Query: Trip Outfits Advisor: Location-Oriented Clothing Recommendation

Relevant passages:
Learning structural SVMs with latent variables <=SEP=> We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.

Matching-CNN meets KNN: Quasi-parametric human parsing <=SEP=> Both parametric and non-parametric approaches have demonstrated encouraging performances in the human parsing task, namely segmenting a human image into several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim to develop a new solution with the advantages of both methodologies, namely supervision from annotated data and the flexibility to use newly annotated (possibly uncommon) images, and present a quasi-parametric human parsing model. Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict the matching confidence and displacements of the best matched region in the testing image for a particular semantic region in one KNN image. Given a testing image, we first retrieve its KNN images from the annotated/manually-parsed human image corpus. Then each semantic region in each KNN image is matched with confidence to the testing image using M-CNN, and the matched regions from all KNN images are further fused, followed by a superpixel smoothing procedure to obtain the ultimate human parsing result. The M-CNN differs from the classic CNN [12] in that the tailored cross image matching filters are introduced to characterize the matching between the testing image and the semantic region of a KNN image. The cross image matching filters are defined at different convolutional layers, each aiming to capture a particular range of displacements. Comprehensive evaluations over a large dataset with 7,700 annotated human images well demonstrate the significant performance gain from the quasi-parametric model over the state-of-the-arts [29, 30], for the human parsing task.

Max-Margin Markov Networks <=SEP=> In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels independently to each object, losing much useful information. Conversely, probabilistic graphical models, such as Markov networks, can represent correlations between labels, by exploiting problem structure, but cannot handle high-dimensional feature spaces, and lack strong theoretical generalization guarantees. In this paper, we present a new framework that combines the advantages of both approaches: Maximum margin Markov (M ) networks incorporate both kernels, which efficiently deal with highdimensional features, and the ability to capture correlations in structured data. We present an efficient algorithm for learning M networks based on a compact quadratic program formulation. We provide a new theoretical bound for generalization in structured domains. Experiments on the task of handwritten character recognition, demonstrate very significant gains over previous approaches.

Transferring a semantic representation for person re-identification and search <=SEP=> Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-invariant representation. However, existing attribute-centric approaches have thus far underperformed state-of-the-art conventional approaches. This is due to their nonscalable need for extensive domain (camera) specific annotation. In this paper we present a new semantic attribute learning approach for person re-identification and search. Our model is trained on existing fashion photography datasets - either weakly or strongly labelled. It can then be transferred and adapted to provide a powerful semantic description of surveillance person detections, without requiring any surveillance domain supervision. The resulting representation is useful for both unsupervised and supervised person re-identification, achieving state-of-the-art and near state-of-the-art performance respectively. Furthermore, as a semantic representation it allows description-based person search to be integrated within the same framework.

Supervised Descent Method and Its Applications to Face Alignment <=SEP=> Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.

An Optimal Algorithm for Generalized Causal Message Ordering (Abstract) <=SEP=> Asynchronous execution of processes and unpredictable communication delays create nondeterminism in distributed systems that complicates the design, verification, and analysis of distributed programs. To simplify the design and development of distributed applications, the idea of “causal message ordering” (CO) was introduced by Birman and Joseph [1]. If for any two messages M and M’ sent to the same destination d, Send(M) + Send(M’), then CO ensures that Delivered + Deliver-yd(lf’) at d. Existing algorithms that provide CO have high message overhead and use more storage than is required by an optimal algorithm, or make simplifying assumptions by assuming certain topolog y/communicat ion pat t ems and inbuilt synchronize ation. We present an optimal CO algorithm [2] under the following framework:

The Acquisition of Reading Comprehension Skill <=SEP=> How do people acquire skill at comprehending what they read? That is the simple question to which we shall try to make a tentative answer. To begin, we have to acknowledge some complexities about the concept of reading comprehension and what it means to develop it.

