can be used by them to modify their operation or behavior. Under the foundations of EU FP7 SocIoTal project, this work provides an overview about how contextual information can be taken into account by smart objects when making security decisions, by considering such information as a first-class component, in order to realize the so-called context-aware security on IoT scenarios.


title: Third-Eye: A Mobilephone-Enabled Crowdsensing System for Air Quality Monitoring

abstract: Air pollution has raised people's public health concerns in major cities, especially for Particulate Matter under 2.5μm (PM2.5) due to its significant impact on human respiratory and circulation systems. In this paper, we present the design, implementation, and evaluation of a mobile application, Third-Eye, that can turn mobile phones into high-quality PM2.5 monitors, thereby enabling a crowdsensing way for fine-grained PM2.5 monitoring in the city. We explore two ways, crowdsensing and web crawling, to efficiently build large-scale datasets of the outdoor images taken by mobile phone, weather data, and air-pollution data. Then, we leverage two deep learning models, Convolutional Neural Network (CNN) for images and Long Short Term Memory (LSTM) network for weather and air-pollution data, to build an end-to-end framework for training PM2.5 inference models. Our App has been downloaded more than 2,000 times and runs more than 1 year. The real user data based evaluation shows that Third-Eye achieves 17.38 μg/m3 average error and 81.55% classification accuracy, which outperforms 5 state-of-the-art methods, including three scattered interpolations and two image based estimation methods. The results also demonstrate how Third-Eye offers substantial enhancements over typical portable PM2.5 monitors by simultaneously improving accessibility, portability, and accuracy.


A comparative study of control architectures in UAV/UGV-based surveillance system


title: Company Health in Mobile Software Ecosystem (MSECO): Research Perspectives and Challenges

abstract: This paper presents initial perspectives in the searchfor diagnosing the health of a Mobile Software Ecosystem(MSECO) company. The research intends to investigate a companys health through user reviews about mobile applications (Apps)and build and present indicators that can identify problems, failures and differences in the quality of the apps to guide thecompany in the search for "immunization" or "medication" sothat it does not become "sick" from the adoption of measuresrelated to the use of indicators for the type of "disease".


title: Steganalysis via Deep Residual Network

abstract: Recent studies have demonstrated that a well designed deep convolutional neural network (CNN) model achieves competitive performances on detecting the presence of secret message in digital images, compared with the classical rich model based steganalysis. In this paper, we propose to investigate a category of very deep CNN model–the deep residual network (DRN), for steganalysis. DRN is suitable for steganalysis from two aspects. For the first, the DRN model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. For the second, DRN's residual learning (ResL) method actively strengthens the signal coming from secret messages, which is extremely beneficial for the discrimination between cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model achieves very low detection error rates for the state of arts steganographic algorithms. It also outperforms the classical rich model method and several recently proposed CNN based methods.


title: The Effects of 1α, 25-dihydroxyvitamin D3 and Transforming Growth Factor-β3 on Bone Development in an Ex Vivo Organotypic Culture System of Embryonic Chick Femora

abstract: Transforming growth factor-beta3 (TGF-β3) and 1α,25-dihydroxyvitamin D3 (1α,25 (OH) 2D3) are essential factors in chondrogenesis and osteogenesis respectively. These factors also play a fundamental role in the developmental processes and the maintenance of skeletal integrity, but their respective direct effects on these processes are not fully understood. Using an organotypic bone rudiment culture system the current study has examined the direct roles the osteotropic factors 1α,25 (OH)2D3 and TGF-β3 exert on the development and modulation of the three dimensional structure of the embryonic femur. Isolated embryonic chick femurs (E11) were organotypically cultured for 10 days in basal media, or basal media supplemented with either 1α,25 (OH) 2D3 (25 nM) or TGF-β3 (5 ng/mL & 15 ng/mL). Analyses of the femurs were undertaken using micro-computed tomography (μCT), histology and immunohistochemistry. 1α,25 (OH)2D3 supplemented cultures enhanced osteogenesis directly in the developing femurs with elevated levels of osteogenic markers such as type 1 collagen. In marked contrast organotypic femur cultures supplemented with TGF-β3 (5 ng/mL & 15 ng/mL) demonstrated enhanced chondrogenesis with a reduction in osteogenesis. These studies demonstrate the efficacy of the ex vivo organotypic embryonic femur culture employed to elucidate the direct roles of these molecules, 1α,25 (OH) 2D3 and TGF-β3 on the structural development of embryonic bone within a three dimensional framework. We conclude that 1α,25(OH)2D and TGF-β3 modify directly the various cell populations in bone rudiment organotypic cultures effecting tissue metabolism resulting in significant changes in embryonic bone growth and modulation. Understanding the roles of osteotropic agents in the process of skeletal development is integral to developing new strategies for the recapitulation of bone tissue in later life.


