title: Fetal Deaths in Brazil: Historical Series Descriptive Analysis 1996–2012

abstract: Objective We seek to comprehensively assess stillbirths in Brazil, to compare the Brazilian findings with global trends and to identify the limitations of the fetal death record system. Method We studied fetal deaths in Brazil between 1996 and 2012 within the following five regions of the country: the North, Northeast, Southeast, South, and Central-West, through an analysis of data obtained from the Unified Health System's (SUS) Informatics Department. The rates of stillbirth in Brazil and in these regions were calculated in relation to the maternal and gestational age and education, birth weight, type of pregnancy, delivery type, weight ranges and cause of death. Results There were 579,661 recorded fetal deaths and a decrease of 22.9 % in the stillbirth rate. In 2012, the overall rate was 10.0/1000 births; the North and Northeast regions had the highest rates (10.3 and 12.1, respectively) and the South region had the lowest rate (7.7/1000 births). Two-thirds of the deaths occurred in pregnancies of 28 or more weeks. Low education was an important risk factor, with rates of 24.3/1000 birth in women with no formal education and 4.7/1000 birth in women with 12 or more years of study in 2012. More than 40 % of the causes of deaths were nonspecific. Conclusions Despite the gradual decline in stillbirth rates, Brazil still has stillbirth rates that are nearly two times higher than those found in developed countries. There are inequalities between country regions portrayed by the significant variation in mortality rates specified by cause.


Extracting Word Relationships from Unstructured Data ( Learning Human Activities from General Websites )


title: From nuchal translucency to intracranial translucency: towards the early detection of spina bifida

abstract: It is now clear that the vast majority of major fetal abnormalities can be diagnosed prenatally by ultrasound, that most of these abnormalities can be detected in the first trimester of pregnancy and that women want firsttrimester rather than later diagnosis. It is also clear that effective diagnosis of fetal abnormalities often necessitates the identification of easily recognizable markers which direct the attention of the sonographer to the specific abnormality. Good examples of such markers are the scalloping of the frontal bones (the ‘lemon’ sign) and caudal displacement of the cerebellum (the ‘banana’ sign), observed in the second trimester in most fetuses with open spina bifida, and increased nuchal translucency thickness (NT) which identifies in the first trimester the majority of fetuses with major aneuploidies, lethal skeletal dysplasias and a high proportion of major cardiac defects. It is now widely accepted that increased NT at 11–13 weeks is the single most effective marker of trisomy 21 and all other major aneuploidies. First-trimester screening by a combination of maternal age, fetal NT, nasal bone, Doppler assessment of blood flow in the ductus venosus and across the tricuspid valve together with maternal serum free β-hCG and PAPP-A can identify more than 95% of all major aneuploidies for a screen-positive rate of less than 3%. A major remaining challenge in first-trimester ultrasonography has been the diagnosis of open spina bifida. This challenge, however, may now have been resolved by the realization that open spina bifida can be suspected by an easily detectable marker within the brain in the same mid-sagittal plane of the fetal face as for measurement of NT and assessment of the nasal bone. In normal fetuses the fourth cerebral ventricle presents as an intracranial translucency (IT) parallel to the NT, while in fetuses with open spina bifida there may be absence of the IT 1 .


title: OpinionMiner: a novel machine learning system for web opinion mining and extraction

abstract: Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. Unfortunately, reading through all customer reviews is difficult, especially for popular items, the number of reviews can be up to hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision. The OpinionMiner system designed in this work aims to mine customer reviews of a product and extract high detailed product entities on which reviewers express their opinions. Opinion expressions are identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that employed rule-based or statistical techniques, we propose a novel machine learning approach built under the framework of lexicalized HMMs. The approach naturally integrates multiple important linguistic features into automatic learning. In this paper, we describe the architecture and main components of the system. The evaluation of the proposed method is presented based on processing the online product reviews from Amazon and other publicly available datasets.


title: Qualitative Data Cleaning

abstract: Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions. ::: ::: Data cleaning exercise often consist of two phases: error detection and error repairing. Error detection techniques can either be quantitative or qualitative; and error repairing is performed by applying data transformation scripts or by involving human experts, and sometimes both. ::: ::: In this tutorial, we discuss the main facets and directions in designing qualitative data cleaning techniques. We present a taxonomy of current qualitative error detection techniques, as well as a taxonomy of current data repairing techniques. We will also discuss proposals for tackling the challenges for cleaning "big data" in terms of scale and distribution.


title: Characteristics of online and offline health information seekers and factors that discriminate between them.

abstract: Increasing number of individuals are using the internet to meet their health information needs; however, little is known about the characteristics of online health information seekers and whether they differ from individuals who search for health information from offline sources. Researchers must examine the primary characteristics of online and offline health information seekers in order to better recognize their needs, highlight improvements that may be made in the arena of internet health information quality and availability, and understand factors that discriminate between those who seek online vs. offline health information. This study examines factors that differentiate between online and offline health information seekers in the United States. Data for this study are from a subsample (n = 385) of individuals from the 2000 General Social Survey. The subsample includes those respondents who were asked Internet and health seeking module questions. Similar to prior research, results of this study show that the majority of both online and offline health information seekers report reliance upon health care professionals as a source of health information. This study is unique in that the results illustrate that there are several key factors (age, income, and education) that discriminate between US online and offline health information seekers; this suggests that general "digital divide" characteristics influence where health information is sought. In addition to traditional digital divide factors, those who are healthier and happier are less likely to look exclusively offline for health information. Implications of these findings are discussed in terms of the digital divide and the patient-provider relationship.


title: Snakebite envenoming from a global perspective: Towards an integrated approach

abstract: Snakebite envenoming is a neglected public health challenge of compelling importance in many regions of the world, particularly sub-Saharan Africa, Asia, Latin America and Papua- New Guinea. Addressing the problem of snakebite effectively demands an integrated multifocal approach, targeting complex problems and involving many participants. It must comprise:


title: A subsymbolic model of language pathology in schizophrenia

abstract: A Subsymbolic Model of Language Pathology in Schizophrenia Uli Grasemann Risto Miikkulainen Department of Computer Sciences, The University of Texas at Austin {uli, risto}@cs.utexas.edu Ralph Hoffman Department of Psychiatry, Yale University School of Medicine ralph.hoffman@yale.edu Abstract schizophrenia based on dysfunctional inhibition and prim- ing mechanisms. Cohen and colleagues (Cohen & Servan- Schreiber, 1992; Braver, Barch, & Cohen, 1999) modeled the interactions between prefrontal cortex and the DA system to explain impaired processing of context in schizophrenia. In a study more closely related to the present paper, Hoff- man & McGlashan (1997, 2006) used simple recurrent net- works (Harris & Elman, 1989) to simulate speech percep- tion in order to understand the mechanisms underlying hal- lucinated speech, which is one characteristic symptom of schizophrenia. Excessive pruning of recurrent connections caused spontaneous speech percepts to be generated, thus em- ulating hallucinated speech. This result is particularly inter- esting because significant reductions in cortical connectivity occur ordinarily during adolescence. That schizophrenia usu- ally first emerges during or shortly after this developmental period suggests that abnormal neurodevelopmental pruning may indeed be involved in the genesis of this disorder. The study reported in this paper extends this hypothesis to three other critical manifestations of schizophrenia, specifi- cally those that disrupt understanding and recall of stories. First, patients with delusions often discover spurious plots around them due to a tendency to confuse the actors in their personal stories with those of the shared stories of their cul- ture. Secondly, patients with language disorganization tend to insert extraneous language material that derails story re- call (Hoffman RE, Watts A, Varanko M, Lane D, Quinlan D, unpublished data). Finally, after an initial active stage of the disorder, patients tend to compensate for these positive symp- toms, showing reduced symptoms at the cost of curtailed lan- guage output (Ventura et al., 2004). The goal of this paper is to demonstrate that pathologies hypothesized to underlie schizophrenia can produce the ab- normalities described above in a computational model of hu- man story processing. The model used is an expanded ver- sion of DISCERN (Miikkulainen, 1993), a system that sim- ulates human story understanding and recall using a multi- modular neural network. DISCERN has been previously ex- tended to include a memory architecture that permits the sys- tem to store and recall complex stories composed of multiple story segments, or scripts (Fidelman, Miikkulainen, & Hoff- man, 2005). Further extensions of of the model include the capacity to process emotional content and an output filtering mechanism that allows the system to discard distorted lan- guage. This paper reports first results of a simulation of language pathology in schizophrenia. Using DISCERN, a subsymbolic model of story understanding and recall, the impact of differ- ent simulated lesions hypothesized to underlie schizophrenia is investigated. In response to excessive connection pruning, the model reproduces symptoms of delusions and disorganized language seen in schizophrenia, as well as the reduced out- put seen in compensated later states of the disorder. The ef- fects of other lesions are less consistent with the symptoms of schizophrenia. The model therefore forms a promising ba- sis for future computational investigations into the underlying causes of schizophrenia. Keywords: Cognitive Modeling; Schizophrenia; Neural Net- works; Natural Language Processing. Introduction Schizophrenia is a disabling psychiatric disorder character- ized by complex alterations of language and thought. Estab- lishing the physiological basis of its symptoms would greatly enhance our understanding of this disorder, possibly leading to more effective treatments. Yet after decades of intensive research, underlying brain processes remain uncertain (Ka- pur, 2003; Pantelis et al., 2005). Competing theories include reduced connectivity within and between cortical networks, drop-out of certain types of neurons, and disturbances in- volving subcortical dopamine (DA) neurons that affect corti- cal systems. Unfortunately currently available neuroimaging, pharmacological, and postmortem methods remain limited in characterizing neural substrates of schizophrenia. Thus, the schizophrenic brain remains a vexing “black-box” where out- put deviations challenge researchers to infer underlying pro- cesses. It is not surprising therefore that schizophrenia has become an important focus in the emerging field of computa- tional models of psychopathology. During the last two decades, researchers have tried to cap- ture aspects of schizophrenia and many other disorders us- ing connectionist models, with the goal of informing clinical research and resolving competing hypotheses about underly- ing brain mechanisms. In one of the earliest models of psy- chopathology, Hoffman (1987) investigated the emergence of “parasitic” stable states in attractor networks (Hopfield, 1982) to model delusions and hallucinations seen in schizophrenia. More recently, Ruppin, Reggia, and Horn (1996) used attrac- tor networks in a computational model of Stevens’s (1992) theory of the pathogenesis of schizophrenia. Spitzer (1997) and Silberman, Bentin, and Miikkulainen (2007) used self- organizing feature maps to model impaired lexical access in


