Query: Pooled motion features for first-person videos

Relevant passages:
Action Recognition with Improved Trajectories <=SEP=> Recently dense trajectories were shown to be an efficient video representation for action recognition and achieved state-of-the-art results on a variety of datasets. This paper improves their performance by taking into account camera motion to correct them. To estimate camera motion, we match feature points between frames using SURF descriptors and dense optical flow, which are shown to be complementary. These matches are, then, used to robustly estimate a homography with RANSAC. Human motion is in general different from camera motion and generates inconsistent matches. To improve the estimation, a human detector is employed to remove these matches. Given the estimated camera motion, we remove trajectories consistent with it. We also use this estimation to cancel out camera motion from the optical flow. This significantly improves motion-based descriptors, such as HOF and MBH. Experimental results on four challenging action datasets (i.e., Hollywood2, HMDB51, Olympic Sports and UCF50) significantly outperform the current state of the art.

First-Person Activity Recognition: What Are They Doing to Me? <=SEP=> This paper discusses the problem of recognizing interaction-level human activities from a first-person viewpoint. The goal is to enable an observer (e.g., a robot or a wearable camera) to understand 'what activity others are performing to it' from continuous video inputs. These include friendly interactions such as 'a person hugging the observer' as well as hostile interactions like 'punching the observer' or 'throwing objects to the observer', whose videos involve a large amount of camera ego-motion caused by physical interactions. The paper investigates multi-channel kernels to integrate global and local motion information, and presents a new activity learning/recognition methodology that explicitly considers temporal structures displayed in first-person activity videos. In our experiments, we not only show classification results with segmented videos, but also confirm that our new approach is able to detect activities from continuous videos reliably.

Improving the Fisher Kernel for Large-Scale Image Classification <=SEP=> The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9% to 58.3%. Similarly, we demonstrate state-of-the-art accuracy on CalTech 256. A major advantage is that these results are obtained using only SIFT descriptors and costless linear classifiers. Equipped with this representation, we can now explore image classification on a larger scale. In the second part, as an application, we compare two abundant resources of labeled images to learn classifiers: ImageNet and Flickr groups. In an evaluation involving hundreds of thousands of training images we show that classifiers learned on Flickr groups perform surprisingly well (although they were not intended for this purpose) and that they can complement classifiers learned on more carefully annotated datasets.

Social interactions: A first-person perspective <=SEP=> This paper presents a method for the detection and recognition of social interactions in a day-long first-person video of u social event, like a trip to an amusement park. The location and orientation of faces are estimated and used to compute the line of sight for each face. The context provided by all the faces in a frame is used to convert the lines of sight into locations in space to which individuals attend. Further, individuals are assigned roles based on their patterns of attention. The rotes and locations of individuals are analyzed over time to detect and recognize the types of social interactions. In addition to patterns of face locations and attention, the head movements of the first-person can provide additional useful cues as to their attentional focus. We demonstrate encouraging results on detection and recognition of social interactions in first-person videos captured from multiple days of experience in amusement parks.

Learning realistic human actions from movies <=SEP=> The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multi-channel non-linear SVMs. The method is shown to improve state-of-the-art results on the standard KTH action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.

A wirelessly controlled implantable LED system for deep brain optogenetic stimulation <=SEP=> In recent years optogenetics has rapidly become an essential technique in neuroscience. Its temporal and spatial specificity, combined with efficacy in manipulating neuronal activity, are especially useful in studying the behavior of awake behaving animals. Conventional optogenetics, however, requires the use of lasers and optic fibers, which can place considerable restrictions on behavior. Here we combined a wirelessly controlled interface and small implantable light-emitting diode (LED) that allows flexible and precise placement of light source to illuminate any brain area. We tested this wireless LED system in vivo, in transgenic mice expressing channelrhodopsin-2 in striatonigral neurons expressing D1-like dopamine receptors. In all mice tested, we were able to elicit movements reliably. The frequency of twitches induced by high power stimulation is proportional to the frequency of stimulation. At lower power, contraversive turning was observed. Moreover, the implanted LED remains effective over 50 days after surgery, demonstrating the long-term stability of the light source. Our results show that the wireless LED system can be used to manipulate neural activity chronically in behaving mice without impeding natural movements.

