DuDe: The Duplicate Detection Toolkit

Duplicate detection, also known as entity matching or record linkage, was first defined by Newcombe et al. [19] and has been a research topic for several decades. The challenge is to effectively and efficiently identify pairs of records that represent the same real world entity. Researchers have developed and described a variety of methods to measure the similarity of records and/or to reduce the number of required comparisons. Comparing these methods to each other is essential to assess their quality and efficiency. However, it is still difficult to compare results, as there usually are differences in the evaluated datasets, the similarity measures, the implementation of the algorithms, or simply the hardware on which the code is executed. To face this challenge, we are developing the comprehensive duplicate detection toolkit “DuDe”. DuDe already provides multiple methods and datasets for duplicate detection and consists of several components with clear interfaces that can be easily served with individual code. In this paper, we present the DuDe architecture and its workflow for duplicate detection. We show that DuDe allows to easily compare different algorithms and similarity measures, which is an important step towards a duplicate detection benchmark. 1. DUPLICATE DETECTION FRAMEWORKS The basic problem of duplicate detection has been studied under various names, such as entity matching, record linkage, merge/purge or record reconciliation. Given a set of entities, the goal is to identify the represented set of distinct real-world entities. Proposed algorithms in the area of duplicate detection aim to improve the efficiency or the effectiveness of the duplicate detection process. The goal of efficiency is usually to reduce the number of pairwise comparisons. In a naive approach this is quadratic in the number of records. By making intelligent guesses which records have a high probability of representing the same real-world entity, the search space is reduced with the drawback that some duPermission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, to post on servers or to redistribute to lists, requires a fee and/or special permission from the publisher, ACM. VLDB ‘10, September 13-17, 2010, Singapore Copyright 2010 VLDB Endowment, ACM 000-0-00000-000-0/00/00. plicates might be missed. Effectiveness, on the other hand, aims at classifying pairs of records accurately as duplicate or non-duplicate [17]. Elmagarmid et al. have compiled a survey of existing algorithms and techniques for duplicate detection [11]. Köpcke and Rahm give a comprehensive overview about existing duplicate detection frameworks [15]. They compare eleven frameworks and distinguish between frameworks without training (BN [16], MOMA [24], SERF [1]), training-based frameworks (Active Atlas [22], [23], MARLIN [2, 3], Multiple Classifier System [27], Operator Trees [4]) and hybrid frameworks (TAILOR [10], FEBRL [6], STEM [14], Context Based Framework [5]). Not included in the overview is STRINGER [12], which deals with approximate string matching in large data sources. Köpcke and Rahm use several comparison criteria, such as supported entity types (e.g. relational entities, XML), availability of partitioning methods to reduce the search space, used matchers to determine whether two entities are similar enough to represent the same real-world entity, the ability to combine several matchers, and, where necessary, the selection of training data. In their summary, Köpcke and Rahm criticize that the frameworks use diverse methodologies, measures, and test problems for evaluation and that it is therefore difficult to assess the efficiency and effectiveness of each single system. They argue that standardized entity matching benchmarks are needed and that researchers should provide prototype implementations and test data with their algorithms. This agrees with Neiling et al. [18], where desired properties of a test framework for object identification solutions are discussed. Moreover, Weis et al. [25] argue for a duplicate detection benchmark. Both papers see the necessity for standardized data from real-world or artificial datasets, which must also contain information about the real-world pairs. Additionally, clearly defined quality criteria with a description of their computation, and a detailed specification of the test procedure are required. An overview of quality and complexity measures for data linkage and deduplication can be found in Christen and Goiser [7] With DuDe, we provide a toolkit for duplicate detection that can easily be extended by new algorithms and components. Conducted experiments are comprehensible and can be compared with former ones. Additionally, several algorithms, similarity measures, and datasets with gold standards are provided, which is a requirement for a duplicate detection benchmark. DuDe and several datasets are available for download at http://www.hpi.uni-potsdam. de/naumann/projekte/dude.html.


One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models

While deep learning methods have achieved state-of-theart performance in many challenging inverse problems like image inpainting and super-resolution, they invariably involve problem-specific training of the networks. Under this approach, each inverse problem requires its own dedicated network. In scenarios where we need to solve a wide variety of problems, e.g., on a mobile camera, it is inefficient and expensive to use these problem-specific networks. On the other hand, traditional methods using analytic signal priors can be used to solve any linear inverse problem; this often comes with a performance that is worse than learning-based methods. In this work, we provide a middle ground between the two kinds of methods — we propose a general framework to train a single deep neural network that solves arbitrary linear inverse problems. We achieve this by training a network that acts as a quasi-projection operator for the set of natural images and show that any linear inverse problem involving natural images can be solved using iterative methods. We empirically show that the proposed framework demonstrates superior performance over traditional methods using wavelet sparsity prior while achieving performance comparable to specially-trained networks on tasks including compressive sensing and pixel-wise inpainting.


Automatic Deobfuscation and Reverse Engineering of Obfuscated Code

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Static test case prioritization using topic models

Software development teams use test suites to test changes to their source code. In many situations, the test suites are so large that executing every test for every source code change is infeasible, due to time and resource constraints. Development teams need to prioritize their test suite so that as many distinct faults as possible are detected early in the execution of the test suite. We consider the problem of static black-box test case prioritization (TCP), where test suites are prioritized without the availability of the source code of the system under test (SUT). We propose a new static black-box TCP technique that represents test cases using a previously unused data source in the test suite: the linguistic data of the test cases, i.e., their identifier names, comments, and string literals. Our technique applies a text analysis algorithm called topic modeling to the linguistic data to approximate the functionality of each test case, allowing our technique to give high priority to test cases that test different functionalities of the SUT. We compare our proposed technique with existing static black-box TCP techniques in a case study of multiple real-world open source systems: several versions of Apache Ant and Apache Derby. We find that our static black-box TCP technique outperforms existing static black-box TCP techniques, and has comparable or better performance than two existing execution-based TCP techniques. Static black-box TCP methods are widely applicable because the only input they require is the source code of the test cases themselves. This contrasts with other TCP techniques which require access to the SUT runtime behavior, to the SUT specification models, or to the SUT source code.


A survey of current paradigms in machine translation

This paper is a survey of the current machine translation research in the US, Europe and Japan. A short history of machine translation is presented rst, followed by an overview of the current research work. Representative examples of a wide range of diierent approaches adopted by machine translation researchers are presented. These are described in detail along with a discussion of the practicalities of scaling up these approaches for operational environments. In support of this discussion, issues in, and techniques for, evaluating machine translation systems are addressed.


On-line monitoring of power curves

A data-driven approach to the performance analysis of wind turbines is presented. Turbine performance is captured with a power curve. The power curves are constructed using historical wind turbine data. Three power curve models are developed, one by the least squares method and the other by the maximum likelihood estimation method. The models are solved by an evolutionary strategy algorithm. The power curve model constructed by the least squares method outperforms the one built by the maximum likelihood approach. The third model is non-parametric and is built with the k-nearest neighbor (k-NN) algorithm. The least squares (parametric) model and the non-parametric model are used for on-line monitoring of the power curve and their performance is analyzed. 2008 Elsevier Ltd. All rights reserved.


The infrared camera-based system to evaluate the human sleepiness

The eye's blinking is a significant indicator of the sleepiness. The existing systems of blink detection and sleepiness analysis require usually to fix camera on spectacle frame or a special helmet that is not convenient and can affect the obtained result. In this paper, the infrared camera-based contact-less system is proposed to evaluate the human's sleepiness. The infrared light switching is used to detect the pupil in each frame and, as a result, the blink event. The active pan-tilt unit is used to make possible free head movements. The algorithm is pointed out to process the camera frames in order to distinguish the involuntary blinks from voluntary ones. Preliminary experimental tests are shown with the intent to validate the proposed hardware and software system pointed out.


