title: Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt

abstract: Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.


Automatic template creation for information extraction


title: MaxMax: a graph-based soft clustering algorithm applied to word sense induction

abstract: This paper introduces a linear time graph-based soft clustering algorithm. The algorithm applies a simple idea: given a graph, vertex pairs are assigned to the same cluster if either vertex has maximal affinity to the other. Clusters of varying size, shape, and density are found automatically making the algorithm suited to tasks such Word Sense Induction (WSI), where the number of classes is unknown and where class distributions may be skewed. The algorithm is applied to two WSI tasks, obtaining results comparable with those of systems adopting existing, state-of-the-art methods.


title: Dengue Epidemics Prediction: A Survey of the State-of-the-Art Based on Data Science Processes

abstract: Dengue infection is a mosquitoborne disease caused by dengue viruses, which are carried by several species of mosquito of the genus Aedes , principally Ae. aegypti . Dengue outbreaks are endemic in tropical and sub-tropical regions of the world, mainly in urban and sub-urban areas. The outbreak is one of the top 10 diseases causing the most deaths worldwide. According to the World Health Organization, dengue infection has increased 30-fold globally over the past five decades. About 50–100 million new infections occur annually in more than 80 countries. Many researchers are working on measures to prevent and control the spread. One avenue of research is collaboration between computer science and the epidemiology researchers in developing methods of predicting potential outbreaks of dengue infection. An important research objective is to develop models that enable, or enhance, forecasting of outbreaks of dengue, giving medical professionals the opportunity to develop plans for handling the outbreak, well in advance. Researchers have been gathering and analyzing data to better identify the relational factors driving the spread of the disease, as well as the development of a variety of methods of predictive modeling using statistical and mathematical analysis and machine learning. In this substantial review of the literature on the state of the art of research over the past decades, we identified six main issues to be explored and analyzed: 1) the available data sources; 2) data preparation techniques; 3) data representations; 4) forecasting models and methods; 5) dengue forecasting models evaluation approaches; and 6) future challenges and possibilities in forecasting modeling of dengue outbreaks. Our comprehensive exploration of the issues provides a valuable information foundation for new researchers in this important area of public health research and epidemiology.


title: Semi-hierarchical naïve Bayes classifier

abstract: The classification of high dimensional data is an arduous task especially with the emergence of high quality data acquisition techniques. This problem is accentuated when the whole set of features is needed to learn a classifier such as the case of genomic data. The Bayesian approach is suitable for these applications because it represents graphically and statistically the dependencies between the features. Unfortunately, learning a Bayesian classifier using a high number of features does not ensure a tradeoff between the dimensions' reduction, the semantic of the model and the predictive performance. We propose a new semi-hierarchical naïve Bayes that uses the latent variables for abstracting the features of a given dataset in order to reduce the dimensionality. These variables are suitable for finding graphically and semantically analyzable models. We combined them with the observed variables in a tree-augmented naïve Bayes structure in order to improve the prediction accuracy. An excessive experimental study showed that our method is suitable for high dimensional data and overcomes the existing methods.


Biomedical Semantic Indexing using Dense Word Vectors in BioASQ


title: Multiple-Kernel Local-Patch Descriptor

abstract: We propose a multiple-kernel local-patch descriptor based on efficient match kernels of patch gradients. It combines two parametrizations of gradient position and direction, each parametrization provides robustness to a different type of patch miss-registration: polar parametrization for noise in the patch dominant orientation detection, Cartesian for imprecise location of the feature point. Even though handcrafted, the proposed method consistently outperforms the state-of-the-art methods on two local patch benchmarks.


title: Ranking experts using author-document-topic graphs

abstract: Expert search or recommendation involves the retrieval of people (experts) in response to a query and on occasion, a given set of constraints. In this paper, we address expert recommendation in academic domains that are different from web and intranet environments studied in TREC. We propose and study graph-based models for expertise retrieval with the objective of enabling search using either a topic (e.g. "Information Extraction") or a name (e.g. "Bruce Croft"). We show that graph-based ranking schemes despite being "generic" perform on par with expert ranking models specific to topic-based and name-based querying.


title: Power Control and Management in a Hybrid AC/DC Microgrid

abstract: Hybrid AC/DC microgrids have been planned for the better interconnection of different distributed generation systems (DG) to the power grid, and exploiting the prominent features of both ac and dc microgrids. Connecting these microgrids requires an interlinking AC/DC converter (IC) with a proper power management and control strategy. During the islanding operation of the hybrid AC/DC microgrid, the IC is intended to take the role of supplier to one microgrid and at the same time acts as a load to the other microgrid and the power management system should be able to share the power demand between the existing AC and dc sources in both microgrids. This paper considers the power flow control and management issues amongst multiple sources dispersed throughout both ac and dc microgrids. The paper proposes a decentralized power sharing method in order to eliminate the need for any communication between DGs or microgrids. This hybrid microgrid architecture allows different ac or dc loads and sources to be flexibly located in order to decrease the required power conversions stages and hence the system cost and efficiency. The performance of the proposed power control strategy is validated for different operating conditions, using simulation studies in the PSCAD/EMTDC software environment.


Drinking in College


Robust Localization in 3D Prior Maps for Autonomous Driving.


title: Dealing with multiple classes in online class imbalance learning

abstract: Online class imbalance learning deals with data streams having very skewed class distributions in a timely fashion. Although a few methods have been proposed to handle such problems, most of them focus on two-class cases. Multi-class imbalance imposes additional challenges in learning. This paper studies the combined challenges posed by multiclass imbalance and online learning, and aims at a more effective and adaptive solution. First, we introduce two resampling-based ensemble methods, called MOOB and MUOB, which can process multi-class data directly and strictly online with an adaptive sampling rate. Then, we look into the impact of multi-minority and multi-majority cases on MOOB and MUOB in comparison to other methods under stationary and dynamic scenarios. Both multi-minority and multi-majority make a negative impact. MOOB shows the best and most stable G-mean in most stationary and dynamic cases.


title: Image quality assessment: from error visibility to structural similarity

abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.


title: The role of arginine methylation in the DNA damage response

abstract: Abstract Post-translational modifications are well-known modulators of DNA damage signaling and epigenetic gene expression. Protein arginine methylation is a covalent modification that results in the addition of methyl groups to the nitrogen atoms of the arginine side chains and is catalyzed by a family of protein arginine methyltransferases (PRMTs). In the past, arginine methylation was mainly observed on abundant proteins such as RNA-binding proteins and histones, but recent advances have revealed a plethora of arginine methylated proteins implicated in a variety of cellular processes including RNA metabolism, epigenetic regulation and DNA repair pathways. Herein, we discuss these recent advances, focusing on the role of PRMTs in DNA damage signaling and its importance for maintaining genomic stability.


title: Reducing False Positives of User-to-Entity First-Access Alerts for User Behavior Analytics

abstract: Detecting security threats from compromised account or malicious insider by leveraging enterprise traffic logs is the goal of user behavior-based analytics. For its ease of interpretation, a common analytic indicator used in the industry for user behavior analytics is whether a user accesses a network entity, such as a machine or process, for the first time. While this popular indicator does correlate well with the threat activities, it has the potential of generating volumes of false positives. This creates a problem for an analytic system of which the first-time access alerting capability is a part. We believe that the false positive rate from the indicator can be reduced by learning from users' historical entity access patterns and user context information. If the first-time access is expected, then its corresponding alert is suppressed. In this paper, we propose a user-to-entity prediction score which uses a recommender system for learning user data. In particular, we use factorization machines, along with necessary data normalization steps, to make predictions on real-world enterprise logs. We demonstrate this novel method is capable of reducing false positives of users' first-time entity access alerts in user behavior analytics applications.


title: Glycoprotein hormones and their common A-subunit stimulate prolactin production by explant cultures of human leiomyoma and myometrium

abstract: Abstract OBJECTIVE: The purpose of this study was to examine the effect of the gonadotropins luteinizing hormone, follicle-stimulating hormone, and human chorionic gonadotropin on prolactin production by human leiomyomas and myometrium. A secondary goal was to explore the effect of thyroid-stimulating hormone and the A-subunit common to all these glycoproteins in the same system. STUDY DESIGN: Explant cultures were established, and harvested medium was assayed for total protein and prolactin. Dose-response studies were performed with follicle-stimulating hormone, luteinizing hormone, and human chorionic gonadotropin. A second set of studies was conducted with a single dose of human chorionic gonadotropin, thyroid-stimulating hormone, and A-subunit. RESULTS: Gonadotropins, thyroid-stimulating hormone, and A-subunit all stimulated prolactin production in both leiomyoma and myometrium. Prolactin production was significantly higher in leiomyoma than in myometrium. A positive effect of time, dose, and gonadotropin treatment on prolactin production was seen in each tissue. There was no treatment effect on total protein secretion. CONCLUSIONS: All four glycoprotein hormones and their common A-subunit stimulate prolactin production. This appears to be a specific effect. (AM J OBSTET GYNECOL 1994;170:677-83.)