title: Automated glaucoma diagnosis using deep learning approach

abstract: Glaucoma is one of the common causes of blindness worldwide. It leads to deterioration in vision and quality of life if it is not cured early. This paper addresses the feasibility of developing an automatic feature learning technique for detecting glaucoma in colored retinal fundus images using a deep learning method. A fully automated system based on convolutional neural network (CNN) is developed to distinguish between normal and glaucomatous patterns for diagnostic decisions. Unlike traditional methods where the optic disc features are handcrafted, the features are extracted automatically from the raw images by CNN and fed to the SVM classifier to classify the images into normal or abnormal. We demonstrate an accuracy, specificity and sensitivity of 88.2%, 90.8%, and 85%, respectively which compared favorably to the-state-of-the-art but at considerably lower computational cost. The obtained preliminary results clearly demonstrate that the proposed deep learning method is promising in automatic diagnosis of glaucoma.


title: Sentence-Similarity Based DocumentClustering Using Birch Algorithm

abstract: Document clustering is the process of automatically grouping the related documents into clusters. Instead of searching entire documents for relevant information, these clusters will improve the efficiency and avoid overlapping of contents. Relevant document can be efficiently retrieved and accessed by means of document clustering. When compared with hard clustering, birch algorithms allow patterns to belong to all the clusters with differing degrees of membership. Birch algorithm is important in domains such as sentence clustering, since a sentence is related to more than one theme or topic present within a document or set of documents. In our proposed system, birch clustering algorithm operates on cluster Start with initial threshold and inserts points into the tree. Results obtained while applying the algorithm to sentence clustering tasks demonstrate that birch algorithm is capable of identifying overlapping clusters of semantically related sentences and its performance improvement can be proved by comparing with k-means. Performance measures document clustering and its application in document summarization


title: Error bounds for approximations with deep ReLU networks

abstract: We study expressive power of shallow and deep neural networks with piece-wise linear activation functions. We establish new rigorous upper and lower bounds for the network complexity in the setting of approximations in Sobolev spaces. In particular, we prove that deep ReLU networks more efficiently approximate smooth functions than shallow networks. In the case of approximations of 1D Lipschitz functions we describe adaptive depth-6 network architectures more efficient than the standard shallow architecture.


title: An efficient and flexible host-FPGA PCIe communication library

abstract: A high-performance interconnection between a host processor and FPGA accelerators is in much demand. Among various interconnection methods, a PCIe bus is an attractive choice for loosely coupled accelerators. Because there is no standard host-FPGA communication library, FPGA developers have to write significant amounts of PCIe related code at both the FPGA side and the host processor side. A high-performance host-FPGA PCIe communication library holds the key to broadening the use of FPGA accelerators. In this paper we target efficiency and flexibility as two important features in such a library. We discuss the challenges in providing these features, and present our solution to these challenges. We propose EPEE, an efficient and flexible host-FPGA PCIe communication library and describe its design. We implemented EPEE in various generations of Xilinx FPGAs with up to 26.24 Gbps half-duplex and 43.02 Gbps full-duplex aggregate throughput in the PCIe Gen2 X8 mode; these are at the best utilization levels that a host-FPGA PCIe library can achieve. The EPEE library has been integrated into four different FPGA applications with different data usage patterns in various institutes.


title: Explaining employee job performance: the role of online and offline workplace communication networks

abstract: By distinguishing between employees' online and offline workplace communication networks, this paper incorporates technology into social network theory to understand employees' job performance. Specifically, we conceptualize network ties as direct and indirect ties in both online and offline workplace communication networks, thus resulting in four distinct types of ties. We theorize that employees' ties in online and offline workplace communication networks are complementary resources that interact to influence their job performance. We found support for our model in a field study among 104 employees in a large telecommunication company. The paper concludes with theoretical and practical implications.