Context-Independent Claim Detection for Argument Mining

Argumentation mining aims to automatically identify structured argument data from unstructured natural language text. This challenging, multifaceted task is recently gaining a growing attention, especially due to its many potential applications. One particularly important aspect of argumentation mining is claim identification. Most of the current approaches are engineered to address specific domains. However, argumentative sentences are often characterized by common rhetorical structures, independently of the domain. We thus propose a method that exploits structured parsing information to detect claims without resorting to contextual information, and yet achieve a performance comparable to that of state-of-the-art methods that heavily rely on the context.


Identifying Appropriate Support for Propositions in Online User Comments

The ability to analyze the adequacy of supporting information is necessary for determining the strength of an argument.1 This is especially the case for online user comments, which often consist of arguments lacking proper substantiation and reasoning. Thus, we develop a framework for automatically classifying each proposition as UNVERIFIABLE, VERIFIABLE NONEXPERIENTIAL, or VERIFIABLE EXPERIENTIAL2, where the appropriate type of support is reason, evidence, and optional evidence, respectively3. Once the existing support for propositions are identified, this classification can provide an estimate of how adequately the arguments have been supported. We build a goldstandard dataset of 9,476 sentences and clauses from 1,047 comments submitted to an eRulemaking platform and find that Support Vector Machine (SVM) classifiers trained with n-grams and additional features capturing the verifiability and experientiality exhibit statistically significant improvement over the unigram baseline, achieving a macro-averaged F1 of 68.99%.


Why do humans reason? Arguments for an argumentative theory.

Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given the exceptional dependence of humans on communication and their vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. Poor performance in standard reasoning tasks is explained by the lack of argumentative context. When the same problems are placed in a proper argumentative setting, people turn out to be skilled arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views. This explains the notorious confirmation bias. This bias is apparent not only when people are actually arguing, but also when they are reasoning proactively from the perspective of having to defend their opinions. Reasoning so motivated can distort evaluations and attitudes and allow erroneous beliefs to persist. Proactively used reasoning also favors decisions that are easy to justify but not necessarily better. In all these instances traditionally described as failures or flaws, reasoning does exactly what can be expected of an argumentative device: Look for arguments that support a given conclusion, and, ceteris paribus, favor conclusions for which arguments can be found.


Report on a general problem-solving program.


Effect of Speech Recognition Errors on Text Understandability for People who are Deaf or Hard of Hearing

Recent advancements in the accuracy of Automated Speech Recognition (ASR) technologies have made them a potential candidate for the task of captioning. However, the presence of errors in the output may present challenges in their use in a fully automatic system. In this research, we are looking more closely into the impact of different inaccurate transcriptions from the ASR system on the understandability of captions for Deaf or Hard-of-Hearing (DHH) individuals. Through a user study with 30 DHH users, we studied the effect of the presence of an error in a text on its understandability for DHH users. We also investigated different prediction models to capture this relation accurately. Among other models, our random forest based model provided the best mean accuracy of 62.04% on the task. Further, we plan to improve this model with more data and use it to advance our investigation on ASR technologies to improve ASR based captioning for DHH users.


IoT security: A layered approach for attacks & defenses

Internet of Things (IoT) has been a massive advancement in the Information and Communication Technology (ICT). It is projected that over 50 billion devices will become part of the IoT in the next few years. Security of the IoT network should be the foremost priority. In this paper, we evaluate the security challenges in the four layers of the IoT architecture and their solutions proposed from 2010 to 2016. Furthermore, important security technologies like encryption are also analyzed in the IoT context. Finally, we discuss countermeasures of the security attacks on different layers of IoT and highlight the future research directions within the IoT architecture.


Foldio: Digital Fabrication of Interactive and Shape-Changing Objects With Foldable Printed Electronics

Foldios are foldable interactive objects with embedded input sensing and output capabilities. Foldios combine the advantages of folding for thin, lightweight and shape-changing objects with the strengths of thin-film printed electronics for embedded sensing and output. To enable designers and end-users to create highly custom interactive foldable objects, we contribute a new design and fabrication approach. It makes it possible to design the foldable object in a standard 3D environment and to easily add interactive high-level controls, eliminating the need to manually design a fold pattern and low-level circuits for printed electronics. Second, we contribute a set of printable user interface controls for touch input and display output on folded objects. Moreover, we contribute controls for sensing and actuation of shape-changeable objects. We demonstrate the versatility of the approach with a variety of interactive objects that have been fabricated with this framework.


Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices

To verify cluster separation in high-dimensional data, analysts often reduce the data with a dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive 3D Scatterplots, or Scatterplot Matrices (SPLOMs). With the goal of providing guidance between these visual encoding choices, we conducted an empirical data study in which two human coders manually inspected a broad set of 816 scatterplots derived from 75 datasets, 4 DR techniques, and the 3 previously mentioned scatterplot techniques. Each coder scored all color-coded classes in each scatterplot in terms of their separability from other classes. We analyze the resulting quantitative data with a heatmap approach, and qualitatively discuss interesting scatterplot examples. Our findings reveal that 2D scatterplots are often 'good enough', that is, neither SPLOM nor interactive 3D adds notably more cluster separability with the chosen DR technique. If 2D is not good enough, the most promising approach is to use an alternative DR technique in 2D. Beyond that, SPLOM occasionally adds additional value, and interactive 3D rarely helps but often hurts in terms of poorer class separation and usability. We summarize these results as a workflow model and implications for design. Our results offer guidance to analysts during the DR exploration process.


Genetic analysis of host resistance: Toll-like receptor signaling and immunity at large.

Classical genetic methods, driven by phenotype rather than hypotheses, generally permit the identification of all proteins that serve nonredundant functions in a defined biological process. Long before this goal is achieved, and sometimes at the very outset, genetics may cut to the heart of a biological puzzle. So it was in the field of mammalian innate immunity. The positional cloning of a spontaneous mutation that caused lipopolysaccharide resistance and susceptibility to Gram-negative infection led directly to the understanding that Toll-like receptors (TLRs) are essential sensors of microbial infection. Other mutations, induced by the random germ line mutagen ENU (N-ethyl-N-nitrosourea), have disclosed key molecules in the TLR signaling pathways and helped us to construct a reasonably sophisticated portrait of the afferent innate immune response. A still broader genetic screen--one that detects all mutations that compromise survival during infection--is permitting fresh insight into the number and types of proteins that mammals use to defend themselves against microbes.


Parallel geometric algorithms for multi-core computers

Computers with multiple processor cores using shared memory are now ubiquitous. In this paper, we present several parallel geometric algorithms that specifically target this environment, with the goal of exploiting the additional computing power. The d-dimensional algorithms we describe are (a) spatial sorting of points, as is typically used for preprocessing before using incremental algorithms, (b) kd-tree construction, (c) axis-aligned box intersection computation, and finally (d) bulk insertion of points in Delaunay triangulations for mesh generation algorithms or simply computing Delaunay triangulations. We show experimental results for these algorithms in 3D, using our implementations based on the Computational Geometry Algorithms Library (CGAL, http://www.cgal.org/). This work is a step towards what we hope will become a parallel mode for CGAL, where algorithms automatically use the available parallel resources without requiring significant user intervention.


Facial Component-Landmark Detection With Weakly-Supervised LR-CNN

In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously. Most of the existing course-to-fine facial detectors fail to detect landmark accurately without lots of fully labeled data, which are costly to obtain. We can handle the task with a small amount of finely labeled data. First, deep convolutional generative adversarial networks are utilized to generate training samples with weak labels, as data preparation. Then, through weakly supervised learning, our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Detecting unblocked components first helps us to focus on the informative area, resulting in a better performance. Additionally, to improve the performance of the above tasks, we design two models as follows: 1) we add AnchorAlign in the region proposal networks to accurately localize components and 2) we propose a two-branch model consisting classification branch and regression branch to detect landmark. Extensive evaluations on benchmark datasets indicate that our proposed approach is able to complete the multi-task facial detection and outperforms the state-of-the-art facial component and landmark detection algorithms.


On-Line Fingerprint Verification

Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy.