title: Investigating success of an e-government initiative: Validation of an integrated IS success model

abstract: The purpose of this paper is to examine the success (by measuring intention to use and user satisfaction) of the online public grievance redressal system (OPGRS) from the perspective of the citizens of India. The success of this e-government system is examined using an integrated IS success model. The model developed includes the constructs such as system quality, information quality, service quality, perceived usefulness, perceived ease of use, perceived satisfaction, perceived risk, and behavioral intention. The proposed integrated research model of IS was validated using the response taken from 419 citizens from different cities of India. The empirical outcomes provided the positive significant connections between all 12 hypothesised relationships between eight constructs. The empirical evidence and discussion presented in the study can help the government to improve upon and fully utilise the potential of the OPGRS as a useful tool toward a transparent and corruption free country.


title: Interaction, usability and aesthetics: what influences users' preferences?

abstract: In this paper we describe an evaluation of two websites with the same content but different interface styles (traditional menu-based and interactive metaphors). A formative usability evaluation was carried out with heuristic assessment of aesthetics, and questionnaire assessment of aesthetics, content, information quality, usability and post-test memory. The study revealed that perception of information quality is affected by the interaction style implemented in the interface, in a manner resembling the halo effect in person perception. Implications for website design and evaluation are discussed.


title: The adjustable nanosecond pulse generation based on highl-voltage MOSFET

abstract: A computer-controlled adjustable nanosecond pulse generator based on high-voltage MOSFET is designed in this paper, which owns stable performance and miniaturization profile of 32×30×7 cm3. The experiment results show that the pulser can generate electrical pulse with Gaussian rising time of 20 nanosecond, section-adjustable index falling time of 40–200 nanosecond, continuously adjustable repitition frequency of 0–5 kHz, quasi-continuously adjustable amplitude of 0–1 kV at 50 Ω load. And the pulser could meet the requiremen.


title: Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder

abstract: Surgical tool segmentation is used for detection, tracking and pose estimation of the tools in the vicinity of surgical scenes. It is considered as an essential task in surgical phase recognition and flow identification. Surgical flow identification is an unresolved task in the domain of context-aware surgical systems, which is used extensively on computer assisted intervention (CAI). CAI is used for staff assignment, automated guidance during intervention, surgical alert systems, automatic indexing of surgical video databases and optimisation of the real-time scheduling of operating room. Semantic segmentation is used for accurate delineation of surgical tools from the background. In semantic segmentation, each label is assigned to a class as a tool or a background. In this presented work, we applied a hybrid method utilising both recurrent and convolutional networks to achieve higher accuracy of surgical tools segmentation. The proposed method is trained and tested using a public dataset MICCAI 2016 Endoscopic Vision Challenge Robotic Instruments dataset "EndoVis". We achieved better performance using the proposed method compared to state-of-the-art methods on the same dataset for benchmarking. We achieved a balanced accuracy of 93.3% and Jaccard index of 82.7%.


title: Transforming Auto-encoders

abstract: The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a whole vector of outputs including an explicit representation of the pose of the feature. We show how neural networks can be used to learn features that output a whole vector of instantiation parameters and we argue that this is a much more promising way of dealing with variations in position, orientation, scale and lighting than the methods currently employed in the neural networks community. It is also more promising than the hand-engineered features currently used in computer vision because it provides an efficient way of adapting the features to the domain.


title: Single-control testability of RTL data paths for BIST

abstract: This paper presents a new BIST method for RTL data paths based on single-control testability a new concept of testability. The BIST method adopts hierarchical test. Test pattern generators are placed only on primary inputs and test patterns are propagated to and fed into each module. Test responses are similarly propagated to response analyzers placed only on primary outputs. For the propagation of test patterns and test responses, paths existing in the data path are utilized. The DFT method for the single-control testability is also proposed. The advantages of the proposed method are high fault coverage (for single stuck-at faults), low hardware overhead and capability of at-speed testing. Moreover test patterns generated by test pattern generators can be fed into each module at consecutive system clocks, and thus, the BIST can also detect some faults of other fault models (e.g., transition faults and delay faults) that require consecutive application of test patterns at the speed of the system clock.


title: Public Opinion Polling with Twitter

abstract: Solicited public opinion surveys reach a limited subpopulation of willing participants and are expensive to conduct, leading to poor time resolution and a restricted pool of expert-chosen survey topics. In this study, we demonstrate that unsolicited public opinion polling through sentiment analysis applied to Twitter correlates well with a range of traditional measures, and has predictive power for issues of global importance. We also examine Twitter's potential to canvas topics seldom surveyed, including ideas, personal feelings, and perceptions of commercial enterprises. Two of our major observations are that appropriately filtered Twitter sentiment (1) predicts President Obama's job approval three months in advance, and (2) correlates well with surveyed consumer sentiment. To make possible a full examination of our work and to enable others' research, we make public over 10,000 data sets, each a seven-year series of daily word counts for tweets containing a frequently used search term.


title: Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

abstract: A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention’s context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).1


title: The Technology Acceptance Model: Past, Present, and Future

abstract: While the technology acceptance model (TAM), introduced in 1986, continues to be the most widely applied theoretical model in the IS field, few previous efforts examined its accomplishments and limitations. This study traces TAM’s history, investigates its findings, and cautiously predicts its future trajectory. One hundred and one articles published by leading IS journals and conferences in the past eighteen years are examined and summarized. An openended survey of thirty-two leading IS researchers assisted in critically examining TAM and specifying future directions.


title: Grand challenges in altmetrics: heterogeneity, data quality and dependencies

abstract: With increasing uptake among researchers, social media are finding their way into scholarly communication and, under the umbrella term altmetrics, are starting to be utilized in research evaluation. Fueled by technological possibilities and an increasing demand to demonstrate impact beyond the scientific community, altmetrics have received great attention as potential democratizers of the scientific reward system and indicators of societal impact. This paper focuses on the current challenges for altmetrics. Heterogeneity, data quality and particular dependencies are identified as the three major issues and discussed in detail with an emphasis on past developments in bibliometrics. The heterogeneity of altmetrics reflects the diversity of the acts and online events, most of which take place on social media platforms. This heterogeneity has made it difficult to establish a common definition or conceptual framework. Data quality issues become apparent in the lack of accuracy, consistency and replicability of various altmetrics, which is largely affected by the dynamic nature of social media events. Furthermore altmetrics are shaped by technical possibilities and are particularly dependent on the availability of APIs and DOIs, strongly dependent on data providers and aggregators, and potentially influenced by the technical affordances of underlying platforms.


title: Kirigami robot: Making paper robot using desktop cutting plotter and inkjet printer

abstract: Much attention has recently been given to a printing method because they are easily designable, have a low cost, and can be mass produced. Numerous electronic devices are fabricated using printing methods because of these advantages. In paper mechatronics, attempts have been made to fabricate robots by printing on paper substrates. The robots are given structures through self-folding and functions using printed actuators. We developed a new system and device to fabricate more sophisticated printed robots. First, we successfully fabricated complex self-folding structures by applying an automatic cutting. Second, a rapidly created and low-voltage electrothermal actuator was developed using an inkjet printed circuit. Finally, a printed robot was fabricated by combining two techniques and two types of paper; a structure design paper and a circuit design paper. Gripper and conveyor robots were fabricated, and their functions were verified. These works demonstrate the possibility of paper mechatronics for rapid and low-cost prototyping as well as of printed robots.


Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study


title: Why is test-restudy practice beneficial for memory? An evaluation of the mediator shift hypothesis.

abstract: Although the memorial benefits of testing are well established empirically, the mechanisms underlying this benefit are not well understood. The authors evaluated the mediator shift hypothesis, which states that test–restudy practice is beneficial for memory because retrieval failures during practice allow individuals to evaluate the effectiveness of mediators and to shift from less effective to more effective mediators. Across a series of experiments, participants used a keyword encoding strategy to learn word pairs with test–restudy practice or restudy only. Robust testing effects were obtained in all experiments, and results supported predictions of the mediator shift hypothesis. First, a greater proportion of keyword shifts occurred during test–restudy practice versus restudy practice. Second, a greater proportion of keyword shifts occurred after retrieval failure trials versus retrieval success trials during test–restudy practice. Third, a greater proportion of keywords were recalled on a final keyword recall test after test–restudy versus restudy practice.


title: Wireless Interference Identification with Convolutional Neural Networks

abstract: The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference avoidance, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 μs and an acquisition bandwidth of 10 MHz. The CNN distinguishes between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95 % for signal-to-noise ratio of at least −5 dB.


title: Data-driven 3D Voxel Patterns for object category recognition

abstract: Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark [17] and the outdoor-scene dataset [41]. We improve state-of-the-art results on car detection and pose estimation with notable margins (6% in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.


title: Skill Acquisition With Text-Entry Interfaces : Particularly Older Users Benefit From Minimized Information-Processing Demands

abstract: Operating information technology challenges older users if it requires executive control, which generally declines with age. Especially for novel and occasional tasks, cognitive demands can be high. We demonstrate how interface design can reduce cognitive demands by studying skill acquisition with the destination entry interfaces of two customary route guidance systems. Young, middle-aged, and older adults performed manual destination entry either with a system operated with multiple buttons in a dialogue encompassing spelling and list selection, or with a system operated by a single rotary encoder, in which an intelligent speller constrained destination entry to a single line of action. Each participant performed 100 training trials. A retention test after at least 10 weeks encompassed 20 trials. The same task was performed faster, more accurately, and produced much less age-related performance differences especially at the beginning of training if interface design reduced demand for executive control, p...


title: Descent methods for elastic body simulation on the GPU

abstract: We show that many existing elastic body simulation approaches can be interpreted as descent methods, under a nonlinear optimization framework derived from implicit time integration. The key question is how to find an effective descent direction with a low computational cost. Based on this concept, we propose a new gradient descent method using Jacobi preconditioning and Chebyshev acceleration. The convergence rate of this method is comparable to that of L-BFGS or nonlinear conjugate gradient. But unlike other methods, it requires no dot product operation, making it suitable for GPU implementation. To further improve its convergence and performance, we develop a series of step length adjustment, initialization, and invertible model conversion techniques, all of which are compatible with GPU acceleration. Our experiment shows that the resulting simulator is simple, fast, scalable, memory-efficient, and robust against very large time steps and deformations. It can correctly simulate the deformation behaviors of many elastic materials, as long as their energy functions are second-order differentiable and their Hessian matrices can be quickly evaluated. For additional speedups, the method can also serve as a complement to other techniques, such as multi-grid.


title: Iterative user-interface design

abstract: A method for developing user interfaces by refining them iteratively over several versions is presented. Each iteration is subjected to user testing or other usability-evaluation methods designed to uncover usability problems. This method not only eliminates problems of this nature, but also allows designers to take advantage of any insights into user needs that emerge from the tests. The author describes four case studies where the median improvement in overall usability from the first to the last iteration was 165%, and the median improvement per iteration was 38%. >


title: A probabilistic graphical model approach for human activity recognition using skeleton data

abstract: In this paper we introduce a general probabilistic graphical model for human everyday activity recognition. The proposed model is a discriminative graphical model with hidden variables for modeling body pose and sequential order of them. We use a unified framework for prediction task that is faster and more efficient than structured support vector machine and hidden conditional random fields. We have trained and tested the model on RGB-D videos and the result was comparable to the state of the art.


title: A Parametrization of the Auction Design Space

abstract: We present an extensive breakdown of the auction design space that captures the essential similarities and differences of many auction mechanisms in a format more descriptive and useful than simple taxonomies. This parametrization serves as an organizational framework in which to classify work within the field and uncovers parameter combinations corresponding to novel mechanisms. The structured characterization of auction rules can be exploited for the modular design of configurable auction servers. It also facilitates the communication of auction rules to software agents, enabling the automation of flexible market-based negotiation. Journal of Economic Literature Classification Numbers: C70, D44.


title: Screening for Internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale.

abstract: The aim of this study was to establish the optimal cut-off points of the Chen Internet Addiction Scale (CIAS), to screen for and diagnose Internet addiction among adolescents in the community by using the well-established diagnostic criteria of Internet addiction. This survey of 454 adolescents used screening (57/58) and diagnostic (63/64) cut-off points of the CIAS, a self-reported instrument, based on the results of systematic diagnostic interviews by psychiatrists. The area under the curve of the receiver operating characteristic curve revealed that CIAS has good diagnostic accuracy (89.6%). The screening cut-off point had high sensitivity (85.6%) and the diagnostic cut-off point had the highest diagnostic accuracy, classifying 87.6% of participants correctly. Accordingly, the screening point of the CIAS could provide a screening function in two-stage diagnosis, and the diagnostic point could serve as a diagnostic criterion in one-stage massive epidemiologic research.


title: ML-CNN: A novel deep learning based disease named entity recognition architecture

abstract: In this paper, we present a deep learning based disease named entity recognition architecture. First, the word-level embedding, character-level embedding and lexicon feature embedding are concatenated as input. Then multiple convolutional layers are stacked over the input to extract useful features automatically. Finally, multiple label strategy, which is firstly introduced, is applied to the output layer to capture the correlation information between neighboring labels. Experimental results on both NCBI and CDR corpora show that ML-CNN can achieve the state-of-the-art performance.


title: Why are lotteries valued less ? Multiple tests of a direct risk-aversion mechanism

abstract: Recent studies have identified the uncertainty effect (UE), whereby risky prospects (e.g., a binary lottery that offers either a $50 or $100 gift certificate) are valued less than their worst possible outcome (a $50 certificate). This effect has been proposed to result from ``direct risk-aversion'' which posits that the mere uncertainty of a lottery directly decreases its value. However, this effect may also be driven by the potential disappointment inherent in not receiving the better of the two outcomes (disappointment aversion), or the mere fact that the risky prospect is referred to as a ``lottery''. The results of two experiments do not support either of these two alternatives. Specifically, the results of Experiment 1 indicate that the UE is observed even when the values of the two lottery outcomes are similar, or even identical. Experiment 2 further replicates the UE in a context in which the word ``lottery'' is never used (a company promotional). These results are consistent with a direct risk-aversion mechanism (Gneezy et al., 2006; Simonsohn, 2009) and suggest that the UE obtains across a number of different contexts.


title: Gene Expression Profiles Characterize Inflammation Stages in the Acute Lung Injury in Mice

abstract: Acute Lung Injury (ALI) carries about 50 percent mortality and is frequently associated with an infection (sepsis). Life-support treatment with mechanical ventilation rescues many patients, although superimposed infection or multiple organ failure can result in death. The outcome of a patient developing sepsis depends on two factors: the infection and the pre-existing inflammation. In this study, we described each stage of the inflammation process using a transcriptional approach and an animal model. Female C57BL6/J mice received an intravenous oleic acid injection to induce an acute lung injury (ALI). Lung expression patterns were analyzed using a 9900 cDNA mouse microarray (MUSV29K). Our gene-expression analysis revealed marked changes in the immune and inflammatory response metabolic pathways, notably lipid metabolism and transcription. The early stage (1 hour–1.5 hours) is characterized by a pro-inflammatory immune response. Later (3 hours–4 hours), the immune cells migrate into inflamed tissues through interaction with vascular endothelial cells. Finally, at late stages of lung inflammation (18 hours–24 hours), metabolism is deeply disturbed. Highly expressed pro-inflammatory cytokines activate transcription of many genes and lipid metabolism. In this study, we described a global overview of critical events occurring during lung inflammation which is essential to understand infectious pathologies such as sepsis where inflammation and infection are intertwined. Based on these data, it becomes possible to isolate the impact of a pathogen at the transcriptional level from the global gene expression modifications resulting from the infection associated with the inflammation.


title: Pose-Driven Deep Convolutional Model for Person Re-identification

abstract: Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Our deep architecture explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts. To match the features from global human body and local body parts, a pose driven feature weighting sub-network is further designed to learn adaptive feature fusions. Extensive experimental analyses and results on three popular datasets demonstrate significant performance improvements of our model over all published state-of-the-art methods.


title: Joint Multi-View Face Alignment in the Wild

abstract: The de facto algorithm for facial landmark estimation involves running a face detector with a subsequent deformable model fitting on the bounding box. This encompasses two basic problems: i) the detection and deformable fitting steps are performed independently, while the detector might not provide best-suited initialisation for the fitting step, ii) the face appearance varies hugely across different poses, which makes the deformable face fitting very challenging and thus distinct models have to be used (\eg, one for profile and one for frontal faces). In this work, we propose the first, to the best of our knowledge, joint multi-view convolutional network to handle large pose variations across faces in-the-wild, and elegantly bridge face detection and facial landmark localisation tasks. Existing joint face detection and landmark localisation methods focus only on a very small set of landmarks. By contrast, our method can detect and align a large number of landmarks for semi-frontal (68 landmarks) and profile (39 landmarks) faces. We evaluate our model on a plethora of datasets including standard static image datasets such as IBUG, 300W, COFW, and the latest Menpo Benchmark for both semi-frontal and profile faces. Significant improvement over state-of-the-art methods on deformable face tracking is witnessed on 300VW benchmark. We also demonstrate state-of-the-art results for face detection on FDDB and MALF datasets.