title: A Dual-CNN Model for Multi-label Classification by Leveraging Co-occurrence Dependencies Between Labels

abstract: In recent years, deep convolutional neural network (CNN) has demonstrated its great power in image classification. In real world, there are many images contain abundant contents so that they have multiple labels. Moreover, there are correlations between labels. Traditional deep methods for such data rarely take into account such correlations. In this paper, we propose a dual-CNN model, i.e., Dual-CNN model for Multi-Label classification (Dual-CNN-ML), which can make full use of the dependencies of labels to enhance classification performance. Specifically, we first obtain co-occurrence dependency matrix from training datasets; then, we merge the co-occurrence dependency matrix and image representation together; finally, we use the new representation to predict labels of samples. Extensive experiments on public benchmark datasets demonstrate that the proposed method obtains satisfying results and outperforms several state-of-the-art methods.


title: RF varactor design based on evolutionary algorithms

abstract: This paper introduces an optimization methodology for the design of RF varactors. The characterization of the varactor behaviour is supported by a set of equations based on technological parameters, granting the accuracy of the results, as well as the adaptability of the model to any technology. The varactor design is achieved through the implementation of a Genetic Algorithms (GA) optimization methodology, which is able to deal with continuous and/or discrete variables, making possible to suit both technological and layout constraints. A set of working examples for UMC130 technology are addressed. The results presented, spotlight the potential of varactor analytical model, combined with a GA optimization procedure, when integrated in optimization design tools. The accuracy of the results is checked against HSPICE simulator.


title: Printem: Instant Printed Circuit Boards with Standard Office Printers & Inks

abstract: Printem film, a novel method for the fabrication of Printed Circuit Boards (PCBs) for small batch/prototyping use, is presented. Printem film enables a standard office inkjet or laser printer, using standard inks, to produce a PCB: the user prints a negative of the PCB onto the film, exposes it to UV or sunlight, and then tears-away the unneeded portion of the film, leaving-behind a copper PCB. PCBs produced with Printem film are as conductive as PCBs created using standard industrial methods. Herein, the composition of Printem film is described, and advantages of various materials discussed. Sample applications are also described, each of which demonstrates some unique advantage of Printem film over current prototyping methods: conductivity, flexibility, the ability to be cut with a pair of scissors, and the ability to be mounted to a rigid backplane. NOTE: publication of full-text held until November 9, 2015.


title: Cyberspace psychopathology.

abstract: The authors, after a initial description of the "Internet phenomenon" and more specifically concerning the psychological and psychopathological risks related to its use, propose a series of unpublished papers on this theme, developed during the last year.


title: Systematic search for lambda expressions

abstract: This paper presents a system for searching for desired small functional programs by just generating a sequence of type-correct programs in a systematic and exhaustive manner and evaluating them. The main goal of this line of research is to ease functional programming, along with the subgoal to provide an axis to evaluate heuristic approaches to program synthesis such as genetic programming by telling the best performance possible by exhaustive search algorithms. While our previous approach to that goal used combinatory expressions in order to simplify the synthesis process, which led to redundant combinator expressions with complex types, this time we use de Bruijn lambda expressions and enjoy improved results.


title: The consciousness state space (CSS)—a unifying model for consciousness and self

abstract: Every experience, those we are aware of and those we are not, is embedded in a subjective timeline, is tinged with emotion, and inevitably evokes a certain sense of self. Here, we present a phenomenological model for consciousness and selfhood which relates time, awareness, and emotion within one framework. The consciousness state space (CSS) model is a theoretical one. It relies on a broad range of literature, hence has high explanatory and integrative strength, and helps in visualizing the relationship between different aspects of experience.Briefly, it is suggested that all phenomenological states fall into two categories of consciousness, core and extended (CC and EC, respectively). CC supports minimal selfhood that is short of temporal extension, its scope being the here and now. EC supports narrative selfhood, which involves personal identity and continuity across time, as well as memory, imagination and conceptual thought. The CSS is a phenomenological space, created by three dimensions: time, awareness and emotion. Each of the three dimensions is shown to have a dual phenomenological composition, falling within CC and EC. The neural spaces supporting each of these dimensions, as well as CC and EC, are laid out based on the neuroscientific literature.The CSS dynamics includes two simultaneous trajectories, one in CC and one in EC, typically antagonistic in normal experiences. However, this characteristic behavior is altered in states in which a person experiences an altered sense of self. Two examples are laid out, flow and meditation. The CSS model creates a broad theoretical framework with explanatory and unificatory power. It constructs a detailed map of the consciousness and selfhood phenomenology, which offers constraints for the science of consciousness. We conclude by outlaying several testable predictions raised by the CSS model.


title: Validating Predictive Performance of Classifier Models for Multiclass Problem in Educational Data Mining

abstract: Classification is one of the most frequently studied problems in data mining and machine learning research areas. It consists of predicting the value of a class attribute based on the values of other attributes. There are different classifications models were proposed in educational data mining (EDM) and it is used to evaluate student’s academic performance in educational institutions and based on the results of the models, preventive measures to be taken in advance to enhance the students learning ability so that students’ academic performance can be improved. The main objective of this study is to explore different predictive measures and assess the quality of predictive performance ability of the classifier models in educational data mining.


title: Design Optimization of Direct-Coupled Ironless Axial Flux Permanent Magnet Synchronous Wind Generator With Low Cost and High Annual Energy Yield

abstract: In this paper, an improved design of an ironless axial flux permanent magnet synchronous generator (AFPMSG) is presented for direct-coupled wind turbine application considering wind speed characteristics. The partial swarm optimization method is used to perform a multi-objective design optimization of the ironless AFPMSG in order to decrease the active material cost and increase the annual energy yield of the generator over the entire range of operating wind speed. General practical and mechanical limitations in the design of the generator are considered as optimization constraints. For accurate analytical design of the generator, distribution of the flux in all parts of the machine is obtained through a modified magnetic equivalent circuit model of AFPMSG. In this model, the magnetic saturation of the rotor back iron cores is considered using a nonlinear iterative algorithm. Various combinations of pole and coil numbers are studied in the design of a 30 kW AFPMSG via the optimization procedure. Finally, 3-D finite-element model of the generator was prepared to confirm the validity of the proposed design procedure and the generator performance for various wind speeds.