Real-Time EMG Driven Lower Limb Actuated Orthosis for Assistance As Needed Movement Strategy <=SEP=> This paper presents a new approach to control a wearable knee joint exoskeleton driven through the wearer’ s intention. A realistic bio-inspired musculoskeletal knee joint model is used to control the exoskeleton. This model takes in to account changes in muscle length and joint moment arms as well as the dynamics of muscle activation and muscle contrac tion during lower limb movements. Identification of the model parameters is done through an unconstrained optimization problem formulation. A control law strategy based on the principle of assistance as needed is proposed. This approach guarantees asymptotic stability of the knee joint orthosis and adaptat ion to human-orthosis interaction. Moreover, the proposed contr ol law is robust with respect to external disturbances. Experimental validations are conducted online on a healthy subject during flexion and extenion of their knee joint. The proposed control strategy has shown satisfactory performances in terms of tr acking trajectory and adaptation to human tasks completion.

Implementing GrabCut <=SEP=> GrabCut is an innovative 2D image segmentation technique developed by Rother et al. [2004]. This paper provides implementation details omitted from the original paper. Details covered in background papers are summarized here so that future implementors can refer to a single paper. Our implementation of GrabCut is described and results are included. Our main contribution is correcting errors in equation (9) and in equation (11) of the original paper. We also discuss weaknesses of the algorithm that were not discussed in the original paper. We present possible research directions to address these problems. CR Categories: I.4.6 [Image Processing and Computer Vision]: Segmentation—Pixel classification

Coronal positioning of existing gingiva: short term results in the treatment of shallow marginal tissue recession. <=SEP=> Although not a new procedure, coronal positioning of existing gingiva may be used to enhance esthetics and reduce sensitivity. Unfortunately when recession is minimal and the marginal tissue is healthy, many periodontists do not suggest treatment. This article outlines a simple surgical technique with the criteria for its use which results in a high degree of predictability and patient satisfaction.

Effective Dynamic Voltage Scaling Through CPU-Boundedness Detection <=SEP=> Dynamic voltage scaling (DVS) allows a program to execute at a non-peak CPU frequency in order to reduce CPU power, and hence, energy consumption; however, it is done at the cost of performance degradation. For a program whose execution time is bounded by peripherals’ performance rather than the CPU speed, applying DVS to the program will result in negligible performance penalty. Unfortunately, existing DVS-based power-management algorithms are conservative in the sense that they overly exaggerate the impact that the CPU speed has on the execution time, e.g., they assume that the execution time will double if the CPU speed is halved. Based on a new single-coefficient performance model, we propose a DVS algorithm that detects the CPU-boundedness of a program on the fly (via a regression method on the past MIPS rate) and then adjusts the CPU frequency accordingly. To illustrate its effectiveness, we compare our algorithm with other DVS algorithms on real systems via physical measurements.

The Relationships among Acceleration , Agility , Sprinting Ability , Speed Dribbling Ability and Vertical Jump Ability in 14-Year-Old Soccer Players <=SEP=> The aim of this study was to evaluate the relationships among acceleration, agility, sprinting ability, speed dribbling ability and vertical jump ability in 14-year-old soccer players. Twenty-five young soccer players (average age 13.52 ± 0.51 years; height 158.81 ± 5.76 cm; weight 48.92 ± 6.48 kg; training age 3.72 ± 0.64 years) performed a series of physical tests: Yo-Yo Intermittent Recovery Test Level 1 (YYIRT); zigzag agility with the ball (ZAWB); without the ball (ZAWOB); sprinting ability (10-m, 20-m and 30-m); speed dribbling ability (SDA), acceleration ability (FLST) and jumping ability (counter-movement jump (CMJ), squat jump (SJ) and drop jump (DJ). The results showed that 10-m sprint was correlated with 20-m (r = 0.682), 30-m sprint (r = 0.634) and also SDA, FLST and ZAWOB (r = 0.540, r = 0.421; r = 0.533 respectively). Similarly, 20-m sprint was correlated with 30-m sprint (r = 0.491) and ZAWOB (r = 0.631). 30-m sprint was negatively correlated with CMJ (r = -0.435), while strong and moderate correlated with FLST (r = 0.742), ZAWOB (r = 0.657). In addition, CMJ strong correlated with SJ and DJ (r = 0.779, r = 0.824 respectively). CMJ, SJ and DJ were negatively correlated with ZAWOB. Furthermore, SDA strong and moderate correlated with FLST (r = 0.875), ZAWB (r = 0.718) and ZAWOB (r = 0.645). Similarly, a correlation was also found among FLST, ZAWB and ZAWOB (r = 0.421, r = 0.614 respectively). Finally, ZAWOB was strong and moderate correlated with the performance of soccer players in different field tests. In conclusion, the findings of the present study indicated that agility without the ball associated with the all physical fitness components. In addition, agility performance affects acceleration, sprint and jumping performances in young soccer players. Therefore, soccer players should focus on agility exercises in order to improve their acceleration, sprinting and jumping performance.