title: Integrated Tin Monoxide P-Channel Thin-Film Transistors for Digital Circuit Applications

abstract: High-performance integrated tin monoxide bottom-gate staggered p-channel thin-film transistors (TFTs) are realized and reported. The active layer has been formed by thermal vacuum evaporation and rapid thermal annealing under a continuous nitrogen flow, resulting in field-effect mobilities up to 1.6 cm2/( $\textsf {V}\cdot \textsf {s}$ ) and contact resistances of around $148~\Omega \cdot \textsf {cm}$ . The integration of these TFTs in elementary building blocks for digital circuit applications such as inverters and ring-oscillators is demonstrated, resulting in stage delays down to 300 ns. Furthermore, a unipolar p-type-only 8-bit radio frequency identification code generator is realized, achieving 12.2 kb/s and comprising 294 tin monoxide TFTs.


title: A head-eye coordination model for animating gaze shifts of virtual characters

abstract: We present a parametric, computational model of head-eye coordination that can be used in the animation of directed gaze shifts for virtual characters. The model is based on research in human neurophysiology. It incorporates control parameters that allow for adapting gaze shifts to the characteristics of the environment, the gaze targets, and the idiosyncratic behavioral attributes of the virtual character. A user study confirms that the model communicates gaze targets as effectively as real humans do, while being preferred subjectively to state-of-the-art models.


title: Oblivious multi-party machine learning on trusted processors

abstract: Privacy-preserving multi-party machine learning allows multiple organizations to perform collaborative data analytics while guaranteeing the privacy of their individual datasets. Using trusted SGX-processors for this task yields high performance, but requires a careful selection, adaptation, and implementation of machine-learning algorithms to provably prevent the exploitation of any side channels induced by data-dependent access patterns. ::: ::: We propose data-oblivious machine learning algorithms for support vector machines, matrix factorization, neural networks, decision trees, and k-means clustering. We show that our efficient implementation based on Intel Skylake processors scales up to large, realistic datasets, with overheads several orders of magnitude lower than with previous approaches based on advanced cryptographic multi-party computation schemes.


title: Distributed Perception by Collaborative Robots

abstract: Recognition ability and, more broadly, machine learning techniques enable robots to perform complex tasks and allow them to function in diverse situations. In fact, robots can easily access an abundance of sensor data that are recorded in real time such as speech, image, and video. Since such data are time sensitive, processing them in real time is a necessity. Moreover, machine learning techniques are known to be computationally intensive and resource hungry. As a result, an individual resource-constrained robot, in terms of computation power and energy supply, is often unable to handle such heavy real-time computations alone. To overcome this obstacle, we propose a framework to harvest the aggregated computational power of several low-power robots for enabling efficient, dynamic, and real-time recognition. Our method adapts to the availability of computing devices at runtime and adjusts to the inherit dynamics of the network. Our framework can be applied to any distributed robot system. To demonstrate, with several Raspberry-Pi3-based robots (up to 12) each equipped with a camera, we implement a state-of-the-art action recognition model for videos and two recognition models for images. Our approach allows a group of multiple low-power robots to obtain a similar performance (in terms of the number of images or video frames processed per second) compared to a high-end embedded platform, Nvidia Tegra TX2.


title: Concentrated winding segmented rotor switched reluctance machine (SRM) using three-phase standard inverters

abstract: Two new topologies of three-phase segmented rotor switched reluctance machine (SRM) that enables the use of standard voltage source inverters (VSIs) for its operation are presented. The topologies has shorter end-turn length, axial length compared to SRM topologies that use three-phase inverters; compared to the conventional SRM (CSRM), these new topologies has the advantage of shorter flux paths that results in lower core losses. FEA based optimization have been performed for a given design specification. The new concentrated winding segmented SRMs demonstrate competitive performance with three-phase standard inverters compared to CSRM.


title: An Efficient Real-Time Traffic Sign Recognition System for Intelligent Vehicles with Smart Phones

abstract: In recent years, intelligent vehicles and smart phones have become more and more popular. The traffic sign recognition system is one kind of driving assistance system (DAS) which is used to automatically inform the driver the traffic sign information by a head up display (HUD), monitor, or speaker device. Hence this system is helpful to reduce driver distraction to increase driver safety. In this paper, an efficient real-time traffic sign recognition scheme is proposed for intelligent vehicles. The proposed scheme can integrate in-vehicle computing devices and smart phones to construe an in-vehicle traffic sign recognition system. This scheme contains four major stages: video frame capturing and transmitting, image preprocess, traffic sign detection, and character/icon extraction and recognition. The smart phone first captures videos and then extracts video frames in certain frame-rate. These extracted frames can be transmitted to an in-vehicle computing device by a wireless network (Bluetooth, WiMAX, Wi-Fi etc.) The preprocess employs some image processing to improve and transform the video frames to keep a stable quality for following detection and recognition schemes. At following stages, this paper presents some efficient and accurate traffic sign detection and recognition schemes which contain color selection, shape recognition, character/icon extraction and recognition. The experimental results shows that the proposed scheme only averagely spends 0.085 seconds for a video frame and its average accuracy can achieve about 98%. Hence, the proposed scheme can keep lower computing complexity, however it still can obtain a well accuracy.


title: Confusion Matrix-based Feature Selection

abstract: This paper introduces a new technique for feature selection and illustrates it on a real data set. Namely, the proposed approach creates subsets of attributes based on two criteria: (1) individual attributes have high discrimination (classification) power; and (2) the attributes in the subset are complementary that is, they misclassify different classes. The method uses information from a confusion matrix and evaluates one attribute at a time.


title: Virtual team collaboration: building shared meaning, resolving breakdowns and creating translucence

abstract: Managing international teams with geographically distributed participants is a complex task. The risk of communication breakdowns increases due to cultural and organizational differences grounded in the geographical distribution of the participants. Such breakdowns indicate general misunderstandings and a lack of shared meaning between participants. In this paper, we address the complexity of building shared meaning. We examine the communication breakdowns that occurred in two globally distributed virtual teams by providing an analytical distinction of the organizational context as the foundation for building shared meaning at three levels. Also we investigate communication breakdowns that can be attributed to differences in lifeworld structures, organizational structures, and work process structures within a virtual team. We find that all communication breakdowns are manifested and experienced by the participants at the work process level; however, resolving breakdowns may require critical reflection at other levels. Where previous research argues that face-to-face interaction is an important variable for virtual team performance, our empirical observations reveal that communication breakdowns related to a lack of shared meaning at the lifeworld level often becomes more salient when the participants are co-located than when geographically distributed. Last, we argue that creating translucence in communication structures is essential for building shared meanings at all three levels.


title: [Rupture of a dissecting aneurysm of the ascending aorta 10 years after therapy of coarctation. Apropos of a case of a 10-year-old girl].

abstract: This is a rare but life-threatening association. After surgical repair of a coarctation, delayed aneurysm of the ascending aorta may occur, with dissection in some instances. The case we report exemplifies this possibility. Surgical repair is difficult. Data from the medical literature is consistent with the speculation that the aneurysm is not an acquired lesion above the coarctation but a distinct disease of the aortic wall which runs an independent course but which may be promoted by the arterial hypertension. The course and prognosis are so serious that, in spite of its low prevalence, patients should be screened at regular intervals for this condition by ultrasonography of the ascending aorta.


title: Us vs. Them: Understanding Artificial Intelligence Technophobia over the Google DeepMind Challenge Match

abstract: Various forms of artificial intelligence (AI), such as Apple's Siri and Google Now, have permeated our everyday lives. However, the advent of such "human-like" technology has stirred both awe and a great deal of fear. Many consider it a woe to have an unimaginable future where human intelligence is exceeded by AI. This paper investigates how people perceive and understand AI with a case study of the Google DeepMind Challenge Match, a Go match between Lee Sedol and AlphaGo, in March 2016. This study explores the underlying and changing perspectives toward AI as users experienced this historic event. Interviews with 22 participants show that users tacitly refer to AlphaGo as an "other" as if it were comparable to a human, while dreading that it would come back to them as a potential existential threat. Our work illustrates a confrontational relationship between users and AI, and suggests the need to prepare for a new kind of user experience in this nascent socio- technological change. It calls for a collaborative research effort from the HCI community to study and accommodate users for a future where they interact with algorithms, not just interfaces.