title: The ROSE Source-to-Source Compiler Infrastructure

abstract: The development of future software for proposed new computer architectures is expected to make significant demands on compiler technologies. Significant rewriting of applications will be likely to support the use of new hardware features and preserve the current software investment. Existing compilers provide little support for the broad range of research efforts addressing exascale challenges, such as parallelism, locality, resiliency, power efficiency, etc. The economics of how new machines will change an existing code base of software, that is too expensive to manually rewrite, may well drive automated mechanisms to transform existing software to take advantage of future machine features. This approach will lessen the cost and delay of the move to new, and possibly radically different, future architectures. Source-to-source compilers provides a pragmatic vehicle to support research, development, and deployment of novel compiler technologies by compiler experts or even advanced application developers. Within a source-to-source approach the input source code is read by the compiler, an internal representation (IR) is constructed, the IR is the basis of analysis that is used to guide transformations, the transformations occur on the IR, the IR is used to regenerate new source code, which is then compiled by a backend compiler. Our source-to-source compiler, ROSE, is a project to support the requirements of DOE. Work on ROSE has focused on the development of a community based project to define source-to-source compilation for a broad range of languages especially targeted at DOE applications (addressing robustness and large scale codes as required for DOE applications). Novel research areas are most easily supported when they can leverage significant tool chains that interact and use source code while allowing the hardware vendor’s own compiler for low level optimizations. In fact, high level optimization are rarely feasible for existing low level compilers for common languages such as C, C++, and Fortran. ROSE addresses the economics of how compiler research can be moved closer to the audience with significant technical performance problems and for whom the hardware is likely to be changing significantly in the next decade. Within ROSE it is less the goal to solve all problems than to permit domain experts to better solve their own problems. This talk will focus on the design and motivation for ROSE as an open community source-to-source compiler infrastructure to support performance optimization, tools for analysis, verification and software assurance, and general cus


title: Implementation of Evolutionary Algorithms for Deep Architectures

abstract: Deep learning is becoming an increasingly interesting and powerful machine learning method with successful applications in many domains, such as natural language processing, image recognition, and hand-written character recognition. Despite of its eminent success, limitations of traditional learning approach may still prevent deep learning from achieving a wide range of realistic learning tasks. Due to the flexibility and proven effectiveness of evolutionary learning techniques, they may therefore play a crucial role towards unleashing the full potential of deep learning in practice. Unfortunately, many researchers with a strong background on evolutionary computation are not fully aware of the stateof-the-art research on deep learning. To close this knowledge gap and to promote the research on evolutionary inspired deep learning techniques, this paper presents a comprehensive review of the latest deep architectures and surveys important evolutionary algorithms that can potentially be explored for training these deep architectures. Index terms — Deep Architectures, Deep Learning, Evolutionary Algorithms


title: Improved seam carving for image resizing

abstract: As displays become cheaper and are incorporated into more and more devices, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or resampling can introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting methods have been proposed ([1][2][4][6][7]) which produce exceptional results. In particular, seam carving, proposed by Avidan and Shamir, has gained attention as an effective solution. However, there are many cases where it can fail. In this paper we propose an improved seam carving algorithm which incorporates antialiasing and thresholding techniques. Experiments have demonstrated superior performance over the current seam carving methods.


Bim with Building Thermal and Daylighting Modeling


title: Closely-Packed UWB MIMO/Diversity Antenna With Different Patterns and Polarizations for USB Dongle Applications

abstract: A closely-packed ultrawideband (UWB) multiple- input multiple-output (MIMO)/diversity antenna (of two elements) with a size of 25 mm by 40 mm is proposed for USB dongle applications. Wideband isolation can be achieved through the different patterns and polarizations of the two antenna elements. Moreover, the slot that is formed between the monopole and the ground plane of the half slot antenna is conveniently used to further enhance the isolation at the lower frequencies and to provide an additional resonance at one antenna element in order to increase its bandwidth. The underlying mechanisms of the antenna's wide impedance bandwidth and low mutual coupling are analyzed in detail. Based on the measurement results, the proposed antenna can cover the lower UWB band of 3.1-5.15 GHz, and within the required band, the isolation exceeds 26 dB. The gains and total efficiencies of the two antenna elements are also measured. Furthermore, a chassis mode can be excited when a physical connection is required between the ground planes of the two antenna elements. Without affecting the performance of the half slot element, the monopole can now cover the band of 1.78-3 GHz, apart from the UWB band. The proposed antenna structure is found to provide good MIMO/diversity performance, with very low envelope correlation of less than 0.1 across the UWB band.


title: Dual-Band and Dual-Sense Omnidirectional Circularly Polarized Antenna

abstract: A circularly polarized (CP) dielectric resonator antenna (DRA) loaded with a modified circular patch is proposed for dual-band applications. The antenna is centrally fed by a probe, giving omnidirectional patterns with different senses of circular polarization in two bands. It is observed that the lower and upper bands are controlled by the patch and dielectric resonator (DR), respectively, making design of the dual-band antenna very easy. For demonstration, a prototype has been designed, fabricated, and measured. The antenna features 10-dB impedance bandwidths of 2.64% and 18.03% and 3-dB axial-ratio (AR) bandwidths of 3.16% and 5.06% in lower and upper frequency bands, respectively.


title: Learning knowledge to support domain-independent narrative intelligence

abstract: ed from artificially segmented perceptual inputs of the human senses. This dissertation follows the three-tier model proposed by Bal [5]. Thus, I consider a narrative to contain three tiers, as defined below: Definition 3 (Fabula). A fabula is the complete set of events that include but are not limited to all events presented in the narrative, that happen to and around all story characters within a continuous time period, and that are temporally partially ordered as multiple events can happen simultaneously. Definition 4 (Sjuzhet). A sjuzhet is a linear sequence of events that are selected from the fabula to constitute the narrative, possibly in a different ordering from the fabula and with possible repetition of events. One event may be repeated from multiple viewpoints, such as viewpoints of an omniscient narrator or different story characters. Definition 5 (Media). The media is the form of representation in which the sjuzhet is actualized and presented to the audience, including but not limited to oral narration, audio recordings, video footages, theatrical performances, text, multimedia hypertext, and so on. An example story that illustrates the differences between the three tiers of fabula, sjuzhet, and media is shown in Figure 2. Many different literary techniques may be used to transform a fabula into a sjuzhet, including but not limited to: linearization, which turns partial-ordered events into a single sequence that is suitable to telling; reorganization, which orders the events differently from the order in which they actually occur (e.g. flashbacks); compression and expansion, which selects more or less events from the fabula, creating a slower or faster pace; focalization, which tells events from different points of view. This dissertation follows this three-tier model and proposes methods to generate fabula, sjuzhet, and text respectively. For the purpose of this dissertation, the task Other complex models have been proposed. See, for example, Branigan’s 8-tier model [16], whose structure is vastly different from those reviewed.


title: Detecting Surgical Tools by Modelling Local Appearance and Global Shape

abstract: Detecting tools in surgical videos is an important ingredient for context-aware computer-assisted surgical systems. To this end, we present a new surgical tool detection dataset and a method for joint tool detection and pose estimation in 2d images. Our two-stage pipeline is data-driven and relaxes strong assumptions made by previous works regarding the geometry, number, and position of tools in the image. The first stage classifies each pixel based on local appearance only, while the second stage evaluates a tool-specific shape template to enforce global shape. Both local appearance and global shape are learned from training data. Our method is validated on a new surgical tool dataset of 2 476 images from neurosurgical microscopes, which is made freely available. It improves over existing datasets in size, diversity and detail of annotation. We show that our method significantly improves over competitive baselines from the computer vision field. We achieve 15% detection miss-rate at $10^{-1}$ false positives per image (for the suction tube) over our surgical tool dataset. Results indicate that performing semantic labelling as an intermediate task is key for high quality detection.


Traffic signals for lymphocyte recirculation and leukocyte emigration: The multistep paradigm


Reconceptualizing politics, socialization, and education : international perspectives for the 21st century


title: Semi-Supervised Event Extraction with Paraphrase Clusters

abstract: Supervised event extraction systems are limited in their accuracy due to the lack of available training data. We present a method for self-training event extraction systems by bootstrapping additional training data. This is done by taking advantage of the occurrence of multiple mentions of the same event instances across newswire articles from multiple sources. If our system can make a highconfidence extraction of some mentions in such a cluster, it can then acquire diverse training examples by adding the other mentions as well. Our experiments show significant performance improvements on multiple event extractors over ACE 2005 and TAC-KBP 2015 datasets.


title: Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation and Shape Classification

abstract: This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when the training data is limited. The generative network learns intrinsic features of tumors while the adversarial network enforces segmentations to be similar to the ground truth. Experiments performed on dozens of malignant tumors extracted from the public DDSM dataset and from our in-house private dataset confirm our hypothesis with very high Dice coefficient and Jaccard index (>94% and>89%, respectively) outperforming the scores obtained by other state-of-the-art approaches. Furthermore, in order to detect portray significant morphological features of the segmented tumor, a specific Convolutional Neural Network (CNN) have also been designed for classifying the segmented tumor areas into four types (irregular, lobular, oval and round), which provides an overall accuracy about 72% with the DDSM dataset.


title: End-to-End Learnable Histogram Filters

abstract: Problem-specific algorithms and generic machine learning approaches have complementary strengths and weaknesses, trading-off data efficiency and generality. To find the right balance between these, we propose to use problem-specific information encoded in algorithms together with the ability to learn details about the problem-instance from data. We demonstrate this approach in the context of state estimation in robotics, where we propose end-to-end learnable histogram filters---a differentiable implementation of histogram filters that encodes the structure of recursive state estimation using prediction and measurement update but allows the specific models to be learned end-to-end, i.e. in such a way that they optimize the performance of the filter, using either supervised or unsupervised learning.


title: Defining deadlock with fungible resources

abstract: In a previous paper we discussed resource deadlock within a classification of all dead states. We differentiated between the different types of anomalies that are called deadlock in the computer science literature. Our primary aim was to correct inconsistencies that are currently being taught in operating systems' textbooks. We introduced a model that allowed us to precisely define deadlock, but we only provided a partial definition of resource deadlock, which, as we noted, was limited to deadlocks with uniquely identifiable resource units. In response to readers of OSR, we now distinguish between different types of resource deadlock and provide a comprehensive definition. We also suggest that it may be desirable to maintain separate definitions.