Vehicle Applications of Controller Area Network <=SEP=> The Controller Area Network (CAN) is a serial bus communications protocol developed by Bosch in the early 1980s. It defines a standard for efficient and reliable communication between sensor, actuator, controller, and other nodes in real-time applications. CAN is the de facto standard in a large variety of networked embedded control systems. The early CAN development was mainly supported by the vehicle industry: CAN is found in a variety of passenger cars, trucks, boats, spacecraft, and other types of vehicles. The protocol is also widely used today in industrial automation and other areas of networked embedded control, with applications in diverse products such as production machinery, medical equipment, building automation, weaving machines, and wheelchairs. In the automotive industry, embedded control has grown from stand-alone systems to highly integrated and networked control systems [11, 7]. By networking electro-mechanical subsystems, it becomes possible to modularize functionalities and hardware, which facilitates reuse and adds capabilities. Fig. 1 shows an example of an electronic control unit (ECU) mounted on a diesel engine of a Scania truck. The ECU handles the control of engine, turbo, fan, etc. but also the CAN communication. Combining networks and mechatronic modules makes it possible to reduce both the cabling and the number

A Universal Intelligent System-on-Chip Based Sensor Interface <=SEP=> The need for real-time/reliable/low-maintenance distributed monitoring systems, e.g., wireless sensor networks, has been becoming more and more evident in many applications in the environmental, agro-alimentary, medical, and industrial fields. The growing interest in technologies related to sensors is an important indicator of these new needs. The design and the realization of complex and/or distributed monitoring systems is often difficult due to the multitude of different electronic interfaces presented by the sensors available on the market. To address these issues the authors propose the concept of a Universal Intelligent Sensor Interface (UISI), a new low-cost system based on a single commercial chip able to convert a generic transducer into an intelligent sensor with multiple standardized interfaces. The device presented offers a flexible analog and/or digital front-end, able to interface different transducer typologies (such as conditioned, unconditioned, resistive, current output, capacitive and digital transducers). The device also provides enhanced processing and storage capabilities, as well as a configurable multi-standard output interface (including plug-and-play interface based on IEEE 1451.3). In this work the general concept of UISI and the design of reconfigurable hardware are presented, together with experimental test results validating the proposed device.

Efficient Nonsmooth Nonconvex Optimization for Image Restoration and Segmentation <=SEP=> In this article, we introduce variational image restoration and segmentation models that incorporate the L1 data-fidelity measure and a nonsmooth, nonconvex regularizer. The L1 fidelity term allows us to restore or segment an image with low contrast or outliers, and the nonconvex regularizer enables homogeneous regions of the objective function (a restored image or an indicator function of a segmented region) to be efficiently smoothed while edges are well preserved. To handle the nonconvexity of the regularizer, a multistage convex relaxation method is adopted. This provides a better solution than the classical convex total variation regularizer, or than the standard L1 convex relaxation method. Furthermore, we design fast and efficient optimization algorithms that can handle the non-differentiability of both the fidelity and regularization terms. The proposed iterative algorithms asymptotically solve the original nonconvex problems. Our algorithms output a restored image or segmented regions in the image, as well as an edge indicator that characterizes the edges of the output, similar to Mumford–Shah-like regularizing functionals. Numerical examples demonstrate the promising results of the proposed restoration and segmentation models.

Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry <=SEP=> Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a credit rating indicator to help identify the financial status and operational competence of banks. A credit rating provides financial entities with an assessment of credit worthiness, investment risk, and default probability. Although numerous models have been proposed to solve credit rating problems, they have the following drawbacks: (1) lack of explanatory power; (2) reliance on the restrictive assumptions of statistical techniques; and (3) numerous variables, which result in multiple dimensions and complex data. To overcome these shortcomings, this work applies two hybrid models that solve the practical problems in credit rating classification. For model verification, this work uses an experimental dataset collected from the Bankscope database for the period 1998–2007. Experimental results demonstrate that the proposed hybrid models for credit rating classification outperform the listing models in this work. A set of decision rules for classifying credit ratings is extracted. Finally, study findings and managerial implications are provided for academics and practitioners. 2012 Elsevier B.V. All rights reserved.

A 5.4-Gbit/s Adaptive Continuous-Time Linear Equalizer Using Asynchronous Undersampling Histograms <=SEP=> We demonstrate a new type of adaptive continuous-time linear equalizer (CTLE) based on asynchronous undersampling histograms. Our CTLE automatically selects the optimal equalizing filter coefficient among several predetermined values by searching for the coefficient that produces the largest peak value in histograms obtained with asynchronous undersampling. This scheme is simple and robust and does not require clock synchronization for its operation. A prototype chip realized in 0.13-μm CMOS technology successfully achieves equalization for 5.4-Gbit/s 231 - 1 pseudorandom bit sequence data through 40-, 80-, and 120-cm PCB traces and 3-m DisplayPort cable. In addition, we present the results of statistical analysis with which we verify the reliability of our scheme for various sample sizes. The results of this analysis are confirmed with experimental data.

Social media and internet-based data in global systems for public health surveillance: a systematic review. <=SEP=> CONTEXT
The exchange of health information on the Internet has been heralded as an opportunity to improve public health surveillance. In a field that has traditionally relied on an established system of mandatory and voluntary reporting of known infectious diseases by doctors and laboratories to governmental agencies, innovations in social media and so-called user-generated information could lead to faster recognition of cases of infectious disease. More direct access to such data could enable surveillance epidemiologists to detect potential public health threats such as rare, new diseases or early-level warnings for epidemics. But how useful are data from social media and the Internet, and what is the potential to enhance surveillance? The challenges of using these emerging surveillance systems for infectious disease epidemiology, including the specific resources needed, technical requirements, and acceptability to public health practitioners and policymakers, have wide-reaching implications for public health surveillance in the 21st century.


METHODS
This article divides public health surveillance into indicator-based surveillance and event-based surveillance and provides an overview of each. We did an exhaustive review of published articles indexed in the databases PubMed, Scopus, and Scirus between 1990 and 2011 covering contemporary event-based systems for infectious disease surveillance.


FINDINGS
Our literature review uncovered no event-based surveillance systems currently used in national surveillance programs. While much has been done to develop event-based surveillance, the existing systems have limitations. Accordingly, there is a need for further development of automated technologies that monitor health-related information on the Internet, especially to handle large amounts of data and to prevent information overload. The dissemination to health authorities of new information about health events is not always efficient and could be improved. No comprehensive evaluations show whether event-based surveillance systems have been integrated into actual epidemiological work during real-time health events.


CONCLUSIONS
The acceptability of data from the Internet and social media as a regular part of public health surveillance programs varies and is related to a circular challenge: the willingness to integrate is rooted in a lack of effectiveness studies, yet such effectiveness can be proved only through a structured evaluation of integrated systems. Issues related to changing technical and social paradigms in both individual perceptions of and interactions with personal health data, as well as social media and other data from the Internet, must be further addressed before such information can be integrated into official surveillance systems.

Functional cultures and health benefits <=SEP=> A number of health benefits have been claimed for probiotic bacteria such as Lactobacillus acidophilus, Bifidobacterium spp., and L. casei. These benefits include antimutagenic effects, anticarcinogenic properties, improvement in lactose metabolism, reduction in serum cholesterol, and immune system stimulation. Because of the potential health benefits, these organisms are increasingly being incorporated into dairy foods, particularly yoghurt. In addition to yoghurt, fermented functional foods with health benefits based on bioactive peptides released by probiotic organisms, including Evolus and Calpis, have been introduced in the market. To maximize effectiveness of bifidus products, prebiotics are used in probiotic foods. Synbiotics are products that contain both prebiotics and probiotics. r 2007 Elsevier Ltd. All rights reserved.