Improving Acoustic Models in TORGO Dysarthric Speech Database

Assistive speech-based technologies can improve the quality of life for people affected with dysarthria, a motor speech disorder. In this paper, we explore multiple ways to improve Gaussian mixture model and deep neural network (DNN) based hidden Markov model (HMM) automatic speech recognition systems for TORGO dysarthric speech database. This work shows significant improvements over the previous attempts in building such systems in TORGO. We trained speaker-specific acoustic models by tuning various acoustic model parameters, using speaker normalized cepstral features and building complex DNN-HMM models with dropout and sequence-discrimination strategies. The DNN-HMM models for severe and severe-moderate dysarthric speakers were further improved by leveraging specific information from dysarthric speech to DNN models trained on audio files from both dysarthric and normal speech, using generalized distillation framework. To the best of our knowledge, this paper presents the best recognition accuracies for TORGO database till date.


A wearable system to assist walking of Parkinson s disease patients.

BACKGROUND
About 50% of the patients with advanced Parkinson's disease (PD) suffer from freezing of gait (FOG), which is a sudden and transient inability to walk. It often causes falls, interferes with daily activities and significantly impairs quality of life. Because gait deficits in PD patients are often resistant to pharmacologic treatment, effective non-pharmacologic treatments are of special interest.

OBJECTIVES
The goal of our study is to evaluate the concept of a wearable device that can obtain real-time gait data, processes them and provides assistance based on pre-determined specifications.

METHODS
We developed a real-time wearable FOG detection system that automatically provides a cueing sound when FOG is detected and which stays until the subject resumes walking. We evaluated our wearable assistive technology in a study with 10 PD patients. Over eight hours of data was recorded and a questionnaire was filled out by each patient.

RESULTS
Two hundred and thirty-seven FOG events have been identified by professional physiotherapists in a post-hoc video analysis. The device detected the FOG events online with a sensitivity of 73.1% and a specificity of 81.6% on a 0.5 sec frame-based evaluation.

CONCLUSIONS
With this study we show that online assistive feedback for PD patients is possible. We present and discuss the patients' and physiotherapists' perspectives on wearability and performance of the wearable assistant as well as their gait performance when using the assistant and point out the next research steps. Our results demonstrate the benefit of such a context-aware system and motivate further studies.


Acne and Rosacea

Acne, one of the most common skin diseases, affects approximately 85% of the adolescent population, and occurs most prominently at skin sites with a high density of sebaceous glands such as the face, back, and chest. Although often considered a disease of teenagers, acne is occurring at an increasingly early age. Rosacea is a chronic facial inflammatory dermatosis characterized by flushing (or transient facial erythema), persistent central facial erythema, inflammatory papules/pustules, and telangiectasia. Both acne and rosacea have a multifactorial pathology that is incompletely understood. Increased sebum production, keratinocyte hyper-proliferation, inflammation, and altered bacterial colonization with Propionibacterium acnes are considered to be the underlying disease mechanisms in acne, while the multifactorial pathology of rosacea is thought to involve both vasoactive and neurocutaneous mechanisms. Several advances have taken place in the past decade in the research field of acne and rosacea, encompassing pathogenesis and epidemiology, as well as the development of new therapeutic interventions. In this article, we provide an overview of current perspectives on the pathogenesis and treatment of acne and rosacea, including a summary of findings from recent landmark pathophysiology studies considered to have important implications for future clinical practice. The advancement of our knowledge of the different pathways and regulatory mechanisms underlying acne and rosacea is thought to lead to further advances in the therapeutic pipeline for both conditions, ultimately providing a greater array of treatments to address gaps in current management practices.


Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a per-pixel reconstruction error based on an `-distance. This procedure, however, leads to large residuals whenever the reconstruction includes slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that they cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity that examines inter-dependencies between local image regions, taking into account luminance, contrast, and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over state-of-the-art approaches for unsupervised defect segmentation that use per-pixel reconstruction


Cellulose: fascinating biopolymer and sustainable raw material.

As the most important skeletal component in plants, the polysaccharide cellulose is an almost inexhaustible polymeric raw material with fascinating structure and properties. Formed by the repeated connection of D-glucose building blocks, the highly functionalized, linear stiff-chain homopolymer is characterized by its hydrophilicity, chirality, biodegradability, broad chemical modifying capacity, and its formation of versatile semicrystalline fiber morphologies. In view of the considerable increase in interdisciplinary cellulose research and product development over the past decade worldwide, this paper assembles the current knowledge in the structure and chemistry of cellulose, and in the development of innovative cellulose esters and ethers for coatings, films, membranes, building materials, drilling techniques, pharmaceuticals, and foodstuffs. New frontiers, including environmentally friendly cellulose fiber technologies, bacterial cellulose biomaterials, and in-vitro syntheses of cellulose are highlighted together with future aims, strategies, and perspectives of cellulose research and its applications.


Virtual Dermatologist: An application of 3D modeling to tele-healthcare

In this paper, we present preliminary results towards the development of the Virtual Dermatologist: A 3D image and tactile database for virtual examination of dermatology patients. This system, which can be installed and operated by non-dermatologists in remotes areas where access to a dermatologist is difficult, will enhance and broaden the application of tele-healthcare, and it will greatly facilitate the surveillance and consequent diagnosis of various skin diseases. Unlike other systems that monitor the progress of skin diseases using qualitative data on simple baseline (2D) photography, the proposed system will also allow for the quantitative assessment of the progress of the disease over time (e.g. thickness, size, roughness, etc). In fact, the 3D model created by the proposed system will let the dermatologist perform dermatoscopic-like examinations over specially annotated areas of the 3D model of the patient's body (i.e. higher definition areas of the 3D model). As part of its future development, the system will also allow the dermatologist to virtually touch and feel the lesion through a haptic interface. In its current form, the system can detect skin lesions smaller than 1mm, as we demonstrate in the result section.


A tool-supported compliance process for software systems

Laws and regulations impact the design of software systems, as they may introduce additional requirements and possible conflicts with pre-existing requirements. We propose a systematic, tool-supported process for establishing compliance of a software system with a given law. The process elicits new requirements from the law, compares them with existing ones and manages conflicts, exploiting a set of heuristics, partially supported by a tool. We illustrate our proposal through an exploratory study using the Italian Privacy Law. We also present results of a preliminary empirical study that indicates that adoption of the process improves compliance analysis for a simple compliance scenario.


Adapting microsoft SQL server for cloud computing

Cloud SQL Server is a relational database system designed to scale-out to cloud computing workloads. It uses Microsoft SQL Server as its core. To scale out, it uses a partitioned database on a shared-nothing system architecture. Transactions are constrained to execute on one partition, to avoid the need for two-phase commit. The database is replicated for high availability using a custom primary-copy replication scheme. It currently serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure.


An end-to-end approach to host mobility

We present the design and implementation of an end-to-end architecture for Internet host mobility using dynamic updates to the Domain Name System (DNS) to track host location. Existing TCP connections are retained using secure and efficient connection migration, enabling established connections to seamlessly negotiate a change in endpoint IP addresses without the need for a third party. Our architecture is secure—name updates are effected via the secure DNS update protocol, while TCP connection migration uses a novel set of Migrate options—and provides a pure end-system alternative to routing-based approaches such as Mobile IP.
Mobile IP was designed under the principle that fixed Internet hosts and applications were to remain unmodified and only the underlying IP substrate should change. Our architecture requires no changes to the unicast IP substrate, instead modifying transport protocols and applications at the end hosts. We argue that this is not a hindrance to deployment; rather, in a significant number of cases, it allows for an easier deployment path than Mobile IP, while simultaneously giving better performance. We compare and contrast the strengths of end-to-end and network-layer mobility schemes, and argue that end-to-end schemes are better suited to many common mobile applications. Our performance experiments show that hand-off times are governed by TCP migrate latencies, and are on the order of a round-trip time of the communicating peers.


Ocular Pain and Impending Blindness During Facial Cosmetic Injections: Is Your Office Prepared?