Complex Abdominal Wall Defects and Enterocutaneous Fistulae in the Era of Biological Mesh: Did We Make any Real Progress?


title: A Novel Structure Single-Phase Tubular Switched Reluctance Linear Motor

abstract: Tubular switched reluctance linear motor (TSRLM) has lower loss and higher thrust density than flat-type switched reluctance linear motor. Two kinds of single-phase TSRLMs with different surrounding magnetic flux paths are introduced in this paper. The structure of transverse flux TSRLM (TF-TSRLM) is compared with the structure of the longitudinal flux TSRLM (LF-TSRLM). The structure selection of TF-TSRLM with two poles, four poles, six poles, and eight poles is made for enhancing average electromagnetic thrust. The comparisons between LF-TSRLM and TF-TSRLM are made in the same stator outer diameter, the same mover laminated thickness, and the same excited current on static electromagnetic thrust, magnetization curves, average thrust per unit volume, and average thrust per unit mass. They show that the TF-TSRLM with the six-pole structure is considered as a relatively more reasonable structure for enhancing the space effective utilization rate and increasing the electromagnetic thrust.


title: A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment

abstract: In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multiplier. However, to do so requires autonomous coordination among the UAVs, similar to swarming behaviors seen in animals and insects. This paper looks at flocking with small fixed-wing UAVs in the context of a model-free reinforcement learning problem. In particular, Peng's Q(λ) with a variable learning rate is employed by the followers to learn a control policy that facilitates flocking in a leader-follower topology. The problem is structured as a Markov decision process, where the agents are modeled as small fixed-wing UAVs that experience stochasticity due to disturbances such as winds and control noises, as well as weight and balance issues. Learned policies are compared to ones solved using stochastic optimal control (i.e., dynamic programming) by evaluating the average cost incurred during flight according to a cost function. Simulation results demonstrate the feasibility of the proposed learning approach at enabling agents to learn how to flock in a leader-follower topology, while operating in a nonstationary stochastic environment.


A Brief Review of Speaker Recognition Technology


title: The use of a low cost 3D scanning and printing tool in the manufacture of custom-made foot orthoses: a preliminary study

abstract: Background ::: Custom foot orthoses are currently recognized as the gold standard for treatment of foot and lower limb pathology. While foam and plaster casting methods are most widely used in clinical practice, technology has emerged, permitting the use of 3D scanning, computer aided design (CAD) and computer aided manufacturing (CAM) for fabrication of foot molds and custom foot orthotic components. Adoption of 3D printing, as a form of CAM, requires further investigation for use as a clinical tool. ::: ::: This study provides a preliminary description of a new method to manufacture foot orthoses using a novel 3D scanner and printer and compare gait kinematic outputs from shod and traditional plaster casted orthotics.


Relationship among smartphone addiction, age, lack of sleep, fear of missing out and social networking sites use among high school students


title: Venturing for Others with Heart and Head: How Compassion Encourages Social Entrepreneurship

abstract: Social entrepreneurship has emerged as a complex yet promising organizational form in which market-based methods are used to address seemingly intractable social issues, but its motivations remain undertheorized. Research asserts that compassion may supplement traditional self-oriented motivations in encouraging social entrepreneurship. We draw on research on compassion and prosocial motivation to build a model of three mechanisms (integrative thinking, prosocial cost-benefit analysis, and commitment to alleviating others' suffering) that transform compassion into social entrepreneurship, and we identify the institutional conditions under which they are most likely to do so. We conclude by discussing the model's contribution to and implications for the positive organizational scholarship literature, entrepreneurship literature, and social entrepreneurship literature.


title: Privacy-by-design in big data analytics and social mining

abstract: Privacy is ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.


title: What is hippotherapy? The indications and effectiveness of hippotherapy

abstract: Hippotherapy is a form of physical, occupational and speech therapy in which a therapist uses the characteristic movements of a horse to provide carefully graded motor and sensory input. A foundation is established to improve neurological function and sensory processing, which can be generalized to a wide range of daily activities. Unlike therapeutic horseback riding (where specific riding skills are taught), the movement of the horse is a means to a treatment goal when utilizing hippotherapy as a treatment strategy. Hippotherapy has been used to treat patients with neurological or other disabilities, such as autism, cerebral palsy, arthritis, multiple sclerosis, head injury, stroke, spinal cord injury, behavioral disorders and psychiatric disorders. The effectiveness of hippotherapy for many of these indications is unclear, and more research has been needed. Here, we purpose to give information about hippotherapy which is not known adequately by many clinicians and health workers.


title: Car detection in low resolution aerial images

abstract: Abstract We present a system to detect passenger cars in aerial images along the road directions where cars appear as small objects. We pose this as a 3D object recognition problem to account for the variation in viewpoint and the shadow. We started from psychological tests to find important features for human detection of cars. Based on these observations, we selected the boundary of the car body, the boundary of the front windshield, and the shadow as the features. Some of these features are affected by the intensity of the car and whether or not there is a shadow along it. This information is represented in the structure of the Bayesian network that we use to integrate all features. Experiments show very promising results even on some very challenging images.


title: 38 GHz and 60 GHz angle-dependent propagation for cellular & peer-to-peer wireless communications

abstract: As the cost of massively broadband® semiconductors continue to be driven down at millimeter wave (mm-wave) frequencies, there is great potential to use LMDS spectrum (in the 28–38 GHz bands) and the 60 GHz band for cellular/mobile and peer-to-peer wireless networks. This work presents urban cellular and peer-to-peer RF wideband channel measurements using a broadband sliding correlator channel sounder and steerable antennas at carrier frequencies of 38 GHz and 60 GHz, and presents measurements showing the propagation time delay spread and path loss as a function of separation distance and antenna pointing angles for many types of real-world environments. The data presented here show that at 38 GHz, unobstructed Line of Site (LOS) channels obey free space propagation path loss while non-LOS (NLOS) channels have large multipath delay spreads and can exploit many different pointing angles to provide propagation links. At 60 GHz, there is notably more path loss, smaller delay spreads, and fewer unique antenna angles for creating a link. For both 38 GHz and 60 GHz, we demonstrate empirical relationships between the RMS delay spread and antenna pointing angles, and observe that excess path loss (above free space) has an inverse relationship with transmitter-to-receiver separation distance.


title: Design of reconfigurable tri-band antenna on chip

abstract: In the modern telecommunication system, Reconfigurable Antennas which can radiate on more than one pattern at different frequencies are required. There is a requirement for increased functionality of an antenna within a confined volume for today's transmitting and receiving system. Reconfigurable on chip antennas are a solution to these problems. This paper describes the design of a reconfigurable tri-band antenna which radiates for 2.4GHz(ISM Band), 5.2GHZ and 7.5GHz. This tri-band reconfigurable antenna was simulated on high frequency simulation software (HFSS) and the results for return loss and VSWR plot was obtained for three different frequencies. Thus this antenna will lead to reduction in space and the multiple functionality from a single structure.


title: Deep Compositional Cross-modal Learning to Rank via Local-Global Alignment

abstract: Cross-modal retrieval is a very hot research topic that is imperative to many applications involving multi-modal data. Discovering an appropriate representation for multi-modal data and learning a ranking function are essential to boost the cross-media retrieval. Motivated by the assumption that a compositional cross-modal semantic representation (pairs of images and text) is more attractive for cross-modal ranking, this paper exploits the existing image-text databases to optimize a ranking function for cross-modal retrieval, called deep compositional cross-modal learning to rank (C2MLR). In this paper, C2MLR considers learning a multi-modal embedding from the perspective of optimizing a pairwise ranking problem while enhancing both local alignment and global alignment. In particular, the local alignment (i.e., the alignment of visual objects and textual words) and the global alignment (i.e., the image-level and sentence-level alignment) are collaboratively utilized to learn the multi-modal embedding common space in a max-margin learning to rank manner. The experiments demonstrate the superiority of our proposed C2MLR due to its nature of multi-modal compositional embedding.