Private Release of Graph Statistics using Ladder Functions <=SEP=> Protecting the privacy of individuals in graph structured data while making accurate versions of the data available is one of the most challenging problems in data privacy. Most efforts to date to perform this data release end up mired in complexity, overwhelm the signal with noise, and are not effective for use in practice. In this paper, we introduce a new method which guarantees differential privacy. It specifies a probability distribution over possible outputs that is carefully defined to maximize the utility for the given input, while still providing the required privacy level. The distribution is designed to form a 'ladder', so that each output achieves the highest 'rung' (maximum probability) compared to less preferable outputs. We show how our ladder framework can be applied to problems of counting the number of occurrences of subgraphs, a vital objective in graph analysis, and give algorithms whose cost is comparable to that of computing the count exactly. Our experimental study confirms that our method outperforms existing methods for counting triangles and stars in terms of accuracy, and provides solutions for some problems for which no effective method was previously known. The results of our algorithms can be used to estimate the parameters of suitable graph models, allowing synthetic graphs to be sampled.

Cloud Computing : Types , Architecture , Applications , Concerns , Virtualization and Role of IT Governance in Cloud <=SEP=> Technology innovation and its adoption are two critical successful factors for any business/organization. Cloud computing is a recent technology paradigm that enables organizations or individuals to share various services in a seamless and cost-effective manner. This paper describes cloud computing, a computing platform for the next generation of the Internet. The paper defines clouds, types of cloud Provides, Comparison of Cloud Computing with Grid Computing, applications and concerns of Cloud Computing , Concept of Virtualization in Cloud Computing. Readers will also discover the working, Architecture and Role of I.T Governance in Cloud Computing.

BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks <=SEP=> In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Coarse guidance using deformation fields obtained by an existing registration method; and (2) Fine guidance using image similarity. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.

Thin Images Reflected in the Water: Narcissism and Girls' Vulnerability to the Thin-Ideal. <=SEP=> The purpose of this research is to test how adolescent girls' narcissistic traits-characterized by a need to impress others and avoid ego-threat-influence acute adverse effects of thin-ideal exposure. Participants (11-15 years; total N = 366; all female) reported their narcissistic traits. Next, in two experiments, they viewed images of either very thin or average-sized models, reported their wishful identification with the models (Experiment 2), and tasted high-calorie foods in an alleged taste test (both experiments). Narcissism kept girls from wishfully identifying with thin models, which is consistent with the view that narcissistic girls are prone to disengage from thin-ideal exposure. Moreover, narcissism protected vulnerable girls (those who experience low weight-esteem) from inhibiting their food intake, and led other girls (those who consider their appearance relatively unimportant) to increase their food intake. These effects did not generalize to conceptually related traits of self-esteem and perfectionism, and were not found for a low-calorie foods outcome, attesting to the specificity of findings. These experiments demonstrate the importance of narcissism at reducing girls' thin-ideal vulnerability. Girls high in narcissism disengage self-protectively from threats to their self-image, a strategy that renders at least subsets of them less vulnerable to the thin-ideal.

Channel Estimation Algorithms for OFDM Systems <=SEP=> In this work we have compared different type of channel estimation algorithm for Orthogonal Frequency Division Multiplexing (OFDM) systems. The result of the Adaptive Boosting (AdaBoost) algorithm was compared with other algorithms such Least Square (LS), Best Linear Unbiased Estimator (BLUE) and Minimum Mean Square Error (MMSE).

EchoFlex: Hand Gesture Recognition using Ultrasound Imaging <=SEP=> Recent improvements in ultrasound imaging enable new opportunities for hand pose detection using wearable devices. Ultrasound imaging has remained under-explored in the HCI community despite being non-invasive, harmless and capable of imaging internal body parts, with applications including smart-watch interaction, prosthesis control and instrument tuition. In this paper, we compare the performance of different forearm mounting positions for a wearable ultrasonographic device. Location plays a fundamental role in ergonomics and performance since the anatomical features differ among positions. We also investigate the performance decrease due to cross-session position shifts and develop a technique to compensate for this misalignment. Our gesture recognition algorithm combines image processing and neural networks to classify the flexion and extension of 10 discrete hand gestures with an accuracy above 98%. Furthermore, this approach can continuously track individual digit flexion with less than 5% NRMSE, and also differentiate between digit flexion at different joints.

Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus <=SEP=> The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.

Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off! <=SEP=> We all have experienced forgetting habitual actions among our daily activities. For example, we probably have forgotten to turn the lights off before leaving a room or turn the stove off after cooking. In this paper, we propose a solution to the problem of issuing notifications on actions that may be missed. This involves learning about interdependencies between actions and being able to predict an ongoing action while segmenting the input video stream. In order to show a proof of concept, we collected a new egocentric dataset, in which people wear a camera while making lattes. We show promising results on the extremely challenging task of issuing correct and timely reminders. We also show that our model reliably segments the actions, while predicting the ongoing one when only a few frames from the beginning of the action are observed. The overall prediction accuracy is 46.2% when only 10 frames of an action are seen (2/3 of a sec). Moreover, the overall recognition and segmentation accuracy is shown to be 72.7% when the whole activity sequence is observed. Finally, the online prediction and segmentation accuracy is 68.3% when the prediction is made at every time step.

Bio-Inspired Robotics Based on Liquid Crystalline Elastomers ( LCEs ) and Flexible Stimulators <=SEP=> 

Indoor Navigation Validation Framework for Visually Impaired Users <=SEP=> In this paper, we introduce the first validation framework of an indoor navigation system for blind and visually impaired (BVI) users, which is a significant step toward the development of cost effective indoor way-finding solutions for BVI users. The BVI users require detailed landmark-based navigation instructions that will help them arrive at the chosen destination independently. These users will interact with the navigation instructions on a smartphone using an accessible user interface. The validation framework includes the following three main components: 1) virtual reality-based simulation that simulates a BVI user traversing and interacting with the physical environment, developed using Unity game engine; 2) generation of action codes that emulate the avatar movement in the virtual environment, developed using a natural language processing parser of the navigation instructions; and 3) accessible user interface, which enables the user to access the navigation instructions, developed using Sikuli script library. We introduce a case study that illustrates the use of the validation tool using PERCEPT system. It is our strong belief that the validation framework we provide in this paper will encourage other developers to invent indoor navigation systems for BVI users. In addition, we would like to mention that this tool is the first step of validating an indoor navigation system and should be followed by trials with human subjects.

Platelet-Rich Plasma: Applications in Dermatology <=SEP=> 

SENTIMENT ANALYSIS IN E-COMMERCE USING RECOMMENDATION SYSTEM <=SEP=> In the field of text mining the sentiment analysis is one of the current research topics. Sentiment analysis is the best solution for opinions and sentiment mining from natural language. Sentiment analysis gives significant information for decision making in various domains. The sentiments include ratings, reviews and emoticons. Several sentiment detection approaches are available which affect the quality of result. This paper finding the sentiments of people related to the services of E-shopping websites. The central goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best product. The proposed system use stochastic learning algorithm which analyze various feedbacks related to the services and then the sentiments are classified as negative, positive and neutral. The pre-processing of the data is greatly affecting the quality of detected sentiments so that the fake reviews in the website are analyzed. Finally the classification technique can be used to analysis the products which are posted in E-shopping website. The proposed system find out fake reviews made by posting fake comments about a product by identifying the MAC address along with review posting patterns. In order to find out the review is fake or genuine the proposed system find out the MAC address of the user if the system observes fake review send by the same MAC address many a times it will inform the admin to remove that review from the system. The proposed system helps the user to find out correct review of the product.

Fetal facial profile markers of Down syndrome in the second and third trimesters of pregnancy. <=SEP=> OBJECTIVES
To investigate the use of the maxilla-nasion-mandible (MNM) angle and fetal profile (FP) line to assess the degree of midfacial hypoplasia in Down-syndrome fetuses in the second and third trimesters of pregnancy.