title: FUSION OF LIDAR DATA AND AERIAL IMAGERY FOR A MORE COMPLETE SURFACE DESCRIPTION

abstract: Photogrammetry is the traditional method of surface reconstruction such as the generation of DTMs. Recently, LIDAR emerged as a new technology for rapidly capturing data on physical surfaces. The high accuracy and automation potential results in a quick delivery of DEMs/DTMs derived from the raw laser data. The two methods deliver complementary surface information. Thus it makes sense to combine data from the two sensors to arrive at a more robust and complete surface reconstruction. This paper describes two aspects of merging aerial imagery and LIDAR data. The establishment of a common reference frame is an absolute prerequisite. We solve this alignment problem by utilizing sensor-invariant features. Such features correspond to the same object space phenomena, for example to breaklines and surface patches. Matched sensor invariant features lend themselves to establishing a common reference frame. Feature-level fusion is performed with sensor specific features that are related to surface characteristics. We show the synergism between these features resulting in a richer and more abstract surface description.


title: A Comprehensive Ontology for Knowledge Representation in the Internet of Things

abstract: Semantic modeling for the Internet of Things has become fundamental to resolve the problem of interoperability given the distributed and heterogeneous nature of the "Things". Most of the current research has primarily focused on devices and resources modeling while paid less attention on access and utilisation of the information generated by the things. The idea that things are able to expose standard service interfaces coincides with the service oriented computing and more importantly, represents a scalable means for business services and applications that need context awareness and intelligence to access and consume the physical world information. We present the design of a comprehensive description ontology for knowledge representation in the domain of Internet of Things and discuss how it can be used to support tasks such as service discovery, testing and dynamic composition.


title: Tracking and Retexturing Cloth for RealTime Virtual Clothing Applications

abstract: In this paper, we describe a dynamic texture overlay method from monocular images for real-time visualization of garments in a virtual mirror environment. Similar to looking into a mirror when trying on clothes, we create the same impression but for virtually textured garments. The mirror is replaced by a large display that shows the mirrored image of a camera capturing e.g. the upper body part of a person. By estimating the elastic deformations of the cloth from a single camera in the 2D image plane and recovering the illumination of the textured surface of a shirt in real time, an arbitrary virtual texture can be realistically augmented onto the moving garment such that the person seems to wear the virtual clothing. The result is a combination of the real video and the new augmented model yielding a realistic impression of the virtual piece of cloth.


title: The Coonrad-Morrey Total Elbow Arthroplasty in Patients Who Have Rheumatoid Arthritis. A Ten to Fifteen-Year Follow-up Study*

abstract: Sixty-nine patients (seventy-eight elbows) who had rheumatoid arthritis were managed with a Coonrad-Morrey total elbow arthroplasty between 1981 and 1986. At the time of the present review, forty-one patients (forty-six elbows) were alive and had been followed for at least ten years after the procedure (Group 1). The remaining twenty-eight patients (thirty-two elbows) had died or had had a revision less than ten years after the procedure or had been followed for less than ten years (Group 2). The patients in Group 1 had a younger mean age at the time of the procedure, but all other preoperative parameters were similar for both groups. At the latest follow-up evaluation, 97 per cent of the elbows (forty-five of the forty-six in Group 1 and thirty-one of the thirty-two in Group 2) were not painful or were only mildly painful. The mean arc of flexion-extension was 28 to 131 degrees; this represents an increase of 13 degrees (15 degrees in Group 1 and 7 degrees in Group 2) compared with the preoperative value. The mean arc of pronation was 68 degrees, and the mean arc of supination was 62 degrees; this represents an increase of 21 degrees. The results for seventy-four of the seventy-eight elbows (all forty-six in Group 1 and twenty-eight of the thirty-two in Group 2) were considered satisfactory by the patients. One patient thought that the status of the elbow was unchanged compared with preoperatively, and three thought that it was worse. Seventy-six of the seventy-eight elbows had long-term radiographic evaluation; the two remaining elbows were excluded because a resection arthroplasty had been performed. There were two loose ulnar components; one was associated with an infection, and the other had been causing no symptoms at the time of the patient's death. In addition, both components were radiographically loose in an elbow that had had a revision without cement after a previous total elbow arthroplasty. Five bushings (7 per cent) were completely worn, and six (8 per cent) were partially worn. Complications occurred in eleven elbows (14 per cent) and were serious, necessitating reoperation, in ten (13 per cent). Delayed complications included three avulsions of the triceps, two deep infections, two ulnar fractures, and one fracture of an ulnar component. In addition, two elbows were revised because of aseptic loosening. No patient had persistent ulnar neuritis or serious skin complications. At the latest clinical follow-up evaluation, according to the Mayo elbow performance score, forty-three of the seventy-eight elbows had an excellent result; twenty-six, a good result; seven, a fair result; and two (both in Group 2), a poor result. The rate of survival of the prosthesis was 92.4 per cent, with 86 per cent good or excellent and 14 per cent fair or poor results.


Generating Multilingual Personalized Descriptions from OWL Ontologies on the Semantic Web: the NaturalOWL System ∗


title: Knowledge actionability: satisfying technical and business interestingness

abstract: Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.


title: Requirements Engineering Based on REBOK (Requirements Engineering Body Of Knowledge) and Its Practice

abstract: We introduce a whole view of current status of requirements engineering based on REBOK (Requirements Engineering Body Of Knowledge) and its practical use with case studies.


title: High gain high dense dielectric patch antenna with a holey superstrate for 5G applications

abstract: High gain low profile of a square dense dielectric patch antenna using a perforated superstrate is presented. The proposed patch antenna is excited by the aperture coupled feeding technique. A dielectric superstrate is used to enhance the gain of the proposed antenna. By drilling a set of identical holes in the superstrate layer, the relative permittivity of the dielectric superstrate is reduced, the antenna impedance matching is improved, and the side lobe level is decreased. The implemented antenna achieved a gain of 14.2 dBi at 28.5 GHz with radiation efficiency of 91 %. Furthermore, the proposed antenna has a good radiation pattern. Simulated results using CST software are presented and discussed. For some attractive advantages such as low profile, low cost, light weight, small size, and ease of implementation, the proposed antenna is a good candidate for millimeter-wave wireless communications.


title: Anatomy and Physiology of Perforator Flaps of the Upper Limb

abstract: Abstract Perforator flaps are an excellent reconstructive option for a functional upper limb reconstruction. This article explores the physiology and general principles of perforator flaps and their indications for use in reconstruction of the upper extremity. Workhorse perforator flaps of the upper extremity, such as the radial artery perforator, ulnar artery perforator, lateral arm perforator, posterior interosseous artery, first dorsal metacarpal artery perforator and perforator-based propeller flaps, are discussed in greater detail.


title: Realization of face image recognition system based on histogram

abstract: In this paper, the face image recognition in the application of MATLAB to preprocess the image, application the toolbox classic image processing of the image, through the example to apply MATLAB image processing function, of a given face image processing, and applied to the face recognition system. In this paper, in summing up the face recognition system of analysis of several commonly used image pre processing method based, using MATLAB to realize a set a variety of pre processing method in one of the generic face image pre processing simulation system, the system as the image pre processing module can be embedded in the face recognition system, and the use of image gray histogram Alignment to achieve the recognition of human face images.


Design and Implementation of an Android EPUB3.0 eBook Learning System


title: Organic acid water treatment reduced Salmonella horizontal transmission in broiler chickens.

abstract: The purpose of the present study was to determine whether an organic acid water treatment could reduce the spread of Salmonella (SAL) to naive birds when infected birds were part of the population. A total of one thousand eighty (1080), day-old Cobb X Cobb male chicks were allocated 60/pen to each of 18 pens by blocks and divided into three treatment groups: T1, unmedicated control; T2 , 0.04%; and T3, 0.08% of an organic acid blend (OAB; ACTIVATE WD). The OAB was added to water from 0-14 days and 42-49 days. Half of the birds in each pen were orally dosed with Naladixic acid resistant-S. heidelberg on Day 0 (tagged) and housed with the remaining uninfected birds (not tagged). Salmonella status of ceca and crops (Day 49 only) was evaluated by random selection of 5 tagged and 5 untagged birds/pen on days 0, 14 and 49. Dragswabs of pens were also obtained on the same days as was mortality, weight gain, feed consumption and feed conversion. On day 49, 22% of T1 untagged birds were SAL+ compared to only 7% of T2 and T3 untagged birds. Similar results were obtained with untagged birds for % SAL+ dragswabs and crops on Day 49. There was no effect of OAB on % SAL+ for tagged birds. Feed conversion was significantly improved by OAB at 42 days of age (1.80 vs 1.77 and 1.77) and at 49 days similar trends were apparent (1.95 vs 1.90 and 1.91) but not different. These results demonstrated that the OAB treatment significantly reduced horizontal spread of SAL to uninfected birds and reduced environmental SAL contamination.