Soft tissue filler injections are the second most common non-surgical procedure performed by the plastic surgeon. Embolization of intravascular material after facial injection is a rare but terrifying outcome due to the high likelihood of long-term sequela such as blindness and cerebrovascular accident. The literature is replete with examples of permanent blindness caused by injection with autologous fat, soft tissue fillers such as hyaluronic acid, PLLA, calcium hydroxyl-apatite, and even corticosteroid suspensions. However, missing from the discussion is an effective treatment algorithm that can be quickly and safely followed by injecting physicians in the case of an intravascular injection with impending blindness. In this report, we present the case of a 64-year-old woman who suffered from blindness and hemiparesis after facial cosmetic injections performed by a family physician. We use this case to create awareness that this complication has become more common as the number of injectors and patients seeking these treatments have increased exponentially over the past few years. We share in this study our experience with the incorporation of a “blindness safety kit” in each of our offices to promptly initiate treatment in someone with embolization and impending blindness. The kit contains a step-by-step protocol to follow in the event of arterial embolization of filler material associated with ocular pain and impending loss of vision. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


CATS: Collection and Analysis of Tweets Made Simple

Twitter presents an unparalleled opportunity for researchers from various ï¬elds to gather valuable and genuine textual data from millions of people. However, the collection pro-cess, as well as the analysis of these data require different kinds of skills (e.g. programing, data mining) which can be an obstacle for people who do not have this background. In this paper we present CATS, an open source, scalable, Web application designed to support researchers who want to carry out studies based on tweets. The purpose of CATS is twofold: (i) allow people to collect tweets (ii) enable them to analyze these tweets thanks to efï¬cient tools (e.g. event detection, named-entity recognition, topic modeling, word-clouds). What is more, CATS relies on a distributed imple-mentation which can deal with massive data streams.


Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation

In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer. Requirements such as continuity and di erentiability of the cost surface add yet another con icting element to the decision process. While \better" solutions should be rated higher than \worse" ones, the resulting cost landscape must also comply with such requirements. Evolutionary algorithms (EAs), which have found application in many areas not amenable to optimization by other methods, possess many characteristics desirable in a multiobjective optimizer, most notably the concerted handling of multiple candidate solutions. However, EAs are essentially unconstrained search techniques which require the assignment of a scalar measure of quality, or tness, to such candidate solutions. After reviewing current evolutionary approaches to multiobjective and constrained optimization, the paper proposes that tness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision making framework based on goals and priorities is subsequently formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies. Finally, the ranking of an arbitrary number of candidates is considered. The e ect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem. The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision strategy. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape.


Model-Driven Design for the Visual Analysis of Heterogeneous Data

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.


Cache-oblivious streaming B-trees

A streaming B-tree is a dictionary that efficiently implements insertions and range queries. We present two cache-oblivious streaming B-trees, the shuttle tree, and the cache-oblivious lookahead array (COLA).
 For block-transfer size B and on N elements, the shuttle tree implements searches in optimal O(log B+1N) transfers, range queries of L successive elements in optimal O(log B+1N +L/B) transfers, and insertions in O((log B+1N)/BΘ(1/(log log B)2)+(log2N)/B) transfers, which is an asymptotic speedup over traditional B-trees if B ≥ (log N)1+c log log log2 N for any constant c >1.
 A COLA implements searches in O(log N) transfers, range queries in O(log N + L/B) transfers, and insertions in amortized O((log N)/B) transfers, matching the bounds for a (cache-aware) buffered repository tree. A partially deamortized COLA matches these bounds but reduces the worst-case insertion cost to O(log N) if memory size M = Ω(log N). We also present a cache-aware version of the COLA, the lookahead array, which achieves the same bounds as Brodal and Fagerberg's (cache-aware) Bε-tree.
 We compare our COLA implementation to a traditional B-tree. Our COLA implementation runs 790 times faster for random inser-tions, 3.1 times slower for insertions of sorted data, and 3.5 times slower for searches.


Dynamic Behaviour of an Electronically Commutated ( Brushless DC ) Motor Drive with Back-emf Sensing

Conventionally, BLDC motors are commutated in six-step pattern with commutation controlled by position sensors. To reduce cost and complexity of the drive system, sensorless drive is preferred. The existing sensorless control scheme with the conventional back EMF sensing based on motor neutral voltage for BLDC has certain drawbacks, which limit its applications. This paper presents the dynamic behaviour of an analytical and circuit model of a Brushless DC (BLDC) motors with back emf sensing. The circuit model was simulated using LTspice and the results obtained were compared with the experimental results. The value of the motor constant and the back emf measured from the experiment agreed with the simulated model. The starting behaviour of the motor, changing of load torque when current are varied and disturbance of sensing method at peak load shows that the dynamic behaviour results of the experiment obtained from oscilloscope are similar to the simulated value.


Quasi-Cyclic LDPC Codes: Influence of Proto- and Tanner-Graph Structure on Minimum Hamming Distance Upper Bounds

Quasi-cyclic (QC) low-density parity-check (LDPC) codes are an important instance of proto-graph-based LDPC codes. In this paper we present upper bounds on the minimum Hamming distance of QC LDPC codes and study how these upper bounds depend on graph structure parameters (like variable degrees, check node degrees, girth) of the Tanner graph and of the underlying proto-graph. Moreover, for several classes of proto-graphs we present explicit QC LDPC code constructions that achieve (or come close to) the respective minimum Hamming distance upper bounds. Because of the tight algebraic connection between QC codes and convolutional codes, we can state similar results for the free Hamming distance of convolutional codes. In fact, some QC code statements are established by first proving the corresponding convolutional code statements and then using a result by Tanner that says that the minimum Hamming distance of a QC code is upper bounded by the free Hamming distance of the convolutional code that is obtained by “unwrapping” the QC code.


Piranha: a scalable architecture based on single-chip multiprocessing

The microprocessor industry is currently struggling with higher development costs and longer design times that arise from exceedingly complex processors that are pushing the limits of instruction-level parallelism. Meanwhile, such designs are especially ill suited for important commercial applications, such as on-line transaction processing (OLTP), which suffer from large memory stall times and exhibit little instruction-level parallelism. Given that commercial applications constitute by far the most important market for high-performance servers, the above trends emphasize the need to consider alternative processor designs that specifically target such workloads. The abundance of explicit thread-level parallelism in commercial workloads, along with advances in semiconductor integration density, identify chip multiprocessing (CMP) as potentially the most promising approach for designing processors targeted at commercial servers.
 This paper describes the Piranha system, a research prototype being developed at Compaq that aggressively exploits chip multi-processing by integrating eight simple Alpha processor cores along with a two-level cache hierarchy onto a single chip. Piranha also integrates further on-chip functionality to allow for scalable multiprocessor configurations to be built in a glueless and modular fashion. The use of simple processor cores combined with an industry-standard ASIC design methodology allow us to complete our prototype within a short time-frame, with a team size and investment that are an order of magnitude smaller than that of a commercial microprocessor. Our detailed simulation results show that while each Piranha processor core is substantially slower than an aggressive next-generation processor, the integration of eight cores onto a single chip allows Piranha to outperform next-generation processors by up to 2.9 times (on a per chip basis) on important workloads such as OLTP. This performance advantage can approach a factor of five by using full-custom instead of ASIC logic. In addition to exploiting chip multiprocessing, the Piranha prototype incorporates several other unique design choices including a shared second-level cache with no inclusion, a highly optimized cache coherence protocol, and a novel I/O architecture.


Short-Circuit Detection for Electrolytic Processes Employing Optibar Intercell Bars

This paper presents a method to detect metallurgical short circuits suitable for Optibar intercell bars in copper electrowinning and electrorefining processes. One of the primary achievements of this bar is to limit short-circuit currents to a maximum of 1.5 p.u. of the actual process current. However, low-current short circuits are more difficult to detect. Thus, conventional short-circuit detection instruments like gaussmeters and infrared cameras become ineffective. To overcome this problem, the proposed method is based on detecting the voltage drop across anode-cathode pairs. The method does not affect the operation of the process and does not require modifications of the industrial plant. In order to verify the performance of this proposal, experimental measurements done over a period of four months at a copper refinery are presented. A 100% success rate was obtained.