title: A 1-V 2.5-ppm/°C second-order compensated bandgap reference

abstract: A new 1-V high precision second-order compensated bandgap voltage reference (BGR) is presented in this paper. It utilizes a conventional first-order current-mode BGR to balance the Complementary to Absolute Temperature (CTAT) voltage component of a forward biased BJT with a Proportional to Absolute Temperature (PTAT) voltage. The residual second-order error is corrected by the difference of two CTAT voltages with different non-linear terms through control of the collector currents of BJTs. All the circuits are designed using a standard 0.18μm CMOS process. The post-simulation results show that the maximum temperature coefficient (TC) of the proposed BGR at 1 V power supply is 2.5 ppm/°C over temperature range of −40 °C to 125 °C. The line regulation is 0.23 mV/V over supply range of 0.9 V–2 V. The low frequency power supply rejection of the circuit is −53 dB at 1 V power supply.


title: Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance

abstract: Real-time traffic surveillance is essential in today’s intelligent transportation systems and will surely play a vital role in tomorrow’s smart cities. The work detailed in this paper reports on the development and implementation of a novel smart wireless sensor for traffic monitoring. Computationally efficient and reliable algorithms for vehicle detection, speed and length estimation, classification, and time-synchronization were fully developed, integrated, and evaluated. Comprehensive system evaluation and extensive data analysis were performed to tune and validate the system for a reliable and robust operation. Several field studies conducted on highway and urban roads for different scenarios and under various traffic conditions resulted in 99.98% detection accuracy, 97.11% speed estimation accuracy, and 97% length-based vehicle classification accuracy. The developed system is portable, reliable, and cost-effective. The system can also be used for short-term or long-term installment on surface of highway, roadway, and roadside. Implementation cost of a single node including enclosure is US $50.


Elliptic Curve Digital Signature Algorithm and its Applications in Bitcoin


title: Simultaneous determination of eugenol, isoeugenol and methyleugenol in fish fillet using gas chromatography coupled to tandem mass spectrometry

abstract: Abstract Gas chromatography (GC) coupled with triple quadrupole tandem mass spectrometry (MS/MS) operated in electron ionization mode (EI) has been shown to have advantages in the trace analysis of chemical compounds. Employing the instrument, a method has been built to simultaneously determine eugenol, isoeugenol‚ and methyleugenol, which have been widely used as fish anesthetic, in the fish fillet. Procedure for the sample preparation was achieved by using hexane extraction followed by phenyl solid phase extraction (SPE) cleanup, which was free of such steps as rotary evaporation and nitrogen blowing by taking the volatility of eugenol and its isomers into consideration. The method was validated by conducting recovery studies on fortified fish fillet samples at four concentrations. The linearity in the range of 5–500 μg·L−1 was forced through the origin giving a coefficient of determination (r2) greater than 0.9982. Limits of detection (LODs) for eugenol, isoeugenol‚ and methyleugenol were 0.4, 1.2‚ and 0.2 μg·kg−1, respectively. The limits of quantification (LOQs) were 1.2, 4‚ and 0.7 μg·kg−1 for eugenol, isoeugenol‚ and methyleugenol, respectively. The recoveries for eugenol and its isomers ranged from 76.4 to 99.9% with relative standard deviations (RSD) in a range from 2.18 to 15.5%. This method is quick, simple and suitable for determining the residues of eugenol, isoeugenol and methyleugenol simultaneously in batch samples of fish fillet.


title: Decrease of hemostatic cardiovascular risk factors by aggressive vs. conventional atorvastatin treatment in patients with Type 2 diabetes mellitus

abstract: BACKGROUND: Patients with Type 2 diabetes mellitus have increased levels of hemostatic risk variables for cardiovascular disease, such as fibrinogen, von Willebrand factor (VWF), factor (F)VIIa, d-dimer and plasminogen activator inhibitor-1 (PAI-1). OBJECTIVES: To evaluate the effect of aggressive vs. standard dose atorvastatin on hemostatic cardiovascular risk factors in patients with Type 2 diabetes mellitus. Patients and methods: The effect of 30 weeks of treatment with atorvastatin 10 and 80 mg on hemostatic cardiovascular risk factors was assessed in a randomized double-blind placebo-controlled trial on 217 patients with Type 2 diabetes mellitus and dyslipidemia. RESULTS AND CONCLUSIONS: Atorvastatin 10 and 80 mg dose-dependently reduced d-dimer (7.4% and 8.5%, respectively, P for trend = 0.004) and PAI-1 antigen levels (9.0% and 18%, respectively, P for trend = 0.021). Levels of fibrinogen, VWF, tissue-type plasminogen activator and FVIIa were not influenced by atorvastatin. In conclusion, in patients with Type 2 diabetes mellitus, atorvastatin dose-dependently improved the levels of the hemostatic risk variables d-dimer and PAI-1. Chemicals/CAS: atorvastatin, 134523-00-5, 134523-03-8; fibrinogen, 9001-32-5; tissue plasminogen activator, 105913-11-9; von Willebrand factor, 109319-16-6; atorvastatin, 110862-48-1; Factor VIIIa, 72175-66-7; Fibrinogen, 9001-32-5; Hemostatics; Heptanoic Acids; Placebos; Pyrroles; Tissue Plasminogen Activator, EC 3.4.21.68; von Willebrand Factor


title: Intent Prediction of Pedestrians via Motion Trajectories Using Stacked Recurrent Neural Networks

abstract: The problem of intent understanding between highly and fully automated vehicles and vulnerable road users (VRUs) such as pedestrians in urban traffic environment has got some momentum over the past few years. Previous work has been tackling the problem using two common approaches, namely dynamical motion modeling and motion planning. In this paper, a novel radical end-to-end data-driven approach is proposed for long-term intent prediction of VRUs in urban traffic environment based solely on their motion trajectories. In the proposed approach, we utilized the widely adopted architecture of recurrent neural networks with Long-Short Term Memory (LSTM) modules to form a deep stacked LSTM network. Three common approaches used in the literature were compared against our proposed approach over two different real-world datasets involving pedestrians collected from vehicle-based stereo cameras. The results over the testing datasets showed that the proposed approach achieved higher accuracies over most of the scenarios of the testing datasets with a small mean lateral position error of 0.48 m. Moreover, the proposed approach showed also a significant generalization capability over totally unobserved testing scenes during the training phase with only 0.58 m in mean lateral position error.


title: Defending IT systems against intelligent malware

abstract: The increasing amount of malware variants seen in the wild is causing problems for Antivirus Software vendors, unable to keep up by creating signatures for each. The methods used to develop a signature, static and dynamic analysis, have various limitations. Machine learning has been used by Antivirus vendors to detect malware based on the information gathered from the analysis process. However, adversarial examples can cause machine learning algorithms to miss-classify new data. In this paper we describe a method for malware analysis by converting malware binaries to images and then preparing those images for training within a Generative Adversarial Network. These unsupervised deep neural networks are not susceptible to adversarial examples. The conversion to images from malware binaries should be faster than using dynamic analysis and it would still be possible to link malware families together. Using the Generative Adversarial Network, malware detection could be much more effective and reliable.


title: Blockchain applications for legal metrology

abstract: Blockchain technology has become a promising approach in many domains. It first came up with the cryptocurrency Bitcoin, where it is used to maintain a distributed ledger, a public list of all transactions. It is assumed that the use of a blockchain as a cryptocurrency, or in general for financial services, was just the first stage, called Phase 1.0. The second phase began with the integration of smart contracts. In Phase 3.0, the focus lies on decentralized autonomous organizations (DAOs), which efficiently implement smart contracts. In the future, it is assumed, that by the use of DAOs many benefits for the public sector emerge. In this paper, blockchain applications of Phase 2.0 and 3.0 are analyzed for measuring instruments under legal control. An example are smart meters, where by smart contracts, a new coin/token within the blockchain can be created whenever the electric meter rotates. Promising areas of application in legal metrology, like a decentralized audit trail to log, e.g., software updates and hinder uncertified manipulations, the complete automation of the legally supervised update mechanism by smart contracts, or an alternative to public key infrastructures are being proposed in this paper.


title: A perpendicular-anisotropy cofebmgo magnetic tunnel junction

abstract: Materials with perpendicular anisotropy receive considerable attention owing to their potential in being employed in efficient memory devices. It is now shown that a type of magnetic tunnel junction widely studied for in-plane magnetic anisotropy has all the properties necessary to realize stable and efficient devices based on perpendicular magnetic anisotropy.


title: Indirectly encoding neural plasticity as a pattern of local rules

abstract: Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve the ability to adapt is by encoding local learning rules. However, a significant problem with most such approaches is that local learning rules for every connection in the network must be discovered separately. This paper aims to show that learning rules can be effectively indirectly encoded by extending the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) method. Adaptive HyperNEAT is introduced to allow not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary learning rules. Several such adaptive models with different levels of generality are explored and compared. The long-term promise of the new approach is to evolve large-scale adaptive ANNs, which is a major goal for neuroevolution.


title: A constant approximation algorithm for the one-warehouse multi-retailer problem

abstract: Deterministic inventory theory provides streamlined optimization models that attempt to capture tradeoffs in managing the flow of goods through a supply chain. We will consider a well-studied inventory model, called the one-warehouse multi-retailer problem (OWMR). and give the first approximation algorithm with constant performance guarantee; more specifically, we give a 2.398-approximation algorithm. Our results are based on an LP-rounding approach, and hence not only provide good algorithmic results, but show strong integrality gaps for these linear programs. Furthermore, we extend this result to obtain a constant performance guarantee for a capacitated variant of this model.


