Query: 3D Texture Recognition Using Bidirectional Feature Histograms

Relevant passages:
A Multichannel Approach to Fingerprint Classification <=SEP=> Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4,000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.

Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. <=SEP=> What chiefly distinguishes cerebral cortex from other parts of the central nervous system is the great diversity of its cell types and inter-connexions. It would be astonishing if such a structure did not profoundly modify the response patterns of fibres coming into it. In the cat's visual cortex, the receptive field arrangements of single cells suggest that there is indeed a degree of complexity far exceeding anything yet seen at lower levels in the visual system. In a previous paper we described receptive fields of single cortical cells, observing responses to spots of light shone on one or both retinas (Hubel & Wiesel, 1959). In the present work this method is used to examine receptive fields of a more complex type (Part I) and to make additional observations on binocular interaction (Part II). This approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours. In the past, the technique of recording evoked slow waves has been used with great success in studies of functional anatomy. It was employed by Talbot & Marshall (1941) and by Thompson, Woolsey & Talbot (1950) for mapping out the visual cortex in the rabbit, cat, and monkey. Daniel & Whitteiidge (1959) have recently extended this work in the primate. Most of our present knowledge of retinotopic projections, binocular overlap, and the second visual area is based on these investigations. Yet the method of evoked potentials is valuable mainly for detecting behaviour common to large populations of neighbouring cells; it cannot differentiate functionally between areas of cortex smaller than about 1 mm2. To overcome this difficulty a method has in recent years been developed for studying cells separately or in small groups during long micro-electrode penetrations through nervous tissue. Responses are correlated with cell location by reconstructing the electrode tracks from histological material. These techniques have been applied to CAT VISUAL CORTEX 107 the somatic sensory cortex of the cat and monkey in a remarkable series of studies by Mountcastle (1957) and Powell & Mountcastle (1959). Their results show that the approach is a powerful one, capable of revealing systems of organization not hinted at by the known morphology. In Part III of the present paper we use this method in studying the functional architecture of the visual cortex. It helped us attempt to explain on anatomical …

Fingerprint classification <=SEP=> A fingerprint classification algorithm is presented in this paper. Fingerprints are classified into five categories: arch, tented arch, left loop, right loop and whorl. The algorithm extracts singular points (cores and deltas) in a fingerprint image and performs classification based on the number and locations of the detected singular points. The classifier is invariant to rotation, translation and small amounts of scale changes. The classifier is rule-based, where the rules are generated independent of a given data set. The classifier was tested on 4000 images in the NIST-4 database and on 5400 images in the NIST-9 database. For he NIST-4 database, classification accuracies of 85.4% for the five-class problem and 91.1% for the four-class problem (with arch and tented arch placed in the same category) were achieved. Using a reject option, the four-class classification error can be reduced to less than 6% with 10% fingerprint images rejected. Similar classification performance was obtained on the NIST-9 database.

Pattern classification and scene analysis <=SEP=> 

Adaptive flow orientation-based feature extraction in fingerprint images <=SEP=> -A reliable method for extracting structural features from fingerprint images is presented. Viewing fingerprint images as a textured image, an orientation flow field is computed. The rest of the stages in the algorithm use the flow field to design adaptive filters for the input image. To accurately locate ridges, a waveform projection-based ridge segmentation algorithm is used. The ridge skeleton image is obtained and smoothed using morphological operators to detect the features. A large number of spurious features from the detected set of minutiae is deleted by a postprocessing stage. The performance of the proposed algorithm has been evaluated by computing a "goodness index" (GI) which compares the results of automatic extraction with manually extracted ground truth. The significance of the observed GI values is determined by comparing the index for a set of fingerprints against the GI values obtained under a baseline distribution. The detected features are observed to be reliable and accurate. Fingerprints Feature extraction Texture Flow orientation Minutiae Segmentation Skeleton

Automated Static Code Analysis for Classifying Android Applications Using Machine Learning <=SEP=> In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted from Android’s Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused on classifying two types of Android applications: tools and games. Successful differentiation between games and tools is expected to provide positive indication about the ability of such methods to learn and model Android benign applications and potentially detect malware files. The results of an evaluation, performed using a test collection comprising 2,285 Android. apk files, indicate that features, extracted statically from. apk files, coupled with ML classification algorithms can provide good indication about the nature of an Android application without running the application, and may assist in detecting malicious applications. This method can be used for rapid examination of Android. apks and informing of suspicious applications.