title: A design model of emotional body expressions in non-humanoid robots

abstract: Robotic emotional expressions could benefit social communication between humans and robots, if the cues such expressions contain were to be intelligible to human observers. In this paper, we present a design framework for modelling emotionally expressive robotic movements. The framework combines approach-avoidance with Shape and Effort dimensions, derived from Laban, and makes use of anatomical body planes that are general to both humanoid and non-humanoid body forms. An experimental validation study is reported with 34 participants rating an implementation of five expressive behaviours on a non-humanoid robotic platform. The results demonstrate that such expressions can encode basic emotional information, in that the parameters of the proposed design model can convey the meaning of emotional dimensions of valence, arousal and dominance. The framework thus creates a basis for implementing a set of emotional expressions that are appropriately adapted to contexts of human-robot joint activity.


title: IRON: A fast interest point descriptor for robust NDT-map matching and its application to robot localization

abstract: This work introduces the IRON keypoint detector and the IRON descriptor which enable high-speed and high-accuracy alignment of 3D depth maps. Instead of using raw point values for storing 3D-scenes, all algorithms were designed to operate on Normal Distribution Transforms (NDT), since NDT-maps provide a highly memory-efficient representation of depth data. By taking into account surface curvature and object shape within NDT-maps, patches with strong surface variability can be recognized and described precisely. In this paper, the whole feature extraction process, as well as descriptor matching, outlier detection, and the final transform calculation between NDT-maps is elaborated. The presented technique is particularly insensitive to an initial offset between both maps, has a high robustness, and it achieves more than 75 NDT-map alignments per second (including complete memory allocation each time as well) in two large publicly available depth datasets while using only a single core of a modern Intel i7 CPU. Even though the main focus of this work was placed on the proposed IRON registration algorithm, two specific applications of this NDT-matching approach are outlined in the second part, namely robot pose tracking and NDT-one-shot localization within densely furnished domestic environments.


title: Using Semantic Preferences To Identify Verbal Participation In Role Switching Alternations

abstract: We propose a method for identifying diathesis alternations where a particular argument type is seen in slots which have different grammatical roles in the alternating forms. The method uses selectional preferences acquired as probability distributions over WordNet. Preferences for the target slots are compared using a measure of distributional similarity. The method is evaluated on the causative and conative alternations, but is generally applicable and does not require a priori knowledge specific to the alternation.


title: How youth get engaged: Grounded-theory research on motivational development in organized youth programs.

abstract: For youth to benefit from many of the developmental opportunities provided by organized programs, they need to not only attend but become psychologically engaged in program activities. This research was aimed at formulating empirically based grounded theory on the processes through which this engagement develops. Longitudinal interviews were conducted with 100 ethnically diverse youth (ages 14–21) in 10 urban and rural arts and leadership programs. Qualitative analysis focused on narrative accounts from the 44 youth who reported experiencing a positive turning point in their motivation or engagement. For 38 of these youth, this change process involved forming a personal connection. Similar to processes suggested by self-determination theory (Ryan & Deci, 2000), forming a personal connection involved youth’s progressive integration of personal goals with the goals of program activities. Youth reported developing a connection to 3 personal goals that linked the self with the activity: learning for the future, developing competence, and pursuing a purpose. The role of purpose for many youth suggests that motivational change can be driven by goals that transcend self-needs. These findings suggest that youth need not enter programs intrinsically engaged—motivation can be fostered—and that programs should be creative in helping youth explore ways to form authentic connections to program activities.


title: Retinal Imaging and Image Analysis

abstract: Many important eye diseases as well as systemic diseases manifest themselves in the retina. While a number of other anatomical structures contribute to the process of vision, this review focuses on retinal imaging and image analysis. Following a brief overview of the most prevalent causes of blindness in the industrialized world that includes age-related macular degeneration, diabetic retinopathy, and glaucoma, the review is devoted to retinal imaging and image analysis methods and their clinical implications. Methods for 2-D fundus imaging and techniques for 3-D optical coherence tomography (OCT) imaging are reviewed. Special attention is given to quantitative techniques for analysis of fundus photographs with a focus on clinically relevant assessment of retinal vasculature, identification of retinal lesions, assessment of optic nerve head (ONH) shape, building retinal atlases, and to automated methods for population screening for retinal diseases. A separate section is devoted to 3-D analysis of OCT images, describing methods for segmentation and analysis of retinal layers, retinal vasculature, and 2-D/3-D detection of symptomatic exudate-associated derangements, as well as to OCT-based analysis of ONH morphology and shape. Throughout the paper, aspects of image acquisition, image analysis, and clinical relevance are treated together considering their mutually interlinked relationships.


title: Term rewriting and all that

abstract: Preface 1. Motivating examples 2. Abstract reduction systems 3. Universal algebra 4. Equational problems 5. Termination 6. Confluence 7. Completion 8. Grobner bases and Buchberger's algorithm 9. Combination problems 10. Equational unification 11. Extensions Appendix 1. Ordered sets Appendix 2. A bluffer's guide to ML Bibliography Index.


title: Iron-based ethylene polymerization catalysts supported by bis(imino)pyridine ligands : Derivatization via deprotonation/alkylation at the ketimine methyl position

abstract: Bis(imino)pyridine ligands, L [where L = 2,6-(ArNCR 1 ) 2 C 5 H 3 N R 1 = Et, i Pr, CH 2 CH 2 Ph or CH(CH 2 Ph) 2 and Ar = 2,4,6-(Me) 3 C 6 H 2 (MES) or 2,6-( i Pr) 2 C 6 H 3 (DIPP)] have been prepared by deprotonation of the parent ketimine ligand (R 1 = Me) using lithium diisopropylamide (LDA), followed by alkylation with the appropriate alkylhalide. The corresponding iron dichloride complexes LFeCl 2 are highly active ethylene polymerisation catalysts upon treatment with methylaluminoxane (MAO), with activities in the range of 3000–18,000 g/mmol bar h. The molecular weights ( M n ) of the resultant polyethylenes lie in the range of 6500–24,000 with broad molecular weight distributions (16.5–38.0). The nature of the imine carbon substituent has a marked effect on the polymer molecular weight whereas the catalyst activity is largely unaffected by changes to this substituent.


title: Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation

abstract: Variational Level Set (LS) has been a widely used method in medical segmentation. However, it is limited when dealing with multi-instance objects in the real world. In addition, its segmentation results are quite sensitive to initial settings and highly depend on the number of iterations. To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS) 1 to employ Gated Recurrent Unit under the energy minimization of a variational LS functional. The curve deformation process in RLS is formed as a hidden state evolution procedure and updated by minimizing an energy functional composed of fitting forces and contour length. By sharing the convolutional features in a fully end-to-end trainable framework , we extend RLS to Contextual RLS (CRLS) to address semantic segmentation in the wild. The experimental results have shown that our proposed RLS improves both computational time and segmentation accuracy against the classic variational LS-based method whereas the fully end-to-end system CRLS achieves competitive performance compared to the state-of-the-art semantic segmentation approaches. 1 Source codes will be publicly available.


title: Rumors and factitious informational blends: The role of the web in speculative politics:

abstract: The World Wide Web has changed the dynamics of information transmission and agenda-setting. Facts mingle with half-truths and untruths to create factitious informational blends (FIBs) that drive speculative politics. We specify an information environment that mirrors and contributes to a polarized political system and develop a methodology that measures the interaction of the two. We do so by examining the evolution of two comparable claims during the 2004 presidential campaign in three streams of data: (1) web pages, (2) Google searches, and (3) media coverage. We find that the web is not sufficient alone for spreading misinformation, but it leads the agenda for traditional media. We find no evidence for equality of influence in network actors.


title: From infancy to adulthood: the development of numerical abilities

abstract: Developmental and school-related changes in basic number, counting, and arithmetic skills from infancy to old age are reviewed. Nearly all of the quantitative competencies that emerge during infancy and the pre-school years appear to reflect the operation of a biological primary, or inherent, cognitive system, and appear to be universal in their expression and development. In contrast, most of the basic quantitative competencies acquired in school and that are of importance in industrial societies do not have a direct inherent foundation. As a result, the development of these secondary quantitative abilities varies considerably with educational practices and can, and often does, vary from one country or generation to the next. Variability in the development of secondary quantitative abilities greatly complicates the study of the relation between pathological (e.g., dyscalculia due to stroke) and age-related processes and these abilities.


title: Consensus on transaction commit

abstract: The distributed transaction commit problem requires reaching agreement on whether a transaction is committed or aborted. The classic Two-Phase Commit protocol blocks if the coordinator fails. Fault-tolerant consensus algorithms also reach agreement, but do not block whenever any majority of the processes are working. Running a Paxos consensus algorithm on the commit/abort decision of each participant yields a transaction commit protocol that uses 2F +1 coordinators and makes progress if at least F +1 of them are working. In the fault-free case, this algorithm requires one extra message delay but has the same stable-storage write delay as Two-Phase Commit. The classic Two-Phase Commit algorithm is obtained as the special F = 0 case of the general Paxos Commit algorithm.