Rating Image Aesthetics Using Deep Learning

This paper investigates unified feature learning and classifier training approaches for image aesthetics assessment . Existing methods built upon handcrafted or generic image features and developed machine learning and statistical modeling techniques utilizing training examples. We adopt a novel deep neural network approach to allow unified feature learning and classifier training to estimate image aesthetics. In particular, we develop a double-column deep convolutional neural network to support heterogeneous inputs, i.e., global and local views, in order to capture both global and local characteristics of images . In addition, we employ the style and semantic attributes of images to further boost the aesthetics categorization performance . Experimental results show that our approach produces significantly better results than the earlier reported results on the AVA dataset for both the generic image aesthetics and content -based image aesthetics. Moreover, we introduce a 1.5-million image dataset (IAD) for image aesthetics assessment and we further boost the performance on the AVA test set by training the proposed deep neural networks on the IAD dataset.


Vehicles Capable of Dynamic Vision

A survey is given on two decades of developments in the field, encompassing an increase in computing power by four orders of magnitude. The '4-D approach' integrating expectation-based methods from systems dynamics and control engineering with methods from AI has allowed to create vehicles with unprecedented capabilities in the technical realm: Autonomous road vehicle guidance in public traffic on freeways at speeds beyond 130 km/h, on-board-autonomous landing approaches of aircraft, and landmark navigation for AGV's, for road vehicles including turn-offs onto cross-roads, and for helicopters in low-level flight (real-time, hardware-in-the-loop simulations in the latter case).


DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map

For applications such as augmented reality, autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. In this paper, we propose a unified framework to tackle these two problems simultaneously. The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system. Specifically, we first have an initial coarse camera pose obtained from consumer-grade GPS/IMU, based on which a label map can be rendered from the 3D semantic map. Then, the rendered label map and the RGB image are jointly fed into a pose CNN, yielding a corrected camera pose. In addition, to incorporate temporal information, a multi-layer recurrent neural network (RNN) is further deployed improve the pose accuracy. Finally, based on the pose from RNN, we render a new label map, which is fed together with the RGB image into a segment CNN which produces perpixel semantic label. In order to validate our approach, we build a dataset with registered 3D point clouds and video camera images. Both the point clouds and the images are semantically-labeled. Each video frame has ground truth pose from highly accurate motion sensors. We show that practically, pose estimation solely relying on images like PoseNet [25] may fail due to street view confusion, and it is important to fuse multiple sensors. Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. In particular, we show that scene parsing and pose estimation are mutually beneficial to achieve a more robust and accurate system.


The learning curve in microtia surgery.

Reconstruction of the auricle is known to be complex. Our objective was to evaluate the improvement of the outcome of the lobulus-type microtia reconstruction. Patient satisfaction was also evaluated. There are no previous reports of the learning process in this field. Postoperative photographs of 51 microtia reconstructions were assessed and rated by a panel made up of six surgeons. The ratings were gathered to generate learning curves. Twenty-two patients assessed the outlook of their reconstructed ears, and the results were analyzed as a self-assessment group. The reliability of the rating by a panel was tested by intraclass correlations. There is a highly significant increasing trend in learning ( P = 0.000001). This trend is not constantly upward, and the steady state was not reached during the study. In the self-assessment group, females were significantly more critical than males ( P = 0.014). Intraclass correlation for six panel members was 0.90, and the rating was considered reliable. Thus, a long and gentle learning curve does exist in microtia reconstruction. To secure good quality and continuity, centralization of the operations and trainee arrangements are highly advisable. Outcomes of plastic surgery can reliably be rated by an evaluation panel.


Comparison of human and computer performance across face recognition experiments

a r t i c l e i n f o Since 2005, human and computer performance has been systematically compared as part of face recognition competitions, with results being reported for both still and video imagery. The key results from these competitions are reviewed. To analyze performance across studies, the cross-modal performance analysis (CMPA) framework is introduced. The CMPA framework is applied to experiments that were part of face a recognition competition. The analysis shows that for matching frontal faces in still images, algorithms are consistently superior to humans. For video and difficult still face pairs, humans are superior. Finally, based on the CMPA framework and a face performance index, we outline a challenge problem for developing algorithms that are superior to humans for the general face recognition problem.


Facilitating relational governance through service level agreements in IT outsourcing: An application of the commitment-trust theory

Article history: Received 18 June 2007 Received in revised form 9 June 2008 Accepted 20 June 2008 Available online 25 June 2008 Firms increasingly rely on outsourcing for strategic IT decisions, and themany sophisticated forms of outsourcing require significant management attention to ensure their success. Two forms of interorganizational governance–formal control and relational–have been used to examine the management of IT outsourcing relationships. Contrary to the conventional substitution view, recent studies have found that these two governance modes are complementary; however, the dynamics of their interactions remain unexplored. Based on the commitment–trust theory, this paper focuses on how the formal controlmechanism can influence the relational governance in an outsourcing engagement. Using service level agreements (SLAs) as a proxy for formal control, this studyfinds that eleven contractual elements, characterized as foundation, governance, and change management variables in an SLA, are positively related to the trust and relationship commitment among the parties. Trust and commitment, in turn, positively influence relational outcomes that we theorize would contribute to outsourcing success. Both research and practical implications of the results are discussed. © 2008 Elsevier B.V. All rights reserved.


Plan 9 , A Distributed System

Plan 9 is a computing environment physically distributed across many machines. The distribution itself is transparent to most programs giving both users and administrators wide latitude in configuring the topology of the environment. Two properties make this possible: a per process group name space and uniform access to all resources by representing them as files.


Measuring the Diversity of Automatic Image Descriptions

Automatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of generic descriptions as a lack of diversity in the output, which we quantify using established metrics and two new metrics that frame image description as a word recall task. This framing allows us to evaluate system performance on the head of the vocabulary, as well as on the long tail, where system performance degrades. We use these metrics to examine the diversity of the sentences generated by nine state-of-the-art systems on the MS COCO data set. We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives. However, the adversarially-trained models only produce more types from the head of the vocabulary and not the tail. Besides vocabulary-based methods, we also look at the compositional capacity of the systems, specifically their ability to create compound nouns and prepositional phrases of different lengths. We conclude that there is still much room for improvement, and offer a toolkit to measure progress towards the goal of generating more diverse image descriptions.


Delaunay Meshing of Isosurfaces

We present an isosurface meshing algorithm, DelIso, based on the Delaunay refinement paradigm. This paradigm has been successfully applied to mesh a variety of domains with guarantees for topology, geometry, mesh gradedness, and triangle shape. A restricted Delaunay tri- angulation, dual of the intersection between the surface and the three dimensional Voronoi diagram, is often the main ingredient in Delaunay refinement. Computing and storing three dimensional Voronoi/Delaunay diagrams become bottlenecks for Delaunay refinement techniques since isosurface computations generally have large input datasets and output meshes. A highlight of our algorithm is that we find a simple way to recover the restricted Delaunay triangulation of the surface without computing the full 3D structure. We employ techniques for efficient ray tracing of isosurfaces to generate surface sample points, and demonstrate the effectiveness of our implementation using a variety of volume datasets.


The Many Faces of Formative Assessment.

In this research paper we consider formative assessment (FA) and discuss ways in which it has been implemented in four different university courses. We illustrate the different aspects of FA by deconstructing it and then demonstrating effectiveness in improving both teaching and student achievement. It appears that specifically “what is done” was less important since there were positive achievement gains in each study. While positive gains were realized with use of technology, gains were also realized with implementation of nontechnology dependent techniques. Further, gains were independent of class size or subject matter.