Automated Text Summarization and the SUMMARIST System <=SEP=> This paper consists of three parts: a preliminary typology of summaries in general; a description of the current and planned modules and performance of the SUMMARIST automated multilingual text summarization system being built sat ISI, and a discussion of three methods to evaluate summaries. 1. T H E N A T U R E O F S U M M A R I E S Early experimentation in the late 1950's and early 60's suggested that text summarization by computer was feasible though not s traightforward (Luhn, 59; Edmundson, 68). The methods developed then were fairly unsophisticated, relying primarily on surface level phenomena such as sentence position and word frequency counts, and focused on producing extracts (passages selected from the text, reproduced verbatim) rather than abstracts (interpreted portions of the text, newly generated). After a hiatus of some decades, the growing presence of large amounts of online text--in corpora and especially on the Web--renewed the interest in automated text summarization. During these intervening decades, progress in Natural Language Processing (NLP), coupled with great increases of computer memory and speed, made possible more sophisticated techniques, with very encouraging results. In the late 1990's, some relatively small research investments in the US (not more than 10 projects, including commercial efforts at Microsoft, Lexis-Nexis, Oracle, SRA, and TextWise, and university efforts at CMU, NMSU, UPenn, and USC/ISI) over three or four years have produced several systems that exhibit potential marketability, as well as several innovations that promise continued improvement. In addition, several recent workshops, a book collection, and several tutorials testify that automated text summarization has become a hot area. However, when one takes a moment to study the various systems and to consider what has really been achieved, one cannot help being struck by their underlying similarity, by the narrowness of their focus, and by the large numbers of unknown factors that surround the problem. For example, what precisely is a summary? No-one seems to know exactly. In our work, we use summary as the generic term and define it as follows: A summary is a text that is produced out of one or more (possibly multimedia) texts, that contains (some of) the same information of the original text(s), and that is no longer than half of the original text(s). To clarify the picture a little, we follow and extend (Sp~irck Jones, 97) by identifying the following aspects of variation. Any summary can be characterized by (at least) three major classes of characteristics: Invut: characteristics of the source text(s) Source size: single-document v s . multi-document: A single-document summary derives from a single input text (though the summarization process itself may employ information compiled earlier from other texts). A multi-document summary is one text that covers the content of more than one input text, and is usually used only when the input texts are thematically related. Specificity: domain-specific vs. general: When the input texts all pertain to a single domain, it may be appropr ia te to apply domain spec i f i c summarization techniques, focus on specific content, and output specific formats, compared to the general case. A domain-specific summary derives from input text(s) whose theme(s) pertain to a single restricted domain. As such, it can assume less term ambiguity, idiosyncratic word and grammar usage, specialized formatting, etc., and can reflect them in the summary.

Asymmetrical duty cycle control with phase limit of LLC resonant inverter for an induction furnace <=SEP=> This paper proposes a power control of LLC resonant inverter for an induction furnace using asymmetrical duty cycle control (ADC) with phase limit to guarantee zero voltage switching (ZVS) operation and protect switching devices from spike current during the operation. The output power can be simply controlled through duty cycle of gate signals of a full-bridge inverter. With the phase limit control, non-ZVS operation and spike current caused by a change of duty cycle with fixed frequency and load Curie's temperature can be eliminated. Theoretical and simulation analyses of the proposed method have been investigated. The experimental results of heating a 300 g of Tin from room temperature until it is melted at 232 °C provided.

Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns <=SEP=> Local binary patterns LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well.

Less is More: Nyström Computational Regularization <=SEP=> We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nyström Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.

Power consumption estimation in CMOS VLSI chips <=SEP=> Power consumption from logic circuits, interconnections, dock distribution, on chip memories, and off chip driving in CMOS VLSI is estimated. Estimation methods are demonstrated and verified. An estimate tool is created. Power consumption distribution between ~ I I ~ I T O M ~ C ~ ~ O I W , clock distribution, logic gates, memories, and off chip driving are analyzed by examples. Comparisons are done between cell library, gate array, and full custom design. Also comparisons between static and dynamic logic are given. Results show that the power consumption of all interconnections and off chip driving can be up to 20% and 65% of the total power consumption respectively. Compared to cell library design, gate array designed chips consume about 10% more power, and power reduction in full custom designed chips could be 15%.