METHODS
The MNM angle and FP line were measured retrospectively in stored two-dimensional images or three-dimensional volumes of fetuses with Down syndrome. Data collected from January 2006 to July 2013 were retrieved from the digital databases of participating units. The MNM angle was expressed as a continuous variable (degrees) and the FP line as positive, negative or zero. Measurements were obtained from stored images in the midsagittal plane by two experienced examiners and compared with our previously reported normal ranges for euploid fetuses. A MNM angle below the 5(th) centile of the reference range and a positive or negative FP line were considered as abnormal.


RESULTS
A total of 133 fetuses with Down syndrome were available for analysis, eight of which were subsequently excluded because of inadequate images. The MNM angle was not influenced by gestational age (P = 0.48) and was significantly smaller in Down-syndrome fetuses than in euploid fetuses (mean, 12.90° vs 13.53°, respectively; P = 0.015). The MNM angle was below the 5th centile for euploid fetuses in 16.8% of fetuses with Down syndrome (P < 0.01). In the cohort of Down-syndrome fetuses, a positive FP line was present in 41.6% of cases (with a false-positive rate (FPR) of 6.3%) and was positively correlated with Down syndrome and gestational age (P < 0.01). There was no case with a negative FP line. In cases of Down syndrome, a positive FP line was correlated with a small MNM angle (P < 0.01).


CONCLUSIONS
A small MNM angle and a positive FP line can be regarded as novel markers for Down syndrome. The FP line is an easy marker to measure, has a low FPR, does not require knowledge of normal reference values and has the potential to differentiate between Down syndrome and trisomy 18, as, in the latter, the FP line is often negative.

Abstraction Learning <=SEP=> ion Learning Fei Deng fei.deng@rutgers.edu Jinsheng Ren rjs17@mails.tsinghua.edu.cn Feng Chen chenfeng@mail.tsinghua.edu.cn

A Low-Noise Wideband Digital Phase-Locked Loop Based on a Coarse–Fine Time-to-Digital Converter With Subpicosecond Resolution <=SEP=> This paper presents the design of a digital PLL which uses a high-resolution time-to-digital converter (TDC) for wide loop bandwidth. The TDC uses a time amplification technique to reduce the quantization noise down to less than 1 ps root mean square (RMS). Additionally TDC input commutation reduces low-frequency spurs due to inaccurate TDC scaling factor in a counter-assisted digital PLL. The loop bandwidth is set to 400 kHz with a 25 MHz reference. The in-band phase noise contribution from the TDC is -116 dBc/Hz, the phase noise is -117 dBc/Hz at high band (1.8 GHz band) 400 kHz offset, and the RMS phase error is 0.3deg.

Continuous Glucose Monitoring Systems: A Review. <=SEP=> There have been continuous advances in the field of glucose monitoring during the last four decades, which have led to the development of highly evolved blood glucose meters, non-invasive glucose monitoring (NGM) devices and continuous glucose monitoring systems (CGMS). Glucose monitoring is an integral part of diabetes management, and the maintenance of physiological blood glucose concentration is the only way for a diabetic to avoid life-threatening diabetic complications. CGMS have led to tremendous improvements in diabetic management, as shown by the significant lowering of glycated hemoglobin (HbA1c) in adults with type I diabetes. Most of the CGMS have been minimally-invasive, although the more recent ones are based on NGM techniques. This manuscript reviews the advances in CGMS for diabetes management along with the future prospects and the challenges involved.

OWL rules: A proposal and prototype implementation <=SEP=> Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present SWRL (the Semantic Web Rules Language), a Horn clause rules extension to OWL that overcomes many of these limitations. SWRL extends OWL in a syntactically and semantically coherent manner: the basic syntax for SWRL rules is an extension of the abstract syntax for OWL DL and OWL Lite; SWRL rules are given formal meaning via an extension of the OWL DL model-theoretic semantics; SWRL rules are given an XML syntax based on the OWL XML presentation syntax; and a mapping from SWRL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. We discuss the expressive power of SWRL, showing that the ontology consistency problem is undecidable, provide several examples of SWRL usage, and discuss a prototype implementation of reasoning support for SWRL.