Evaluation of Hybrid framework for Detection of Sybil Attack in VANET


title: The architecture of the colour centre in the human visual brain: new results and a review *

abstract: We have used the technique of functional magnetic resonance imaging (fMRI) and a variety of colour paradigms to activate the human brain regions selective for colour. We show here that the region defined previously (Lueck et al., 1989; Zeki et al., 1991; McKeefry and Zeki, 1997) as the human colour centre consists of two subdivisions, a posterior one, which we call V4 and an anterior one, which we refer to as V4α, the two together being part of the V4-complex. The posterior area is retinotopically organized while the anterior is not. We discuss our new findings in context of previous studies of the cortical colour processing system in humans and monkeys. Our new insight into the organization of the colour centre in the human brain may also account for the variability in both severity and degree of recovery from lesions producing cerebral colour blindness (achromatopsia).


title: Object tracking in an outdoor environment using fusion of features and cameras

abstract: Abstract This paper presents methods for tracking moving objects in an outdoor environment. A robust tracking is achieved using feature fusion and multiple cameras. The proposed method integrates spatial position, shape and color information to track object blobs. The trajectories obtained from individual cameras are incorporated by an extended Kalman filter (EKF) to resolve object occlusion. Our results show that integrating simple features makes the tracking effective and that EKF improves the tracking accuracy when long-term or temporary occlusion occurs. The tracked objects are successfully classified into three categories: single person, people group, or vehicle.


title: Visual servoing when visual information is missing: Experimental comparison of visual feature prediction schemes

abstract: One way to deal with occlusions or loss of tracking of the visual features used for visual servoing tasks is to predict the feature behavior in the image plane when the measurements are missing. Different prediction and correction methods have already been proposed in the literature. The purpose of this paper is to compare and experimentally validate some of these methods for eye-in-hand and eye-to-hand configurations. In particular, we show that a correction based both on the image and the camera/target pose provides the best results.


Stress and Burnout among Physicians: Prevalence and Risk Factors in a Singaporean Internal Medicine Programme.


title: Facultative Anaerobiosis in the Invertebrates: Pathways and Control Systems

abstract: SYNOPSIS. An increasing number of the invertebrates studied have been found to rely on an anaerobic energy metabolism during at least one stage in their development. Some of the bivalve mollusks, and particularly a relatively large number of parasitic nematodes, cestodes, trematodes and acanthocephala may be classified in this category. Regardless of the aerobic or anaerobic requirements of the parasitic helminths, two features appear to be common to all members of this group which have been examined. First, they are all capable of taking up oxygen. Second, none can oxidize substrates completely to CO2 and H2O; end-products of fermentation invariably accumulate, indicating either the complete absence or the presence of only a limited terminal respiratory pathway. ::: ::: The intes inal nematode, Ascaris lumbricoides , has served as a model system for the elucidation of an anaerobic energy-yielding pathway of carbohydrate dissimilation which appears to beoperative in the anaerobic stages of a number of other invertebrates. This pathway differs in several major respects from those previously described for mammalian and other aerobic tissues. The Ascaris system is discussed in detail and is compared with other invertebrate and vertebrate metabolisms.


title: Keratosis pilaris and ulerythema ophryogenes associated with an 18p deletion caused by a Y/18 translocation

abstract: We present a patient with partial monosomy of the short arm of chromosome 18 caused by de novo translocation t(Y;18) and a generalized form of keratosis pilaris (keratosis pilaris affecting the skin follicles of the trunk, limbs and face—ulerythema ophryogenes). Two-color FISH with centromere-specific Y and 18 DNA probes identified the derivative chromosome 18 as a dicentric with breakpoints in p11.2 on both involved chromosomes. The patient had another normal Y chromosome. This is a third report the presence of a chromosome 18p deletion (and first case of a translocation involving 18p and a sex chromosome) with this genodermatosis. Our data suggest that the short arm of chromosome 18 is a candidate region for a gene causing keratosis pilaris. Unmasking of a recessive mutation at the disease locus by deletion of the wild type allele could be the cause of the recessive genodermatosis. Am. J. Med. Genet. 85:179–182, 1999. © 1999 Wiley-Liss, Inc.


title: Forensics Analysis of Android Mobile VoIP Apps

abstract: Voice over Internet Protocol (VoIP) applications (apps) provide convenient and low cost means for users to communicate and share information with each other in real-time. Day by day, the popularity of such apps is increasing, and people produce and share a huge amount of data, including their personal and sensitive information. This might lead to several privacy issues, such as revealing user contacts, private messages or personal photos. Therefore, having an up-to-date forensic understanding of these apps is necessary. This chapter presents analysis of forensically valuable remnants of three popular Mobile VoIP (mVoIP) apps on Google Play store, namely: Viber, Skype, and WhatsApp Messenger, in order to figure out to what extent these apps reveal forensically valuable information about the users activities. We performed a thorough investigative study of these three mVoIP apps on smartphone devices. Our experimental results show that several artefacts, such as messages, contact details, phone numbers, images, and video files, are recoverable from the smartphone device that is equipped with these mVoIP apps.


title: Analysis of satellite images for disaster detection

abstract: Analysis of satellite images plays an increasingly vital role in environment and climate monitoring, especially in detecting and managing natural disaster. In this paper, we proposed an automatic disaster detection system by implementing one of the advance deep learning techniques, convolutional neural network (CNN), to analysis satellite images. The neural network consists of 3 convolutional layers, followed by max-pooling layers after each convolutional layer, and 2 fully connected layers. We created our own disaster detection training data patches, which is currently focusing on 2 main disasters in Japan and Thailand: landslide and flood. Each disaster's training data set consists of 30000∼40000 patches and all patches are trained automatically in CNN to extract region where disaster occurred instantaneously. The results reveal accuracy of 80%∼90% for both disaster detection. The results presented here may facilitate improvements in detecting natural disaster efficiently by establishing automatic disaster detection system.


title: ECvisual: a visualization tool for elliptic curve based ciphers

abstract: This paper describes a visualization tool ECvisual that helps students understand and instructors teach elliptic curve based ciphers. This tool permits the user to visualize elliptic curves over the real field and over a finite field of prime order, perform arithmetic operations, do encryption and decryption, and convert plaintext to a point on an elliptic curve. The demo mode of ECvisual can be used for classroom presentation and self-study. With the practice mode, the user may go through steps in finite field computations, encryption, decryption and plaintext conversion. The user may compute the output for each operation check each answer for correctness. This helps students understand the primitive operations and how they are used in an elliptic curve cipher. The opportunity for self-study provides an instructor greater flexibility in selecting a lecture pace for this detail-filled material. Classroom evaluation was positive and very encouraging.


title: Schema clustering and retrieval for multi-domain pay-as-you-go data integration systems

abstract: A data integration system offers a single interface to multiple structured data sources. Many application contexts (e.g., searching structured data on the web) involve the integration of large numbers of structured data sources. At web scale, it is impractical to use manual or semi-automatic data integration methods, so a pay-as-you-go approach is more appropriate. A pay-as-you-go approach entails using a fully automatic approximate data integration technique to provide an initial data integration system (i.e., an initial mediated schema, and initial mappings from source schemas to the mediated schema), and then refining the system as it gets used. Previous research has investigated automatic approximate data integration techniques, but all existing techniques require the schemas being integrated to belong to the same conceptual domain. At web scale, it is impractical to classify schemas into domains manually or semi-automatically, which limits the applicability of these techniques. In this paper, we present an approach for clustering schemas into domains without any human intervention and based only on the names of attributes in the schemas. Our clustering approach deals with uncertainty in assigning schemas to domains using a probabilistic model. We also propose a query classifier that determines, for a given a keyword query, the most relevant domains to this query. We experimentally demonstrate the effectiveness of our schema clustering and query classification techniques.


title: Emergent structures in supply chains - a study integrating agent-based and system dynamics modeling

abstract: Supply chain management is a demanding and complicated task due to its broad scope and the strong connectedness of its objects and issues. In order to make theoretical investigations of supply chains feasible and to support decision-making in real world supply chains, simulation models are used. An integration of system dynamics and discrete agent-based modeling is a promising combination of methods for reducing the a priori complexity of the model. The paper discusses the strengths and weaknesses of system dynamics and discrete agent-based modeling. An approach for integration of the two modeling methodologies is presented. Issues concerning the practical coupling of software environments and a simple, prototypical supply chain model are discussed. Experiments for which the integrated simulation solution is applied are described. Insights in emergent structures in supply chains are derived from these simulation analyses.