A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment

Iris recognition is a challenging task in a noisy imaging environment. Nowadays researcher's primary focus is to develop reliable Iris recognition System that can work in noisy imaging environment and to increase the iris recognition rate on different iris database. But there are major issues involved in designing such systems like occlusion by eyelashes, eyelids, glass frames, off-angle imaging, presence of contact lenses, poor illumination, motion blur, close-up of iris image acquired at a large standoff distance and specular reflections etc. Because of these issues the quality of acquired iris image gets affected. The performance of the iris based recognition system will deteriorate abruptly, when the iris mask is not accurate. This results in lower recognition rate. In this review paper different challenges in designing iris recognition systems for noisy imaging environment are reviewed and methodologies involved in overcoming these issues are discussed. At the end, some measures to improve the accuracy of such systems are suggested.


Mining Multi-label Data

A large body of research in supervised learning deals with the analysis of singlelabel data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label. Textual data, such as documents and web pages, are frequently annotated with more than a single label. For example, a news article concerning the reactions of the Christian church to the release of the “Da Vinci Code” film can be labeled as both religion and movies. The categorization of textual data is perhaps the dominant multi-label application. Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization into emotions [11, 12, 13, 14] and directed marketing [15]. Table 1 presents a variety of applications that are discussed in the literature. This chapter reviews past and recent work on the rapidly evolving research area of multi-label data mining. Section 2 defines the two major tasks in learning from multi-label data and presents a significant number of learning methods. Section 3 discusses dimensionality reduction methods for multi-label data. Sections 4 and 5 discuss two important research challenges, which, if successfully met, can significantly expand the real-world applications of multi-label learning methods: a) exploiting label structure and b) scaling up to domains with large number of labels. Section 6 introduces benchmark multi-label datasets and their statistics, while Section 7 presents the most frequently used evaluation measures for multi-label learn-


Prediction of central nervous system embryonal tumour outcome based on gene expression

Embryonal tumours of the central nervous system (CNS) represent a heterogeneous group of tumours about which little is known biologically, and whose diagnosis, on the basis of morphologic appearance alone, is controversial. Medulloblastomas, for example, are the most common malignant brain tumour of childhood, but their pathogenesis is unknown, their relationship to other embryonal CNS tumours is debated, and patients’ response to therapy is difficult to predict. We approached these problems by developing a classification system based on DNA microarray gene expression data derived from 99 patient samples. Here we demonstrate that medulloblastomas are molecularly distinct from other brain tumours including primitive neuroectodermal tumours (PNETs), atypical teratoid/rhabdoid tumours (AT/RTs) and malignant gliomas. Previously unrecognized evidence supporting the derivation of medulloblastomas from cerebellar granule cells through activation of the Sonic Hedgehog (SHH) pathway was also revealed. We show further that the clinical outcome of children with medulloblastomas is highly predictable on the basis of the gene expression profiles of their tumours at diagnosis.


Problems in dealing with missing data and informative censoring in clinical trials

Acommon problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.


Missing value estimation methods for DNA microarrays

MOTIVATION
Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.

RESULTS
We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.


Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.


Multiple Imputation for Missing Data: Concepts and New Development

Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results from different imputed data sets is essentially the same. This results in valid statistical inferences that properly reflect the uncertainty due to missing values. This paper reviews methods for analyzing missing data, including basic concepts and applications of multiple imputation techniques. The paper also presents new SAS R procedures for creating multiple imputations for incomplete multivariate data and for analyzing results from multiply imputed data sets. These procedures are still under development and will be available in experimental form in Release 8.1 of the SAS System. Introduction Most SAS statistical procedures exclude observations with any missing variable values from the analysis. These observations are called incomplete cases. While using only complete cases has its simplicity, you lose information in the incomplete cases. This approach also ignores the possible systematic difference between the complete cases and incomplete cases, and the resulting inference may not be applicable to the population of all cases, especially with a smaller number of complete cases. Some SAS procedures use all the available cases in an analysis, that is, cases with available information. For example, PROC CORR estimates a variable mean by using all cases with nonmissing values on this variable, ignoring the possible missing values in other variables. PROC CORR also estimates a correlation by using all cases with nonmissing values for this pair of variables. This may make better use of the available data, but the resulting correlation matrix may not be positive definite. Another strategy is simple imputation, in which you substitute a value for each missing value. Standard statistical procedures for complete data analysis can then be used with the filled-in data set. For example, each missing value can be imputed from the variable mean of the complete cases, or it can be imputed from the mean conditional on observed values of other variables. This approach treats missing values as if they were known in the complete-data analyses. Single imputation does not reflect the uncertainty about the predictions of the unknown missing values, and the resulting estimated variances of the parameter estimates will be biased toward zero. Instead of filling in a single value for each missing value, a multiple imputation procedure (Rubin 1987) replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. The multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results from different data sets is essentially the same. Multiple imputation does not attempt to estimate each missing value through simulated values but rather to represent a random sample of the missing values. This process results in valid statistical inferences that properly reflect the uncertainty due to missing values; for example, valid confidence intervals for parameters. Multiple imputation inference involves three distinct phases: The missing data are filled in m times to generate m complete data sets. The m complete data sets are analyzed by using standard procedures. The results from the m complete data sets are combined for the inference. A new SAS/STAT R procedure, PROC MI, is a multiple imputation procedure that creates multiply imputed data sets for incomplete p-dimensional multivariate data. It uses methods that incorporate appropriate variability across the m imputations. Once the m complete data sets are analyzed by using standard procedures, another new procedure, PROC MIANALYZE, can be used to generate valid statistical inferences about these parameters by combining results from the m complete data sets. Statistics and Data Analysis


An Image-based Feature Extraction Approach for Phishing Website Detection

Phishing website creators and anti-phishing defenders are in an arms race. Cloning a website is fairly easy and can be automated by any junior programmer. Attempting to recognize numerous phishing links posted in the wild e.g. on social media sites or in email is a constant game of escalation. Automated phishing website detection systems need both speed and accuracy to win. We present a new method of detecting phishing websites and a prototype system LEO (Logo Extraction and cOmparison) that implements it. LEO uses image feature recognition to extract “visual hotspots” of a webpage, and compare these parts with known logo images. LEO can recognize phishing websites that has different layout from the original websites, or logos embedded in images. Comparing to existing visual similaritybased methods, our method has a much wider application range and higher detection accuracy. Our method successfully recognized 24 of 25 random URLs from PhishTank that previously evaded detection of other visual similarity-based methods.


Clustering based anomalous transaction reporting

Anti-money laundering (AML) refers to a set of financial and technological controls that aim to combat the entrance of dirty money into financial systems. A robust AML system must be able to automatically detect any unusual/anomalous financial transactions committed by a customer. The paper presents a hybrid anomaly detection approach that employs clustering to establish customers’ normal behaviors and uses statistical techniques to determine deviation of a particular transaction from the corresponding group behavior. The approach implements a variant of Euclidean Adaptive Resonance Theory, termed as TEART, to group customers in different clusters. The paper also suggests an anomaly index, named AICAF, for ranking transactions as anomalous. The approach has been tested on a real data set comprising of 8.2 million transactions and the results suggest that TEART scales well in terms of the partitions obtained when compared to the traditional K-means algorithm. The presented approach marks transactions having high AICAF values as suspicious.


Decomposing Adult Age Differences in Working Memory

Two studies, involving a total of 460 adults between 18 and 87 years of age, were conducted to determine which of several hypothesized processing components was most responsible for age-related declines in working memory functioning. Significant negative correlations between age and measures of working memory (i.e., from -.39 to -.52) were found in both studies, and these relations were substantially attenuated by partialing measures hypothesized to reflect storage capacity, processing efficiency, coordination effectiveness, and simple comparison speed. Because the greatest attenuation of the age relations occurred with measures of simple processing speed, it was suggested that many of the age differences in working memory may be mediated by age-related reductions in the speed of executing elementary operations.