title: Effect of robot's active touch on people's motivation

abstract: This paper presents the effect of a robot's active touch for improving people's motivation. For services in the education and healthcare fields, a robot might be useful for improving the motivation of performing such repetitive and monotonous tasks as exercising or taking medicine. Previous research demonstrated with a robot the effect of user touch on improving its impressions, but they did not clarify whether a robot's touch, especially an active touch, has enough influence on people's motive. We implemented an active touch behavior and experimentally investigated its effect on motivation. In the experiment, a robot requested participants to perform a monotonous task with a robot's active touch, a passive touch, or no touch. The result of experiment showed that an active touch by a robot increased the number of working actions and the amount of working time for the task. This suggests that a robot's active touch can support people to improve their motivation. We believe that a robot's active touch behavior is useful for such robot's services as education and healthcare.


title: Prediction of rating from comments based on information retrieval and sentiment analysis

abstract: The number of users of an on-line shopping websites is continuously increasing. Such website often provides facility for the users to give comments and ratings to the products being sold on the websites. This information can be useful as the recommendation for other users in making their purchase decision. This paper investigates the problem of predicting rating based on users' comments. A classifier based on information retrieval model is proposed for the prediction. In addition, the effect of integrating sentiment analysis for the rating prediction is also investigated. Based on the results, an improvement in prediction performance can be expected with sentiment analysis where an increase of 54% is achieved.


title: A spectrogram-based audio fingerprinting system for content-based copy detection

abstract: This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the spectrogram matrix of the audio signal by using a threshold based on the average of the spectral values to prune this matrix. We transform each version of this pruned spectrogram matrix into a 2-D binary image. Multiple versions of these 2-D images suppress noise to a varying degree. This varying degree of noise suppression improves likelihood of one of the images matching a reference image. To speed up matching, we convert each image into an n-dimensional vector, and perform a nearest neighbor search based on this n-dimensional vector. We give results with two different feature parameters and their combination. We test this method on TRECVID 2010 content-based copy detection evaluation dataset, and we validate the performance on TRECVID 2009 dataset also. Experimental results show the effectiveness of these features even when the audio is distorted. We compare the proposed method to two state-of-the-art audio copy detection systems, namely NN-based and Shazam systems. Our method by far outperforms Shazam system for all audio transformations (or distortions) in terms of detection performance, number of missed queries and localization accuracy. Compared to NN-based system, our approach reduces minimal Normalized Detection Cost Rate (min NDCR) by 23 % and improves localization accuracy by 24 %.


title: Fully Distributed Scrum: The Secret Sauce for Hyperproductive Offshored Development Teams

abstract: Scrum was designed to achieve a hyperproductive state where productivity increases 5-10 times over industry averages and many collocated teams have achieved this effect. The question for this paper is whether distributed, offshore teams can consistently achieve the hyperproductive state. In particular, can a team establish a localized velocity and then maintain or increase that velocity when distributing teams across continents. Since 2006, Xebia started projects with half Dutch and half Indian team members. After establishing localized hyperproductivity, they move the Indian members of the team to India and show increasing velocity with fully distributed teams. After running XP engineering practices inside many distributed Scrum projects, Xebia has systematically productized a model very similar to the SirsiDynix model (J. Sutherland, 2006) for high performance, distributed, offshore teams with outstanding quality.


title: The use of skeletal anchorage in open bite treatment: a cephalometric evaluation.

abstract: The aims of the present study were to assess the effectiveness of skeletal anchorage for intrusion of maxillary posterior teeth, to correct open bite malocclusion, and to evaluate the usage of titanium miniplates for orthodontic anchorage. Anterior open bite is one of the most difficult malocclusions to treat orthodontically. Currently, surgical impaction of the maxillary posterior segment is considered to be the most effective treatment option in adult patients. Various studies have reported the use of implants as anchorage units at different sites of midfacial bones for orthodontic tooth movement. The zygomatic buttress area could be a valuable anchorage site to achieve intrusion of maxillary posterior teeth. Ten patients, 17 to 23 years old and characterized with an anterior open bite and excessive maxillary posterior growth, were included in this preliminary study. Titanium miniplates were fixed bilaterally to the zygomatic buttress area, and a force was applied bilaterally with nine mm Ni-Ti coil springs between the vertical extension of the miniplate and the first molar buccal tube. The results showed that, with the help of skeletal anchorage, maxillary posterior teeth were intruded effectively. As compared with an osteotomy, this minimally invasive surgical procedure eased treatment and reduced treatment time and did not require headgear wear or anterior box elastics for anterior open bite correction. In conclusion, the zygomatic area was found to be a useful anchorage site for intrusion of the molars in a short period of time.


title: A New Shape Signature for Fourier Descriptors

abstract: Shape-based image description is an important approach to content-based image retrieval (CBIR). A variety of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its advantages and disadvantages. Fourier descriptor (FD), a simple yet powerful technique, has attractive properties such as rotational, scale, and translational invariance. In this paper we investigate this technique and present a novel shape registration method for extracting Fourier descriptors. When evaluated against curvature scale space (CSS) and Zernike moments (ZM) in shape-based image retrieval, the proposed technique exhibits superior performance.


title: A hierarchical model of temporal perception

abstract: Temporal perception comprises subjective phenomena such as simultaneity, successiveness, temporal order, subjective present, temporal continuity and subjective duration. These elementary temporal experiences are hierarchically related to each other. Functional system states with a duration of 30 ms are implemented by neuronal oscillations and they provide a mechanism to define successiveness. These system states are also responsible for the identification of basic events. For a sequential representation of several events time tags are allocated, resulting in an ordinal representation of such events. A mechanism of temporal integration binds successive events into perceptual units of 3 s duration. Such temporal integration, which is automatic and presemantic, is also operative in movement control and other cognitive activities. Because of the omnipresence of this integration mechanism it is used for a pragmatic definition of the subjective present. Temporal continuity is the result of a semantic connection between successive integration intervals. Subjective duration is known to depend on mental load and attentional demand, high load resulting in long time estimates. In the hierarchical model proposed, system states of 30 ms and integration intervals of 3 s, together with a memory store, provide an explanatory neuro-cognitive machinery for differential subjective duration.


title: Towards a Trust Prediction Framework for Cloud Services Based on PSO-Driven Neural Network

abstract: Trustworthiness is an important indicator for service selection and recommendation in the cloud environment. However, predicting the trust rate of a cloud service based on its multifaceted quality of services (QoSs) is not an easy task due to the complicated and non-linear relations between service’s QoS values and the final trust rate of the service. According to the existing studies, the adoption of intelligent technique is a rational way to attack this problem. Neural network (NN) has been validated as an effective way to predict the trust rate of the service. However, the parameter setting of NN, which plays an important role in its prediction performance, has not been properly addressed yet. In the paper, particle swarm optimization (PSO) is introduced to enhance NN by optimizing its initial settings. In the proposed hybrid prediction algorithm named PSO-NN, PSO is used to search the appropriate parameters for NN so as to realize accurate trust prediction of cloud services. In order to investigate the effectiveness of PSO-NN, extensive experiments are performed based on public QoS data set, as well as in-depth comparison analysis. The results show that our proposed approach has better performance than basic classification methods in most cases, and significantly outperforms the basic NN in the terms of prediction precision. In addition, PSO-NN demonstrates better stability than the basic NN.


title: A novel approach for speech feature extraction by Cubic-Log compression in MFCC

abstract: Speech Pre-processing is measured as major step in development of feature vector extraction for an efficient Automatic Speech Recognition (ASR) system. A novel approach for speech feature extraction is by applying the Mel-frequency cepstral co-efficient (MFCC) algorithm using Cubic-Log compression instead of Logarithmic compression in MFCC. In proposed MFCC, the frequency axis is initially warped to the mel-scale which is roughly below 2 kHz and logarithmic above this point. Triangular filter are equally spaced in the mel-scale are applied on the warped spectrum. The result of the filters are compressed using Cubic-Log function and cepstral co-efficient are computed by applying DCT to obtain minimum MFCC feature vector for spoken words. These feature vectors are given as input to classification and Recognition phase. The system is trained and tested by generating MFCC feature vector for 600 isolated words, 256 connected words and 150 sentences in clear and noisy environment. Experiment results shows that with minimum MFCC feature vector is enough for speech recognition system to achieve high recognition rate and its performance is measured based on Mean Square Error (MSE) rate.