title: State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks

abstract: This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$ ) and lithium titanate ( $\text{LTO}$ ) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).


title: Socializing in emergencies - A review of the use of social media in emergency situations

abstract: During disasters social media provides access to relevant and timely information.Social media has changed the information dissemination pathways in emergencies.Social media enable to transform the ways in which emergencies are tracked.Social media are reliable during disasters when other channels are overwhelmed.Social media can self-regulate misinformation in emergencies through the masses. Social media tools are integrated in most parts of our daily lives, as citizens, netizens, researchers or emergency responders. Lessons learnt from disasters and emergencies that occurred globally in the last few years have shown that social media tools may serve as an integral and significant component of crisis response. Communication is one of the fundamental tools of emergency management. It becomes crucial when there are dozens of agencies and organizations responding to a disaster. Regardless of the type of emergency, whether a terrorist attack, a hurricane or an earthquake, communication lines may be overloaded and cellular networks overwhelmed as too many people attempt to use them to access information. Social scientists have presented that post-disaster active public participation was largely altruistic, including activities such as search and rescue, first aid treatment, victim evacuation, and on-line help. Social media provides opportunities for engaging citizens in the emergency management by both disseminating information to the public and accessing information from them. During emergency events, individuals are exposed to large quantities of information without being aware of their validity or risk of misinformation, but users are usually swift to correct them, thus making the social media "self-regulating".


title: ANNarchy: a code generation approach to neural simulations on parallel hardware

abstract: Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.


title: Quality isosurface mesh generation using an extended marching cubes lookup table

abstract: The Marching Cubes Algorithm may return degenerate, zero area isosurface triangles, and often returns isosurface triangles with small areas, edges or angles. We show how to avoid both problems using an extended Marching Cubes lookup table. As opposed to the conventional Marching Cubes lookup table, the extended lookup table differentiates scalar values equal to the isovalue from scalar values greater than the isovalue. The lookup table has 38 = 6561 entries, based on three possible labels, '-' or '=' or '+', of each cube vertex. We present an algorithm based on this lookup table which returns an isosurface close to the Marching Cubes isosurface, but without any degenerate triangles or any small areas, edges or angles


title: Incomplete preference relations to smooth out the cold-start in collaborative Recommender Systems

abstract: E-Commerce companies have developed tools to assist users in finding the most suitable items for their needs or preferences. The most successful tool in this area has been the Recommender Systems. This kind of software obtains information about the users' tastes, opinions, necessities, and with a recommendation algorithm, infers recommendations that lead users to the most suitable items for them. These algorithms usually require a significant quantity of information and that information is not always available or easy to obtain. Overcome this problem, known as the cold-start problem, and reduce the requirements of information is not an easy task. In this contribution, we review this topic and present our proposal: a hybrid recommender system which combines a collaborative filtering algorithm with a knowledge-based one in order to improve the cold-start problem.


title: The role of domain-general cognitive control in language comprehension

abstract: What role does domain-general cognitive control play in understanding linguistic input? Although much evidence has suggested that domain-general cognitive control and working memory resources are sometimes recruited during language comprehension, many aspects of this relationship remain elusive. For example, how frequently do cognitive control mechanisms get engaged when we understand language? And is this engagement necessary for successful comprehension? I here a) review recent brain imaging evidence for the neural separability of the brain regions that support high-level linguistic processing vs. those that support domain-general cognitive control abilities; b) define the space of possibilities for the relationship between these sets of brain regions; and c) review the available evidence that constrains these possibilities to some extent. I argue that we should stop asking whether domain-general cognitive control mechanisms play a role in language comprehension, and instead focus on characterizing the division of labor between the cognitive control brain regions and the more functionally specialized language regions.


title: Text mining based on tax comments as big data analysis using SVM and feature selection

abstract: The tax gives an important role for the contributions of the economy and development of a country. The improvements to the taxation service system continuously done in order to increase the State Budget. One of consideration to know the performance of taxation particularly in Indonesia is to know the public opinion as for the object service. Text mining can be used to know public opinion about the tax system. The rapid growth of data in social media initiates this research to use the data source as big data analysis. The dataset used is derived from Facebook and Twitter as a source of data in processing tax comments. The results of opinions in the form of public sentiment in part of service, website system, and news can be used as consideration to improve the quality of tax services. In this research, text mining is done through the phases of text processing, feature selection and classification with Support Vector Machine (SVM). To reduce the problem of the number of attributes on the dataset in classifying text, Feature Selection used the Information Gain to select the relevant terms to the tax topic. Testing is used to measure the performance level of SVM with Feature Selection from two data sources. Performance measured using the parameters of precision, recall, and F-measure.


title: Cloud computing adoption decision modelling for SMEs: a conjoint analysis

abstract: Cloud computing is an emerging technology that promises competitive advantages, cost savings, enhanced business processes and services, and various other benefits to enterprises. Despite the rapid technological advancement, the adoption of cloud computing is still growing slowly among small and medium-sized enterprises SMEs. This paper presents a model to support the decision-making process, using a multi-criteria decision method PAPRIKA for the socio-technical aspects influencing SMEs cloud adoption decision. Due to the multifaceted nature of the cloud computing adoption process, the evaluation of various cloud services and deployment models have become a major challenge. This paper presents a systematic approach to evaluating cloud computing services and deployment models. Subsequently, we have conducted conjoint analysis activities with five SMEs decision makers as part of the distribution process of this decision modelling based on predetermined criteria. With the help of the proposed model, cloud services and deployment models can be ranked and selected.


title: Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge

abstract: This manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma. The other phases of the competition are not considered. Our proposed algorithm consists of three steps: preprocessing, classification using VGG-NET and Random Forests, and calculation of a final score.


title: Antenna Array Design for Multi-Gbps mmWave Mobile Broadband Communication

abstract: Almost all cellular mobile communications including first generation analog systems, second generation digital systems, third generation WCDMA, and fourth generation OFDMA systems use Ultra High Frequency (UHF) band of radio spectrum with frequencies in the range of 300MHz-3GHz. This band of spectrum is becoming increasingly crowded due to spectacular growth in mobile data and other related services. More recently, there have been proposals to explore mmWave spectrum (3-300GHz) for commercial mobile applications due to its unique advantages such as spectrum availability and small component sizes. In this paper, we discuss system design aspects such as antenna array design, base station and mobile station requirements. We also provide system performance and SINR geometry results to demonstrate the feasibility of an outdoor mmWave mobile broadband communication system. We note that with adaptive antenna array beamforming, multi-Gbps data rates can be supported for mobile cellular deployments.


title: Emotion recognition in the wild from videos using images

abstract: This paper presents the implementation details of the proposed solution to the Emotion Recognition in the Wild 2016 Challenge, in the category of video-based emotion recognition. The proposed approach takes the video stream from the audio-video trimmed clips provided by the challenge as input and produces the emotion label corresponding to this video sequence. This output is encoded as one out of seven classes: the six basic emotions (Anger, Disgust, Fear, Happiness, Sad, Surprise) and Neutral. Overall, the system consists of several pipelined modules: face detection, image pre-processing, deep feature extraction, feature encoding and, finally, an SVM classification. This system achieves 59.42% validation accuracy, surpassing the competition baseline of 38.81%. With regard to test data, our system achieves 56.66% recognition rate, also improving the competition baseline of 40.47%.


title: SGX-Log: Securing System Logs With SGX

abstract: System logs are the greatest forensics assets that capture how an operating system or a program behaves. System logs are often the next immediate attack target once a system is compromised, and it is thus paramount to protect them. This paper introduces SGX-Log, a new logging system that ensures the integrity and confidentiality of log data. The key idea is to redesign a logging system by leveraging a recent hardware extension, called Intel SGX, which provides a secure enclave with sealing and unsealing primitives to protect program code and data in both memory and disk from being modified in an unauthorized manner even from high privilege code. We have implemented SGX-Log atop the recent Ubuntu 14.04 for secure logging using real SGX hardware. Our evaluation shows that SGX-Log introduces no observable performance overhead to the programs that generate the log requests, and it also imposes very small overhead to the log daemons.


title: Power analysis attack: an approach based on machine learning

abstract: In cryptography, a side-channel attack is any attack based on the analysis of measurements related to the physical implementation of a cryptosystem. Nowadays, the possibility of collecting a large amount of observations paves the way to the adoption of machine learning techniques, i.e., techniques able to extract information and patterns from large datasets. The use of statistical techniques for side-channel attacks is not new. Techniques like the template attack have shown their effectiveness in recent years. However, these techniques rely on parametric assumptions and are often limited to small dimensionality settings, which limit their range of application. This paper explores the use of machine learning techniques to relax such assumptions and to deal with high dimensional feature vectors.


title: Deep into the Brain: Artificial Intelligence in Stroke Imaging

abstract: Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.