CS Teacher Experiences with Educational Technology, Problem-BasedLearning, and a CS Principles Curriculum <=SEP=> Little is known about how K-12 Computer Science (CS) teachers use technology and problem-based learning (PBL) to teach CS content in the context of CS Principles curricula. Significantly, little qualitative research has been conducted in these areas in computer science education, so we lack an in-depth understanding of the complicated realities of CS teachers' experiences. This paper describes the practices and experiences of six teachers' use of technology that was implemented to support PBL in the context of a dual enrollment CS Principles course.
 Results from an early offering of this course suggest that (1) while CS teachers used technology, they did not appear to use it to support student inquiry, (2) local adaptations to the curriculum were largely teacher-centric, and (3) the simultaneous adoption of new instructional practices, technologies, and curricula was overwhelming to teachers. This paper then describes how these results were used to modify the curriculum and professional development, leading to increased teacher satisfaction and student success in the course.

Good Question! Statistical Ranking for Question Generation <=SEP=> We address the challenge of automatically generating questions from reading materials for educational practice and assessment. Our approach is to overgenerate questions, then rank them. We use manually written rules to perform a sequence of general purpose syntactic transformations (e.g., subject-auxiliary inversion) to turn declarative sentences into questions. These questions are then ranked by a logistic regression model trained on a small, tailored dataset consisting of labeled output from our system. Experimental results show that ranking nearly doubles the percentage of questions rated as acceptable by annotators, from 27% of all questions to 52% of the top ranked 20% of questions.

Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks <=SEP=> In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.

Maximizing Throughput of Overprovisioned HPC Data Centers Under a Strict Power Budget <=SEP=> Building future generation supercomputers while constraining their power consumption is one of the biggest challenges faced by the HPC community. For example, US Department of Energy has set a goal of 20 MW for an exascale (1018 flops) supercomputer. To realize this goal, a lot of research is being done to revolutionize hardware design to build power efficient computers and network interconnects. In this work, we propose a software-based online resource management system that leverages hardware facilitated capability to constrain the power consumption of each node in order to optimally allocate power and nodes to a job. Our scheme uses this hardware capability in conjunction with an adaptive runtime system that can dynamically change the resource configuration of a running job allowing our resource manager to re-optimize allocation decisions to running jobs as new jobs arrive, or a running job terminates.
 We also propose a performance modeling scheme that estimates the essential power characteristics of a job at any scale. The proposed online resource manager uses these performance characteristics for making scheduling and resource allocation decisions that maximize the job throughput of the supercomputer under a given power budget. We demonstrate the benefits of our approach by using a mix of jobs with different power-response characteristics. We show that with a power budget of 4.75 MW, we can obtain up to 5.2X improvement in job throughput when compared with the SLURM scheduling policy that is power-unaware. We corroborate our results with real experiments on a relatively small scale cluster, in which we obtain a 1.7X improvement.

The Impact of Flow on Online Consumer Behavior <=SEP=> Previous research has acknowledged flow as a useful construct for explaining online consumer behavior. However, there is dearth of knowledge about what dimensions of flow and how they actually influence online consumer behavior as flow is difficult to conceptualize and measure. This research examines flow and its effects on online consumer behavior in a unified model which draws upon theory of planned behavior (TPB). The four important dimensions of flow (concentration, enjoyment, time distortion, telepresence) are explored in terms of their antecedent effects on online consumer behavior. Results of this empirical study show that flow influences online consumer behavior through several important latent constructs. Findings of this research not only extend the existing knowledge of flow and its antecedent effects on online consumer behavior but also provide new insights into how flow can be conceptualized and studied in the e-commerce setting.

DuDe: The Duplicate Detection Toolkit <=SEP=> 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 <=SEP=> 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 <=SEP=> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 CHAPTER

Static test case prioritization using topic models <=SEP=> 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 <=SEP=> 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 <=SEP=> 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 <=SEP=> 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.