Manga content analysis using physiological signals <=SEP=> Recently, the physiological signals have been analyzed more and more, especially in the context of everyday life activities such as watching video or looking at pictures. Tracking these signals gives access to the mental state of the user (interest, tiredness, stress) but also to his emotions (sadness, fright, happiness). The analysis of the reader's physiological signals during reading can provide a better understanding of the reader's feelings but also a better understanding of the documents. Our main research direction is to find the relationship between a change in the reader's physiological signal and the content of the reading. As a first step, we investigate whether it is possible to distinguish a manga (Japanese comic book) from another by analyzing the physiological signals of the reader. We use 3 different manga genres (horror, romance, comedy) and try to predict which one is read by analyzing the features extracted from the physiological signals of the reader. Our method uses the blood volume pulse, the electrodermal activity and the skin temperature of the reader while reading. We show that by using these physiological signals with a support vector machine we can retrieve which manga has been read with a 90% average accuracy.

The neural basis of economic decision-making in the Ultimatum Game. <=SEP=> The nascent field of neuroeconomics seeks to ground economic decision making in the biological substrate of the brain. We used functional magnetic resonance imaging of Ultimatum Game players to investigate neural substrates of cognitive and emotional processes involved in economic decision-making. In this game, two players split a sum of money;one player proposes a division and the other can accept or reject this. We scanned players as they responded to fair and unfair proposals. Unfair offers elicited activity in brain areas related to both emotion (anterior insula) and cognition (dorsolateral prefrontal cortex). Further, significantly heightened activity in anterior insula for rejected unfair offers suggests an important role for emotions in decision-making.

Security/privacy of wearable fitness tracking IoT devices <=SEP=> As wearable fitness trackers gain widespread acceptance among the general population, there is a concomitant need to ensure that associated privacy and security vulnerabilities are kept to a minimum. We discuss potential vulnerabilities of these trackers, in general, and specific vulnerabilities in one such tracker - Fitbit - identified by Rahman et al. (2013) who then proposed means to address identified vulnerabilities. However, the `fix' has its own vulnerabilities. We discuss possible means to alleviate related issues.

Using BPMN to Model a BPEL Process <=SEP=> The Business Process Modeling Notation (BPMN) has been developed to enable business user to develop readily understandable graphical representations of business processes. BPMN is also supported with appropriate graphical object properties that will enable the generation of executable BPEL. Thus, BPMN creates a standardized bridge for the gap between the business process design and process implementation. This paper presents a simple, yet instructive example of how a BPMN diagram can be used to generate a BPEL process.

Cloud computing: state-of-the-art and research challenges <=SEP=> Cloud computing has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the IT industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this paper, we present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation as well as research challenges. The aim of this paper is to provide a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.

Generalized Pythagoras Trees for visualizing hierarchies <=SEP=> Pythagoras Trees are fractals that can be used to depict binary hierarchies. But this binary encoding is an obstacle for visualizing hierarchical data such as file systems or phylogenetic trees, which branch into n sub-hierarchies. Although any hierarchy can be modeled as a binary one by subsequently dividing n-ary branches into a sequence of n − 1 binary branches, we follow a different strategy. In our approach extending Pythagoras Trees to arbitrarily branching trees, we only need a single visual element for an n-ary branch instead of spreading the binary branches along a strand. Each vertex in the hierarchy is visualized as a rectangle sized according to a metric. We analyze several visual parameters such as length, width, order, and color of the nodes against the use of different metrics. The usefulness of our technique is illustrated by two case studies visualizing directory structures and a large phylogenetic tree. We compare our approach with existing tree diagrams and discuss questions of geometry, perception, readability, and aesthetics.