Biometric Recognition: Security and Privacy Concerns


title: Measuring experiment of FMCW ground penetrating radar

abstract: The paper describes measurement results obtained from FMCW GPR radar. Main goal of the article is to present real GPR data after AutoESP (Auto Estimation of Soil Permittivity) processing. The iterative AutoESP method is required to estimate proper value of permittivity. In order to test correct operation of the radar, several objects were buried in the tested ground. Focused SAR algorithm has been applied to achieve good azimuth resolution.


title: Chromosome Numbers in the Some Taxa of Hesperis L. (Brassicaceae) from Turkey

abstract: Hesperis L. genus belongs to Brassicaceae family and has 27 species in Turkey. In this study, chromosome numbers were defined for the first time in five taxa of Hesperis L. which grows up naturally. These taxa are Hesperis schischkinii Tzvelev, H. anatolica A. Duran, H. pendula DC. subsp. dirmilensis A. Duran, H. ozcelikii A. Duran and H. aspera Fourn., according to their floristic order. Diploid chromosome numbers of Hesperis schischkinii, H. anatolica and H. aspera species were counted as 2n = 14, while those of H. pendula subsp. dirmilensis and H. ozcelikii taxa were 2n = 12. There was only one double satellited metaphase chromosome in H. anatolica among these five Hesperis taxa.


title: BlueID: A practical system for Bluetooth device identification

abstract: Despite the widespread use of Bluetooth technology, identity management of Bluetooth devices remains a significant challenge because the MAC address and name of Bluetooth device are easy to forge. In this paper, we present BlueID - a practical system that identifies Bluetooth devices by fingerprinting their clocks. Previous approaches to clock fingerprinting exclusively rely on the timestamps carried by packet headers, which can be easily spoofed by hacking the user-space device driver. In comparison, BlueID performs clock fingerprinting based on the temporal feature of Bluetooth frequency hopping, which is impossible to forge without a customized baseband. Due to the proprietary nature of chipset firmware that implements baseband on commodity Bluetooth devices, BlueID will significantly raise the bar of identity spoofing. Moreover, BlueID employs simple yet efficient techniques to detect and differentiate low power Bluetooth transmissions from a distance, making it suitable for mobile applications like energy efficient localization and tracking. BlueID is implemented on a low cost wireless development platform and extensively evaluated based on 56 commodity devices. We show that BlueID can detect Bluetooth radios from 100m away, and identify different devices with high accuracy, short delay, and low computational overhead. Although this paper focuses on Bluetooth, the design of BlueID is general and can be applied to other frequency hopping based wireless systems.


title: Determining Influential Users in Internet Social Networks

abstract: Abstract The success of Internet social networking sites depends on the number and activity levels of their user members. Although users typically have numerous connections to other site members (i.e., “friends”), only a fraction of those so-called friends may actually influence a member's site usage. Because the influence of potentially hundreds of friends needs to be evaluated for each user, inferring precisely who is influential—and, therefore, of managerial interest for advertising targeting and retention efforts—is difficult. The authors develop an approach to determine which users have significant effects on the activities of others using the longitudinal records of members' log-in activity. They propose a nonstandard form of Bayesian shrinkage implemented in a Poisson regression. Instead of shrinking across panelists, strength is pooled across variables within the model for each user. The approach identifies the specific users who most influence others' activity and does so considerably better than...


title: Open Targets: a platform for therapeutic target identification and validation

abstract: We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.


title: DREAMING and the BRAIN: Toward a Cognitive Neuroscience of Conscious States

abstract: Sleep researchers in different disciplines disagree about how fully dreaming can be explained in terms of brain physiology. Debate has focused on whether REM sleep dreaming is qualitatively different from nonREM (NREM) sleep and waking. A review of psychophysiological studies shows clear quantitative differences between REM and NREM mentation and between REM and waking mentation. Recent neuroimaging and neurophysiological studies also differentiate REM, NREM, and waking in features with phenomenological implications. Both evidence and theory suggest that there are isomorphisms between the phenomenology and the physiology of dreams. We present a three-dimensional model with specific examples from normally and abnormally changing conscious states.


title: Refractory dissecting Cellulitis of the Scalp Successfully controlled with Adalimumab

abstract: Dissecting cellulitis of the scalp (DCS ) is an uncommon inflammatory disease that often results in scarring alopecia. Numerous therapies have either proved ineffective or only temporarily effective in the management of this condition. Recent reports show adequate responses to tumor necrosis factor (TNF) inhibitors in cases of DCS. We report a case of severe recalcitrant DCS successfully treated with adalimumab.


title: A research framework for social commerce adoption

abstract: Purpose – Social relationships on the internet through the emergence of Web 2.0 applications created new opportunities for business. This is mainly because of the growth of social networking sites, which has also developed e‐commerce. The current development in e‐commerce opened a new stream, entitled social commerce, which is using social technologies to create an environment for generating social interactions. These social interactions can drive online social support in e‐commerce, which in turn is creating trust and an increased intention to use social commerce.Design/methodology/approach – This research used social support theory and related theories on intention to use to propose a theoretical framework for the adoption of social commerce.Findings – The model predicts that forums and communities, ratings, reviews, referrals and recommendations are helping to introduce new business plans for e‐vendors. The model also shows trust is an on‐going issue in e‐commerce and can be built through social commer...


title: How to Choose the Best Pivot Language for Automatic Translation of Low-Resource Languages

abstract: Recent research on multilingual statistical machine translation focuses on the usage of pivot languages in order to overcome language resource limitations for certain language pairs. Due to the richness of available language resources, English is, in general, the pivot language of choice. However, factors like language relatedness can also effect the choice of the pivot language for a given language pair, especially for Asian languages, where language resources are currently quite limited. In this article, we provide new insights into what factors make a pivot language effective and investigate the impact of these factors on the overall pivot translation performance for translation between 22 Indo-European and Asian languages. Experimental results using state-of-the-art statistical machine translation techniques revealed that the translation quality of 54.8p of the language pairs improved when a non-English pivot language was chosen. Moreover, 81.0p of system performance variations can be explained by a combination of factors such as language family, vocabulary, sentence length, language perplexity, translation model entropy, reordering, monotonicity, and engine performance.


title: Immunogenicity to Biologics: Mechanisms, Prediction and Reduction

abstract: Currently, there is a significant rise in the development and clinical use of a unique class of pharmaceuticals termed as Biopharmaceuticals or Biologics, in the management of a range of disease conditions with, remarkable therapeutic benefits. However, there is an equally growing concern regarding development of adverse effects like immunogenicity in the form of anti-drug antibodies (ADA) production and hypersensitivity. Immunogenicity to biologics represents a significant hurdle in the continuing therapy of patients in a number of disease settings. Efforts focussed on the identification of factors that contribute towards the onset of immunogenic response to biologics have led to reductions in the incidence of immunogenicity. An in-depth understanding of the cellular and molecular mechanism underpinning immunogenic responses will likely improve the safety profile of biologics. This review addresses the mechanistic basis of ADA generation to biologics, with emphasis on the role of antigen processing and presentation in this process. The article also addresses the potential contribution of complement system in augmenting or modulating this response. Identifying specific factors that influences processing and presentation of biologic-derived antigens in different genotype and disease background may offer additional options for intervention in the immunogenic process and consequently, the management of immunogenicity to biologics.


Neural Response Generation via GAN with an Approximate Embedding Layer


A Generic Framework for Time-Stepping PDEs : general linear methods , object-orientated implementation and application to fluid problems


title: Automatic player position detection in basketball games

abstract: This paper presents us with a framework for the automatic player position detection (APPD) in the game of basketball. Court players are detected in the images broadcasted via television stations. In them, at any point of time, the view is from only one camera. This makes the detection process much more difficult. The player detection is based on the mixture of non-oriented pictorial structures. The detection of body parts is performed by the Support Vector Machine (SVM) algorithm. The results of these detections are combined together with constraints on their locations, which specify the position of one body part with respect to the parent body part. In order to train the whole model, we used a latent form of SVM called the latent SVM (LSVM). Such approach generated the statistical accuracy of about 82 %, which represents one of the best results in basketball player detection framework. Beside players, the algorithm detected a certain number of false positive objects. These are mostly people from the audience and the referees as well. This paper contains a simple and robust solution to remove them all, based on the play court boundaries and the histogram comparison. Separating players in different teams is done by k-means clustering. The inputs to this algorithm are saturation histograms calculated on the jerseys. A spatial transformation is determined by the detected play court boundaries and the actual court measures. Using this transformation, points representing the location of detected players in TV images are mapped to the actual location of players on the court, which was the main goal of our research. The proposed solution is sound and efficient. In addition, it is backed up by the experimental results obtained using the model of the actual footage of basketball games.