The Deep Regression Bayesian Network and Its Applications: Probabilistic Deep Learning for Computer Vision

Deep directed generative models have attracted much attention recently due to their generative modeling nature and powerful data representation ability. In this article, we review different structures of deep directed generative models and the learning and inference algorithms associated with the structures. We focus on a specific structure that consists of layers of Bayesian networks (BNs) due to the property of capturing inherent and rich dependencies among latent variables. The major difficulty of learning and inference with deep directed models with many latent variables is the intractable inference due to the dependencies among the latent variables and the exponential number of latent variable configurations. Current solutions use variational methods, often through an auxiliary network, to approximate the posterior probability inference. In contrast, inference can also be performed directly without using any auxiliary network to maximally preserve the dependencies among the latent variables. Specifically, by exploiting the sparse representation with the latent space, max-max instead of maxsum operation can be used to overcome the exponential number of latent configurations. Furthermore, the max-max operation and augmented coordinate ascent (AugCA) are applied to both supervised and unsupervised learning as well as to various inference. Quantitative evaluations on benchmark data sets of different models are given for both data representation and feature-learning tasks.


Making working memory work: a meta-analysis of executive-control and working memory training in older adults.

This meta-analysis examined the effects of process-based executive-function and working memory training (49 articles, 61 independent samples) in older adults (> 60 years). The interventions resulted in significant effects on performance on the trained task and near-transfer tasks; significant results were obtained for the net pretest-to-posttest gain relative to active and passive control groups and for the net effect at posttest relative to active and passive control groups. Far-transfer effects were smaller than near-transfer effects but were significant for the net pretest-to-posttest gain relative to passive control groups and for the net gain at posttest relative to both active and passive control groups. We detected marginally significant differences in training-induced improvements between working memory and executive-function training, but no differences between the training-induced improvements observed in older adults and younger adults, between the benefits associated with adaptive and nonadaptive training, or between the effects in active and passive control conditions. Gains did not vary with total training time.


Direct Torque and Indirect Flux Control of Brushless DC Motor

In this paper, the position-sensorless direct torque and indirect flux control of brushless dc (BLDC) motor with nonsinusoidal back electromotive force (EMF) has been extensively investigated. In the literature, several methods have been proposed for BLDC motor drives to obtain optimum current and torque control with minimum torque pulsations. Most methods are complicated and do not consider the stator flux linkage control, therefore, possible high-speed operations are not feasible. In this study, a novel and simple approach to achieve a low-frequency torque ripple-free direct torque control (DTC) with maximum efficiency based on dq reference frame is presented. The proposed sensorless method closely resembles the conventional DTC scheme used for sinusoidal ac motors such that it controls the torque directly and stator flux amplitude indirectly using d-axis current. This method does not require pulsewidth modulation and proportional plus integral regulators and also permits the regulation of varying signals. Furthermore, to eliminate the low-frequency torque oscillations, two actual and easily available line-to-line back EMF constants ( kba and kca) according to electrical rotor position are obtained offline and converted to the dq frame equivalents using the new line-to-line park transformation. Then, they are set up in the look-up table for torque estimation. The validity and practical applications of the proposed sensorless three-phase conduction DTC of BLDC motor drive scheme are verified through simulations and experimental results.


Automatic Identification and Classification of Misogynistic Language on Twitter

Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.


Role of IL-33 in inflammation and disease

Interleukin (IL)-33 is a new member of the IL-1 superfamily of cytokines that is expressed by mainly stromal cells, such as epithelial and endothelial cells, and its expression is upregulated following pro-inflammatory stimulation. IL-33 can function both as a traditional cytokine and as a nuclear factor regulating gene transcription. It is thought to function as an 'alarmin' released following cell necrosis to alerting the immune system to tissue damage or stress. It mediates its biological effects via interaction with the receptors ST2 (IL-1RL1) and IL-1 receptor accessory protein (IL-1RAcP), both of which are widely expressed, particularly by innate immune cells and T helper 2 (Th2) cells. IL-33 strongly induces Th2 cytokine production from these cells and can promote the pathogenesis of Th2-related disease such as asthma, atopic dermatitis and anaphylaxis. However, IL-33 has shown various protective effects in cardiovascular diseases such as atherosclerosis, obesity, type 2 diabetes and cardiac remodeling. Thus, the effects of IL-33 are either pro- or anti-inflammatory depending on the disease and the model. In this review the role of IL-33 in the inflammation of several disease pathologies will be discussed, with particular emphasis on recent advances.


A Dual-Fed Aperture-Coupled Microstrip Antenna With Polarization Diversity

This communication presents a dual-port reconfigurable square patch antenna with polarization diversity for 2.4 GHz. By controlling the states of four p-i-n diodes on the patch, the polarization of the proposed antenna can be switched among linear polarization (LP), left- or right-hand circular polarization (CP) at each port. The air substrate and aperture-coupled feed structure are employed to simplify the bias circuit of p-i-n diodes. With high isolation and low cross-polarization level in LP modes, both ports can work simultaneously as a dual linearly polarized antenna for polarimetric radars. Different CP waves are obtained at each port, which are suitable for addressing challenges ranging from mobility, adverse weather conditions and non-line-of-sight applications. The antenna has advantages of simple biasing network, easy fabrication and adjustment, which can be widely applied in polarization diversity applications.


Expression Cloning of TMEM16A as a Calcium-Activated Chloride Channel Subunit

Calcium-activated chloride channels (CaCCs) are major regulators of sensory transduction, epithelial secretion, and smooth muscle contraction. Other crucial roles of CaCCs include action potential generation in Characean algae and prevention of polyspermia in frog egg membrane. None of the known molecular candidates share properties characteristic of most CaCCs in native cells. Using Axolotl oocytes as an expression system, we have identified TMEM16A as the Xenopus oocyte CaCC. The TMEM16 family of "transmembrane proteins with unknown function" is conserved among eukaryotes, with family members linked to tracheomalacia (mouse TMEM16A), gnathodiaphyseal dysplasia (human TMEM16E), aberrant X segregation (a Drosophila TMEM16 family member), and increased sodium tolerance (yeast TMEM16). Moreover, mouse TMEM16A and TMEM16B yield CaCCs in Axolotl oocytes and mammalian HEK293 cells and recapitulate the broad CaCC expression. The identification of this new family of ion channels may help the development of CaCC modulators for treating diseases including hypertension and cystic fibrosis.


An improved wavelet based shock wave detector

In this paper, the detection of shock wave that generated by supersonic bullet is considered. A wavelet based multi-scale products method has been widely used for detection. However, the performance of method decreased at low signal-to-noise ratio (SNR). It is noted that the method does not consider the distribution of the signal and noise. Thus we analyze the method under the standard likelihood ratio test in this paper. It is found that the multi-scale product method is made in an assumption that is extremely restricted, just hold for a special noise condition. Based on the analysis, a general condition is considered for the detection. An improved detector under the standard likelihood ratio test is proposed. Monte Carlo simulations is conducted with simulated shock waves under additive white Gaussian noise. The result shows that this new detection algorithm outperforms the conventional detection algorithm.


Cascaded Random Forest for Fast Object Detection

A Random Forest consists of several independent decision trees arranged in a forest. A majority vote over all trees leads to the final decision. In this paper we propose a Random Forest framework which incorporates a cascade structure consisting of several stages together with a bootstrap approach. By introducing the cascade, 99% of the test images can be rejected by the first and second stage with minimal computational effort leading to a massively speeded-up detection framework. Three different cascade voting strategies are implemented and evaluated. Additionally, the training and classification speed-up is analyzed. Several experiments on public available datasets for pedestrian detection, lateral car detection and unconstrained face detection demonstrate the benefit of our contribution.


A density-based cluster validity approach using multi-representatives

Although the goal of clustering is intuitively compelling and its notion arises in many fields, it is difficult to define a unified approach to address the clustering problem and thus diverse clustering algorithms abound in the research community. These algorithms, under different clustering assumptions, often lead to qualitatively different results. As a consequence the results of clustering algorithms (i.e. data set partitionings) need to be evaluated as regards their validity based on widely accepted criteria. In this paper a cluster validity index, CDbw, is proposed which assesses the compactness and separation of clusters defined by a clustering algorithm. The cluster validity index, given a data set and a set of clustering algorithms, enables: i) the selection of the input parameter values that lead an algorithm to the best possible partitioning of the data set, and ii) the selection of the algorithm that provides the best partitioning of the data set. CDbw handles efficiently arbitrarily shaped clusters by representing each cluster with a number of points rather than by a single representative point. A full implementation and experimental results confirm the reliability of the validity index showing also that its performance compares favourably to that of several others.