A dynamic instruction set computer <=SEP=> One of the key contributions of this paper is the idea of treating the two-dimensional FPGA area as a one-dimensional array of custom instructions, which the paper refers to as linear hardware space. Each custom instruction occupies the full width of the array but can occupy different heights to allow for instructions of varying complexity. Instructions also have a common interface that includes pass-thrus which connect to the previous and next instructions in the array. This communication interface greatly simplifies the problem of relocating a custom instruction to a free area, allowing other instructions to potentially remain in-place. The communication paths for an instruction are always at the same relative positions, and run-time CAD operations (and their corresponding overheads) are completely avoided. Thus, the linear hardware space reduces the overheads and simplifies the process of run-time reconfiguration of custom processor instructions.

Mechanical Design and Modeling of an Omni-directional RoboCup Player <=SEP=> Abstract. This paper covers the mechanical design process of an omni-directional mobile robot developed for the Ohio University RoboCup team player. It covers each design iteration, detailing what we learned from each phase. In addition, the kinematics of the final design is derived, and its inverse Jacobian matrix for the design is presented. The dynamic equations of motion for the final design is derived in a symbolic form, assuming that no slip occurs on the wheel in the spin direction. Finally, a simulation example demonstrates simple independent PID wheel control for the complex coupled nonlinear dynamic system.

Analysis on botnet detection techniques <=SEP=> Botnet detection plays an important role in network security. Botnet are collection of compromised computers called the bot. For detecting the presence of bots in a network, there are many detection techniques available. Network based detection method is the one of the efficient method in detecting bots. Paper reviews four different botnet detection techniques and a comparison of all these techniques are done.

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. <=SEP=> Importance
A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases.


Objective
To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes.


Design, Setting, and Participants
Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes.


Exposures
Use of a deep learning system.


Main Outcomes and Measures
Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard.


Results
In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images).


Conclusions and Relevance
In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

Suicide and Suicide Risk in Lesbian, Gay, Bisexual, and Transgender Populations: Review and Recommendations <=SEP=> Despite strong indications of elevated risk of suicidal behavior in lesbian, gay, bisexual, and transgender people, limited attention has been given to research, interventions or suicide prevention programs targeting these populations. This article is a culmination of a three-year effort by an expert panel to address the need for better understanding of suicidal behavior and suicide risk in sexual minority populations, and stimulate the development of needed prevention strategies, interventions and policy changes. This article summarizes existing research findings, and makes recommendations for addressing knowledge gaps and applying current knowledge to relevant areas of suicide prevention practice.

Watch What You Wear: Preliminary Forensic Analysis of Smart Watches <=SEP=> This work presents preliminary forensic analysis of two popular smart watches, the Samsung Gear 2 Neo and LG G. These wearable computing devices have the form factor of watches and sync with smart phones to display notifications, track footsteps and record voice messages. We posit that as smart watches are adopted by more users, the potential for them becoming a haven for digital evidence will increase thus providing utility for this preliminary work. In our work, we examined the forensic artifacts that are left on a Samsung Galaxy S4 Active phone that was used to sync with the Samsung Gear 2 Neo watch and the LG G watch. We further outline a methodology for physically acquiring data from the watches after gaining root access to them. Our results show that we can recover a swath of digital evidence directly form the watches when compared to the data on the phone that is synced with the watches. Furthermore, to root the LG G watch, the watch has to be reset to its factory settings which is alarming because the process may delete data of forensic relevance. Although this method is forensically intrusive, it may be used for acquiring data from already rooted LG watches. It is our observation that the data at the core of the functionality of at least the two tested smart watches, messages, health and fitness data, e-mails, contacts, events and notifications are accessible directly from the acquired images of the watches, which affirms our claim that the forensic value of evidence from smart watches is worthy of further study and should be investigated both at a high level and with greater specificity and granularity.

Design of Broadband Constant-Beamwidth Conical Corrugated-Horn Antennas [Antenna Designer's Notebook] <=SEP=> In this paper, a new design procedure is proposed for the design of wideband constant-beamwidth conical corrugated-horn antennas, with minimum design and construction complexity. The inputs to the procedure are the operating frequency band, the required minimum beamwidth in the entire frequency band, and the frequency in which the maximum gain is desired to occur. Based on these values, the procedure gives a relatively good design with a relative bandwidth of up to 2.5:1. Based on the proposed procedure, a corrugated-horn antenna with a constant beamwidth over the frequencies of 8 to 18 GHz was designed and simulated using commercial software. The designed antenna was also constructed, and its electromagnetic performance was measured. The measured results of the constructed prototype antenna confirmed the simulation results and satisfied the design requirements, validating the proposed design procedure.