title: A Unified Test Framework for Continuous Integration Testing of SOA Solutions

abstract: The quality of Service Oriented Architecture (SOA) solutions is becoming more and more important along with the increasing adoption of SOA. Continuous Integration Testing (CIT) is an effective technology to discover bugs as early as possible. However, the diversity of programming models used in an SOA solution and the distribution nature of an SOA solution pose new challenges for CIT. Existing testing frameworks more focus on the integration testing of applications developed by a single programming model. In this paper, a unified test framework is proposed to overcome these limitations and enable the CIT of SOA solutions across the whole development lifecycle.This framework is designed following the Model Driven Architecture (MDA). The information of an executable test case is separated into two layers: the behavior layer and the configuration layer. The behavior layer represents the test logic of a test case and is platform independent. The configuration layer contains the platform specific information and is configurable for different programming models. An extensible and pluggable test execution engine is specially designed to execute the integration test cases. A global test case identifier instrumentation approach is used to merge the distributed test case execution traces captured by ITCAM – an IBM integrated management tool. A verification approach supporting Boolean expression and back-end service interaction verification is proposed to verify the test execution result. Initial experiments have shown the effectiveness of this unified test framework.


title: Supporting requirements traceability through refactoring

abstract: Modern traceability tools employ information retrieval (IR) methods to generate candidate traceability links. These methods track textual signs embedded in the system to establish relationships between software artifacts. However, as software systems evolve, new and inconsistent terminology finds its way into the system's taxonomy, thus corrupting its lexical structure and distorting its traceability tracks. In this paper, we argue that the distorted lexical tracks of the system can be systematically re-established through refactoring, a set of behavior-preserving transformations for keeping the system quality under control during evolution. To test this novel hypothesis, we investigate the effect of integrating various types of refactoring on the performance of requirements-to-code automated tracing methods. In particular, we identify the problems of missing, misplaced, and duplicated signs in software artifacts, and then examine to what extent refactorings that restore, move, and remove textual information can overcome these problems respectively. We conduct our experimental analysis using three datasets from different application domains. Results show that restoring textual information in the system has a positive impact on tracing. In contrast, refactorings that remove redundant information impact tracing negatively. Refactorings that move information among the system modules are found to have no significant effect. Our findings address several issues related to code and requirements evolution, as well as refactoring as a mechanism to enhance the practicality of automated tracing tools.


title: Anatomical Landmark Detection in Medical Applications Driven by Synthetic Data

abstract: An important initial step in many medical image analysis applications is the accurate detection of anatomical landmarks. Most successful methods for this task rely on data-driven machine learning algorithms. However, modern machine learning techniques, e.g. convolutional neural networks, need a large corpus of training data, which is often an unrealistic setting for medical datasets. In this work, we investigate how to adapt synthetic image datasets from other computer vision tasks to overcome the under-representation of the anatomical pose and shape variations in medical image datasets. We transform both data domains to a common one in such a way that a convolutional neural network can be trained on the larger synthetic image dataset and fine-tuned on the smaller medical image dataset. Our evaluations on data of MR hand and whole body CT images demonstrate that this approach improves the detection results compared to training a convolutional neural network only on the medical data. The proposed approach may also be usable in other medical applications, where training data is scarce.


title: Impact of High Loading Dose of Atorvastatin in Diabetic Patients with Renal Dysfunction Undergoing Elective Percutaneous Coronary Intervention: A Randomized Controlled Trial

abstract: INTRODUCTION ::: The effectiveness of statin pretreatment in reducing the incidence of contrast-induced nephropathy (CIN) has been examined in some observational and randomized studies, yielding controversial results. ::: ::: ::: AIM ::: This study sought to evaluate the role of atorvastatin in prevention of CIN in diabetic patients with mild-to-moderate chronic kidney disease (CKD), undergoing elective percutaneous coronary intervention (PCI). ::: ::: ::: METHODS ::: 130 patients with mean glomerular filtration rate of 48.5 ± 16 mL/min/1.73 m were prospectively enrolled, then randomly (double blind) assigned in 1:1 ratio to receive atorvastatin (80 mg daily for 48 h) or placebo. Serum creatinine and glomerular filtration rate were measured preintervention, 72 h and 10 days thereafter. An increase in serum creatinine by >0.5 mg/dL (44.2 μmol/L) or >25% of baseline value was considered as CIN. ::: ::: ::: RESULTS ::: Mean age of the study cohort was 56 ± 5 years (males: 62%). Mean serum creatinine level in the placebo group increased significantly 3 days after coronary intervention and declined on the 10th day to a level that did not differ significantly from the baseline level, but still higher. However, in atorvastatin group, mean serum creatinine level showed a nonsignificant rise on the third day and then decreased to a level close to the baseline one, on the 10th day. Incidence of CIN was 7.7% in atorvastatin group and 20% in the placebo group (P < 0.05). ::: ::: ::: CONCLUSION ::: Atorvastatin dose of 80 mg per day for 48 h is associated with decreased incidence of CIN in diabetic patients with CKD undergoing PCI.


title: IT governance in public organization based on ITBSC and cobit 5: The case of Kupang Municipality

abstract: IT Governance becomes a key component in corporative governance because of the influence of information systems and technologies that support every component of the organization. IT governance which is applied to government organizations can provide positive benefits and support the achievement of business objectives to increase the quality of public services. Application of a good IT Governance is used to apply it in accordance with institutional context. The method used is COBIT 5 combined with ITBSC, then mapping with institutional objectives. The process of collecting data using structured interview methods to the stakeholders in Kupang Municipality. This research found that capability level of Kupang Municipality is in position 0 (incomplete process) with target level of capability of 3, it means that IT Governance of Kupang Municipality not in a maximal condition to responding business process. This study also produced a recommendation for improvement in order to increase the value of capability levels that were set based on COBIT 5.


title: Design and control of the phase shift full bridge converter for the on-board battery charger of the electric forklift

abstract: This paper describes the design and control of phase shift full bridge converter with a current doubler, which can be used for the on-board charger for the lead-acid battery of the electric forklift. Unlike the common resistance load the battery has a large capacitance element and it absorbs the entire converter output ripple current thereby shortening the battery life and degrading the system efficiency. In this paper the phase shift full bridge converter with a current doubler has been adopted to decrease the output ripple current and the transformer rating of the charger. The charge controller is designed by using small signal model of the converter considering the internal impedance of the battery. The stability and performance of the battery charger is then verified by the constant current(CC) and constant voltage(CV) charge experiments using an actual lead-acid battery bank for the electric forklift.


title: The VideoToolbox software for visual psychophysics: transforming numbers into movies.

abstract: The VideoToolbox is a free collection of two hundred C subroutines for Macintosh computers that calibrates and controls the computer-display interface to create accurately specified visual stimuli. High-level platform-independent languages like MATLAB are best for creating the numbers that describe the desired images. Low-level, computer-specific VideoToolbox routines control the hardware that transforms those numbers into a movie. Transcending the particular computer and language, we discuss the nature of the computer-display interface, and how to calibrate and control it.


title: Deep Probabilistic Programming Languages: A Qualitative Study

abstract: Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages. If successful, this would be a big step forward in machine learning and programming languages. Unfortunately, as of now, this new crop of languages is hard to use and understand. This paper addresses this problem directly by explaining deep probabilistic programming languages and indirectly by characterizing their current strengths and weaknesses.


title: Workflow extraction for service operation using multiple unstructured trouble tickets

abstract: In current large scale networks, troubleshooting has become more complicated task due to the diversification in the causes of network failures. The increase in the operational costs has become a serious problem. Thus, manualization of the troubleshooting process also becomes important task though it is time-consuming. We propose a method that automatically extracts a workflow for troubleshooting using multiple trouble tickets. Our method extracts an operator's actions from free-format texts and aligns relative sentences between multiple trouble tickets. Finally, we show a novel approach to visualizing a workflow by mining conditional branches using clustering. We validated our method using real trouble ticket data captured from a network operation and showed that it can extract the workflow to identify the cause of failure.


Effects of a DVD-delivered exercise intervention on physical function in older adults with multiple sclerosis: A pilot randomized controlled trial


title: Change-Detection Map Learning Using Matching Pursuit

abstract: Learning can be of great use when dealing with problems in various fields. Inspired by locally linear embedding from manifold, we propose a novel automatic change-detection method through an offline learning approach. The proposed method comprises three steps. First, two coupled dictionaries of the difference image (DI) patches and change-detection map patches are generated from known image pairs. Second, we approximately represent each patch of the input DI with respect to the DI dictionary by using the matching the pursuit algorithm. Third, the coefficients of this representation are applied with the change-detection map dictionary to generate the output change-detection map. This way, we exploit the relationship between the DI patches and the corresponding change-detection map patches based on two coupled dictionaries. In addition, the relationship guides us to construct the change-detection map for any given input DI. Experimental results on real synthetic aperture radar databases show that the proposed method is superior to its counterparts. Our method can obtain promising results, even though the dictionaries are prepared by simple random sampling from fixed training images.