Multilevel Inverters for Electric Vehicle Applications

 This paper presents multilevel inverters as an application for all-electric vehicle (EV) and hybrid-electric vehicle (HEV) motor drives. Diode-clamped inverters and cascaded H-bridge inverters, (1) can generate near-sinusoidal voltages with only fundamental frequency switching; (2) have almost no electromagnetic interference (EMI) and commonmode voltage; and (3) make an EV more accessible/safer and open wiring possible for most of an EV’s power system. This paper explores the benefits and discusses control schemes of the cascade inverter for use as an EV motor drive or a parallel HEV drive and the diode-clamped inverter as a series HEV motor drive. Analytical, simulated, and experimental results show the superiority of these multilevel inverters for this new niche.


X-band FMCW radar system with variable chirp duration

For application in a short range ground based surveillance radar a combination between frequency modulated continuous wave (FMCW) transmit signals and a receive antenna array system is considered in this paper. The target echo signal is directly down converted by the instantaneous transmit frequency. The target range R will be estimated based on the measured frequency shift fB between transmit and receive signal. Due to an extremely short chirp duration Tchirp, the target radial velocity υτ has only a very small influence to the measured frequency shift fB. Therefore the radial velocity υτ will not be measured inside a single FMCW chirp but in a sequence of chirp signals and inside each individual range gate. Finally, the target azimuth angle is calculated utilizing the receive antenna array and applying a digital beamforming scheme. Furthermore, in order to unambiguously measure even high radial velocities, a variable chirp duration is proposed on a dwell to dwell basis.


Code-Red: a case study on the spread and victims of an internet worm

On July 19, 2001, more than 359,000 computers connected to the Internet were infected with the Code-Red (CRv2) worm in less than 14 hours. The cost of this epidemic, including subsequent strains of Code-Red, is estimated to be in excess of $2.6 billion. Despite the global damage caused by this attack, there have been few serious attempts to characterize the spread of the worm, partly due to the challenge of collecting global information about worms. Using a technique that enables global detection of worm spread, we collected and analyzed data over a period of 45 days beginning July 2nd, 2001 to determine the characteristics of the spread of Code-Red throughout the Internet.In this paper, we describe the methodology we use to trace the spread of Code-Red, and then describe the results of our trace analyses. We first detail the spread of the Code-Red and CodeRedII worms in terms of infection and deactivation rates. Even without being optimized for spread of infection, Code-Red infection rates peaked at over 2,000 hosts per minute. We then examine the properties of the infected host population, including geographic location, weekly and diurnal time effects, top-level domains, and ISPs. We demonstrate that the worm was an international event, infection activity exhibited time-of-day effects, and found that, although most attention focused on large corporations, the Code-Red worm primarily preyed upon home and small business users. We also qualified the effects of DHCP on measurements of infected hosts and determined that IP addresses are not an accurate measure of the spread of a worm on timescales longer than 24 hours. Finally, the experience of the Code-Red worm demonstrates that wide-spread vulnerabilities in Internet hosts can be exploited quickly and dramatically, and that techniques other than host patching are required to mitigate Internet worms.


On the Generation of Skeletons from Discrete Euclidean Distance Maps

The skeleton is an important representation for shape analysis. A common approach for generating discrete skeletons takes three steps: 1) computing the distance map, 2) detecting maximal disks from the distance map, and 3) linking the centers of maximal disks (CMDs) into a connected skeleton. Algorithms using approximate distance metrics are abundant and their theory has been well established. However, the resulting skeletons may be inaccurate and sensitive to rotation. In this paper, we study methods for generating skeletons based on the exact Euclidean metric. We first show that no previous algorithms identifies the exact set of discrete maximal disks under the Euclidean metric. We then propose new algorithms and show that they are correct. To link CMDs into connected skeletons, we examine two prevalent approaches: connected thinning and steepest ascent. We point out that the connected thinning approach does not work properly for Euclidean distance maps. Only the steepest ascent algorithm produces skeletons that are truly medially placed. The resulting skeletons have all the desirable properties: they have the same simple connectivity as the figure, they are well-centered, they are insensitive to rotation, and they allow exact reconstruction. The effectiveness of our algorithms is demonstrated with numerous examples.


CloudSpeller: query spelling correction by using a unified hidden markov model with web-scale resources

Query spelling correction is an important component of modern search engines that can help users to express an information need more accurately and thus improve search quality. In this work we proposed and implemented an end-to-end speller correction system, namely CloudSpeller. The CloudSpeller system uses a Hidden Markov Model to effectively model major types of spelling errors in a unified framework, in which we integrate a large-scale lexicon constructed using Wikipedia, an error model trained from high confidence correction pairs, and the Microsoft Web N-gram service. Our system achieves excellent performance on two search query spelling correction datasets, reaching 0.960 and 0.937 F1 scores on the TREC dataset and the MSN dataset respectively.


Unsupervised Event Tracking by Integrating Twitter and Instagram

This paper proposes an unsupervised framework for tracking real world events from their traces on Twitter and Instagram. Empirical data suggests that event detection from Instagram streams errs on the false-negative side due to the relative sparsity of Instagram data (compared to Twitter data), whereas event detection from Twitter can suffer from false-positives, at least if not paired with careful analysis of tweet content. To tackle both problems simultaneously, we design a unified unsupervised algorithm that fuses events detected originally on Instagram (called I-events) and events detected originally on Twitter (called T-events), that occur in adjacent periods, in an attempt to combine the benefits of both sources while eliminating their individual disadvantages. We evaluate the proposed framework with real data crawled from Twitter and Instagram. The results indicate that our algorithm significantly improves tracking accuracy compared to baselines.


Design consideration of recent advanced low-voltage CMOS boost converter for energy harvesting

With the emergence of nanoscale material-based energy harvesters such as thermoelectric generator and microbial fuel cells, energy-harvesting-assisted self-powered electronics systems are gaining popularity. The state-of-the-art low-voltage CMOS boost converter, a critical voltage converter circuit for low power energy harvesting sources will be reviewed in this paper. Fundamentals of the boost converter circuit startup problem are discussed and recent circuit solutions to solve this problem are compared and analyzed. Necessary design considerations and trade-offs regarding circuit topology, component and CMOS process are also addressed.


Syntactic Annotations for the Google Books NGram Corpus

We present a new edition of the Google Books Ngram Corpus, which describes how often words and phrases were used over a period of five centuries, in eight languages; it reflects 6% of all books ever published. This new edition introduces syntactic annotations: words are tagged with their part-of-speech, and headmodifier relationships are recorded. The annotations are produced automatically with statistical models that are specifically adapted to historical text. The corpus will facilitate the study of linguistic trends, especially those related to the evolution of syntax.


A Bayesian framework for word segmentation: Exploring the effects of context

Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning in 8-month-old infants. Science, 274, 1926-1928], there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words--in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what's that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered.


Quantifying the evolutionary dynamics of language

Human language is based on grammatical rules. Cultural evolution allows these rules to change over time. Rules compete with each other: as new rules rise to prominence, old ones die away. To quantify the dynamics of language evolution, we studied the regularization of English verbs over the past 1,200 years. Although an elaborate system of productive conjugations existed in English’s proto-Germanic ancestor, Modern English uses the dental suffix, ‘-ed’, to signify past tense. Here we describe the emergence of this linguistic rule amidst the evolutionary decay of its exceptions, known to us as irregular verbs. We have generated a data set of verbs whose conjugations have been evolving for more than a millennium, tracking inflectional changes to 177 Old-English irregular verbs. Of these irregular verbs, 145 remained irregular in Middle English and 98 are still irregular today. We study how the rate of regularization depends on the frequency of word usage. The half-life of an irregular verb scales as the square root of its usage frequency: a verb that is 100 times less frequent regularizes 10 times as fast. Our study provides a quantitative analysis of the regularization process by which ancestral forms gradually yield to an emerging linguistic rule.