I See What You Say (ISWYS): Arabic lip reading system <=SEP=> The ability of communicating easily with everyone is a blessing people with hearing impairment do not have. They completely rely on their vision around healthy individuals to difficultly read lips. This paper proposes a solution for this problem, ISWYS (I See What You Say) is a research-oriented speech recognition system for the Arabic language that interprets lips movements into readable text. It is accomplished by analyzing a video of lips movements that resemble utterances, and then converting it to readable characters using video analysis and motion estimation. Our algorithm involves dividing the video into n number of frames to generate n-1 image frame which is produced by taking the difference between consecutive frames. Then, video features are extracted to be used by our error function which provided recognition of approximately 70%.

A Novel BiQuad Antenna for 2 . 4 GHz Wireless Link Application : A Proposed Design <=SEP=> An ISM Band (2.4GHz) Design for a Biquad Antenna with Reflector base is presented for satisfying the ISM Band point-to-point link Applications in this paper. This proposed design can be used for Reception as well as Transmission in Wi-Fi's, WLAN, Bluetooth and even Zigbee links. The proposed antenna consists of two squares of the same size of 1⁄4 wavelength as a radiating element and a metallic plate or grid as reflector. This antenna has a beam width of about 70 degrees and a gain in the order of 10-12 dBi. A Prototype of this Antenna is designed and constructed. Parametric study is performed to understand the characterstics of the proposed antenna. Almost good antenna performances such as radiation patterns and antenna gains over the operating bands have been observed and simulated peak gain of the antenna is 10.7 dBi at 2439MHz. The simulated return loss is-35dB, whereas simulated SWR is 1.036 over the operating bands. The Biquad antenna is simple to build and offers good directivity and gain for Point-to-Point communications [1]. It consists of two squares of the same size of 1⁄4 wavelength as a radiating element and a metallic plate or grid as reflector. This antenna has a beam width of about 70 degrees and a gain in the order of 10-12 dBi. It can be used as stand-alone antenna or as feeder for a Parabolic Dish [2]. The polarization is such that looking at the antenna from the front, if the squares are placed side by side the polarization is vertical. The element is made from a length of 2mm thick copper wire, bent into the appropriate shape. Note that the length of each " side " should be close to 31 mm as possible, when measured from center to center of the wire [4-5]. The Details of the proposed antenna design are described in the paper, and simulated results are presented and discussed in the following sections.

Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice <=SEP=> Increasing crop yield is a major challenge for modern agriculture. The development of new plant types, which is known as ideal plant architecture (IPA), has been proposed as a means to enhance rice yield potential over that of existing high-yield varieties. Here, we report the cloning and characterization of a semidominant quantitative trait locus, IPA1 (Ideal Plant Architecture 1), which profoundly changes rice plant architecture and substantially enhances rice grain yield. The IPA1 quantitative trait locus encodes OsSPL14 (SOUAMOSA PROMOTER BINDING PROTEIN-LIKE 14) and is regulated by microRNA (miRNA) OsmiR156 in vivo. We demonstrate that a point mutation in OsSPL14 perturbs OsmiR156-directed regulation of OsSPL14, generating an 'ideal' rice plant with a reduced tiller number, increased lodging resistance and enhanced grain yield. Our study suggests that OsSPL14 may help improve rice grain yield by facilitating the breeding of new elite rice varieties.

Introduction to smart learning analytics: foundations and developments in video-based learning <=SEP=> Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics.

Machine Learning for Semantic Parsing in Review <=SEP=> Spoken Language Understanding (SLU) and more specifically, semantic parsing is an indispensable task in each speech-enabled application. In this survey, we review the current research on SLU and semantic parsing with emphasis on machine learning techniques used for these tasks. Observing the current trends in semantic parsing, we conclude our discussion by suggesting some of the most promising future research trends.

Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce <=SEP=> Classifying products precisely and efficiently is a major challenge in modern e-commerce. The high traffic of new products uploaded daily and the dynamic nature of the categories raise the need for machine learning models that can reduce the cost and time of human editors. In this paper, we propose a decision level fusion approach for multi-modal product classification based on text and image neural network classifiers. We train input specific state-of-the-art deep neural networks for each input source, show the potential of forging them together into a multi-modal architecture and train a novel policy network that learns to choose between them. Finally, we demonstrate that our multi-modal network improves classification accuracy over both networks on a real-world largescale product classification dataset that we collected from Walmart.com. While we focus on image-text fusion that characterizes e-commerce businesses, our algorithms can be easily applied to other modalities such as audio, video, physical sensors, etc.

Balancing Performance, Energy, and Quality in Pervasive Computing <=SEP=> We describe Spectra, a remote execution system for batterypowered clients used in pervasive computing. Spectra enables applications to combine the mobility of small devices with the greater processing power of static compute servers. Spectra is self-tuning: it monitors both application resource usage and the availability of resources in the environment, and dynamically determines how and where to execute application components. In making this determination, Spectra balances the competing goals of performance, energy conservation, and application quality. We have validated Spectra’s approach on the Compaq Itsy v2.2 and IBM ThinkPad 560X using a speech recognizer, a document preparation system, and a natural language translator. Our results confirm that Spectra almost always selects the best execution plan, and that its few suboptimal choices are very close to optimal.

Implicit and explicit ethnocentrism: revisiting the ideologies of prejudice. <=SEP=> Two studies investigated relationships among individual differences in implicit and explicit prejudice, right-wing ideology, and rigidity in thinking. The first study examined these relationships focusing on White Americans' prejudice toward Black Americans. The second study provided the first test of implicit ethnocentrism and its relationship to explicit ethnocentrism by studying the relationship between attitudes toward five social groups. Factor analyses found support for both implicit and explicit ethnocentrism. In both studies, mean explicit attitudes toward out groups were positive, whereas implicit attitudes were negative, suggesting that implicit and explicit prejudices are distinct; however, in both studies, implicit and explicit attitudes were related (r = .37, .47). Latent variable modeling indicates a simple structure within this ethnocentric system, with variables organized in order of specificity. These results lead to the conclusion that (a) implicit ethnocentrism exists and (b) it is related to and distinct from explicit ethnocentrism.

Harlequin fetus. <=SEP=> We report a case of harlequin fetus born to the consanguineous parents. She had the typical skin manifestations of thick armour like scales with fissures, complete ectropion and eclabium, atrophic and crumpled ears and swollen extremities with gangrenous digits. Supportive treatment was given but the neonate died on the 4th day.

Efficient Clustering-Based Outlier Detection Algorithm for Dynamic Data Stream <=SEP=> Anomaly detection is currently an important and active research problem in many fields and involved in numerous applications. Most of the existing methods are based on distance measure. But in case of data stream these methods are not very efficient as computational point of view. Most of the exiting work on outlier detection in data stream declare a point as an outlier/inlier as soon as it arrive due to limited memory resources as compared to the huge data stream, to declare an outlier as it arrive often can lead us to a wrong decision, because of dynamic nature of the incoming data. In this paper we introduced a clustering based approach, which divide the stream in chunks and cluster each chunk using k-mean in fixed number of clusters. Instead of keeping only the summary information, which often used in case of clustering data stream, we keep the candidate outliers and mean value of every cluster for the next fixed number of steam chunks, to make sure that the detected candidate outliers are the real outliers. By employing the mean value of the clusters of previous chunk with mean values of the current chunk of stream, we decide better outlierness for data stream objects. Several experiments on different dataset confirm that our technique can find better outliers with low computational cost than the other exiting distance based approaches of outlier detection in data stream.

Performance modeling for systematic performance tuning <=SEP=> The performance of parallel scientific applications depends on many factors which are determined by the execution environment and the parallel application. Especially on large parallel systems, it is too expensive to explore the solution space with series of experiments. Deriving analytical models for applications and platforms allow estimating and extrapolating their execution performance, bottlenecks, and the potential impact of optimization options. We propose to use such "performance modeling" techniques beginning from the application design process throughout the whole software development cycle and also during the lifetime of supercomputer systems. Such models help to guide supercomputer system design and re-engineering efforts to adopt applications to changing platforms and allow users to estimate costs to solve a particular problem. Models can often be built with the help of well-known performance profiling tools. We discuss how we successfully used modeling throughout the proposal, initial testing, and beginning deployment phase of the Blue Waters supercomputer system.

Programming by Feedback <=SEP=> This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user’s utility function and accounts for the user’s (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.